examples/PCA/PCA_on_cancer_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal component analysis (PCA) on cancer data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates how to use the **hoggorm** package to carry out principal component analysis (PCA) on a multivariate data set on cancer in men across OECD countries. Furthermore, we will learn how to visualize the results of the PCA using the **hoggormPlot** package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import **hoggorm** for analysis of the data and **hoggormPlot** for plotting of the analysis results. We'll also import **pandas** such that we can read the data into a data frame. **numpy** is needed for checking dimensions of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hoggorm as ho\n",
    "import hoggormplot as hop\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, load the cancer data that we are going to analyse using **hoggorm**. The data can be acquired from the OECD (The Organisation for Economic Co-operation and Development) and holds the percentages of various cancer types in men. After the data has been loaded into the pandas data frame, we'll display it in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>20.493429</td>\n",
       "      <td>9.290024</td>\n",
       "      <td>2.954790</td>\n",
       "      <td>5.033474</td>\n",
       "      <td>13.612695</td>\n",
       "      <td>4.049921</td>\n",
       "      <td>0.157038</td>\n",
       "      <td>3.855691</td>\n",
       "      <td>3.107695</td>\n",
       "      <td>4.425986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>22.286929</td>\n",
       "      <td>10.944722</td>\n",
       "      <td>4.584951</td>\n",
       "      <td>6.627842</td>\n",
       "      <td>10.574968</td>\n",
       "      <td>5.666482</td>\n",
       "      <td>0.147902</td>\n",
       "      <td>3.780736</td>\n",
       "      <td>3.031984</td>\n",
       "      <td>1.941209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30.090706</td>\n",
       "      <td>10.450900</td>\n",
       "      <td>3.253582</td>\n",
       "      <td>5.179440</td>\n",
       "      <td>9.162613</td>\n",
       "      <td>3.450769</td>\n",
       "      <td>0.144604</td>\n",
       "      <td>3.569081</td>\n",
       "      <td>4.719337</td>\n",
       "      <td>1.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>27.731624</td>\n",
       "      <td>11.443416</td>\n",
       "      <td>3.011842</td>\n",
       "      <td>5.419733</td>\n",
       "      <td>9.739695</td>\n",
       "      <td>3.863702</td>\n",
       "      <td>0.195163</td>\n",
       "      <td>3.789857</td>\n",
       "      <td>3.534035</td>\n",
       "      <td>1.537569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13.304823</td>\n",
       "      <td>8.022491</td>\n",
       "      <td>17.272511</td>\n",
       "      <td>4.165677</td>\n",
       "      <td>16.195454</td>\n",
       "      <td>5.044745</td>\n",
       "      <td>0.221747</td>\n",
       "      <td>3.009424</td>\n",
       "      <td>2.162034</td>\n",
       "      <td>0.554368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>24.875324</td>\n",
       "      <td>13.970344</td>\n",
       "      <td>4.455083</td>\n",
       "      <td>6.742470</td>\n",
       "      <td>9.455416</td>\n",
       "      <td>3.464326</td>\n",
       "      <td>0.279274</td>\n",
       "      <td>3.371235</td>\n",
       "      <td>3.557417</td>\n",
       "      <td>1.522708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>24.311585</td>\n",
       "      <td>12.168196</td>\n",
       "      <td>3.212602</td>\n",
       "      <td>6.375589</td>\n",
       "      <td>14.301662</td>\n",
       "      <td>3.162987</td>\n",
       "      <td>0.210866</td>\n",
       "      <td>3.100968</td>\n",
       "      <td>4.366162</td>\n",
       "      <td>2.245100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>26.371951</td>\n",
       "      <td>10.264228</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>5.436992</td>\n",
       "      <td>13.008130</td>\n",
       "      <td>2.388211</td>\n",
       "      <td>0.101626</td>\n",
       "      <td>3.861789</td>\n",
       "      <td>3.506098</td>\n",
       "      <td>1.016260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>23.917887</td>\n",
       "      <td>9.818587</td>\n",
       "      <td>4.169319</td>\n",
       "      <td>7.940802</td>\n",
       "      <td>13.574157</td>\n",
       "      <td>4.232973</td>\n",
       "      <td>0.159134</td>\n",
       "      <td>2.737110</td>\n",
       "      <td>2.928071</td>\n",
       "      <td>2.418842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>25.125758</td>\n",
       "      <td>10.161689</td>\n",
       "      <td>3.360656</td>\n",
       "      <td>5.335729</td>\n",
       "      <td>9.885470</td>\n",
       "      <td>6.466427</td>\n",
       "      <td>0.143723</td>\n",
       "      <td>3.535819</td>\n",
       "      <td>4.350999</td>\n",
       "      <td>1.112733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>24.401222</td>\n",
       "      <td>11.177186</td>\n",
       "      <td>4.592273</td>\n",
       "      <td>6.795183</td>\n",
       "      <td>11.012912</td>\n",
       "      <td>4.106844</td>\n",
       "      <td>0.151953</td>\n",
       "      <td>3.609916</td>\n",
       "      <td>3.198410</td>\n",
       "      <td>1.467786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>31.667245</td>\n",
       "      <td>8.503992</td>\n",
       "      <td>4.830499</td>\n",
       "      <td>5.229666</td>\n",
       "      <td>9.013074</td>\n",
       "      <td>1.237996</td>\n",
       "      <td>0.792549</td>\n",
       "      <td>3.823904</td>\n",
       "      <td>5.582552</td>\n",
       "      <td>0.769409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>30.412574</td>\n",
       "      <td>16.081953</td>\n",
       "      <td>5.287679</td>\n",
       "      <td>5.293292</td>\n",
       "      <td>6.797642</td>\n",
       "      <td>3.104126</td>\n",
       "      <td>0.117878</td>\n",
       "      <td>2.559641</td>\n",
       "      <td>3.575639</td>\n",
       "      <td>1.044064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>20.322581</td>\n",
       "      <td>13.225806</td>\n",
       "      <td>4.193548</td>\n",
       "      <td>3.870968</td>\n",
       "      <td>17.096774</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>2.903226</td>\n",
       "      <td>5.806452</td>\n",
       "      <td>0.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>22.957477</td>\n",
       "      <td>12.326804</td>\n",
       "      <td>4.395604</td>\n",
       "      <td>5.805065</td>\n",
       "      <td>12.446249</td>\n",
       "      <td>3.511706</td>\n",
       "      <td>0.238892</td>\n",
       "      <td>3.057812</td>\n",
       "      <td>2.866699</td>\n",
       "      <td>1.887243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>21.822897</td>\n",
       "      <td>12.671360</td>\n",
       "      <td>5.446462</td>\n",
       "      <td>8.725454</td>\n",
       "      <td>7.725083</td>\n",
       "      <td>3.501297</td>\n",
       "      <td>0.351982</td>\n",
       "      <td>5.094479</td>\n",
       "      <td>4.890700</td>\n",
       "      <td>2.278622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>26.067418</td>\n",
       "      <td>10.900444</td>\n",
       "      <td>6.087111</td>\n",
       "      <td>5.398894</td>\n",
       "      <td>7.628006</td>\n",
       "      <td>6.953407</td>\n",
       "      <td>0.250356</td>\n",
       "      <td>3.660018</td>\n",
       "      <td>4.687631</td>\n",
       "      <td>1.131317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>23.990561</td>\n",
       "      <td>11.971776</td>\n",
       "      <td>14.737968</td>\n",
       "      <td>7.315522</td>\n",
       "      <td>5.327754</td>\n",
       "      <td>9.132765</td>\n",
       "      <td>0.051157</td>\n",
       "      <td>2.214981</td>\n",
       "      <td>2.426985</td>\n",
       "      <td>0.151168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>26.204210</td>\n",
       "      <td>10.098575</td>\n",
       "      <td>13.107486</td>\n",
       "      <td>5.630408</td>\n",
       "      <td>3.142353</td>\n",
       "      <td>18.281606</td>\n",
       "      <td>0.094701</td>\n",
       "      <td>1.977960</td>\n",
       "      <td>1.975808</td>\n",
       "      <td>0.271189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>25.183824</td>\n",
       "      <td>11.948529</td>\n",
       "      <td>4.044118</td>\n",
       "      <td>6.066176</td>\n",
       "      <td>7.720588</td>\n",
       "      <td>6.066176</td>\n",
       "      <td>0.183824</td>\n",
       "      <td>4.779412</td>\n",
       "      <td>4.044118</td>\n",
       "      <td>1.838235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>11.495974</td>\n",
       "      <td>6.910581</td>\n",
       "      <td>8.227151</td>\n",
       "      <td>5.088289</td>\n",
       "      <td>16.321514</td>\n",
       "      <td>7.591468</td>\n",
       "      <td>0.740218</td>\n",
       "      <td>6.260771</td>\n",
       "      <td>1.873146</td>\n",
       "      <td>0.802373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>27.155154</td>\n",
       "      <td>11.469393</td>\n",
       "      <td>3.543496</td>\n",
       "      <td>5.334906</td>\n",
       "      <td>11.076157</td>\n",
       "      <td>2.127846</td>\n",
       "      <td>0.157295</td>\n",
       "      <td>3.062874</td>\n",
       "      <td>3.700791</td>\n",
       "      <td>2.154061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>19.773765</td>\n",
       "      <td>13.095497</td>\n",
       "      <td>4.198390</td>\n",
       "      <td>4.763977</td>\n",
       "      <td>12.725691</td>\n",
       "      <td>3.567544</td>\n",
       "      <td>0.195780</td>\n",
       "      <td>4.154884</td>\n",
       "      <td>2.740918</td>\n",
       "      <td>5.286056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>21.398230</td>\n",
       "      <td>13.451327</td>\n",
       "      <td>3.203540</td>\n",
       "      <td>6.194690</td>\n",
       "      <td>17.486726</td>\n",
       "      <td>2.566372</td>\n",
       "      <td>0.176991</td>\n",
       "      <td>3.221239</td>\n",
       "      <td>4.247788</td>\n",
       "      <td>3.309735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>30.656934</td>\n",
       "      <td>11.920225</td>\n",
       "      <td>6.439067</td>\n",
       "      <td>4.550070</td>\n",
       "      <td>8.201621</td>\n",
       "      <td>2.086327</td>\n",
       "      <td>0.195414</td>\n",
       "      <td>2.927371</td>\n",
       "      <td>5.145890</td>\n",
       "      <td>1.415790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>20.390610</td>\n",
       "      <td>14.466792</td>\n",
       "      <td>8.730518</td>\n",
       "      <td>4.733881</td>\n",
       "      <td>11.103926</td>\n",
       "      <td>4.695079</td>\n",
       "      <td>0.155209</td>\n",
       "      <td>3.000711</td>\n",
       "      <td>4.416995</td>\n",
       "      <td>0.821315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>22.663051</td>\n",
       "      <td>15.099187</td>\n",
       "      <td>6.108178</td>\n",
       "      <td>5.651491</td>\n",
       "      <td>7.606679</td>\n",
       "      <td>3.111175</td>\n",
       "      <td>0.285429</td>\n",
       "      <td>2.554588</td>\n",
       "      <td>3.125446</td>\n",
       "      <td>1.541316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>24.945770</td>\n",
       "      <td>13.727921</td>\n",
       "      <td>7.468237</td>\n",
       "      <td>5.113108</td>\n",
       "      <td>11.093895</td>\n",
       "      <td>3.997521</td>\n",
       "      <td>0.154943</td>\n",
       "      <td>2.479083</td>\n",
       "      <td>4.090487</td>\n",
       "      <td>2.169197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>26.777063</td>\n",
       "      <td>14.070260</td>\n",
       "      <td>5.245117</td>\n",
       "      <td>4.827702</td>\n",
       "      <td>8.816002</td>\n",
       "      <td>5.138478</td>\n",
       "      <td>0.196521</td>\n",
       "      <td>2.953901</td>\n",
       "      <td>6.384632</td>\n",
       "      <td>0.885104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>16.053045</td>\n",
       "      <td>12.345140</td>\n",
       "      <td>3.350201</td>\n",
       "      <td>6.962136</td>\n",
       "      <td>20.441459</td>\n",
       "      <td>3.577037</td>\n",
       "      <td>0.157041</td>\n",
       "      <td>3.568313</td>\n",
       "      <td>4.240098</td>\n",
       "      <td>2.748211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>21.607539</td>\n",
       "      <td>10.188470</td>\n",
       "      <td>3.680710</td>\n",
       "      <td>6.241685</td>\n",
       "      <td>14.168514</td>\n",
       "      <td>5.365854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.359202</td>\n",
       "      <td>4.257206</td>\n",
       "      <td>2.372506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>38.971658</td>\n",
       "      <td>7.623419</td>\n",
       "      <td>8.846855</td>\n",
       "      <td>5.211386</td>\n",
       "      <td>7.225854</td>\n",
       "      <td>3.723590</td>\n",
       "      <td>0.219276</td>\n",
       "      <td>3.612927</td>\n",
       "      <td>3.397750</td>\n",
       "      <td>0.571756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>22.803081</td>\n",
       "      <td>10.262614</td>\n",
       "      <td>3.447752</td>\n",
       "      <td>4.946063</td>\n",
       "      <td>12.707596</td>\n",
       "      <td>3.427884</td>\n",
       "      <td>0.191672</td>\n",
       "      <td>3.223356</td>\n",
       "      <td>4.037960</td>\n",
       "      <td>1.521686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>29.141246</td>\n",
       "      <td>9.063132</td>\n",
       "      <td>2.226842</td>\n",
       "      <td>6.211578</td>\n",
       "      <td>9.487245</td>\n",
       "      <td>4.537710</td>\n",
       "      <td>0.237131</td>\n",
       "      <td>4.268031</td>\n",
       "      <td>3.463645</td>\n",
       "      <td>1.993364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>25.991834</td>\n",
       "      <td>10.696822</td>\n",
       "      <td>6.270034</td>\n",
       "      <td>5.933306</td>\n",
       "      <td>9.235451</td>\n",
       "      <td>5.686093</td>\n",
       "      <td>0.194011</td>\n",
       "      <td>3.432922</td>\n",
       "      <td>3.617678</td>\n",
       "      <td>1.304311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trachea-bronchus-lung  Colon, rectum and anus    Stomach  \\\n",
       "MEN                                                                        \n",
       "Australia                   20.493429                9.290024   2.954790   \n",
       "Austria                     22.286929               10.944722   4.584951   \n",
       "Belgium                     30.090706               10.450900   3.253582   \n",
       "Canada                      27.731624               11.443416   3.011842   \n",
       "Chile                       13.304823                8.022491  17.272511   \n",
       "Czech Rep.                  24.875324               13.970344   4.455083   \n",
       "Denmark                     24.311585               12.168196   3.212602   \n",
       "Estonia                     26.371951               10.264228   8.333333   \n",
       "Finland                     23.917887                9.818587   4.169319   \n",
       "France                      25.125758               10.161689   3.360656   \n",
       "Germany                     24.401222               11.177186   4.592273   \n",
       "Greece                      31.667245                8.503992   4.830499   \n",
       "Hungary                     30.412574               16.081953   5.287679   \n",
       "Iceland                     20.322581               13.225806   4.193548   \n",
       "Ireland                     22.957477               12.326804   4.395604   \n",
       "Israel                      21.822897               12.671360   5.446462   \n",
       "Italy                       26.067418               10.900444   6.087111   \n",
       "Japan                       23.990561               11.971776  14.737968   \n",
       "Korea                       26.204210               10.098575  13.107486   \n",
       "Luxembourg                  25.183824               11.948529   4.044118   \n",
       "Mexico                      11.495974                6.910581   8.227151   \n",
       "Netherlands                 27.155154               11.469393   3.543496   \n",
       "New Zealand                 19.773765               13.095497   4.198390   \n",
       "Norway                      21.398230               13.451327   3.203540   \n",
       "Poland                      30.656934               11.920225   6.439067   \n",
       "Portugal                    20.390610               14.466792   8.730518   \n",
       "Slovak Rep.                 22.663051               15.099187   6.108178   \n",
       "Slovenia                    24.945770               13.727921   7.468237   \n",
       "Spain                       26.777063               14.070260   5.245117   \n",
       "Sweden                      16.053045               12.345140   3.350201   \n",
       "Switzerland                 21.607539               10.188470   3.680710   \n",
       "Turkey                      38.971658                7.623419   8.846855   \n",
       "United Kingdom              22.803081               10.262614   3.447752   \n",
       "United States               29.141246                9.063132   2.226842   \n",
       "OECD                        25.991834               10.696822   6.270034   \n",
       "\n",
       "                Pancreas   Prostate      Liver  Hodgkins disease  Leukemia  \\\n",
       "MEN                                                                          \n",
       "Australia       5.033474  13.612695   4.049921          0.157038  3.855691   \n",
       "Austria         6.627842  10.574968   5.666482          0.147902  3.780736   \n",
       "Belgium         5.179440   9.162613   3.450769          0.144604  3.569081   \n",
       "Canada          5.419733   9.739695   3.863702          0.195163  3.789857   \n",
       "Chile           4.165677  16.195454   5.044745          0.221747  3.009424   \n",
       "Czech Rep.      6.742470   9.455416   3.464326          0.279274  3.371235   \n",
       "Denmark         6.375589  14.301662   3.162987          0.210866  3.100968   \n",
       "Estonia         5.436992  13.008130   2.388211          0.101626  3.861789   \n",
       "Finland         7.940802  13.574157   4.232973          0.159134  2.737110   \n",
       "France          5.335729   9.885470   6.466427          0.143723  3.535819   \n",
       "Germany         6.795183  11.012912   4.106844          0.151953  3.609916   \n",
       "Greece          5.229666   9.013074   1.237996          0.792549  3.823904   \n",
       "Hungary         5.293292   6.797642   3.104126          0.117878  2.559641   \n",
       "Iceland         3.870968  17.096774   0.967742          0.645161  2.903226   \n",
       "Ireland         5.805065  12.446249   3.511706          0.238892  3.057812   \n",
       "Israel          8.725454   7.725083   3.501297          0.351982  5.094479   \n",
       "Italy           5.398894   7.628006   6.953407          0.250356  3.660018   \n",
       "Japan           7.315522   5.327754   9.132765          0.051157  2.214981   \n",
       "Korea           5.630408   3.142353  18.281606          0.094701  1.977960   \n",
       "Luxembourg      6.066176   7.720588   6.066176          0.183824  4.779412   \n",
       "Mexico          5.088289  16.321514   7.591468          0.740218  6.260771   \n",
       "Netherlands     5.334906  11.076157   2.127846          0.157295  3.062874   \n",
       "New Zealand     4.763977  12.725691   3.567544          0.195780  4.154884   \n",
       "Norway          6.194690  17.486726   2.566372          0.176991  3.221239   \n",
       "Poland          4.550070   8.201621   2.086327          0.195414  2.927371   \n",
       "Portugal        4.733881  11.103926   4.695079          0.155209  3.000711   \n",
       "Slovak Rep.     5.651491   7.606679   3.111175          0.285429  2.554588   \n",
       "Slovenia        5.113108  11.093895   3.997521          0.154943  2.479083   \n",
       "Spain           4.827702   8.816002   5.138478          0.196521  2.953901   \n",
       "Sweden          6.962136  20.441459   3.577037          0.157041  3.568313   \n",
       "Switzerland     6.241685  14.168514   5.365854          0.000000  3.359202   \n",
       "Turkey          5.211386   7.225854   3.723590          0.219276  3.612927   \n",
       "United Kingdom  4.946063  12.707596   3.427884          0.191672  3.223356   \n",
       "United States   6.211578   9.487245   4.537710          0.237131  4.268031   \n",
       "OECD            5.933306   9.235451   5.686093          0.194011  3.432922   \n",
       "\n",
       "                 Bladder      Skin  \n",
       "MEN                                 \n",
       "Australia       3.107695  4.425986  \n",
       "Austria         3.031984  1.941209  \n",
       "Belgium         4.719337  1.018798  \n",
       "Canada          3.534035  1.537569  \n",
       "Chile           2.162034  0.554368  \n",
       "Czech Rep.      3.557417  1.522708  \n",
       "Denmark         4.366162  2.245100  \n",
       "Estonia         3.506098  1.016260  \n",
       "Finland         2.928071  2.418842  \n",
       "France          4.350999  1.112733  \n",
       "Germany         3.198410  1.467786  \n",
       "Greece          5.582552  0.769409  \n",
       "Hungary         3.575639  1.044064  \n",
       "Iceland         5.806452  0.967742  \n",
       "Ireland         2.866699  1.887243  \n",
       "Israel          4.890700  2.278622  \n",
       "Italy           4.687631  1.131317  \n",
       "Japan           2.426985  0.151168  \n",
       "Korea           1.975808  0.271189  \n",
       "Luxembourg      4.044118  1.838235  \n",
       "Mexico          1.873146  0.802373  \n",
       "Netherlands     3.700791  2.154061  \n",
       "New Zealand     2.740918  5.286056  \n",
       "Norway          4.247788  3.309735  \n",
       "Poland          5.145890  1.415790  \n",
       "Portugal        4.416995  0.821315  \n",
       "Slovak Rep.     3.125446  1.541316  \n",
       "Slovenia        4.090487  2.169197  \n",
       "Spain           6.384632  0.885104  \n",
       "Sweden          4.240098  2.748211  \n",
       "Switzerland     4.257206  2.372506  \n",
       "Turkey          3.397750  0.571756  \n",
       "United Kingdom  4.037960  1.521686  \n",
       "United States   3.463645  1.993364  \n",
       "OECD            3.617678  1.304311  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load OECD data for cancer in men\n",
    "\n",
    "# Insert code for reading data from other folder in repository instead of directly from same repository.\n",
    "data_df = pd.read_csv('Cancer_men_perc.txt', index_col=0, sep='\\t')\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the dimensions of the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are observations for 34 countries as well as all OECD countries together, which results in 35 rows. Furthermore, there are 10 columns where each column represents one type of cancer in men."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``nipalsPCA`` class in hoggorm accepts only **numpy** arrays with numerical values and not pandas data frames. Therefore, the pandas data frame holding the imported data needs to be \"taken apart\" into three parts: \n",
    "* a numpy array holding the numeric values\n",
    "* a Python list holding variable (column) names\n",
    "* a Python list holding object (row) names. \n",
    "\n",
    "The array with values will be used as input for the ``nipalsPCA`` class for analysis. The Python lists holding the variable and row names will be used later in the plotting function from the **hoggormPlot** package when visualizing the results of the analysis. Below is the code needed to access both data, variable names and object names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the values from the data frame\n",
    "data = data_df.values\n",
    "\n",
    "# Get the variable or columns names\n",
    "data_varNames = list(data_df.columns)\n",
    "\n",
    "# Get the object or row names\n",
    "data_objNames = list(data_df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a quick look at the column or variable names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Trachea-bronchus-lung',\n",
       " 'Colon, rectum and anus',\n",
       " 'Stomach',\n",
       " 'Pancreas',\n",
       " 'Prostate',\n",
       " 'Liver',\n",
       " 'Hodgkins disease',\n",
       " 'Leukemia',\n",
       " 'Bladder',\n",
       " 'Skin']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_varNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now show the object or row names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Australia',\n",
       " 'Austria',\n",
       " 'Belgium',\n",
       " 'Canada',\n",
       " 'Chile',\n",
       " 'Czech Rep.',\n",
       " 'Denmark',\n",
       " 'Estonia',\n",
       " 'Finland',\n",
       " 'France',\n",
       " 'Germany',\n",
       " 'Greece',\n",
       " 'Hungary',\n",
       " 'Iceland',\n",
       " 'Ireland',\n",
       " 'Israel',\n",
       " 'Italy',\n",
       " 'Japan',\n",
       " 'Korea',\n",
       " 'Luxembourg',\n",
       " 'Mexico',\n",
       " 'Netherlands',\n",
       " 'New Zealand',\n",
       " 'Norway',\n",
       " 'Poland',\n",
       " 'Portugal',\n",
       " 'Slovak Rep.',\n",
       " 'Slovenia',\n",
       " 'Spain',\n",
       " 'Sweden',\n",
       " 'Switzerland',\n",
       " 'Turkey',\n",
       " 'United Kingdom',\n",
       " 'United States',\n",
       " 'OECD']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_objNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply PCA to our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run PCA on the data using the ``nipalsPCA`` class. The documentation provides a [description of the input parameters](https://hoggorm.readthedocs.io/en/latest/pca.html). Using input parameter ``arrX`` we define which numpy array we would like to analyse. By setting input parameter ``Xstand=False`` we make sure that the variables are only mean centered, not scaled to unit variance. This is the default setting and actually doesn't need to expressed explicitly. Setting parameter ``cvType=[\"loo\"]`` we make sure that we compute the PCA model using full cross validation. ``\"loo\"`` means \"Leave One Out\". By setting parameter ``numpComp=4`` we ask for four principal components (PC) to be computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loo\n"
     ]
    }
   ],
   "source": [
    "model = ho.nipalsPCA(arrX=data, Xstand=False, cvType=[\"loo\"], numComp=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, the PCA model has been computed. Now we would like to inspect the results by visualising them. We can do this using the taylor-made plotting function for PCA from the separate [**hoggormPlot** package](https://hoggormplot.readthedocs.io/en/latest/). If we wish to plot the results for component 1 and component 2, we can do this by setting the input argument ``comp=[1, 2]``. The input argument ``plots=[1, 2, 3, 4, 6]`` lets the user define which plots are to be plotted. If this list for example contains value ``1``, the function will generate the scores plot for the model. If the list contains value ``2``, then the loadings plot will be plotted. Value ``3`` stands for correlation loadings plot and value ``4`` stands for bi-plot and ``6`` stands for explained variance plot. The hoggormPlot documentation provides a [description of input parameters](https://hoggormplot.readthedocs.io/en/latest/mainPlot.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hop.plot(model, comp=[1, 2], \n",
    "         plots=[1, 2, 3, 4, 6], \n",
    "         objNames=data_objNames, \n",
    "         XvarNames=data_varNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing numerical results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have visualized the PCA results, we may also want to access the numerical results. Below are some examples. For a complete list of accessible results, please see this part of the documentation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>-4.804277</td>\n",
       "      <td>2.310263</td>\n",
       "      <td>-0.320919</td>\n",
       "      <td>2.835885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>-1.491013</td>\n",
       "      <td>-0.521756</td>\n",
       "      <td>-1.669103</td>\n",
       "      <td>1.558577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>5.930459</td>\n",
       "      <td>2.930098</td>\n",
       "      <td>0.758349</td>\n",
       "      <td>1.199125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.580658</td>\n",
       "      <td>2.560206</td>\n",
       "      <td>-0.593118</td>\n",
       "      <td>1.027471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>-12.382151</td>\n",
       "      <td>-9.441836</td>\n",
       "      <td>6.727705</td>\n",
       "      <td>-3.070356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>1.319773</td>\n",
       "      <td>1.315515</td>\n",
       "      <td>-2.350299</td>\n",
       "      <td>-1.668611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>-1.711772</td>\n",
       "      <td>3.914209</td>\n",
       "      <td>0.076722</td>\n",
       "      <td>0.251714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>0.524115</td>\n",
       "      <td>0.435027</td>\n",
       "      <td>4.308677</td>\n",
       "      <td>-1.262844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>-1.741210</td>\n",
       "      <td>1.886170</td>\n",
       "      <td>0.830626</td>\n",
       "      <td>2.085349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.435297</td>\n",
       "      <td>0.232770</td>\n",
       "      <td>-1.275385</td>\n",
       "      <td>2.792210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.057056</td>\n",
       "      <td>0.964567</td>\n",
       "      <td>-0.388893</td>\n",
       "      <td>0.658498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>7.226501</td>\n",
       "      <td>3.290313</td>\n",
       "      <td>3.784990</td>\n",
       "      <td>0.607414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>7.452514</td>\n",
       "      <td>1.392996</td>\n",
       "      <td>-2.187983</td>\n",
       "      <td>-3.928233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>-6.500124</td>\n",
       "      <td>4.719514</td>\n",
       "      <td>1.199494</td>\n",
       "      <td>-2.495195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>-1.937233</td>\n",
       "      <td>1.729734</td>\n",
       "      <td>-0.639801</td>\n",
       "      <td>-0.376054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>-0.450615</td>\n",
       "      <td>-0.495134</td>\n",
       "      <td>-3.286539</td>\n",
       "      <td>-1.172208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>3.378048</td>\n",
       "      <td>-2.357099</td>\n",
       "      <td>-1.225306</td>\n",
       "      <td>1.105300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2.711983</td>\n",
       "      <td>-11.101208</td>\n",
       "      <td>-0.021016</td>\n",
       "      <td>-2.765627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>6.055118</td>\n",
       "      <td>-15.670778</td>\n",
       "      <td>-2.856998</td>\n",
       "      <td>3.790745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>2.513495</td>\n",
       "      <td>-0.481077</td>\n",
       "      <td>-2.925284</td>\n",
       "      <td>1.238478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>-13.796612</td>\n",
       "      <td>-5.092872</td>\n",
       "      <td>1.371124</td>\n",
       "      <td>3.851659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.307706</td>\n",
       "      <td>3.593764</td>\n",
       "      <td>0.571465</td>\n",
       "      <td>-0.149753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>-5.020797</td>\n",
       "      <td>1.731151</td>\n",
       "      <td>-2.325881</td>\n",
       "      <td>-0.523972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>-5.926939</td>\n",
       "      <td>4.986128</td>\n",
       "      <td>-0.018785</td>\n",
       "      <td>-0.672933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6.812707</td>\n",
       "      <td>1.554399</td>\n",
       "      <td>1.749005</td>\n",
       "      <td>-2.223861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>-3.313726</td>\n",
       "      <td>-2.641853</td>\n",
       "      <td>-1.211839</td>\n",
       "      <td>-3.820953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>0.388879</td>\n",
       "      <td>-0.724607</td>\n",
       "      <td>-3.411846</td>\n",
       "      <td>-3.827329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>0.459487</td>\n",
       "      <td>-0.347168</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-2.798297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>3.471632</td>\n",
       "      <td>0.319985</td>\n",
       "      <td>-1.971708</td>\n",
       "      <td>-1.789486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>-11.957737</td>\n",
       "      <td>3.990669</td>\n",
       "      <td>-0.043580</td>\n",
       "      <td>0.608510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>-3.913395</td>\n",
       "      <td>1.499904</td>\n",
       "      <td>-0.226503</td>\n",
       "      <td>2.412778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>14.236427</td>\n",
       "      <td>-0.244792</td>\n",
       "      <td>7.015958</td>\n",
       "      <td>0.957182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>-2.149667</td>\n",
       "      <td>2.373420</td>\n",
       "      <td>0.262543</td>\n",
       "      <td>1.224744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.865291</td>\n",
       "      <td>2.751178</td>\n",
       "      <td>0.272190</td>\n",
       "      <td>3.571268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>2.370123</td>\n",
       "      <td>-1.361798</td>\n",
       "      <td>0.021916</td>\n",
       "      <td>0.768806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      PC1        PC2       PC3       PC4\n",
       "Australia       -4.804277   2.310263 -0.320919  2.835885\n",
       "Austria         -1.491013  -0.521756 -1.669103  1.558577\n",
       "Belgium          5.930459   2.930098  0.758349  1.199125\n",
       "Canada           3.580658   2.560206 -0.593118  1.027471\n",
       "Chile          -12.382151  -9.441836  6.727705 -3.070356\n",
       "Czech Rep.       1.319773   1.315515 -2.350299 -1.668611\n",
       "Denmark         -1.711772   3.914209  0.076722  0.251714\n",
       "Estonia          0.524115   0.435027  4.308677 -1.262844\n",
       "Finland         -1.741210   1.886170  0.830626  2.085349\n",
       "France           1.435297   0.232770 -1.275385  2.792210\n",
       "Germany          0.057056   0.964567 -0.388893  0.658498\n",
       "Greece           7.226501   3.290313  3.784990  0.607414\n",
       "Hungary          7.452514   1.392996 -2.187983 -3.928233\n",
       "Iceland         -6.500124   4.719514  1.199494 -2.495195\n",
       "Ireland         -1.937233   1.729734 -0.639801 -0.376054\n",
       "Israel          -0.450615  -0.495134 -3.286539 -1.172208\n",
       "Italy            3.378048  -2.357099 -1.225306  1.105300\n",
       "Japan            2.711983 -11.101208 -0.021016 -2.765627\n",
       "Korea            6.055118 -15.670778 -2.856998  3.790745\n",
       "Luxembourg       2.513495  -0.481077 -2.925284  1.238478\n",
       "Mexico         -13.796612  -5.092872  1.371124  3.851659\n",
       "Netherlands      2.307706   3.593764  0.571465 -0.149753\n",
       "New Zealand     -5.020797   1.731151 -2.325881 -0.523972\n",
       "Norway          -5.926939   4.986128 -0.018785 -0.672933\n",
       "Poland           6.812707   1.554399  1.749005 -2.223861\n",
       "Portugal        -3.313726  -2.641853 -1.211839 -3.820953\n",
       "Slovak Rep.      0.388879  -0.724607 -3.411846 -3.827329\n",
       "Slovenia         0.459487  -0.347168  0.000022 -2.798297\n",
       "Spain            3.471632   0.319985 -1.971708 -1.789486\n",
       "Sweden         -11.957737   3.990669 -0.043580  0.608510\n",
       "Switzerland     -3.913395   1.499904 -0.226503  2.412778\n",
       "Turkey          14.236427  -0.244792  7.015958  0.957182\n",
       "United Kingdom  -2.149667   2.373420  0.262543  1.224744\n",
       "United States    4.865291   2.751178  0.272190  3.571268\n",
       "OECD             2.370123  -1.361798  0.021916  0.768806"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get scores and store in numpy array\n",
    "scores = model.X_scores()\n",
    "\n",
    "# Get scores and store in pandas dataframe with row and column names\n",
    "scores_df = pd.DataFrame(model.X_scores())\n",
    "scores_df.index = data_objNames\n",
    "scores_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_scores().shape[1])]\n",
    "scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_scores in module hoggorm.pca:\n",
      "\n",
      "X_scores(self)\n",
      "    Returns array holding scores T. First column holds scores for\n",
      "    component 1, second column holds scores for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of the scores\n",
    "np.shape(model.X_scores())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the numpy array holds the scores for all countries and OECD (35 in total) for four components as required when computing the PCA model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <td>0.845199</td>\n",
       "      <td>0.224593</td>\n",
       "      <td>0.314566</td>\n",
       "      <td>0.046389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <td>0.015660</td>\n",
       "      <td>0.106296</td>\n",
       "      <td>-0.550679</td>\n",
       "      <td>-0.668953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stomach</th>\n",
       "      <td>-0.027941</td>\n",
       "      <td>-0.680376</td>\n",
       "      <td>0.498526</td>\n",
       "      <td>-0.471499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pancreas</th>\n",
       "      <td>0.001799</td>\n",
       "      <td>-0.000626</td>\n",
       "      <td>-0.141297</td>\n",
       "      <td>0.084208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prostate</th>\n",
       "      <td>-0.525513</td>\n",
       "      <td>0.354902</td>\n",
       "      <td>0.453070</td>\n",
       "      <td>0.086309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liver</th>\n",
       "      <td>0.035896</td>\n",
       "      <td>-0.562988</td>\n",
       "      <td>-0.327117</td>\n",
       "      <td>0.518504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <td>-0.005208</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.003083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leukemia</th>\n",
       "      <td>-0.036068</td>\n",
       "      <td>0.034901</td>\n",
       "      <td>0.010028</td>\n",
       "      <td>0.165016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bladder</th>\n",
       "      <td>0.040952</td>\n",
       "      <td>0.128914</td>\n",
       "      <td>-0.023781</td>\n",
       "      <td>-0.107264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skin</th>\n",
       "      <td>-0.064443</td>\n",
       "      <td>0.120724</td>\n",
       "      <td>-0.127032</td>\n",
       "      <td>0.076852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             PC1       PC2       PC3       PC4\n",
       "Trachea-bronchus-lung   0.845199  0.224593  0.314566  0.046389\n",
       "Colon, rectum and anus  0.015660  0.106296 -0.550679 -0.668953\n",
       "Stomach                -0.027941 -0.680376  0.498526 -0.471499\n",
       "Pancreas                0.001799 -0.000626 -0.141297  0.084208\n",
       "Prostate               -0.525513  0.354902  0.453070  0.086309\n",
       "Liver                   0.035896 -0.562988 -0.327117  0.518504\n",
       "Hodgkins disease       -0.005208  0.005020  0.015071  0.003083\n",
       "Leukemia               -0.036068  0.034901  0.010028  0.165016\n",
       "Bladder                 0.040952  0.128914 -0.023781 -0.107264\n",
       "Skin                   -0.064443  0.120724 -0.127032  0.076852"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get loadings and store in numpy array\n",
    "loadings = model.X_loadings()\n",
    "\n",
    "# Get loadings and store in pandas dataframe with row and column names\n",
    "loadings_df = pd.DataFrame(model.X_loadings())\n",
    "loadings_df.index = data_varNames\n",
    "loadings_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_loadings in module hoggorm.pca:\n",
      "\n",
      "X_loadings(self)\n",
      "    Returns array holding loadings P of array X. Rows represent variables\n",
      "    and columns represent components. First column holds loadings for\n",
      "    component 1, second column holds scores for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_loadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(model.X_loadings())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the array holds the loadings for the 10 variables in the data across four components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <td>0.965908</td>\n",
       "      <td>0.191746</td>\n",
       "      <td>0.149441</td>\n",
       "      <td>0.020209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <td>0.043048</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>-0.629289</td>\n",
       "      <td>-0.701062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stomach</th>\n",
       "      <td>-0.048240</td>\n",
       "      <td>-0.877575</td>\n",
       "      <td>0.357808</td>\n",
       "      <td>-0.310349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pancreas</th>\n",
       "      <td>0.010313</td>\n",
       "      <td>-0.002680</td>\n",
       "      <td>-0.336703</td>\n",
       "      <td>0.184027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prostate</th>\n",
       "      <td>-0.829307</td>\n",
       "      <td>0.418408</td>\n",
       "      <td>0.297220</td>\n",
       "      <td>0.051922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liver</th>\n",
       "      <td>0.071639</td>\n",
       "      <td>-0.839369</td>\n",
       "      <td>-0.271383</td>\n",
       "      <td>0.394491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <td>-0.180713</td>\n",
       "      <td>0.130129</td>\n",
       "      <td>0.217384</td>\n",
       "      <td>0.040783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leukemia</th>\n",
       "      <td>-0.258370</td>\n",
       "      <td>0.186778</td>\n",
       "      <td>0.029859</td>\n",
       "      <td>0.450652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bladder</th>\n",
       "      <td>0.230094</td>\n",
       "      <td>0.541118</td>\n",
       "      <td>-0.055545</td>\n",
       "      <td>-0.229764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skin</th>\n",
       "      <td>-0.352154</td>\n",
       "      <td>0.492845</td>\n",
       "      <td>-0.288573</td>\n",
       "      <td>0.160107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             PC1       PC2       PC3       PC4\n",
       "Trachea-bronchus-lung   0.965908  0.191746  0.149441  0.020209\n",
       "Colon, rectum and anus  0.043048  0.218298 -0.629289 -0.701062\n",
       "Stomach                -0.048240 -0.877575  0.357808 -0.310349\n",
       "Pancreas                0.010313 -0.002680 -0.336703  0.184027\n",
       "Prostate               -0.829307  0.418408  0.297220  0.051922\n",
       "Liver                   0.071639 -0.839369 -0.271383  0.394491\n",
       "Hodgkins disease       -0.180713  0.130129  0.217384  0.040783\n",
       "Leukemia               -0.258370  0.186778  0.029859  0.450652\n",
       "Bladder                 0.230094  0.541118 -0.055545 -0.229764\n",
       "Skin                   -0.352154  0.492845 -0.288573  0.160107"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get loadings and store in numpy array\n",
    "loadings = model.X_corrLoadings()\n",
    "\n",
    "# Get loadings and store in pandas dataframe with row and column names\n",
    "loadings_df = pd.DataFrame(model.X_corrLoadings())\n",
    "loadings_df.index = data_varNames\n",
    "loadings_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_corrLoadings().shape[1])]\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_corrLoadings in module hoggorm.pca:\n",
      "\n",
      "X_corrLoadings(self)\n",
      "    Returns array holding correlation loadings of array X. First column\n",
      "    holds correlation loadings for component 1, second column holds\n",
      "    correlation loadings for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_corrLoadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calibrated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>49.949783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>27.877279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>8.631727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>7.259616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     calibrated explained variance\n",
       "PC1                      49.949783\n",
       "PC2                      27.877279\n",
       "PC3                       8.631727\n",
       "PC4                       7.259616"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get calibrated explained variance of each component\n",
    "calExplVar = model.X_calExplVar()\n",
    "\n",
    "# Get calibrated explained variance and store in pandas dataframe with row and column names\n",
    "calExplVar_df = pd.DataFrame(model.X_calExplVar())\n",
    "calExplVar_df.columns = ['calibrated explained variance']\n",
    "calExplVar_df.index = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "calExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_calExplVar in module hoggorm.pca:\n",
      "\n",
      "X_calExplVar(self)\n",
      "    Returns a list holding the calibrated explained variance for\n",
      "    each component. First number in list is for component 1, second number\n",
      "    for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_calExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative calibrated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>49.949783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>77.827062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>86.458789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>93.718405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cumulative calibrated explained variance\n",
       "PC0                                  0.000000\n",
       "PC1                                 49.949783\n",
       "PC2                                 77.827062\n",
       "PC3                                 86.458789\n",
       "PC4                                 93.718405"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative calibrated explained variance\n",
    "cumCalExplVar = model.X_cumCalExplVar()\n",
    "\n",
    "# Get cumulative calibrated explained variance and store in pandas dataframe with row and column names\n",
    "cumCalExplVar_df = pd.DataFrame(model.X_cumCalExplVar())\n",
    "cumCalExplVar_df.columns = ['cumulative calibrated explained variance']\n",
    "cumCalExplVar_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumCalExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumCalExplVar in module hoggorm.pca:\n",
      "\n",
      "X_cumCalExplVar(self)\n",
      "    Returns a list holding the cumulative validated explained variance\n",
      "    for array X after each component. First number represents zero\n",
      "    components, second number represents component 1, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumCalExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>93.297874</td>\n",
       "      <td>0.185312</td>\n",
       "      <td>0.232708</td>\n",
       "      <td>0.010637</td>\n",
       "      <td>68.775042</td>\n",
       "      <td>0.513211</td>\n",
       "      <td>3.265701</td>\n",
       "      <td>6.675528</td>\n",
       "      <td>5.294306</td>\n",
       "      <td>12.401232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>96.974604</td>\n",
       "      <td>4.950729</td>\n",
       "      <td>77.246454</td>\n",
       "      <td>0.011355</td>\n",
       "      <td>86.281381</td>\n",
       "      <td>70.967161</td>\n",
       "      <td>4.959056</td>\n",
       "      <td>10.164105</td>\n",
       "      <td>34.575287</td>\n",
       "      <td>36.690812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>99.207876</td>\n",
       "      <td>44.551154</td>\n",
       "      <td>90.049020</td>\n",
       "      <td>11.348255</td>\n",
       "      <td>95.115368</td>\n",
       "      <td>78.332080</td>\n",
       "      <td>9.684653</td>\n",
       "      <td>10.253262</td>\n",
       "      <td>34.883801</td>\n",
       "      <td>45.018208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>99.248721</td>\n",
       "      <td>93.700489</td>\n",
       "      <td>99.680558</td>\n",
       "      <td>14.734767</td>\n",
       "      <td>95.384980</td>\n",
       "      <td>93.894274</td>\n",
       "      <td>9.850987</td>\n",
       "      <td>30.561992</td>\n",
       "      <td>40.162969</td>\n",
       "      <td>47.581571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Trachea-bronchus-lung  Colon, rectum and anus    Stomach   Pancreas  \\\n",
       "PC0               0.000000                0.000000   0.000000   0.000000   \n",
       "PC1              93.297874                0.185312   0.232708   0.010637   \n",
       "PC2              96.974604                4.950729  77.246454   0.011355   \n",
       "PC3              99.207876               44.551154  90.049020  11.348255   \n",
       "PC4              99.248721               93.700489  99.680558  14.734767   \n",
       "\n",
       "      Prostate      Liver  Hodgkins disease   Leukemia    Bladder       Skin  \n",
       "PC0   0.000000   0.000000          0.000000   0.000000   0.000000   0.000000  \n",
       "PC1  68.775042   0.513211          3.265701   6.675528   5.294306  12.401232  \n",
       "PC2  86.281381  70.967161          4.959056  10.164105  34.575287  36.690812  \n",
       "PC3  95.115368  78.332080          9.684653  10.253262  34.883801  45.018208  \n",
       "PC4  95.384980  93.894274          9.850987  30.561992  40.162969  47.581571  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative calibrated explained variance for each variable\n",
    "cumCalExplVar_ind = model.X_cumCalExplVar_indVar()\n",
    "\n",
    "# Get cumulative calibrated explained variance for each variable and store in pandas dataframe with row and column names\n",
    "cumCalExplVar_ind_df = pd.DataFrame(model.X_cumCalExplVar_indVar())\n",
    "cumCalExplVar_ind_df.columns = data_varNames\n",
    "cumCalExplVar_ind_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumCalExplVar_ind_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumCalExplVar_indVar in module hoggorm.pca:\n",
      "\n",
      "X_cumCalExplVar_indVar(self)\n",
      "    Returns an array holding the cumulative calibrated explained variance\n",
      "    for each variable in X after each component. First row represents zero\n",
      "    components, second row represents one component, third row represents\n",
      "    two components, etc. Columns represent variables.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumCalExplVar_indVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>20.264145</td>\n",
       "      <td>11.348532</td>\n",
       "      <td>5.914738</td>\n",
       "      <td>5.710003</td>\n",
       "      <td>13.528109</td>\n",
       "      <td>4.394810</td>\n",
       "      <td>0.250634</td>\n",
       "      <td>3.612515</td>\n",
       "      <td>3.604950</td>\n",
       "      <td>1.980947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>23.064511</td>\n",
       "      <td>11.400417</td>\n",
       "      <td>5.822161</td>\n",
       "      <td>5.715964</td>\n",
       "      <td>11.786947</td>\n",
       "      <td>4.513742</td>\n",
       "      <td>0.233378</td>\n",
       "      <td>3.493013</td>\n",
       "      <td>3.740634</td>\n",
       "      <td>1.767432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>29.337131</td>\n",
       "      <td>11.516635</td>\n",
       "      <td>5.614795</td>\n",
       "      <td>5.729317</td>\n",
       "      <td>7.886869</td>\n",
       "      <td>4.780140</td>\n",
       "      <td>0.194727</td>\n",
       "      <td>3.225336</td>\n",
       "      <td>4.044555</td>\n",
       "      <td>1.289172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>27.351081</td>\n",
       "      <td>11.479838</td>\n",
       "      <td>5.680452</td>\n",
       "      <td>5.725089</td>\n",
       "      <td>9.121720</td>\n",
       "      <td>4.695792</td>\n",
       "      <td>0.206965</td>\n",
       "      <td>3.310089</td>\n",
       "      <td>3.948327</td>\n",
       "      <td>1.440599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13.859335</td>\n",
       "      <td>11.229864</td>\n",
       "      <td>6.126475</td>\n",
       "      <td>5.696369</td>\n",
       "      <td>17.510377</td>\n",
       "      <td>4.122798</td>\n",
       "      <td>0.290101</td>\n",
       "      <td>3.885833</td>\n",
       "      <td>3.294624</td>\n",
       "      <td>2.469286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>25.440185</td>\n",
       "      <td>11.444433</td>\n",
       "      <td>5.743624</td>\n",
       "      <td>5.721021</td>\n",
       "      <td>10.309843</td>\n",
       "      <td>4.614636</td>\n",
       "      <td>0.218740</td>\n",
       "      <td>3.391634</td>\n",
       "      <td>3.855740</td>\n",
       "      <td>1.586297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>22.877926</td>\n",
       "      <td>11.396960</td>\n",
       "      <td>5.828329</td>\n",
       "      <td>5.715567</td>\n",
       "      <td>11.902959</td>\n",
       "      <td>4.505817</td>\n",
       "      <td>0.234528</td>\n",
       "      <td>3.500975</td>\n",
       "      <td>3.731593</td>\n",
       "      <td>1.781658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>24.767695</td>\n",
       "      <td>11.431973</td>\n",
       "      <td>5.765856</td>\n",
       "      <td>5.719590</td>\n",
       "      <td>10.727972</td>\n",
       "      <td>4.586076</td>\n",
       "      <td>0.222883</td>\n",
       "      <td>3.420331</td>\n",
       "      <td>3.823156</td>\n",
       "      <td>1.637571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>22.853045</td>\n",
       "      <td>11.396499</td>\n",
       "      <td>5.829152</td>\n",
       "      <td>5.715514</td>\n",
       "      <td>11.918429</td>\n",
       "      <td>4.504761</td>\n",
       "      <td>0.234682</td>\n",
       "      <td>3.502037</td>\n",
       "      <td>3.730388</td>\n",
       "      <td>1.783555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>25.537825</td>\n",
       "      <td>11.446242</td>\n",
       "      <td>5.740396</td>\n",
       "      <td>5.721229</td>\n",
       "      <td>10.249134</td>\n",
       "      <td>4.618783</td>\n",
       "      <td>0.218138</td>\n",
       "      <td>3.387467</td>\n",
       "      <td>3.860471</td>\n",
       "      <td>1.578852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>24.372937</td>\n",
       "      <td>11.424659</td>\n",
       "      <td>5.778906</td>\n",
       "      <td>5.718750</td>\n",
       "      <td>10.973417</td>\n",
       "      <td>4.569310</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>3.437177</td>\n",
       "      <td>3.804030</td>\n",
       "      <td>1.667670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>30.432544</td>\n",
       "      <td>11.536931</td>\n",
       "      <td>5.578582</td>\n",
       "      <td>5.731649</td>\n",
       "      <td>7.205783</td>\n",
       "      <td>4.826662</td>\n",
       "      <td>0.187977</td>\n",
       "      <td>3.178591</td>\n",
       "      <td>4.097630</td>\n",
       "      <td>1.205651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>30.623569</td>\n",
       "      <td>11.540470</td>\n",
       "      <td>5.572266</td>\n",
       "      <td>5.732055</td>\n",
       "      <td>7.087011</td>\n",
       "      <td>4.834775</td>\n",
       "      <td>0.186800</td>\n",
       "      <td>3.170439</td>\n",
       "      <td>4.106886</td>\n",
       "      <td>1.191086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>18.830817</td>\n",
       "      <td>11.321975</td>\n",
       "      <td>5.962123</td>\n",
       "      <td>5.706952</td>\n",
       "      <td>14.419298</td>\n",
       "      <td>4.333937</td>\n",
       "      <td>0.259466</td>\n",
       "      <td>3.673681</td>\n",
       "      <td>3.535502</td>\n",
       "      <td>2.090232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>22.687367</td>\n",
       "      <td>11.393429</td>\n",
       "      <td>5.834629</td>\n",
       "      <td>5.715162</td>\n",
       "      <td>12.021441</td>\n",
       "      <td>4.497724</td>\n",
       "      <td>0.235702</td>\n",
       "      <td>3.509107</td>\n",
       "      <td>3.722360</td>\n",
       "      <td>1.796187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>23.943854</td>\n",
       "      <td>11.416709</td>\n",
       "      <td>5.793091</td>\n",
       "      <td>5.717836</td>\n",
       "      <td>11.240205</td>\n",
       "      <td>4.551087</td>\n",
       "      <td>0.227960</td>\n",
       "      <td>3.455488</td>\n",
       "      <td>3.783240</td>\n",
       "      <td>1.700385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>27.179835</td>\n",
       "      <td>11.476665</td>\n",
       "      <td>5.686113</td>\n",
       "      <td>5.724725</td>\n",
       "      <td>9.228194</td>\n",
       "      <td>4.688519</td>\n",
       "      <td>0.208020</td>\n",
       "      <td>3.317396</td>\n",
       "      <td>3.940030</td>\n",
       "      <td>1.453656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>26.616879</td>\n",
       "      <td>11.466235</td>\n",
       "      <td>5.704724</td>\n",
       "      <td>5.723526</td>\n",
       "      <td>9.578219</td>\n",
       "      <td>4.664611</td>\n",
       "      <td>0.211489</td>\n",
       "      <td>3.341420</td>\n",
       "      <td>3.912753</td>\n",
       "      <td>1.496579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>29.442492</td>\n",
       "      <td>11.518587</td>\n",
       "      <td>5.611312</td>\n",
       "      <td>5.729541</td>\n",
       "      <td>7.821360</td>\n",
       "      <td>4.784614</td>\n",
       "      <td>0.194077</td>\n",
       "      <td>3.220840</td>\n",
       "      <td>4.049660</td>\n",
       "      <td>1.281138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>26.449117</td>\n",
       "      <td>11.463126</td>\n",
       "      <td>5.710270</td>\n",
       "      <td>5.723169</td>\n",
       "      <td>9.682527</td>\n",
       "      <td>4.657486</td>\n",
       "      <td>0.212523</td>\n",
       "      <td>3.348579</td>\n",
       "      <td>3.904625</td>\n",
       "      <td>1.509370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>12.663835</td>\n",
       "      <td>11.207714</td>\n",
       "      <td>6.165997</td>\n",
       "      <td>5.693825</td>\n",
       "      <td>18.253694</td>\n",
       "      <td>4.072025</td>\n",
       "      <td>0.297467</td>\n",
       "      <td>3.936849</td>\n",
       "      <td>3.236699</td>\n",
       "      <td>2.560438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>26.275184</td>\n",
       "      <td>11.459904</td>\n",
       "      <td>5.716020</td>\n",
       "      <td>5.722799</td>\n",
       "      <td>9.790672</td>\n",
       "      <td>4.650099</td>\n",
       "      <td>0.213594</td>\n",
       "      <td>3.356001</td>\n",
       "      <td>3.896197</td>\n",
       "      <td>1.522632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>20.081142</td>\n",
       "      <td>11.345141</td>\n",
       "      <td>5.920788</td>\n",
       "      <td>5.709614</td>\n",
       "      <td>13.641893</td>\n",
       "      <td>4.387038</td>\n",
       "      <td>0.251762</td>\n",
       "      <td>3.620325</td>\n",
       "      <td>3.596083</td>\n",
       "      <td>1.994900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>19.315272</td>\n",
       "      <td>11.330951</td>\n",
       "      <td>5.946107</td>\n",
       "      <td>5.707983</td>\n",
       "      <td>14.118082</td>\n",
       "      <td>4.354512</td>\n",
       "      <td>0.256481</td>\n",
       "      <td>3.653007</td>\n",
       "      <td>3.558975</td>\n",
       "      <td>2.053295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>30.082805</td>\n",
       "      <td>11.530451</td>\n",
       "      <td>5.590144</td>\n",
       "      <td>5.730904</td>\n",
       "      <td>7.423238</td>\n",
       "      <td>4.811808</td>\n",
       "      <td>0.190132</td>\n",
       "      <td>3.193516</td>\n",
       "      <td>4.080684</td>\n",
       "      <td>1.232317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>21.523957</td>\n",
       "      <td>11.371874</td>\n",
       "      <td>5.873090</td>\n",
       "      <td>5.712685</td>\n",
       "      <td>12.744805</td>\n",
       "      <td>4.448314</td>\n",
       "      <td>0.242871</td>\n",
       "      <td>3.558754</td>\n",
       "      <td>3.665991</td>\n",
       "      <td>1.884892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>24.653394</td>\n",
       "      <td>11.429856</td>\n",
       "      <td>5.769634</td>\n",
       "      <td>5.719347</td>\n",
       "      <td>10.799040</td>\n",
       "      <td>4.581221</td>\n",
       "      <td>0.223588</td>\n",
       "      <td>3.425209</td>\n",
       "      <td>3.817618</td>\n",
       "      <td>1.646286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>24.713071</td>\n",
       "      <td>11.430961</td>\n",
       "      <td>5.767661</td>\n",
       "      <td>5.719474</td>\n",
       "      <td>10.761935</td>\n",
       "      <td>4.583756</td>\n",
       "      <td>0.223220</td>\n",
       "      <td>3.422662</td>\n",
       "      <td>3.820510</td>\n",
       "      <td>1.641736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>27.258933</td>\n",
       "      <td>11.478131</td>\n",
       "      <td>5.683498</td>\n",
       "      <td>5.724893</td>\n",
       "      <td>9.179014</td>\n",
       "      <td>4.691879</td>\n",
       "      <td>0.207532</td>\n",
       "      <td>3.314021</td>\n",
       "      <td>3.943862</td>\n",
       "      <td>1.447625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>14.218050</td>\n",
       "      <td>11.236511</td>\n",
       "      <td>6.114616</td>\n",
       "      <td>5.697133</td>\n",
       "      <td>17.287342</td>\n",
       "      <td>4.138033</td>\n",
       "      <td>0.287890</td>\n",
       "      <td>3.870525</td>\n",
       "      <td>3.312004</td>\n",
       "      <td>2.441935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>21.017117</td>\n",
       "      <td>11.362483</td>\n",
       "      <td>5.889846</td>\n",
       "      <td>5.711606</td>\n",
       "      <td>13.059939</td>\n",
       "      <td>4.426789</td>\n",
       "      <td>0.245994</td>\n",
       "      <td>3.580383</td>\n",
       "      <td>3.641433</td>\n",
       "      <td>1.923536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>36.357323</td>\n",
       "      <td>11.646705</td>\n",
       "      <td>5.382714</td>\n",
       "      <td>5.744261</td>\n",
       "      <td>3.521979</td>\n",
       "      <td>5.078287</td>\n",
       "      <td>0.151468</td>\n",
       "      <td>2.925758</td>\n",
       "      <td>4.384698</td>\n",
       "      <td>0.753913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>22.507818</td>\n",
       "      <td>11.390103</td>\n",
       "      <td>5.840565</td>\n",
       "      <td>5.714779</td>\n",
       "      <td>12.133078</td>\n",
       "      <td>4.490099</td>\n",
       "      <td>0.236809</td>\n",
       "      <td>3.516769</td>\n",
       "      <td>3.713661</td>\n",
       "      <td>1.809877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>28.436852</td>\n",
       "      <td>11.499955</td>\n",
       "      <td>5.644557</td>\n",
       "      <td>5.727400</td>\n",
       "      <td>8.446629</td>\n",
       "      <td>4.741905</td>\n",
       "      <td>0.200274</td>\n",
       "      <td>3.263755</td>\n",
       "      <td>4.000935</td>\n",
       "      <td>1.357814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>26.327938</td>\n",
       "      <td>11.460881</td>\n",
       "      <td>5.714276</td>\n",
       "      <td>5.722911</td>\n",
       "      <td>9.757871</td>\n",
       "      <td>4.652339</td>\n",
       "      <td>0.213269</td>\n",
       "      <td>3.353750</td>\n",
       "      <td>3.898753</td>\n",
       "      <td>1.518609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trachea-bronchus-lung  Colon, rectum and anus   Stomach  \\\n",
       "Australia                   20.264145               11.348532  5.914738   \n",
       "Austria                     23.064511               11.400417  5.822161   \n",
       "Belgium                     29.337131               11.516635  5.614795   \n",
       "Canada                      27.351081               11.479838  5.680452   \n",
       "Chile                       13.859335               11.229864  6.126475   \n",
       "Czech Rep.                  25.440185               11.444433  5.743624   \n",
       "Denmark                     22.877926               11.396960  5.828329   \n",
       "Estonia                     24.767695               11.431973  5.765856   \n",
       "Finland                     22.853045               11.396499  5.829152   \n",
       "France                      25.537825               11.446242  5.740396   \n",
       "Germany                     24.372937               11.424659  5.778906   \n",
       "Greece                      30.432544               11.536931  5.578582   \n",
       "Hungary                     30.623569               11.540470  5.572266   \n",
       "Iceland                     18.830817               11.321975  5.962123   \n",
       "Ireland                     22.687367               11.393429  5.834629   \n",
       "Israel                      23.943854               11.416709  5.793091   \n",
       "Italy                       27.179835               11.476665  5.686113   \n",
       "Japan                       26.616879               11.466235  5.704724   \n",
       "Korea                       29.442492               11.518587  5.611312   \n",
       "Luxembourg                  26.449117               11.463126  5.710270   \n",
       "Mexico                      12.663835               11.207714  6.165997   \n",
       "Netherlands                 26.275184               11.459904  5.716020   \n",
       "New Zealand                 20.081142               11.345141  5.920788   \n",
       "Norway                      19.315272               11.330951  5.946107   \n",
       "Poland                      30.082805               11.530451  5.590144   \n",
       "Portugal                    21.523957               11.371874  5.873090   \n",
       "Slovak Rep.                 24.653394               11.429856  5.769634   \n",
       "Slovenia                    24.713071               11.430961  5.767661   \n",
       "Spain                       27.258933               11.478131  5.683498   \n",
       "Sweden                      14.218050               11.236511  6.114616   \n",
       "Switzerland                 21.017117               11.362483  5.889846   \n",
       "Turkey                      36.357323               11.646705  5.382714   \n",
       "United Kingdom              22.507818               11.390103  5.840565   \n",
       "United States               28.436852               11.499955  5.644557   \n",
       "OECD                        26.327938               11.460881  5.714276   \n",
       "\n",
       "                Pancreas   Prostate     Liver  Hodgkins disease  Leukemia  \\\n",
       "Australia       5.710003  13.528109  4.394810          0.250634  3.612515   \n",
       "Austria         5.715964  11.786947  4.513742          0.233378  3.493013   \n",
       "Belgium         5.729317   7.886869  4.780140          0.194727  3.225336   \n",
       "Canada          5.725089   9.121720  4.695792          0.206965  3.310089   \n",
       "Chile           5.696369  17.510377  4.122798          0.290101  3.885833   \n",
       "Czech Rep.      5.721021  10.309843  4.614636          0.218740  3.391634   \n",
       "Denmark         5.715567  11.902959  4.505817          0.234528  3.500975   \n",
       "Estonia         5.719590  10.727972  4.586076          0.222883  3.420331   \n",
       "Finland         5.715514  11.918429  4.504761          0.234682  3.502037   \n",
       "France          5.721229  10.249134  4.618783          0.218138  3.387467   \n",
       "Germany         5.718750  10.973417  4.569310          0.225316  3.437177   \n",
       "Greece          5.731649   7.205783  4.826662          0.187977  3.178591   \n",
       "Hungary         5.732055   7.087011  4.834775          0.186800  3.170439   \n",
       "Iceland         5.706952  14.419298  4.333937          0.259466  3.673681   \n",
       "Ireland         5.715162  12.021441  4.497724          0.235702  3.509107   \n",
       "Israel          5.717836  11.240205  4.551087          0.227960  3.455488   \n",
       "Italy           5.724725   9.228194  4.688519          0.208020  3.317396   \n",
       "Japan           5.723526   9.578219  4.664611          0.211489  3.341420   \n",
       "Korea           5.729541   7.821360  4.784614          0.194077  3.220840   \n",
       "Luxembourg      5.723169   9.682527  4.657486          0.212523  3.348579   \n",
       "Mexico          5.693825  18.253694  4.072025          0.297467  3.936849   \n",
       "Netherlands     5.722799   9.790672  4.650099          0.213594  3.356001   \n",
       "New Zealand     5.709614  13.641893  4.387038          0.251762  3.620325   \n",
       "Norway          5.707983  14.118082  4.354512          0.256481  3.653007   \n",
       "Poland          5.730904   7.423238  4.811808          0.190132  3.193516   \n",
       "Portugal        5.712685  12.744805  4.448314          0.242871  3.558754   \n",
       "Slovak Rep.     5.719347  10.799040  4.581221          0.223588  3.425209   \n",
       "Slovenia        5.719474  10.761935  4.583756          0.223220  3.422662   \n",
       "Spain           5.724893   9.179014  4.691879          0.207532  3.314021   \n",
       "Sweden          5.697133  17.287342  4.138033          0.287890  3.870525   \n",
       "Switzerland     5.711606  13.059939  4.426789          0.245994  3.580383   \n",
       "Turkey          5.744261   3.521979  5.078287          0.151468  2.925758   \n",
       "United Kingdom  5.714779  12.133078  4.490099          0.236809  3.516769   \n",
       "United States   5.727400   8.446629  4.741905          0.200274  3.263755   \n",
       "OECD            5.722911   9.757871  4.652339          0.213269  3.353750   \n",
       "\n",
       "                 Bladder      Skin  \n",
       "Australia       3.604950  1.980947  \n",
       "Austria         3.740634  1.767432  \n",
       "Belgium         4.044555  1.289172  \n",
       "Canada          3.948327  1.440599  \n",
       "Chile           3.294624  2.469286  \n",
       "Czech Rep.      3.855740  1.586297  \n",
       "Denmark         3.731593  1.781658  \n",
       "Estonia         3.823156  1.637571  \n",
       "Finland         3.730388  1.783555  \n",
       "France          3.860471  1.578852  \n",
       "Germany         3.804030  1.667670  \n",
       "Greece          4.097630  1.205651  \n",
       "Hungary         4.106886  1.191086  \n",
       "Iceland         3.535502  2.090232  \n",
       "Ireland         3.722360  1.796187  \n",
       "Israel          3.783240  1.700385  \n",
       "Italy           3.940030  1.453656  \n",
       "Japan           3.912753  1.496579  \n",
       "Korea           4.049660  1.281138  \n",
       "Luxembourg      3.904625  1.509370  \n",
       "Mexico          3.236699  2.560438  \n",
       "Netherlands     3.896197  1.522632  \n",
       "New Zealand     3.596083  1.994900  \n",
       "Norway          3.558975  2.053295  \n",
       "Poland          4.080684  1.232317  \n",
       "Portugal        3.665991  1.884892  \n",
       "Slovak Rep.     3.817618  1.646286  \n",
       "Slovenia        3.820510  1.641736  \n",
       "Spain           3.943862  1.447625  \n",
       "Sweden          3.312004  2.441935  \n",
       "Switzerland     3.641433  1.923536  \n",
       "Turkey          4.384698  0.753913  \n",
       "United Kingdom  3.713661  1.809877  \n",
       "United States   4.000935  1.357814  \n",
       "OECD            3.898753  1.518609  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get calibrated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from calibration using 1 component\n",
    "X_from_1_component = model.X_predCal()[1]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_1_component_df = pd.DataFrame(model.X_predCal()[1])\n",
    "X_from_1_component_df.index = data_objNames\n",
    "X_from_1_component_df.columns = data_varNames\n",
    "X_from_1_component_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>20.813615</td>\n",
       "      <td>9.873754</td>\n",
       "      <td>2.845786</td>\n",
       "      <td>5.992706</td>\n",
       "      <td>14.447389</td>\n",
       "      <td>4.669554</td>\n",
       "      <td>0.266138</td>\n",
       "      <td>4.157895</td>\n",
       "      <td>3.606217</td>\n",
       "      <td>2.518563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>22.494585</td>\n",
       "      <td>11.221481</td>\n",
       "      <td>4.610192</td>\n",
       "      <td>6.083374</td>\n",
       "      <td>10.980075</td>\n",
       "      <td>6.161605</td>\n",
       "      <td>0.210409</td>\n",
       "      <td>3.715255</td>\n",
       "      <td>3.545885</td>\n",
       "      <td>2.036253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30.289386</td>\n",
       "      <td>10.608328</td>\n",
       "      <td>3.433897</td>\n",
       "      <td>5.721306</td>\n",
       "      <td>9.373847</td>\n",
       "      <td>3.504211</td>\n",
       "      <td>0.224562</td>\n",
       "      <td>3.533081</td>\n",
       "      <td>4.275626</td>\n",
       "      <td>1.638726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>27.787173</td>\n",
       "      <td>11.391266</td>\n",
       "      <td>3.158412</td>\n",
       "      <td>5.893813</td>\n",
       "      <td>9.850298</td>\n",
       "      <td>3.981193</td>\n",
       "      <td>0.214046</td>\n",
       "      <td>3.563045</td>\n",
       "      <td>4.182266</td>\n",
       "      <td>1.903987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13.712647</td>\n",
       "      <td>8.575350</td>\n",
       "      <td>17.352077</td>\n",
       "      <td>4.493128</td>\n",
       "      <td>16.942574</td>\n",
       "      <td>5.645705</td>\n",
       "      <td>0.334629</td>\n",
       "      <td>3.117106</td>\n",
       "      <td>2.246791</td>\n",
       "      <td>0.238829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>24.918911</td>\n",
       "      <td>13.994751</td>\n",
       "      <td>4.463644</td>\n",
       "      <td>5.911778</td>\n",
       "      <td>9.567855</td>\n",
       "      <td>3.777658</td>\n",
       "      <td>0.184778</td>\n",
       "      <td>3.138631</td>\n",
       "      <td>4.260203</td>\n",
       "      <td>1.915438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>23.792840</td>\n",
       "      <td>11.602391</td>\n",
       "      <td>3.084761</td>\n",
       "      <td>5.723473</td>\n",
       "      <td>13.348603</td>\n",
       "      <td>2.407581</td>\n",
       "      <td>0.256110</td>\n",
       "      <td>3.679893</td>\n",
       "      <td>4.207363</td>\n",
       "      <td>2.263796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>26.162181</td>\n",
       "      <td>9.950300</td>\n",
       "      <td>8.213290</td>\n",
       "      <td>5.004174</td>\n",
       "      <td>12.725499</td>\n",
       "      <td>2.276930</td>\n",
       "      <td>0.286110</td>\n",
       "      <td>3.270333</td>\n",
       "      <td>3.912231</td>\n",
       "      <td>1.045696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>23.634688</td>\n",
       "      <td>9.744582</td>\n",
       "      <td>3.976695</td>\n",
       "      <td>5.772572</td>\n",
       "      <td>13.144150</td>\n",
       "      <td>4.252419</td>\n",
       "      <td>0.263098</td>\n",
       "      <td>3.920313</td>\n",
       "      <td>3.730104</td>\n",
       "      <td>2.066009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>25.318437</td>\n",
       "      <td>10.305455</td>\n",
       "      <td>3.629688</td>\n",
       "      <td>6.136417</td>\n",
       "      <td>9.994900</td>\n",
       "      <td>6.352708</td>\n",
       "      <td>0.208694</td>\n",
       "      <td>3.843561</td>\n",
       "      <td>3.621303</td>\n",
       "      <td>1.983556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>24.497786</td>\n",
       "      <td>11.300840</td>\n",
       "      <td>4.618283</td>\n",
       "      <td>5.828546</td>\n",
       "      <td>11.196383</td>\n",
       "      <td>4.494917</td>\n",
       "      <td>0.226327</td>\n",
       "      <td>3.575605</td>\n",
       "      <td>3.866990</td>\n",
       "      <td>1.884125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>32.390331</td>\n",
       "      <td>9.396033</td>\n",
       "      <td>4.940451</td>\n",
       "      <td>5.245931</td>\n",
       "      <td>10.140811</td>\n",
       "      <td>2.051066</td>\n",
       "      <td>0.263410</td>\n",
       "      <td>3.431617</td>\n",
       "      <td>4.366631</td>\n",
       "      <td>1.168737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>30.065936</td>\n",
       "      <td>15.521221</td>\n",
       "      <td>5.385899</td>\n",
       "      <td>5.709550</td>\n",
       "      <td>6.251035</td>\n",
       "      <td>2.729456</td>\n",
       "      <td>0.148707</td>\n",
       "      <td>2.548894</td>\n",
       "      <td>4.759854</td>\n",
       "      <td>1.335305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>20.152357</td>\n",
       "      <td>12.832275</td>\n",
       "      <td>4.525541</td>\n",
       "      <td>5.324398</td>\n",
       "      <td>16.422357</td>\n",
       "      <td>-0.009238</td>\n",
       "      <td>0.293543</td>\n",
       "      <td>3.438680</td>\n",
       "      <td>4.383032</td>\n",
       "      <td>2.315855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>22.857148</td>\n",
       "      <td>12.181181</td>\n",
       "      <td>4.516112</td>\n",
       "      <td>5.772814</td>\n",
       "      <td>12.312995</td>\n",
       "      <td>3.538208</td>\n",
       "      <td>0.233584</td>\n",
       "      <td>3.501006</td>\n",
       "      <td>4.000899</td>\n",
       "      <td>2.057382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>22.744440</td>\n",
       "      <td>13.958059</td>\n",
       "      <td>5.044240</td>\n",
       "      <td>6.083814</td>\n",
       "      <td>9.474277</td>\n",
       "      <td>5.297130</td>\n",
       "      <td>0.172329</td>\n",
       "      <td>3.211816</td>\n",
       "      <td>3.923304</td>\n",
       "      <td>1.968020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>26.316282</td>\n",
       "      <td>11.161471</td>\n",
       "      <td>6.157831</td>\n",
       "      <td>5.992407</td>\n",
       "      <td>7.931905</td>\n",
       "      <td>6.989459</td>\n",
       "      <td>0.181128</td>\n",
       "      <td>3.405235</td>\n",
       "      <td>3.546747</td>\n",
       "      <td>1.409696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>23.988724</td>\n",
       "      <td>12.147866</td>\n",
       "      <td>14.551232</td>\n",
       "      <td>5.500557</td>\n",
       "      <td>5.390162</td>\n",
       "      <td>9.487348</td>\n",
       "      <td>0.146917</td>\n",
       "      <td>2.497388</td>\n",
       "      <td>2.778810</td>\n",
       "      <td>-0.053480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>25.200082</td>\n",
       "      <td>8.890300</td>\n",
       "      <td>13.061711</td>\n",
       "      <td>6.462246</td>\n",
       "      <td>1.292533</td>\n",
       "      <td>16.507169</td>\n",
       "      <td>0.084039</td>\n",
       "      <td>3.270790</td>\n",
       "      <td>1.690815</td>\n",
       "      <td>0.043556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>25.478326</td>\n",
       "      <td>12.194399</td>\n",
       "      <td>3.995312</td>\n",
       "      <td>6.241093</td>\n",
       "      <td>8.293326</td>\n",
       "      <td>6.527392</td>\n",
       "      <td>0.169839</td>\n",
       "      <td>3.506822</td>\n",
       "      <td>3.779329</td>\n",
       "      <td>1.918078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>12.129994</td>\n",
       "      <td>7.334733</td>\n",
       "      <td>8.498550</td>\n",
       "      <td>5.827617</td>\n",
       "      <td>17.399875</td>\n",
       "      <td>8.487836</td>\n",
       "      <td>0.304439</td>\n",
       "      <td>4.408436</td>\n",
       "      <td>2.134407</td>\n",
       "      <td>2.067437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>27.255134</td>\n",
       "      <td>11.627392</td>\n",
       "      <td>3.626408</td>\n",
       "      <td>5.627193</td>\n",
       "      <td>11.312093</td>\n",
       "      <td>2.362268</td>\n",
       "      <td>0.239786</td>\n",
       "      <td>3.462448</td>\n",
       "      <td>4.361955</td>\n",
       "      <td>1.872382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>19.713997</td>\n",
       "      <td>13.160483</td>\n",
       "      <td>3.830496</td>\n",
       "      <td>5.993047</td>\n",
       "      <td>13.157271</td>\n",
       "      <td>3.901573</td>\n",
       "      <td>0.223784</td>\n",
       "      <td>3.570956</td>\n",
       "      <td>3.930767</td>\n",
       "      <td>2.459085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>20.397995</td>\n",
       "      <td>12.321463</td>\n",
       "      <td>2.861589</td>\n",
       "      <td>5.650850</td>\n",
       "      <td>15.821075</td>\n",
       "      <td>1.204606</td>\n",
       "      <td>0.279154</td>\n",
       "      <td>3.715797</td>\n",
       "      <td>4.274383</td>\n",
       "      <td>2.605910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>30.878927</td>\n",
       "      <td>12.220196</td>\n",
       "      <td>6.453041</td>\n",
       "      <td>5.295536</td>\n",
       "      <td>8.575377</td>\n",
       "      <td>2.211490</td>\n",
       "      <td>0.217438</td>\n",
       "      <td>2.898333</td>\n",
       "      <td>4.478015</td>\n",
       "      <td>1.026882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>20.372164</td>\n",
       "      <td>14.314427</td>\n",
       "      <td>8.867988</td>\n",
       "      <td>5.563813</td>\n",
       "      <td>10.928376</td>\n",
       "      <td>4.350881</td>\n",
       "      <td>0.199566</td>\n",
       "      <td>2.823879</td>\n",
       "      <td>3.764091</td>\n",
       "      <td>1.426250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>23.239857</td>\n",
       "      <td>15.791968</td>\n",
       "      <td>6.366330</td>\n",
       "      <td>5.879592</td>\n",
       "      <td>8.665737</td>\n",
       "      <td>4.120755</td>\n",
       "      <td>0.156731</td>\n",
       "      <td>2.734135</td>\n",
       "      <td>4.215880</td>\n",
       "      <td>1.698084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>24.505298</td>\n",
       "      <td>13.265975</td>\n",
       "      <td>7.323272</td>\n",
       "      <td>5.484049</td>\n",
       "      <td>10.397215</td>\n",
       "      <td>3.328272</td>\n",
       "      <td>0.212850</td>\n",
       "      <td>2.948782</td>\n",
       "      <td>4.075912</td>\n",
       "      <td>1.384766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>26.627555</td>\n",
       "      <td>13.795004</td>\n",
       "      <td>5.326583</td>\n",
       "      <td>5.852600</td>\n",
       "      <td>8.244807</td>\n",
       "      <td>4.228854</td>\n",
       "      <td>0.173906</td>\n",
       "      <td>3.010123</td>\n",
       "      <td>4.223950</td>\n",
       "      <td>1.599199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>15.128844</td>\n",
       "      <td>11.277638</td>\n",
       "      <td>3.090824</td>\n",
       "      <td>5.752034</td>\n",
       "      <td>18.736412</td>\n",
       "      <td>2.221103</td>\n",
       "      <td>0.309143</td>\n",
       "      <td>4.109782</td>\n",
       "      <td>3.762220</td>\n",
       "      <td>2.976007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>21.394659</td>\n",
       "      <td>10.032613</td>\n",
       "      <td>3.618807</td>\n",
       "      <td>5.945846</td>\n",
       "      <td>13.697881</td>\n",
       "      <td>4.907488</td>\n",
       "      <td>0.257549</td>\n",
       "      <td>4.028607</td>\n",
       "      <td>3.581372</td>\n",
       "      <td>2.318812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>38.553730</td>\n",
       "      <td>7.116832</td>\n",
       "      <td>8.595589</td>\n",
       "      <td>4.833684</td>\n",
       "      <td>6.696434</td>\n",
       "      <td>3.417367</td>\n",
       "      <td>0.258927</td>\n",
       "      <td>3.145521</td>\n",
       "      <td>4.083622</td>\n",
       "      <td>-0.093331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>23.180272</td>\n",
       "      <td>10.678515</td>\n",
       "      <td>3.779165</td>\n",
       "      <td>5.779330</td>\n",
       "      <td>13.200065</td>\n",
       "      <td>3.703044</td>\n",
       "      <td>0.256456</td>\n",
       "      <td>3.804340</td>\n",
       "      <td>3.882012</td>\n",
       "      <td>2.157179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>29.306034</td>\n",
       "      <td>9.253495</td>\n",
       "      <td>2.224565</td>\n",
       "      <td>5.987948</td>\n",
       "      <td>9.854582</td>\n",
       "      <td>4.955702</td>\n",
       "      <td>0.229198</td>\n",
       "      <td>3.951821</td>\n",
       "      <td>3.966056</td>\n",
       "      <td>1.929831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>26.064646</td>\n",
       "      <td>10.789763</td>\n",
       "      <td>6.289244</td>\n",
       "      <td>5.785407</td>\n",
       "      <td>9.350852</td>\n",
       "      <td>5.810476</td>\n",
       "      <td>0.209133</td>\n",
       "      <td>3.433306</td>\n",
       "      <td>3.640212</td>\n",
       "      <td>1.410508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trachea-bronchus-lung  Colon, rectum and anus    Stomach  \\\n",
       "Australia                   20.813615                9.873754   2.845786   \n",
       "Austria                     22.494585               11.221481   4.610192   \n",
       "Belgium                     30.289386               10.608328   3.433897   \n",
       "Canada                      27.787173               11.391266   3.158412   \n",
       "Chile                       13.712647                8.575350  17.352077   \n",
       "Czech Rep.                  24.918911               13.994751   4.463644   \n",
       "Denmark                     23.792840               11.602391   3.084761   \n",
       "Estonia                     26.162181                9.950300   8.213290   \n",
       "Finland                     23.634688                9.744582   3.976695   \n",
       "France                      25.318437               10.305455   3.629688   \n",
       "Germany                     24.497786               11.300840   4.618283   \n",
       "Greece                      32.390331                9.396033   4.940451   \n",
       "Hungary                     30.065936               15.521221   5.385899   \n",
       "Iceland                     20.152357               12.832275   4.525541   \n",
       "Ireland                     22.857148               12.181181   4.516112   \n",
       "Israel                      22.744440               13.958059   5.044240   \n",
       "Italy                       26.316282               11.161471   6.157831   \n",
       "Japan                       23.988724               12.147866  14.551232   \n",
       "Korea                       25.200082                8.890300  13.061711   \n",
       "Luxembourg                  25.478326               12.194399   3.995312   \n",
       "Mexico                      12.129994                7.334733   8.498550   \n",
       "Netherlands                 27.255134               11.627392   3.626408   \n",
       "New Zealand                 19.713997               13.160483   3.830496   \n",
       "Norway                      20.397995               12.321463   2.861589   \n",
       "Poland                      30.878927               12.220196   6.453041   \n",
       "Portugal                    20.372164               14.314427   8.867988   \n",
       "Slovak Rep.                 23.239857               15.791968   6.366330   \n",
       "Slovenia                    24.505298               13.265975   7.323272   \n",
       "Spain                       26.627555               13.795004   5.326583   \n",
       "Sweden                      15.128844               11.277638   3.090824   \n",
       "Switzerland                 21.394659               10.032613   3.618807   \n",
       "Turkey                      38.553730                7.116832   8.595589   \n",
       "United Kingdom              23.180272               10.678515   3.779165   \n",
       "United States               29.306034                9.253495   2.224565   \n",
       "OECD                        26.064646               10.789763   6.289244   \n",
       "\n",
       "                Pancreas   Prostate      Liver  Hodgkins disease  Leukemia  \\\n",
       "Australia       5.992706  14.447389   4.669554          0.266138  4.157895   \n",
       "Austria         6.083374  10.980075   6.161605          0.210409  3.715255   \n",
       "Belgium         5.721306   9.373847   3.504211          0.224562  3.533081   \n",
       "Canada          5.893813   9.850298   3.981193          0.214046  3.563045   \n",
       "Chile           4.493128  16.942574   5.645705          0.334629  3.117106   \n",
       "Czech Rep.      5.911778   9.567855   3.777658          0.184778  3.138631   \n",
       "Denmark         5.723473  13.348603   2.407581          0.256110  3.679893   \n",
       "Estonia         5.004174  12.725499   2.276930          0.286110  3.270333   \n",
       "Finland         5.772572  13.144150   4.252419          0.263098  3.920313   \n",
       "France          6.136417   9.994900   6.352708          0.208694  3.843561   \n",
       "Germany         5.828546  11.196383   4.494917          0.226327  3.575605   \n",
       "Greece          5.245931  10.140811   2.051066          0.263410  3.431617   \n",
       "Hungary         5.709550   6.251035   2.729456          0.148707  2.548894   \n",
       "Iceland         5.324398  16.422357  -0.009238          0.293543  3.438680   \n",
       "Ireland         5.772814  12.312995   3.538208          0.233584  3.501006   \n",
       "Israel          6.083814   9.474277   5.297130          0.172329  3.211816   \n",
       "Italy           5.992407   7.931905   6.989459          0.181128  3.405235   \n",
       "Japan           5.500557   5.390162   9.487348          0.146917  2.497388   \n",
       "Korea           6.462246   1.292533  16.507169          0.084039  3.270790   \n",
       "Luxembourg      6.241093   8.293326   6.527392          0.169839  3.506822   \n",
       "Mexico          5.827617  17.399875   8.487836          0.304439  4.408436   \n",
       "Netherlands     5.627193  11.312093   2.362268          0.239786  3.462448   \n",
       "New Zealand     5.993047  13.157271   3.901573          0.223784  3.570956   \n",
       "Norway          5.650850  15.821075   1.204606          0.279154  3.715797   \n",
       "Poland          5.295536   8.575377   2.211490          0.217438  2.898333   \n",
       "Portugal        5.563813  10.928376   4.350881          0.199566  2.823879   \n",
       "Slovak Rep.     5.879592   8.665737   4.120755          0.156731  2.734135   \n",
       "Slovenia        5.484049  10.397215   3.328272          0.212850  2.948782   \n",
       "Spain           5.852600   8.244807   4.228854          0.173906  3.010123   \n",
       "Sweden          5.752034  18.736412   2.221103          0.309143  4.109782   \n",
       "Switzerland     5.945846  13.697881   4.907488          0.257549  4.028607   \n",
       "Turkey          4.833684   6.696434   3.417367          0.258927  3.145521   \n",
       "United Kingdom  5.779330  13.200065   3.703044          0.256456  3.804340   \n",
       "United States   5.987948   9.854582   4.955702          0.229198  3.951821   \n",
       "OECD            5.785407   9.350852   5.810476          0.209133  3.433306   \n",
       "\n",
       "                 Bladder      Skin  \n",
       "Australia       3.606217  2.518563  \n",
       "Austria         3.545885  2.036253  \n",
       "Belgium         4.275626  1.638726  \n",
       "Canada          4.182266  1.903987  \n",
       "Chile           2.246791  0.238829  \n",
       "Czech Rep.      4.260203  1.915438  \n",
       "Denmark         4.207363  2.263796  \n",
       "Estonia         3.912231  1.045696  \n",
       "Finland         3.730104  2.066009  \n",
       "France          3.621303  1.983556  \n",
       "Germany         3.866990  1.884125  \n",
       "Greece          4.366631  1.168737  \n",
       "Hungary         4.759854  1.335305  \n",
       "Iceland         4.383032  2.315855  \n",
       "Ireland         4.000899  2.057382  \n",
       "Israel          3.923304  1.968020  \n",
       "Italy           3.546747  1.409696  \n",
       "Japan           2.778810 -0.053480  \n",
       "Korea           1.690815  0.043556  \n",
       "Luxembourg      3.779329  1.918078  \n",
       "Mexico          2.134407  2.067437  \n",
       "Netherlands     4.361955  1.872382  \n",
       "New Zealand     3.930767  2.459085  \n",
       "Norway          4.274383  2.605910  \n",
       "Poland          4.478015  1.026882  \n",
       "Portugal        3.764091  1.426250  \n",
       "Slovak Rep.     4.215880  1.698084  \n",
       "Slovenia        4.075912  1.384766  \n",
       "Spain           4.223950  1.599199  \n",
       "Sweden          3.762220  2.976007  \n",
       "Switzerland     3.581372  2.318812  \n",
       "Turkey          4.083622 -0.093331  \n",
       "United Kingdom  3.882012  2.157179  \n",
       "United States   3.966056  1.929831  \n",
       "OECD            3.640212  1.410508  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predicted X for a given number of components\n",
    "\n",
    "# Predicted X from calibration using 4 components\n",
    "X_from_4_component = model.X_predCal()[4]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_4_component_df = pd.DataFrame(model.X_predCal()[4])\n",
    "X_from_4_component_df.index = data_objNames\n",
    "X_from_4_component_df.columns = data_varNames\n",
    "X_from_4_component_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_predCal in module hoggorm.pca:\n",
      "\n",
      "X_predCal(self)\n",
      "    Returns a dictionary holding the predicted arrays Xhat from\n",
      "    calibration after each computed component. Dictionary key represents\n",
      "    order of component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_predCal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>41.228104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>27.515848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>3.577173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>16.787521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     validated explained variance\n",
       "PC1                     41.228104\n",
       "PC2                     27.515848\n",
       "PC3                      3.577173\n",
       "PC4                     16.787521"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated explained variance of each component\n",
    "valExplVar = model.X_valExplVar()\n",
    "\n",
    "# Get calibrated explained variance and store in pandas dataframe with row and column names\n",
    "valExplVar_df = pd.DataFrame(model.X_valExplVar())\n",
    "valExplVar_df.columns = ['validated explained variance']\n",
    "valExplVar_df.index = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "valExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_valExplVar in module hoggorm.pca:\n",
      "\n",
      "X_valExplVar(self)\n",
      "    Returns a list holding the validated explained variance for X after\n",
      "    each component. First number in list is for component 1, second number\n",
      "    for component 2, third number for component 3, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_valExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative validated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>41.228104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>68.743952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>72.321125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>89.108645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cumulative validated explained variance\n",
       "PC0                                 0.000000\n",
       "PC1                                41.228104\n",
       "PC2                                68.743952\n",
       "PC3                                72.321125\n",
       "PC4                                89.108645"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative validated explained variance\n",
    "cumValExplVar = model.X_cumValExplVar()\n",
    "\n",
    "# Get cumulative validated explained variance and store in pandas dataframe with row and column names\n",
    "cumValExplVar_df = pd.DataFrame(model.X_cumValExplVar())\n",
    "cumValExplVar_df.columns = ['cumulative validated explained variance']\n",
    "cumValExplVar_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumValExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumValExplVar in module hoggorm.pca:\n",
      "\n",
      "X_cumValExplVar(self)\n",
      "    Returns a list holding the cumulative validated explained variance\n",
      "    for array X after each component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumValExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>90.824851</td>\n",
       "      <td>-17.145967</td>\n",
       "      <td>-19.815931</td>\n",
       "      <td>-5.240408</td>\n",
       "      <td>62.379405</td>\n",
       "      <td>-10.961539</td>\n",
       "      <td>-9.643134</td>\n",
       "      <td>-5.138706</td>\n",
       "      <td>-6.705916</td>\n",
       "      <td>2.898408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>95.904674</td>\n",
       "      <td>-17.188115</td>\n",
       "      <td>63.295538</td>\n",
       "      <td>-15.408414</td>\n",
       "      <td>84.244357</td>\n",
       "      <td>41.440343</td>\n",
       "      <td>-17.843876</td>\n",
       "      <td>-15.248051</td>\n",
       "      <td>28.054297</td>\n",
       "      <td>26.713650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>97.104389</td>\n",
       "      <td>1.232204</td>\n",
       "      <td>67.630816</td>\n",
       "      <td>-10.748362</td>\n",
       "      <td>90.128127</td>\n",
       "      <td>40.872884</td>\n",
       "      <td>-17.770772</td>\n",
       "      <td>-23.139446</td>\n",
       "      <td>24.826724</td>\n",
       "      <td>34.032033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>98.475855</td>\n",
       "      <td>88.493629</td>\n",
       "      <td>99.498470</td>\n",
       "      <td>-12.666695</td>\n",
       "      <td>91.756606</td>\n",
       "      <td>79.683951</td>\n",
       "      <td>-33.877348</td>\n",
       "      <td>-18.734551</td>\n",
       "      <td>24.589304</td>\n",
       "      <td>31.520312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Trachea-bronchus-lung  Colon, rectum and anus    Stomach   Pancreas  \\\n",
       "PC0               0.000000                0.000000   0.000000   0.000000   \n",
       "PC1              90.824851              -17.145967 -19.815931  -5.240408   \n",
       "PC2              95.904674              -17.188115  63.295538 -15.408414   \n",
       "PC3              97.104389                1.232204  67.630816 -10.748362   \n",
       "PC4              98.475855               88.493629  99.498470 -12.666695   \n",
       "\n",
       "      Prostate      Liver  Hodgkins disease   Leukemia    Bladder       Skin  \n",
       "PC0   0.000000   0.000000          0.000000   0.000000   0.000000   0.000000  \n",
       "PC1  62.379405 -10.961539         -9.643134  -5.138706  -6.705916   2.898408  \n",
       "PC2  84.244357  41.440343        -17.843876 -15.248051  28.054297  26.713650  \n",
       "PC3  90.128127  40.872884        -17.770772 -23.139446  24.826724  34.032033  \n",
       "PC4  91.756606  79.683951        -33.877348 -18.734551  24.589304  31.520312  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative validated explained variance for each variable\n",
    "cumCalExplVar_ind = model.X_cumCalExplVar_indVar()\n",
    "\n",
    "# Get cumulative validated explained variance for each variable and store in pandas dataframe with row and column names\n",
    "cumValExplVar_ind_df = pd.DataFrame(model.X_cumValExplVar_indVar())\n",
    "cumValExplVar_ind_df.columns = data_varNames\n",
    "cumValExplVar_ind_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumValExplVar_ind_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumValExplVar_indVar in module hoggorm.pca:\n",
      "\n",
      "X_cumValExplVar_indVar(self)\n",
      "    Returns an array holding the cumulative validated explained variance\n",
      "    for each variable in X after each component. First row represents\n",
      "    zero components, second row represents component 1, third row for\n",
      "    compnent 2, etc. Columns represent variables.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumValExplVar_indVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>20.346812</td>\n",
       "      <td>11.452455</td>\n",
       "      <td>6.105403</td>\n",
       "      <td>5.743157</td>\n",
       "      <td>13.435601</td>\n",
       "      <td>4.446251</td>\n",
       "      <td>0.254297</td>\n",
       "      <td>3.591813</td>\n",
       "      <td>3.628411</td>\n",
       "      <td>1.842250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>23.098502</td>\n",
       "      <td>11.415033</td>\n",
       "      <td>5.860404</td>\n",
       "      <td>5.687173</td>\n",
       "      <td>11.820143</td>\n",
       "      <td>4.476746</td>\n",
       "      <td>0.236020</td>\n",
       "      <td>3.483532</td>\n",
       "      <td>3.763559</td>\n",
       "      <td>1.761308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>29.146256</td>\n",
       "      <td>11.573484</td>\n",
       "      <td>5.847552</td>\n",
       "      <td>5.763295</td>\n",
       "      <td>7.834264</td>\n",
       "      <td>4.924500</td>\n",
       "      <td>0.197677</td>\n",
       "      <td>3.203712</td>\n",
       "      <td>3.982234</td>\n",
       "      <td>1.300025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>27.278466</td>\n",
       "      <td>11.477010</td>\n",
       "      <td>5.818147</td>\n",
       "      <td>5.737198</td>\n",
       "      <td>9.114227</td>\n",
       "      <td>4.747381</td>\n",
       "      <td>0.207564</td>\n",
       "      <td>3.290940</td>\n",
       "      <td>3.958260</td>\n",
       "      <td>1.435539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>17.147842</td>\n",
       "      <td>11.842768</td>\n",
       "      <td>3.732281</td>\n",
       "      <td>5.903233</td>\n",
       "      <td>16.242627</td>\n",
       "      <td>3.460482</td>\n",
       "      <td>0.284343</td>\n",
       "      <td>3.913106</td>\n",
       "      <td>3.740709</td>\n",
       "      <td>2.648065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>25.441970</td>\n",
       "      <td>11.365015</td>\n",
       "      <td>5.785915</td>\n",
       "      <td>5.689270</td>\n",
       "      <td>10.345341</td>\n",
       "      <td>4.651024</td>\n",
       "      <td>0.216961</td>\n",
       "      <td>3.392898</td>\n",
       "      <td>3.863856</td>\n",
       "      <td>1.589068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>22.877615</td>\n",
       "      <td>11.371955</td>\n",
       "      <td>5.918591</td>\n",
       "      <td>5.694592</td>\n",
       "      <td>11.791892</td>\n",
       "      <td>4.557505</td>\n",
       "      <td>0.234934</td>\n",
       "      <td>3.511302</td>\n",
       "      <td>3.712101</td>\n",
       "      <td>1.761700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>24.709957</td>\n",
       "      <td>11.466478</td>\n",
       "      <td>5.690023</td>\n",
       "      <td>5.727979</td>\n",
       "      <td>10.666493</td>\n",
       "      <td>4.650982</td>\n",
       "      <td>0.226537</td>\n",
       "      <td>3.407674</td>\n",
       "      <td>3.832053</td>\n",
       "      <td>1.656775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>22.845354</td>\n",
       "      <td>11.447777</td>\n",
       "      <td>5.885934</td>\n",
       "      <td>5.644067</td>\n",
       "      <td>11.845279</td>\n",
       "      <td>4.517876</td>\n",
       "      <td>0.236904</td>\n",
       "      <td>3.525032</td>\n",
       "      <td>3.756839</td>\n",
       "      <td>1.760221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>25.536053</td>\n",
       "      <td>11.486346</td>\n",
       "      <td>5.816234</td>\n",
       "      <td>5.733147</td>\n",
       "      <td>10.269548</td>\n",
       "      <td>4.560135</td>\n",
       "      <td>0.220550</td>\n",
       "      <td>3.383306</td>\n",
       "      <td>3.844408</td>\n",
       "      <td>1.594376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>24.371904</td>\n",
       "      <td>11.431935</td>\n",
       "      <td>5.813820</td>\n",
       "      <td>5.687086</td>\n",
       "      <td>10.972379</td>\n",
       "      <td>4.582907</td>\n",
       "      <td>0.227475</td>\n",
       "      <td>3.432105</td>\n",
       "      <td>3.821834</td>\n",
       "      <td>1.673564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>29.988759</td>\n",
       "      <td>11.760878</td>\n",
       "      <td>5.769006</td>\n",
       "      <td>5.773468</td>\n",
       "      <td>7.166377</td>\n",
       "      <td>5.209923</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>3.133014</td>\n",
       "      <td>3.946455</td>\n",
       "      <td>1.240776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>30.432727</td>\n",
       "      <td>11.129622</td>\n",
       "      <td>5.663199</td>\n",
       "      <td>5.766419</td>\n",
       "      <td>7.208318</td>\n",
       "      <td>5.027657</td>\n",
       "      <td>0.193566</td>\n",
       "      <td>3.230453</td>\n",
       "      <td>4.123417</td>\n",
       "      <td>1.205169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>19.045072</td>\n",
       "      <td>11.176274</td>\n",
       "      <td>6.205299</td>\n",
       "      <td>5.830261</td>\n",
       "      <td>13.937207</td>\n",
       "      <td>4.701215</td>\n",
       "      <td>0.231198</td>\n",
       "      <td>3.703216</td>\n",
       "      <td>3.375083</td>\n",
       "      <td>2.111165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>22.690428</td>\n",
       "      <td>11.361906</td>\n",
       "      <td>5.886459</td>\n",
       "      <td>5.712189</td>\n",
       "      <td>11.996754</td>\n",
       "      <td>4.534695</td>\n",
       "      <td>0.235493</td>\n",
       "      <td>3.523132</td>\n",
       "      <td>3.750129</td>\n",
       "      <td>1.791230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>24.018899</td>\n",
       "      <td>11.379755</td>\n",
       "      <td>5.802910</td>\n",
       "      <td>5.628857</td>\n",
       "      <td>11.336708</td>\n",
       "      <td>4.582601</td>\n",
       "      <td>0.224217</td>\n",
       "      <td>3.406475</td>\n",
       "      <td>3.751077</td>\n",
       "      <td>1.682347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>27.184279</td>\n",
       "      <td>11.501753</td>\n",
       "      <td>5.653202</td>\n",
       "      <td>5.737154</td>\n",
       "      <td>9.332802</td>\n",
       "      <td>4.577990</td>\n",
       "      <td>0.206790</td>\n",
       "      <td>3.306558</td>\n",
       "      <td>3.912044</td>\n",
       "      <td>1.473370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>26.228981</td>\n",
       "      <td>11.445194</td>\n",
       "      <td>5.375725</td>\n",
       "      <td>5.668874</td>\n",
       "      <td>10.046941</td>\n",
       "      <td>4.461135</td>\n",
       "      <td>0.220210</td>\n",
       "      <td>3.402577</td>\n",
       "      <td>3.946523</td>\n",
       "      <td>1.586448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>27.730339</td>\n",
       "      <td>11.575703</td>\n",
       "      <td>5.162100</td>\n",
       "      <td>5.728087</td>\n",
       "      <td>9.321042</td>\n",
       "      <td>3.946935</td>\n",
       "      <td>0.211874</td>\n",
       "      <td>3.366191</td>\n",
       "      <td>4.084304</td>\n",
       "      <td>1.504239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>26.466206</td>\n",
       "      <td>11.445000</td>\n",
       "      <td>5.769489</td>\n",
       "      <td>5.710523</td>\n",
       "      <td>9.772187</td>\n",
       "      <td>4.603458</td>\n",
       "      <td>0.213747</td>\n",
       "      <td>3.299525</td>\n",
       "      <td>3.898795</td>\n",
       "      <td>1.499993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>14.823905</td>\n",
       "      <td>12.330597</td>\n",
       "      <td>4.708767</td>\n",
       "      <td>5.809731</td>\n",
       "      <td>18.140365</td>\n",
       "      <td>2.574636</td>\n",
       "      <td>0.209335</td>\n",
       "      <td>3.454833</td>\n",
       "      <td>3.783557</td>\n",
       "      <td>2.955884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>26.186871</td>\n",
       "      <td>11.456603</td>\n",
       "      <td>5.805600</td>\n",
       "      <td>5.735989</td>\n",
       "      <td>9.772606</td>\n",
       "      <td>4.745666</td>\n",
       "      <td>0.215727</td>\n",
       "      <td>3.367532</td>\n",
       "      <td>3.897608</td>\n",
       "      <td>1.502747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>20.191262</td>\n",
       "      <td>11.244010</td>\n",
       "      <td>6.045521</td>\n",
       "      <td>5.757741</td>\n",
       "      <td>13.613356</td>\n",
       "      <td>4.460347</td>\n",
       "      <td>0.253996</td>\n",
       "      <td>3.586785</td>\n",
       "      <td>3.639054</td>\n",
       "      <td>1.808438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>19.463314</td>\n",
       "      <td>11.188321</td>\n",
       "      <td>6.226537</td>\n",
       "      <td>5.680406</td>\n",
       "      <td>13.654525</td>\n",
       "      <td>4.581480</td>\n",
       "      <td>0.258470</td>\n",
       "      <td>3.660036</td>\n",
       "      <td>3.508576</td>\n",
       "      <td>1.932281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>29.921540</td>\n",
       "      <td>11.489156</td>\n",
       "      <td>5.578672</td>\n",
       "      <td>5.820119</td>\n",
       "      <td>7.389165</td>\n",
       "      <td>5.069093</td>\n",
       "      <td>0.189600</td>\n",
       "      <td>3.215776</td>\n",
       "      <td>3.985895</td>\n",
       "      <td>1.218740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>21.668124</td>\n",
       "      <td>11.251377</td>\n",
       "      <td>5.735740</td>\n",
       "      <td>5.751282</td>\n",
       "      <td>12.764150</td>\n",
       "      <td>4.426888</td>\n",
       "      <td>0.245906</td>\n",
       "      <td>3.578680</td>\n",
       "      <td>3.644499</td>\n",
       "      <td>1.923347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>24.701635</td>\n",
       "      <td>11.321178</td>\n",
       "      <td>5.759912</td>\n",
       "      <td>5.721322</td>\n",
       "      <td>10.900040</td>\n",
       "      <td>4.624149</td>\n",
       "      <td>0.221828</td>\n",
       "      <td>3.451396</td>\n",
       "      <td>3.837558</td>\n",
       "      <td>1.650205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>24.702075</td>\n",
       "      <td>11.362842</td>\n",
       "      <td>5.717365</td>\n",
       "      <td>5.737422</td>\n",
       "      <td>10.754704</td>\n",
       "      <td>4.600925</td>\n",
       "      <td>0.225268</td>\n",
       "      <td>3.450790</td>\n",
       "      <td>3.812320</td>\n",
       "      <td>1.626453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>27.234718</td>\n",
       "      <td>11.368016</td>\n",
       "      <td>5.706269</td>\n",
       "      <td>5.760888</td>\n",
       "      <td>9.221238</td>\n",
       "      <td>4.674359</td>\n",
       "      <td>0.208281</td>\n",
       "      <td>3.330966</td>\n",
       "      <td>3.842222</td>\n",
       "      <td>1.473031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>14.401049</td>\n",
       "      <td>10.978468</td>\n",
       "      <td>7.150693</td>\n",
       "      <td>5.492561</td>\n",
       "      <td>16.026316</td>\n",
       "      <td>4.742420</td>\n",
       "      <td>0.301095</td>\n",
       "      <td>3.862850</td>\n",
       "      <td>3.081189</td>\n",
       "      <td>2.231894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>21.027227</td>\n",
       "      <td>11.414215</td>\n",
       "      <td>6.004838</td>\n",
       "      <td>5.688268</td>\n",
       "      <td>12.976067</td>\n",
       "      <td>4.399588</td>\n",
       "      <td>0.256229</td>\n",
       "      <td>3.586549</td>\n",
       "      <td>3.613891</td>\n",
       "      <td>1.897293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>34.708058</td>\n",
       "      <td>12.730771</td>\n",
       "      <td>4.438275</td>\n",
       "      <td>5.901250</td>\n",
       "      <td>3.193107</td>\n",
       "      <td>5.370252</td>\n",
       "      <td>0.138639</td>\n",
       "      <td>2.812942</td>\n",
       "      <td>4.581269</td>\n",
       "      <td>0.917090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>22.522778</td>\n",
       "      <td>11.427796</td>\n",
       "      <td>5.928451</td>\n",
       "      <td>5.740641</td>\n",
       "      <td>12.092165</td>\n",
       "      <td>4.533439</td>\n",
       "      <td>0.238070</td>\n",
       "      <td>3.524824</td>\n",
       "      <td>3.702613</td>\n",
       "      <td>1.815952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>28.256565</td>\n",
       "      <td>11.620894</td>\n",
       "      <td>5.875856</td>\n",
       "      <td>5.701849</td>\n",
       "      <td>8.446227</td>\n",
       "      <td>4.783065</td>\n",
       "      <td>0.198763</td>\n",
       "      <td>3.213984</td>\n",
       "      <td>4.013630</td>\n",
       "      <td>1.325812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>26.332338</td>\n",
       "      <td>11.488461</td>\n",
       "      <td>5.689744</td>\n",
       "      <td>5.715559</td>\n",
       "      <td>9.784644</td>\n",
       "      <td>4.611067</td>\n",
       "      <td>0.214029</td>\n",
       "      <td>3.351589</td>\n",
       "      <td>3.909208</td>\n",
       "      <td>1.527713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trachea-bronchus-lung  Colon, rectum and anus   Stomach  \\\n",
       "Australia                   20.346812               11.452455  6.105403   \n",
       "Austria                     23.098502               11.415033  5.860404   \n",
       "Belgium                     29.146256               11.573484  5.847552   \n",
       "Canada                      27.278466               11.477010  5.818147   \n",
       "Chile                       17.147842               11.842768  3.732281   \n",
       "Czech Rep.                  25.441970               11.365015  5.785915   \n",
       "Denmark                     22.877615               11.371955  5.918591   \n",
       "Estonia                     24.709957               11.466478  5.690023   \n",
       "Finland                     22.845354               11.447777  5.885934   \n",
       "France                      25.536053               11.486346  5.816234   \n",
       "Germany                     24.371904               11.431935  5.813820   \n",
       "Greece                      29.988759               11.760878  5.769006   \n",
       "Hungary                     30.432727               11.129622  5.663199   \n",
       "Iceland                     19.045072               11.176274  6.205299   \n",
       "Ireland                     22.690428               11.361906  5.886459   \n",
       "Israel                      24.018899               11.379755  5.802910   \n",
       "Italy                       27.184279               11.501753  5.653202   \n",
       "Japan                       26.228981               11.445194  5.375725   \n",
       "Korea                       27.730339               11.575703  5.162100   \n",
       "Luxembourg                  26.466206               11.445000  5.769489   \n",
       "Mexico                      14.823905               12.330597  4.708767   \n",
       "Netherlands                 26.186871               11.456603  5.805600   \n",
       "New Zealand                 20.191262               11.244010  6.045521   \n",
       "Norway                      19.463314               11.188321  6.226537   \n",
       "Poland                      29.921540               11.489156  5.578672   \n",
       "Portugal                    21.668124               11.251377  5.735740   \n",
       "Slovak Rep.                 24.701635               11.321178  5.759912   \n",
       "Slovenia                    24.702075               11.362842  5.717365   \n",
       "Spain                       27.234718               11.368016  5.706269   \n",
       "Sweden                      14.401049               10.978468  7.150693   \n",
       "Switzerland                 21.027227               11.414215  6.004838   \n",
       "Turkey                      34.708058               12.730771  4.438275   \n",
       "United Kingdom              22.522778               11.427796  5.928451   \n",
       "United States               28.256565               11.620894  5.875856   \n",
       "OECD                        26.332338               11.488461  5.689744   \n",
       "\n",
       "                Pancreas   Prostate     Liver  Hodgkins disease  Leukemia  \\\n",
       "Australia       5.743157  13.435601  4.446251          0.254297  3.591813   \n",
       "Austria         5.687173  11.820143  4.476746          0.236020  3.483532   \n",
       "Belgium         5.763295   7.834264  4.924500          0.197677  3.203712   \n",
       "Canada          5.737198   9.114227  4.747381          0.207564  3.290940   \n",
       "Chile           5.903233  16.242627  3.460482          0.284343  3.913106   \n",
       "Czech Rep.      5.689270  10.345341  4.651024          0.216961  3.392898   \n",
       "Denmark         5.694592  11.791892  4.557505          0.234934  3.511302   \n",
       "Estonia         5.727979  10.666493  4.650982          0.226537  3.407674   \n",
       "Finland         5.644067  11.845279  4.517876          0.236904  3.525032   \n",
       "France          5.733147  10.269548  4.560135          0.220550  3.383306   \n",
       "Germany         5.687086  10.972379  4.582907          0.227475  3.432105   \n",
       "Greece          5.773468   7.166377  5.209923          0.142857  3.133014   \n",
       "Hungary         5.766419   7.208318  5.027657          0.193566  3.230453   \n",
       "Iceland         5.830261  13.937207  4.701215          0.231198  3.703216   \n",
       "Ireland         5.712189  11.996754  4.534695          0.235493  3.523132   \n",
       "Israel          5.628857  11.336708  4.582601          0.224217  3.406475   \n",
       "Italy           5.737154   9.332802  4.577990          0.206790  3.306558   \n",
       "Japan           5.668874  10.046941  4.461135          0.220210  3.402577   \n",
       "Korea           5.728087   9.321042  3.946935          0.211874  3.366191   \n",
       "Luxembourg      5.710523   9.772187  4.603458          0.213747  3.299525   \n",
       "Mexico          5.809731  18.140365  2.574636          0.209335  3.454833   \n",
       "Netherlands     5.735989   9.772606  4.745666          0.215727  3.367532   \n",
       "New Zealand     5.757741  13.613356  4.460347          0.253996  3.586785   \n",
       "Norway          5.680406  13.654525  4.581480          0.258470  3.660036   \n",
       "Poland          5.820119   7.389165  5.069093          0.189600  3.215776   \n",
       "Portugal        5.751282  12.764150  4.426888          0.245906  3.578680   \n",
       "Slovak Rep.     5.721322  10.900040  4.624149          0.221828  3.451396   \n",
       "Slovenia        5.737422  10.754704  4.600925          0.225268  3.450790   \n",
       "Spain           5.760888   9.221238  4.674359          0.208281  3.330966   \n",
       "Sweden          5.492561  16.026316  4.742420          0.301095  3.862850   \n",
       "Switzerland     5.688268  12.976067  4.399588          0.256229  3.586549   \n",
       "Turkey          5.901250   3.193107  5.370252          0.138639  2.812942   \n",
       "United Kingdom  5.740641  12.092165  4.533439          0.238070  3.524824   \n",
       "United States   5.701849   8.446227  4.783065          0.198763  3.213984   \n",
       "OECD            5.715559   9.784644  4.611067          0.214029  3.351589   \n",
       "\n",
       "                 Bladder      Skin  \n",
       "Australia       3.628411  1.842250  \n",
       "Austria         3.763559  1.761308  \n",
       "Belgium         3.982234  1.300025  \n",
       "Canada          3.958260  1.435539  \n",
       "Chile           3.740709  2.648065  \n",
       "Czech Rep.      3.863856  1.589068  \n",
       "Denmark         3.712101  1.761700  \n",
       "Estonia         3.832053  1.656775  \n",
       "Finland         3.756839  1.760221  \n",
       "France          3.844408  1.594376  \n",
       "Germany         3.821834  1.673564  \n",
       "Greece          3.946455  1.240776  \n",
       "Hungary         4.123417  1.205169  \n",
       "Iceland         3.375083  2.111165  \n",
       "Ireland         3.750129  1.791230  \n",
       "Israel          3.751077  1.682347  \n",
       "Italy           3.912044  1.473370  \n",
       "Japan           3.946523  1.586448  \n",
       "Korea           4.084304  1.504239  \n",
       "Luxembourg      3.898795  1.499993  \n",
       "Mexico          3.783557  2.955884  \n",
       "Netherlands     3.897608  1.502747  \n",
       "New Zealand     3.639054  1.808438  \n",
       "Norway          3.508576  1.932281  \n",
       "Poland          3.985895  1.218740  \n",
       "Portugal        3.644499  1.923347  \n",
       "Slovak Rep.     3.837558  1.650205  \n",
       "Slovenia        3.812320  1.626453  \n",
       "Spain           3.842222  1.473031  \n",
       "Sweden          3.081189  2.231894  \n",
       "Switzerland     3.613891  1.897293  \n",
       "Turkey          4.581269  0.917090  \n",
       "United Kingdom  3.702613  1.815952  \n",
       "United States   4.013630  1.325812  \n",
       "OECD            3.909208  1.527713  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from validation using 1 component\n",
    "X_from_1_component_val = model.X_predVal()[1]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_1_component_val_df = pd.DataFrame(model.X_predVal()[1])\n",
    "X_from_1_component_val_df.index = data_objNames\n",
    "X_from_1_component_val_df.columns = data_varNames\n",
    "X_from_1_component_val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trachea-bronchus-lung</th>\n",
       "      <th>Colon, rectum and anus</th>\n",
       "      <th>Stomach</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Prostate</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Hodgkins disease</th>\n",
       "      <th>Leukemia</th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>20.764530</td>\n",
       "      <td>11.883816</td>\n",
       "      <td>4.351302</td>\n",
       "      <td>5.783851</td>\n",
       "      <td>14.210242</td>\n",
       "      <td>3.152660</td>\n",
       "      <td>0.263723</td>\n",
       "      <td>3.671416</td>\n",
       "      <td>3.953540</td>\n",
       "      <td>2.148731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>22.479761</td>\n",
       "      <td>12.312983</td>\n",
       "      <td>5.506251</td>\n",
       "      <td>5.888952</td>\n",
       "      <td>10.919635</td>\n",
       "      <td>5.230857</td>\n",
       "      <td>0.210053</td>\n",
       "      <td>3.425437</td>\n",
       "      <td>3.753513</td>\n",
       "      <td>1.886682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30.212489</td>\n",
       "      <td>11.532262</td>\n",
       "      <td>4.064188</td>\n",
       "      <td>5.660435</td>\n",
       "      <td>9.259651</td>\n",
       "      <td>2.843325</td>\n",
       "      <td>0.225970</td>\n",
       "      <td>3.310212</td>\n",
       "      <td>4.379648</td>\n",
       "      <td>1.592228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>27.738536</td>\n",
       "      <td>12.108949</td>\n",
       "      <td>3.703828</td>\n",
       "      <td>5.823744</td>\n",
       "      <td>9.773033</td>\n",
       "      <td>3.415657</td>\n",
       "      <td>0.211989</td>\n",
       "      <td>3.369483</td>\n",
       "      <td>4.333096</td>\n",
       "      <td>1.835485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13.075733</td>\n",
       "      <td>9.646726</td>\n",
       "      <td>8.913721</td>\n",
       "      <td>6.307958</td>\n",
       "      <td>13.900808</td>\n",
       "      <td>10.670921</td>\n",
       "      <td>0.264012</td>\n",
       "      <td>4.068782</td>\n",
       "      <td>2.150252</td>\n",
       "      <td>1.824376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Rep.</th>\n",
       "      <td>25.098286</td>\n",
       "      <td>12.488010</td>\n",
       "      <td>3.669759</td>\n",
       "      <td>5.987947</td>\n",
       "      <td>9.881577</td>\n",
       "      <td>4.755909</td>\n",
       "      <td>0.189882</td>\n",
       "      <td>3.458486</td>\n",
       "      <td>4.077980</td>\n",
       "      <td>2.054321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>23.748048</td>\n",
       "      <td>11.746771</td>\n",
       "      <td>3.217476</td>\n",
       "      <td>5.663136</td>\n",
       "      <td>13.257003</td>\n",
       "      <td>2.235640</td>\n",
       "      <td>0.257956</td>\n",
       "      <td>3.669418</td>\n",
       "      <td>4.224329</td>\n",
       "      <td>2.241527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>25.977504</td>\n",
       "      <td>8.716484</td>\n",
       "      <td>6.600062</td>\n",
       "      <td>5.281250</td>\n",
       "      <td>12.517250</td>\n",
       "      <td>3.840790</td>\n",
       "      <td>0.301266</td>\n",
       "      <td>3.609970</td>\n",
       "      <td>3.712430</td>\n",
       "      <td>1.346756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>23.484804</td>\n",
       "      <td>11.287266</td>\n",
       "      <td>4.966832</td>\n",
       "      <td>5.517355</td>\n",
       "      <td>12.847988</td>\n",
       "      <td>3.179513</td>\n",
       "      <td>0.258285</td>\n",
       "      <td>3.601377</td>\n",
       "      <td>3.998405</td>\n",
       "      <td>1.893054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>25.264965</td>\n",
       "      <td>12.140194</td>\n",
       "      <td>5.192369</td>\n",
       "      <td>5.874895</td>\n",
       "      <td>9.880548</td>\n",
       "      <td>4.711927</td>\n",
       "      <td>0.206250</td>\n",
       "      <td>3.367113</td>\n",
       "      <td>3.904670</td>\n",
       "      <td>1.750883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>24.471358</td>\n",
       "      <td>11.755519</td>\n",
       "      <td>4.948300</td>\n",
       "      <td>5.738860</td>\n",
       "      <td>11.146549</td>\n",
       "      <td>4.152540</td>\n",
       "      <td>0.226706</td>\n",
       "      <td>3.461806</td>\n",
       "      <td>3.960749</td>\n",
       "      <td>1.843174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>32.315134</td>\n",
       "      <td>10.583494</td>\n",
       "      <td>5.399023</td>\n",
       "      <td>5.196190</td>\n",
       "      <td>10.136267</td>\n",
       "      <td>1.745528</td>\n",
       "      <td>0.155814</td>\n",
       "      <td>3.129398</td>\n",
       "      <td>4.267369</td>\n",
       "      <td>1.200123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>30.378073</td>\n",
       "      <td>11.861468</td>\n",
       "      <td>3.760067</td>\n",
       "      <td>6.000479</td>\n",
       "      <td>7.095720</td>\n",
       "      <td>4.787683</td>\n",
       "      <td>0.181130</td>\n",
       "      <td>3.320748</td>\n",
       "      <td>4.341984</td>\n",
       "      <td>1.596543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>20.227861</td>\n",
       "      <td>10.997202</td>\n",
       "      <td>3.146196</td>\n",
       "      <td>5.781847</td>\n",
       "      <td>16.337650</td>\n",
       "      <td>1.570692</td>\n",
       "      <td>0.257341</td>\n",
       "      <td>3.968692</td>\n",
       "      <td>3.898049</td>\n",
       "      <td>2.707126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>22.875723</td>\n",
       "      <td>11.904691</td>\n",
       "      <td>4.342052</td>\n",
       "      <td>5.803498</td>\n",
       "      <td>12.342237</td>\n",
       "      <td>3.744655</td>\n",
       "      <td>0.234745</td>\n",
       "      <td>3.584067</td>\n",
       "      <td>4.003327</td>\n",
       "      <td>2.092452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>23.125626</td>\n",
       "      <td>12.641666</td>\n",
       "      <td>4.580049</td>\n",
       "      <td>5.917360</td>\n",
       "      <td>10.074275</td>\n",
       "      <td>6.055285</td>\n",
       "      <td>0.172077</td>\n",
       "      <td>3.325237</td>\n",
       "      <td>3.675352</td>\n",
       "      <td>1.985619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>26.290041</td>\n",
       "      <td>11.955581</td>\n",
       "      <td>6.761001</td>\n",
       "      <td>5.914960</td>\n",
       "      <td>7.888150</td>\n",
       "      <td>6.328044</td>\n",
       "      <td>0.174375</td>\n",
       "      <td>3.190602</td>\n",
       "      <td>3.611133</td>\n",
       "      <td>1.326765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>24.221279</td>\n",
       "      <td>9.685499</td>\n",
       "      <td>12.405529</td>\n",
       "      <td>5.304529</td>\n",
       "      <td>5.927250</td>\n",
       "      <td>11.321808</td>\n",
       "      <td>0.188055</td>\n",
       "      <td>3.218558</td>\n",
       "      <td>2.527670</td>\n",
       "      <td>0.234515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>25.402589</td>\n",
       "      <td>11.882237</td>\n",
       "      <td>16.291011</td>\n",
       "      <td>5.836304</td>\n",
       "      <td>2.376307</td>\n",
       "      <td>8.385048</td>\n",
       "      <td>0.137746</td>\n",
       "      <td>2.959834</td>\n",
       "      <td>2.654578</td>\n",
       "      <td>-0.690457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>25.511190</td>\n",
       "      <td>13.184020</td>\n",
       "      <td>4.926473</td>\n",
       "      <td>6.071516</td>\n",
       "      <td>8.345538</td>\n",
       "      <td>5.603757</td>\n",
       "      <td>0.166709</td>\n",
       "      <td>3.127996</td>\n",
       "      <td>3.931822</td>\n",
       "      <td>1.760704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>12.391547</td>\n",
       "      <td>11.509568</td>\n",
       "      <td>10.399259</td>\n",
       "      <td>5.705564</td>\n",
       "      <td>16.763155</td>\n",
       "      <td>6.062712</td>\n",
       "      <td>0.149478</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>2.868827</td>\n",
       "      <td>2.136482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>27.264210</td>\n",
       "      <td>11.530376</td>\n",
       "      <td>3.557542</td>\n",
       "      <td>5.658308</td>\n",
       "      <td>11.337331</td>\n",
       "      <td>2.464355</td>\n",
       "      <td>0.244981</td>\n",
       "      <td>3.511832</td>\n",
       "      <td>4.382769</td>\n",
       "      <td>1.867873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>19.847536</td>\n",
       "      <td>12.615937</td>\n",
       "      <td>3.612401</td>\n",
       "      <td>6.126305</td>\n",
       "      <td>13.298844</td>\n",
       "      <td>4.234944</td>\n",
       "      <td>0.231075</td>\n",
       "      <td>3.626668</td>\n",
       "      <td>3.960792</td>\n",
       "      <td>2.228559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>20.350495</td>\n",
       "      <td>11.679687</td>\n",
       "      <td>2.525859</td>\n",
       "      <td>5.656758</td>\n",
       "      <td>15.641944</td>\n",
       "      <td>1.497086</td>\n",
       "      <td>0.292522</td>\n",
       "      <td>3.893807</td>\n",
       "      <td>4.186937</td>\n",
       "      <td>2.574396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>30.863768</td>\n",
       "      <td>10.683579</td>\n",
       "      <td>5.082660</td>\n",
       "      <td>5.630259</td>\n",
       "      <td>8.707421</td>\n",
       "      <td>3.693953</td>\n",
       "      <td>0.222339</td>\n",
       "      <td>3.322107</td>\n",
       "      <td>4.139811</td>\n",
       "      <td>1.234604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>20.773941</td>\n",
       "      <td>11.319348</td>\n",
       "      <td>7.082734</td>\n",
       "      <td>5.894732</td>\n",
       "      <td>11.475380</td>\n",
       "      <td>6.271428</td>\n",
       "      <td>0.221338</td>\n",
       "      <td>3.500289</td>\n",
       "      <td>3.290746</td>\n",
       "      <td>1.728424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Rep.</th>\n",
       "      <td>23.984321</td>\n",
       "      <td>11.900879</td>\n",
       "      <td>4.980590</td>\n",
       "      <td>6.053141</td>\n",
       "      <td>9.861055</td>\n",
       "      <td>6.019048</td>\n",
       "      <td>0.187696</td>\n",
       "      <td>3.512518</td>\n",
       "      <td>3.751198</td>\n",
       "      <td>1.862199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>24.623444</td>\n",
       "      <td>11.325025</td>\n",
       "      <td>5.955342</td>\n",
       "      <td>5.737772</td>\n",
       "      <td>10.630254</td>\n",
       "      <td>4.798362</td>\n",
       "      <td>0.223524</td>\n",
       "      <td>3.438775</td>\n",
       "      <td>3.767088</td>\n",
       "      <td>1.584056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>26.775612</td>\n",
       "      <td>12.226788</td>\n",
       "      <td>4.524750</td>\n",
       "      <td>6.043729</td>\n",
       "      <td>8.546323</td>\n",
       "      <td>5.138220</td>\n",
       "      <td>0.183747</td>\n",
       "      <td>3.359587</td>\n",
       "      <td>3.866305</td>\n",
       "      <td>1.762706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>14.995524</td>\n",
       "      <td>11.542715</td>\n",
       "      <td>3.453728</td>\n",
       "      <td>5.431998</td>\n",
       "      <td>18.189938</td>\n",
       "      <td>1.583005</td>\n",
       "      <td>0.338882</td>\n",
       "      <td>4.099645</td>\n",
       "      <td>3.736133</td>\n",
       "      <td>2.945017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>21.299826</td>\n",
       "      <td>11.698535</td>\n",
       "      <td>4.847546</td>\n",
       "      <td>5.712640</td>\n",
       "      <td>13.449388</td>\n",
       "      <td>3.570984</td>\n",
       "      <td>0.262217</td>\n",
       "      <td>3.640261</td>\n",
       "      <td>3.818733</td>\n",
       "      <td>2.111408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>34.685849</td>\n",
       "      <td>13.269931</td>\n",
       "      <td>5.350938</td>\n",
       "      <td>5.761327</td>\n",
       "      <td>3.089725</td>\n",
       "      <td>4.889617</td>\n",
       "      <td>0.138391</td>\n",
       "      <td>2.615668</td>\n",
       "      <td>4.655886</td>\n",
       "      <td>0.742734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>23.135092</td>\n",
       "      <td>11.563119</td>\n",
       "      <td>4.402602</td>\n",
       "      <td>5.709889</td>\n",
       "      <td>13.093267</td>\n",
       "      <td>3.064259</td>\n",
       "      <td>0.254849</td>\n",
       "      <td>3.616245</td>\n",
       "      <td>4.011357</td>\n",
       "      <td>2.085617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>29.030028</td>\n",
       "      <td>11.865958</td>\n",
       "      <td>4.045997</td>\n",
       "      <td>5.663223</td>\n",
       "      <td>9.517251</td>\n",
       "      <td>3.071514</td>\n",
       "      <td>0.215477</td>\n",
       "      <td>3.298146</td>\n",
       "      <td>4.397172</td>\n",
       "      <td>1.636963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD</th>\n",
       "      <td>26.029230</td>\n",
       "      <td>11.328335</td>\n",
       "      <td>6.665261</td>\n",
       "      <td>5.712566</td>\n",
       "      <td>9.287666</td>\n",
       "      <td>5.399807</td>\n",
       "      <td>0.207244</td>\n",
       "      <td>3.301529</td>\n",
       "      <td>3.726988</td>\n",
       "      <td>1.353495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trachea-bronchus-lung  Colon, rectum and anus    Stomach  \\\n",
       "Australia                   20.764530               11.883816   4.351302   \n",
       "Austria                     22.479761               12.312983   5.506251   \n",
       "Belgium                     30.212489               11.532262   4.064188   \n",
       "Canada                      27.738536               12.108949   3.703828   \n",
       "Chile                       13.075733                9.646726   8.913721   \n",
       "Czech Rep.                  25.098286               12.488010   3.669759   \n",
       "Denmark                     23.748048               11.746771   3.217476   \n",
       "Estonia                     25.977504                8.716484   6.600062   \n",
       "Finland                     23.484804               11.287266   4.966832   \n",
       "France                      25.264965               12.140194   5.192369   \n",
       "Germany                     24.471358               11.755519   4.948300   \n",
       "Greece                      32.315134               10.583494   5.399023   \n",
       "Hungary                     30.378073               11.861468   3.760067   \n",
       "Iceland                     20.227861               10.997202   3.146196   \n",
       "Ireland                     22.875723               11.904691   4.342052   \n",
       "Israel                      23.125626               12.641666   4.580049   \n",
       "Italy                       26.290041               11.955581   6.761001   \n",
       "Japan                       24.221279                9.685499  12.405529   \n",
       "Korea                       25.402589               11.882237  16.291011   \n",
       "Luxembourg                  25.511190               13.184020   4.926473   \n",
       "Mexico                      12.391547               11.509568  10.399259   \n",
       "Netherlands                 27.264210               11.530376   3.557542   \n",
       "New Zealand                 19.847536               12.615937   3.612401   \n",
       "Norway                      20.350495               11.679687   2.525859   \n",
       "Poland                      30.863768               10.683579   5.082660   \n",
       "Portugal                    20.773941               11.319348   7.082734   \n",
       "Slovak Rep.                 23.984321               11.900879   4.980590   \n",
       "Slovenia                    24.623444               11.325025   5.955342   \n",
       "Spain                       26.775612               12.226788   4.524750   \n",
       "Sweden                      14.995524               11.542715   3.453728   \n",
       "Switzerland                 21.299826               11.698535   4.847546   \n",
       "Turkey                      34.685849               13.269931   5.350938   \n",
       "United Kingdom              23.135092               11.563119   4.402602   \n",
       "United States               29.030028               11.865958   4.045997   \n",
       "OECD                        26.029230               11.328335   6.665261   \n",
       "\n",
       "                Pancreas   Prostate      Liver  Hodgkins disease  Leukemia  \\\n",
       "Australia       5.783851  14.210242   3.152660          0.263723  3.671416   \n",
       "Austria         5.888952  10.919635   5.230857          0.210053  3.425437   \n",
       "Belgium         5.660435   9.259651   2.843325          0.225970  3.310212   \n",
       "Canada          5.823744   9.773033   3.415657          0.211989  3.369483   \n",
       "Chile           6.307958  13.900808  10.670921          0.264012  4.068782   \n",
       "Czech Rep.      5.987947   9.881577   4.755909          0.189882  3.458486   \n",
       "Denmark         5.663136  13.257003   2.235640          0.257956  3.669418   \n",
       "Estonia         5.281250  12.517250   3.840790          0.301266  3.609970   \n",
       "Finland         5.517355  12.847988   3.179513          0.258285  3.601377   \n",
       "France          5.874895   9.880548   4.711927          0.206250  3.367113   \n",
       "Germany         5.738860  11.146549   4.152540          0.226706  3.461806   \n",
       "Greece          5.196190  10.136267   1.745528          0.155814  3.129398   \n",
       "Hungary         6.000479   7.095720   4.787683          0.181130  3.320748   \n",
       "Iceland         5.781847  16.337650   1.570692          0.257341  3.968692   \n",
       "Ireland         5.803498  12.342237   3.744655          0.234745  3.584067   \n",
       "Israel          5.917360  10.074275   6.055285          0.172077  3.325237   \n",
       "Italy           5.914960   7.888150   6.328044          0.174375  3.190602   \n",
       "Japan           5.304529   5.927250  11.321808          0.188055  3.218558   \n",
       "Korea           5.836304   2.376307   8.385048          0.137746  2.959834   \n",
       "Luxembourg      6.071516   8.345538   5.603757          0.166709  3.127996   \n",
       "Mexico          5.705564  16.763155   6.062712          0.149478  2.977442   \n",
       "Netherlands     5.658308  11.337331   2.464355          0.244981  3.511832   \n",
       "New Zealand     6.126305  13.298844   4.234944          0.231075  3.626668   \n",
       "Norway          5.656758  15.641944   1.497086          0.292522  3.893807   \n",
       "Poland          5.630259   8.707421   3.693953          0.222339  3.322107   \n",
       "Portugal        5.894732  11.475380   6.271428          0.221338  3.500289   \n",
       "Slovak Rep.     6.053141   9.861055   6.019048          0.187696  3.512518   \n",
       "Slovenia        5.737772  10.630254   4.798362          0.223524  3.438775   \n",
       "Spain           6.043729   8.546323   5.138220          0.183747  3.359587   \n",
       "Sweden          5.431998  18.189938   1.583005          0.338882  4.099645   \n",
       "Switzerland     5.712640  13.449388   3.570984          0.262217  3.640261   \n",
       "Turkey          5.761327   3.089725   4.889617          0.138391  2.615668   \n",
       "United Kingdom  5.709889  13.093267   3.064259          0.254849  3.616245   \n",
       "United States   5.663223   9.517251   3.071514          0.215477  3.298146   \n",
       "OECD            5.712566   9.287666   5.399807          0.207244  3.301529   \n",
       "\n",
       "                 Bladder      Skin  \n",
       "Australia       3.953540  2.148731  \n",
       "Austria         3.753513  1.886682  \n",
       "Belgium         4.379648  1.592228  \n",
       "Canada          4.333096  1.835485  \n",
       "Chile           2.150252  1.824376  \n",
       "Czech Rep.      4.077980  2.054321  \n",
       "Denmark         4.224329  2.241527  \n",
       "Estonia         3.712430  1.346756  \n",
       "Finland         3.998405  1.893054  \n",
       "France          3.904670  1.750883  \n",
       "Germany         3.960749  1.843174  \n",
       "Greece          4.267369  1.200123  \n",
       "Hungary         4.341984  1.596543  \n",
       "Iceland         3.898049  2.707126  \n",
       "Ireland         4.003327  2.092452  \n",
       "Israel          3.675352  1.985619  \n",
       "Italy           3.611133  1.326765  \n",
       "Japan           2.527670  0.234515  \n",
       "Korea           2.654578 -0.690457  \n",
       "Luxembourg      3.931822  1.760704  \n",
       "Mexico          2.868827  2.136482  \n",
       "Netherlands     4.382769  1.867873  \n",
       "New Zealand     3.960792  2.228559  \n",
       "Norway          4.186937  2.574396  \n",
       "Poland          4.139811  1.234604  \n",
       "Portugal        3.290746  1.728424  \n",
       "Slovak Rep.     3.751198  1.862199  \n",
       "Slovenia        3.767088  1.584056  \n",
       "Spain           3.866305  1.762706  \n",
       "Sweden          3.736133  2.945017  \n",
       "Switzerland     3.818733  2.111408  \n",
       "Turkey          4.655886  0.742734  \n",
       "United Kingdom  4.011357  2.085617  \n",
       "United States   4.397172  1.636963  \n",
       "OECD            3.726988  1.353495  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from validation using 3 components\n",
    "X_from_3_component_val = model.X_predVal()[3]\n",
    "\n",
    "# Predicted X from calibration using 3 components stored in pandas data frame with row and columns names\n",
    "X_from_3_component_val_df = pd.DataFrame(model.X_predVal()[3])\n",
    "X_from_3_component_val_df.index = data_objNames\n",
    "X_from_3_component_val_df.columns = data_varNames\n",
    "X_from_3_component_val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_predVal in module hoggorm.pca:\n",
      "\n",
      "X_predVal(self)\n",
      "    Returns a dictionary holding the predicted arrays Xhat from\n",
      "    validation after each computed component. Dictionary key represents\n",
      "    order of component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_predVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC0</th>\n",
       "      <th>PC1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new object 0</th>\n",
       "      <td>-4.906931</td>\n",
       "      <td>1.599417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 1</th>\n",
       "      <td>-1.219690</td>\n",
       "      <td>-0.434143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 2</th>\n",
       "      <td>6.424203</td>\n",
       "      <td>2.823349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 3</th>\n",
       "      <td>3.880636</td>\n",
       "      <td>2.890535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC0       PC1\n",
       "new object 0 -4.906931  1.599417\n",
       "new object 1 -1.219690 -0.434143\n",
       "new object 2  6.424203  2.823349\n",
       "new object 3  3.880636  2.890535"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predicted scores for new measurements (objects) of X\n",
    "\n",
    "# First pretend that we acquired new X data by using part of the existing data and overlaying some noise\n",
    "import numpy.random as npr\n",
    "new_data = data[0:4, :] + npr.rand(4, np.shape(data)[1])\n",
    "np.shape(new_data)\n",
    "\n",
    "# Now insert the new data into the existing model and compute scores for two components (numComp=2)\n",
    "pred_scores = model.X_scores_predict(new_data, numComp=2)\n",
    "\n",
    "# Same as above, but results stored in a pandas dataframe with row names and column names\n",
    "pred_scores_df = pd.DataFrame(model.X_scores_predict(new_data, numComp=2))\n",
    "pred_scores_df.columns = ['PC{0}'.format(x) for x in range(2)]\n",
    "pred_scores_df.index = ['new object {0}'.format(x) for x in range(np.shape(new_data)[0])]\n",
    "pred_scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_scores_predict in module hoggorm.pca:\n",
      "\n",
      "X_scores_predict(self, Xnew, numComp=None)\n",
      "    Returns array of X scores from new X data using the exsisting model.\n",
      "    Rows represent objects and columns represent components.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_scores_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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