From 45a96eef6ff6c88b9e6c55b8b5e043301ec49802 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Tue, 12 Mar 2024 17:50:05 +0100 Subject: [PATCH 01/11] minor error in documentation --- R/geodist.R | 1 + 1 file changed, 1 insertion(+) diff --git a/R/geodist.R b/R/geodist.R index 742b2424..439c6c7a 100644 --- a/R/geodist.R +++ b/R/geodist.R @@ -81,6 +81,7 @@ #' st_crs(dat) <- 26911 #' trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] #' predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] +#' trainDat$week <- lubridate::week(trainDat$Date) #' cvfolds <- CreateSpacetimeFolds(trainDat,timevar = "week") #' #' dist <- geodist(trainDat,preddata = predictionDat,cvfolds = cvfolds$indexOut,type="time",time_unit="days") From e19853ce16b235b9c378d1139ed9034319aca34c Mon Sep 17 00:00:00 2001 From: JanLinnenbrink Date: Wed, 13 Mar 2024 09:39:59 +0100 Subject: [PATCH 02/11] kNNDM in feature space now uses gower distances for categorical variables --- DESCRIPTION | 2 +- R/knndm.R | 202 +++++++++++++++++++++++------------- man/knndm.Rd | 3 +- tests/testthat/test-knndm.R | 35 ++++++- 4 files changed, 164 insertions(+), 78 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 041f0e02..6489880e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -20,7 +20,7 @@ URL: https://github.com/HannaMeyer/CAST, Encoding: UTF-8 LazyData: false Depends: R (>= 4.1.0) -Imports: caret, stats, utils, ggplot2, graphics, FNN, plyr, zoo, methods, grDevices, data.table, lattice, sf, forcats +Imports: caret, stats, utils, ggplot2, graphics, FNN, plyr, zoo, methods, grDevices, data.table, lattice, sf, forcats, PCAmixdata, gower, clustMixType Suggests: doParallel, randomForest, diff --git a/R/knndm.R b/R/knndm.R index f4a680b8..b29109a3 100644 --- a/R/knndm.R +++ b/R/knndm.R @@ -64,7 +64,8 @@ #' In this case, nearest neighbour distances are calculated in n-dimensional feature space rather than in geographical space. #' `tpoints` and `predpoints` can be data frames or sf objects containing the values of the features. Note that the names of `tpoints` and `predpoints` must be the same. #' `predpoints` can also be missing, if `modeldomain` is of class SpatRaster. In this case, the values of of the SpatRaster will be extracted to the `predpoints`. -#' In the case of any categorical features, 0/1 encoding will be performed (pŕovisionally). +#' In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances. If categorical +#' features are present, and `clustering` = "kmeans", K-Prototype clustering will be performed instead. #' #' @references #' \itemize{ @@ -305,16 +306,17 @@ knndm <- function(tpoints, modeldomain = NULL, predpoints = NULL, # kNNDM in the geographical / feature space if(isTRUE(space == "geographical")){ - # Prior checks + # prior checks check_knndm_geo(tpoints, predpoints, space, k, maxp, clustering, islonglat) - + # kNNDM in geographical space knndm_res <- knndm_geo(tpoints, predpoints, k, maxp, clustering, linkf, islonglat) } else if (isTRUE(space == "feature")) { + # prior checks check_knndm_feature(tpoints, predpoints, space, k, maxp, clustering, islonglat, catVars) - - knndm_res <- knndm_feature(tpoints, predpoints, k, maxp, clustering, catVars) + # kNNDM in feature space + knndm_res <- knndm_feature(tpoints, predpoints, k, maxp, clustering, linkf, catVars) } @@ -394,9 +396,9 @@ knndm_geo <- function(tpoints, predpoints, k, maxp, clustering, linkf, islonglat clust <- sample(rep(1:k, ceiling(nrow(tpoints)/k)), size = nrow(tpoints), replace=F) if(isTRUE(islonglat)){ - Gjstar <- distclust_geo(distmat, clust) + Gjstar <- distclust_distmat(distmat, clust) }else{ - Gjstar <- distclust_proj(tcoords, clust) + Gjstar <- distclust_euclidean(tcoords, clust) } k_final <- "random CV" W_final <- twosamples::wass_stat(Gjstar, Gij) @@ -467,9 +469,9 @@ knndm_geo <- function(tpoints, predpoints, k, maxp, clustering, linkf, islonglat if(!any(table(clust_k)/length(clust_k)>maxp)){ if(isTRUE(islonglat)){ - Gjstar_i <- distclust_geo(distmat, clust_k) + Gjstar_i <- distclust_distmat(distmat, clust_k) }else{ - Gjstar_i <- distclust_proj(tcoords, clust_k) + Gjstar_i <- distclust_euclidean(tcoords, clust_k) } clustgrid$W[clustgrid$nk==nk] <- twosamples::wass_stat(Gjstar_i, Gij) clustgroups[[paste0("nk", nk)]] <- clust_k @@ -481,9 +483,9 @@ knndm_geo <- function(tpoints, predpoints, k, maxp, clustering, linkf, islonglat W_final <- min(clustgrid$W, na.rm=T) clust <- clustgroups[[paste0("nk", k_final)]] if(isTRUE(islonglat)){ - Gjstar <- distclust_geo(distmat, clust) + Gjstar <- distclust_distmat(distmat, clust) }else{ - Gjstar <- distclust_proj(tcoords, clust) + Gjstar <- distclust_euclidean(tcoords, clust) } } @@ -499,7 +501,7 @@ knndm_geo <- function(tpoints, predpoints, k, maxp, clustering, linkf, islonglat # kNNDM in the feature space -knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { +knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, linkf, catVars) { # rescale data if(is.null(catVars)) { @@ -507,7 +509,7 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { scale_attr <- attributes(scale(tpoints)) tpoints <- scale(tpoints) |> as.data.frame() predpoints <- scale(predpoints,center=scale_attr$`scaled:center`, - scale=scale_attr$`scaled:scale`) |> + scale=scale_attr$`scaled:scale`) |> as.data.frame() } else { @@ -520,49 +522,27 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { scale_attr <- attributes(scale(tpoints_num)) tpoints <- scale(tpoints_num) |> as.data.frame() predpoints <- scale(predpoints_num,center=scale_attr$`scaled:center`, - scale=scale_attr$`scaled:scale`) |> + scale=scale_attr$`scaled:scale`) |> as.data.frame() tpoints <- as.data.frame(cbind(tpoints, lapply(tpoints_cat, as.factor))) predpoints <- as.data.frame(cbind(predpoints, lapply(predpoints_cat, as.factor))) - - # 0/1 encode categorical variables (as in R/trainDI.R) - for (catvar in catVars){ - # mask all unknown levels in newdata as NA - tpoints[,catvar]<-droplevels(tpoints[,catvar]) - predpoints[,catvar]<-droplevels(predpoints[,catvar]) - - # then create dummy variables for the remaining levels in train: - dvi_train <- predict(caret::dummyVars(paste0("~",catvar), data = tpoints), - tpoints) - dvi_predpoints <- predict(caret::dummyVars(paste0("~",catvar), data = predpoints), - predpoints) - tpoints <- data.frame(tpoints,dvi_train) - predpoints <- data.frame(predpoints,dvi_predpoints) - - } - tpoints <- tpoints[,-which(names(tpoints)%in%catVars)] - predpoints <- predpoints[,-which(names(predpoints)%in%catVars)] - } # Gj and Gij calculation - if (clustering=="kmeans") { - # calculate euclidean NNDs + if(is.null(catVars)) { + + # use FNN with Euclidean distances if no categorical variables are present Gj <- c(FNN::knn.dist(tpoints, k = 1)) Gij <- c(FNN::knnx.dist(query = predpoints, data = tpoints, k = 1)) + } else { - # calculate euclidean distance matrix - distmat <- stats::dist(tpoints, upper=TRUE, diag=TRUE) |> as.matrix() - diag(distmat) <- NA - Gj <- apply(distmat, 1, function(x) min(x, na.rm=TRUE)) - Gij <- outer( - 1:nrow(predpoints), - 1:nrow(tpoints), - FUN = Vectorize(function(x,y) dist(rbind(predpoints[x,],tpoints[y,]))) - ) - Gij <- apply(Gij, 1, min) + + # use Gower distances if categorical variables are present + Gj <- sapply(1:nrow(tpoints), function(i) gower::gower_topn(tpoints[i,], tpoints[-i,], n=1)$distance[[1]]) + Gij <- c(gower::gower_topn(predpoints, tpoints, n = 1)$distance) + } @@ -572,11 +552,10 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { clust <- sample(rep(1:k, ceiling(nrow(tpoints)/k)), size = nrow(tpoints), replace=F) - - if(clustering == "kmeans") { - Gjstar <- distclust_proj(tpoints, clust) + if(is.null(catVars)) { + Gjstar <- distclust_euclidean(tpoints, clust) } else { - Gjstar <- distclust_geo(distmat, clust) + Gjstar <- distclust_gower(tpoints, clust) } k_final <- "random CV" @@ -585,6 +564,30 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { }else{ + if(clustering == "hierarchical"){ + + # calculate distance matrix which is needed for hierarchical clustering + if(is.null(catVars)) { + + # calculate distance matrix with Euclidean distances if no categorical variables are present + distmat <- stats::dist(tpoints, upper=TRUE, diag=TRUE) |> as.matrix() + diag(distmat) <- NA + + } else { + + # calculate distance matrix with Gower distances if categorical variables are present + distmat <- matrix(nrow=nrow(tpoints), ncol=nrow(tpoints)) + for (i in 1:nrow(tpoints)){ + + trainDist <- gower::gower_dist(tpoints[i,], tpoints) + + trainDist[i] <- NA + distmat[i,] <- trainDist + } + } + hc <- stats::hclust(d = stats::as.dist(distmat), method = linkf) + } + # Build grid of number of clusters to try - we sample low numbers more intensively clustgrid <- data.frame(nk = as.integer(round(exp(seq(log(k), log(nrow(tpoints)-2), length.out = 100))))) @@ -593,26 +596,55 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { clustgroups <- list() # Compute 1st PC for ordering clusters - pcacoords <- stats::prcomp(tpoints, center = TRUE, scale. = FALSE, rank = 1) + if(is.null(catVars)) { + pcacoords <- stats::prcomp(tpoints, center = TRUE, scale. = FALSE, rank = 1) + } else { + pcacoords <- PCAmixdata::PCAmix(X.quanti = tpoints[,!(names(tpoints) %in% catVars), drop=FALSE], + X.quali = tpoints[,names(tpoints) %in% catVars, drop=FALSE], + graph = FALSE) + } # We test each number of clusters - for(nk in clustgrid$nk){ + for(nk in clustgrid$nk) { # Create nk clusters - clust_nk <- tryCatch(stats::kmeans(tpoints, nk)$cluster, - error=function(e) e) - + if(clustering == "hierarchical"){ + clust_nk <- stats::cutree(hc, k=nk) + } else if(clustering == "kmeans"){ + if(is.null(catVars)) { + clust_nk <- tryCatch(stats::kmeans(tpoints, nk)$cluster, + error=function(e) e) + + } else { + # prototype clustering for mixed data sets + clust_nk <- tryCatch(clustMixType::kproto(tpoints, nk,verbose=FALSE)$cluster, + error=function(e) e) + } + } if (!inherits(clust_nk,"error")){ tabclust <- as.data.frame(table(clust_nk)) tabclust$clust_k <- NA # compute cluster centroids and apply PC loadings to shuffle along the 1st dimension - centr_tpoints <- sapply(tabclust$clust_nk, function(x){ - centrpca <- matrix(apply(tpoints[clust_nk %in% x, , drop=FALSE], 2, mean), nrow = 1) - colnames(centrpca) <- colnames(tpoints) - return(predict(pcacoords, centrpca)) - }) + if(is.null(catVars)) { + centr_tpoints <- sapply(tabclust$clust_nk, function(x){ + centrpca <- matrix(apply(tpoints[clust_nk %in% x, , drop=FALSE], 2, mean), nrow = 1) + colnames(centrpca) <- colnames(tpoints) + return(predict(pcacoords, centrpca)) + }) + } else { + centr_tpoints <- sapply(tabclust$clust_nk, function(x){ + centrpca_num <- matrix(apply(tpoints[clust_nk %in% x, !(names(tpoints) %in% catVars), drop=FALSE], 2, mean), nrow = 1) + centrpca_cat <- matrix(apply(tpoints[clust_nk %in% x, names(tpoints) %in% catVars, drop=FALSE], 2, + function(y) names(which.max(table(y)))), nrow = 1) + colnames(centrpca_num) <- colnames(tpoints[,!(names(tpoints) %in% catVars), drop=FALSE]) + colnames(centrpca_cat) <- colnames(tpoints[,names(tpoints) %in% catVars, drop=FALSE]) + + return(predict(pcacoords, centrpca_num, centrpca_cat)[,1]) + + }) + } tabclust$centrpca <- centr_tpoints tabclust <- tabclust[order(tabclust$centrpca),] @@ -636,12 +668,17 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { clust_k <- tabclust2$clust_k # Compute W statistic if not exceeding maxp - if(!any(table(clust_k)/length(clust_k)>maxp)){ + if(!(any(table(clust_k)/length(clust_k)>maxp))){ if(clustering == "kmeans") { - Gjstar_i <- distclust_proj(tpoints, clust_k) + if(is.null(catVars)) { + Gjstar_i <- distclust_euclidean(tpoints, clust_k) + } else { + Gjstar_i <- distclust_gower(tpoints, clust_k) + } + } else { - Gjstar_i <- distclust_geo(distmat, clust_k) + Gjstar_i <- distclust_distmat(distmat, clust_k) } clustgrid$W[clustgrid$nk==nk] <- twosamples::wass_stat(Gjstar_i, Gij) @@ -650,22 +687,28 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { } else { message(paste("skipped nk", nk)) } + } - # Final configuration - k_final <- clustgrid$nk[which.min(clustgrid$W)] - W_final <- min(clustgrid$W, na.rm=T) - clust <- clustgroups[[paste0("nk", k_final)]] + # Final configuration + k_final <- clustgrid$nk[which.min(clustgrid$W)] + W_final <- min(clustgrid$W, na.rm=T) + clust <- clustgroups[[paste0("nk", k_final)]] - if(clustering == "kmeans") { - Gjstar <- distclust_proj(tpoints, clust) + if(clustering == "kmeans") { + if(is.null(catVars)) { + Gjstar <- distclust_euclidean(tpoints, clust) } else { - Gjstar <- distclust_geo(distmat, clust) + Gjstar <- distclust_gower(tpoints, clust) } - + } else { + Gjstar <- distclust_distmat(distmat, clust) } } + + + # Output cfolds <- CAST::CreateSpacetimeFolds(data.frame(clust=clust), spacevar = "clust", k = k) res <- list(clusters = clust, @@ -677,8 +720,8 @@ knndm_feature <- function(tpoints, predpoints, k, maxp, clustering, catVars) { } -# Helper function: Compute out-of-fold NN distance (geographical) -distclust_geo <- function(distm, folds){ +# Helper function: Compute out-of-fold NN distance (geographical coordinates / numerical variables) +distclust_distmat <- function(distm, folds){ alldist <- rep(NA, length(folds)) for(f in unique(folds)){ alldist[f == folds] <- apply(distm[f == folds, f != folds, drop=FALSE], 1, min) @@ -686,8 +729,8 @@ distclust_geo <- function(distm, folds){ alldist } -# Helper function: Compute out-of-fold NN distance (projected) -distclust_proj <- function(tr_coords, folds){ +# Helper function: Compute out-of-fold NN distance (projected coordinates / numerical variables) +distclust_euclidean <- function(tr_coords, folds){ alldist <- rep(NA, length(folds)) for(f in unique(folds)){ alldist[f == folds] <- c(FNN::knnx.dist(query = tr_coords[f == folds,,drop=FALSE], @@ -695,3 +738,14 @@ distclust_proj <- function(tr_coords, folds){ } alldist } + +# Helper function: Compute out-of-fold NN distance (categorical variables) +distclust_gower <- function(tr_coords, folds){ + + alldist <- rep(NA, length(folds)) + for(f in unique(folds)){ + alldist[f == folds] <- c(gower::gower_topn(tr_coords[f == folds,,drop=FALSE], + tr_coords[f != folds,,drop=FALSE], n=1))$distance[[1]] + } + unlist(alldist) +} diff --git a/man/knndm.Rd b/man/knndm.Rd index 4d4d4e8b..6d3fcd26 100644 --- a/man/knndm.Rd +++ b/man/knndm.Rd @@ -92,7 +92,8 @@ When using either `modeldomain` or `predpoints`, we advise to plot the study are In this case, nearest neighbour distances are calculated in n-dimensional feature space rather than in geographical space. `tpoints` and `predpoints` can be data frames or sf objects containing the values of the features. Note that the names of `tpoints` and `predpoints` must be the same. `predpoints` can also be missing, if `modeldomain` is of class SpatRaster. In this case, the values of of the SpatRaster will be extracted to the `predpoints`. -In the case of any categorical features, 0/1 encoding will be performed (pŕovisionally). +In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances. If categorical +features are present, and `clustering` = "kmeans", K-Prototype clustering will be performed instead. } \examples{ ######################################################################## diff --git a/tests/testthat/test-knndm.R b/tests/testthat/test-knndm.R index 98eabba9..42edae69 100644 --- a/tests/testthat/test-knndm.R +++ b/tests/testthat/test-knndm.R @@ -303,8 +303,39 @@ test_that("kNNDM works in feature space with categorical variables and predpoint train_points$fct <- factor(sample(LETTERS[1:4], nrow(pts), replace=TRUE)) - knndm_folds <- knndm(tpoints=train_points, predpoints = prediction_points, space="feature", clustering = "kmeans") + knndm_folds <- knndm(tpoints=train_points, predpoints = prediction_points, + space="feature", clustering = "hierarchical") - expect_equal(round(as.numeric(knndm_folds$Gjstar[40]),4), 1.1464) + expect_equal(round(as.numeric(knndm_folds$Gjstar[40]),3), 0.057) }) + + +test_that("kNNDM works in feature space with clustered training points, categorical features ", { + + set.seed(1234) + predictor_stack <- terra::rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST")) + predictors <- c("DEM","TWI", "NDRE.M", "Easting", "Northing", "fct") + predictor_stack$fct <- factor(c(rep(LETTERS[1], terra::ncell(predictor_stack)/2), + rep(LETTERS[2], terra::ncell(predictor_stack)/2))) + + + predictor_stack <- predictor_stack[[predictors]] + studyArea <- predictor_stack + studyArea[!is.na(studyArea)] <- 1 + studyArea <- terra::as.polygons(studyArea, values = FALSE, na.all = TRUE) |> + sf::st_as_sf() |> + sf::st_union() + + pts <- clustered_sample(studyArea, 30, 5, 60) + pts <- sf::st_transform(pts, crs = sf::st_crs(studyArea)) + pts <- terra::extract(predictor_stack, terra::vect(pts), ID=FALSE) + + knndm_folds_kproto <- knndm(tpoints=pts, modeldomain = predictor_stack, space="feature", clustering = "kmeans") + knndm_folds_hclust <- knndm(tpoints=pts, modeldomain = predictor_stack, space="feature", clustering = "hierarchical") + + expect_equal(round(as.numeric(knndm_folds_kproto$Gjstar[20]),3), 0.077) + expect_equal(round(as.numeric(knndm_folds_hclust$Gjstar[20]),3), 0.078) + +}) + From e09c8806685d1e11b521f5eff57968dbc77ab9e0 Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 10:34:58 +0100 Subject: [PATCH 03/11] dataset documentation --- R/cookfarm.R | 27 +++++++++++ data-raw/create-cookfarm.R | 6 +++ data-raw/create-splotdata.R | 93 ++++++++++++++++++++++++++++++++++++ data/cookfarm.rda | Bin 0 -> 653170 bytes data/splotdata.rda | Bin 43899 -> 43866 bytes man/cookfarm.Rd | 37 ++++++++++++++ 6 files changed, 163 insertions(+) create mode 100644 R/cookfarm.R create mode 100644 data-raw/create-cookfarm.R create mode 100644 data-raw/create-splotdata.R create mode 100644 data/cookfarm.rda create mode 100644 man/cookfarm.Rd diff --git a/R/cookfarm.R b/R/cookfarm.R new file mode 100644 index 00000000..e459a0ff --- /dev/null +++ b/R/cookfarm.R @@ -0,0 +1,27 @@ +#' Cookfarm soil logger data +#' +#' spatio-temporal data of soil properties and associated predictors for the Cookfarm in South Africa +#' @format +#' A sf data.frame with 128545 rows and 17 columns: +#' \describe{ +#' \item{SOURCEID}{sPlotOpen Metadata} +#' \item{VW}{Response Variable - Soil Moisture} +#' \item{altitude}{Measurement depth of VW} +#' \item{Date, cdata}{Measurement Date, Cumulative Date} +#' \item{Easting, Northing}{Location in EPSG:????} +#' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +#' } +#' @source \itemize{ +#' \item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} +#' \item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} +#' } +#' +#' @references \itemize{ +#' \item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}} +#' \item{Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation \doi{https://doi.org/10.1016/j.envsoft.2017.12.001}} +#' +#' } +#' @usage data(cookfarm) +#' +"cookfarm" + diff --git a/data-raw/create-cookfarm.R b/data-raw/create-cookfarm.R new file mode 100644 index 00000000..ec2d215f --- /dev/null +++ b/data-raw/create-cookfarm.R @@ -0,0 +1,6 @@ +# This script creates the cookfarm dataset +# + + +cookfarm = readRDS("inst/extdata/Cookfarm.RDS") +save(cookfarm, file = "data/cookfarm.rda") diff --git a/data-raw/create-splotdata.R b/data-raw/create-splotdata.R new file mode 100644 index 00000000..c7cff000 --- /dev/null +++ b/data-raw/create-splotdata.R @@ -0,0 +1,93 @@ +## This script creates the example dataset "splotdata" of the CAST package. +## It downloads splotopen data points and associated worldclim predictors for South America. +## A lower resolution predictor stack (terra rast) is also created for Chile. +## For more information, please check out the Book Chapter and Repository CAST4Ecology + +library(geodata) +library(rnaturalearth) +library(terra) +library(sf) +library(tidyverse) +library(geodata) + + +##### Download Predictors -------------------------------- +## Warning: This downloads ~ 1 GB of data +dir.create("data-raw/raw/") + +wcf = geodata::worldclim_global(var = "bio", path = "data-raw/raw/", res = 0.5) +wc = geodata::worldclim_global(var = "bio", path = "data-raw/raw/", res = 5) +elevf = geodata::elevation_global(res = 0.5, path = "data-raw/raw/") +elev = geodata::elevation_global(res = 5, path = "data-raw/raw/") + +wcf = c(wcf, elevf) +wc = c(wc, elev) + +##### Download sPlotOpen ------------------------------------- +if(!file.exists("data-raw/raw/splotopen")){ + download.file("https://idata.idiv.de/ddm/Data/DownloadZip/3474?version=5779", destfile = "data-raw/raw/splotopen.zip") + unzip(zipfile = "data-raw/raw/splotopen.zip", exdir = "data-raw/raw/splotopen") + unzip(zipfile = "data-raw/raw/splotopen/sPlotOpen.RData(2).zip", exdir = "data-raw/raw/splotopen") +} + + + +##### Clean up and save necessary files ---------------------------------- +# define region: all of south america +region = rnaturalearth::ne_countries(continent = "South America", returnclass = "sf", scale = 110) + + +# Predictor clean up +wc = crop(wc, region) +names(wc) = names(wc) |> str_remove(pattern = "wc2.1_5m_") +p = c("bio_1", "bio_4", "bio_5", "bio_6", "bio_8", "bio_9", "bio_12", "bio_13", "bio_14", "bio_15", "elev") +wc = wc[[p]] + +# worldclim in full resolution for extracting the training data +wcf = crop(wcf, region) +names(wcf) = names(wcf) |> str_remove(pattern = "wc2.1_30s_") +wcf = wcf[[p]] +wcf$lat = terra::init(wcf, "y") +wcf$lon = terra::init(wcf, "x") + + +# Gather Response Variable: sPlotOpen Species Richness for South America +## see Appendix 1 of https://doi.org/10.1111/geb.13346 +load("data-raw/raw/splotopen/sPlotOpen.RData") + +splot = header.oa |> + #filter(Resample_1 == TRUE) |> + filter(Continent == "South America") |> + st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) |> + left_join(CWM_CWV.oa |> select(c("PlotObservationID", "Species_richness"))) |> + select(c("PlotObservationID", "GIVD_ID", "Country", "Biome", + "Species_richness")) |> + na.omit() + +# extract predictor values and attach to response +splot = terra::extract(wcf, splot, ID = FALSE, bind = TRUE) |> + st_as_sf() |> + na.omit() + + +# only keep unique locations +## some reference sample locations are in the same predictor stack pixel +## this can lead to erroneous models and misleading validations +splotdata = splot[!duplicated(c(splot$lat, splot$lon)),] +splotdata = splotdata |> na.omit() +splotdata$lat = NULL +splotdata$lon = NULL + + +# save splotdata +splotdata$Biome = droplevels(splotdata$Biome) +save(splotdata, file = "data/splotdata.rda") + +## save predictors for chile 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zYHyE6ZdRGDwsjoG4#$Y{k|wOSd#P%?d6grO3p%ej^r`_3yTcKrT#6Nu4O6e-V$O%0 zt^mTN6W%bXfdj<)le%ywr}u z%QcBK!IK^ENGsr*FUzLM-`N5S<8r&l9t#b=--#Q8cXcAF-ZtT(es4glF^a7{@=5pn zYpW+>mOnfMEd*K{g&E&1AGL1AcR6_&8~X)}^HtSwIRHniV8`w3Xm>6-3t$oi73KZz dM#F~>>KxmcF5unS+uHw$^B&BvB>(51{tt`_tpNZ4 diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd new file mode 100644 index 00000000..e64ab5dd --- /dev/null +++ b/man/cookfarm.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cookfarm.R +\docType{data} +\name{cookfarm} +\alias{cookfarm} +\title{Cookfarm soil logger data} +\format{ +A sf data.frame with 128545 rows and 17 columns: +\describe{ + \item{SOURCEID}{sPlotOpen Metadata} + \item{VW}{Response Variable - Soil Moisture} + \item{altitude}{Measurement depth of VW} + \item{Date, cdata}{Measurement Date, Cumulative Date} + \item{Easting, Northing}{Location in EPSG:????} + \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +} +} +\source{ +\itemize{ +\item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} +\item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} +} +} +\usage{ +data(cookfarm) +} +\description{ +spatio-temporal data of soil properties and associated predictors for the Cookfarm in South Africa +} +\references{ +\itemize{ +\item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}} +\item{Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation \doi{https://doi.org/10.1016/j.envsoft.2017.12.001}} + +} +} +\keyword{datasets} From 2328df0d590e0358e083a62b189941f752d862bb Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 10:48:41 +0100 Subject: [PATCH 04/11] data-raw in buildignore --- .Rbuildignore | 1 + NEWS.md | 2 ++ R/cookfarm.R | 4 ---- man/cookfarm.Rd | 6 ------ 4 files changed, 3 insertions(+), 10 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index eb54bd38..57f050d9 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -8,3 +8,4 @@ vignettes/CAST-intro_files ^docs$ ^pkgdown$ ^\.github$ +data-raw/ diff --git a/NEWS.md b/NEWS.md index 329f5ea5..245e8dfc 100644 --- a/NEWS.md +++ b/NEWS.md @@ -3,9 +3,11 @@ * option of spatial error profiles (errorProfiles with variable="geodist") * normalize_DI for a more intuitive interpretation * geodist allows calculating temporal distances + * ffs now can be run in parallel (Linux only) * modifications: * function DItoErrormetric renamed to errorProfiles and allows for other dissimilarity measures * Improvement and homogenization of plotting methods for nndm, knndm and geodist objects + * aoa and trainDI `weight` now allows list input * deprecated: *plot_geodist (replaced by plot.geodist) *plot_ffs (replaced by plot.ffs) diff --git a/R/cookfarm.R b/R/cookfarm.R index e459a0ff..17df3a8e 100644 --- a/R/cookfarm.R +++ b/R/cookfarm.R @@ -11,10 +11,6 @@ #' \item{Easting, Northing}{Location in EPSG:????} #' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} #' } -#' @source \itemize{ -#' \item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} -#' \item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} -#' } #' #' @references \itemize{ #' \item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}} diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd index e64ab5dd..406c670a 100644 --- a/man/cookfarm.Rd +++ b/man/cookfarm.Rd @@ -15,12 +15,6 @@ A sf data.frame with 128545 rows and 17 columns: \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} } } -\source{ -\itemize{ -\item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} -\item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} -} -} \usage{ data(cookfarm) } From ea40f6ad070c83807ce21d79de5557d880c71c74 Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 11:13:59 +0100 Subject: [PATCH 05/11] Cookfarm documentation bugfix --- R/cookfarm.R | 5 ++--- man/cookfarm.Rd | 5 ++--- man/geodist.Rd | 1 + 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/R/cookfarm.R b/R/cookfarm.R index 17df3a8e..93b6c16f 100644 --- a/R/cookfarm.R +++ b/R/cookfarm.R @@ -8,14 +8,13 @@ #' \item{VW}{Response Variable - Soil Moisture} #' \item{altitude}{Measurement depth of VW} #' \item{Date, cdata}{Measurement Date, Cumulative Date} -#' \item{Easting, Northing}{Location in EPSG:????} -#' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +#' \item{Easting, Northing}{Location Coordinates (EPSG:26911)} +#' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum}{Predictor Variables} #' } #' #' @references \itemize{ #' \item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}} #' \item{Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation \doi{https://doi.org/10.1016/j.envsoft.2017.12.001}} -#' #' } #' @usage data(cookfarm) #' diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd index 406c670a..3e7f5d42 100644 --- a/man/cookfarm.Rd +++ b/man/cookfarm.Rd @@ -11,8 +11,8 @@ A sf data.frame with 128545 rows and 17 columns: \item{VW}{Response Variable - Soil Moisture} \item{altitude}{Measurement depth of VW} \item{Date, cdata}{Measurement Date, Cumulative Date} - \item{Easting, Northing}{Location in EPSG:????} - \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} + \item{Easting, Northing}{Location Coordinates (EPSG:26911)} + \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum}{Predictor Variables} } } \usage{ @@ -25,7 +25,6 @@ spatio-temporal data of soil properties and associated predictors for the Cookfa \itemize{ \item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}} \item{Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation \doi{https://doi.org/10.1016/j.envsoft.2017.12.001}} - } } \keyword{datasets} diff --git a/man/geodist.Rd b/man/geodist.Rd index 4820c6d3..8838741e 100644 --- a/man/geodist.Rd +++ b/man/geodist.Rd @@ -118,6 +118,7 @@ dat <- st_as_sf(dat,coords=c("Easting","Northing")) st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] +trainDat$week <- lubridate::week(trainDat$Date) cvfolds <- CreateSpacetimeFolds(trainDat,timevar = "week") dist <- geodist(trainDat,preddata = predictionDat,cvfolds = cvfolds$indexOut,type="time",time_unit="days") From 261ae836d82235ee94d2e9cc9ac12d82c7acac23 Mon Sep 17 00:00:00 2001 From: JanLinnenbrink Date: Wed, 13 Mar 2024 11:19:09 +0100 Subject: [PATCH 06/11] geodist works with gower distances if some predictors are categorical --- R/geodist.R | 174 +++++++++++++++++++++++++++------- tests/testthat/test-geodist.R | 47 ++++++++- tests/testthat/test-knndm.R | 3 - 3 files changed, 183 insertions(+), 41 deletions(-) diff --git a/R/geodist.R b/R/geodist.R index 439c6c7a..1b861723 100644 --- a/R/geodist.R +++ b/R/geodist.R @@ -19,12 +19,12 @@ #' @return A data.frame containing the distances. Unit of returned geographic distances is meters. attributes contain W statistic between prediction area and either sample data, CV folds or test data. See details. #' @details The modeldomain is a sf polygon or a raster that defines the prediction area. The function takes a regular point sample (amount defined by samplesize) from the spatial extent. #' If type = "feature", the argument modeldomain (and if provided then also the testdata and/or preddata) has to include predictors. Predictor values for x, testdata and preddata are optional if modeldomain is a raster. -#' If not provided they are extracted from the modeldomain rasterStack. +#' If not provided they are extracted from the modeldomain rasterStack. If some predictors are categorical (i.e., of class factor or character), gower distances will be used. #' W statistic describes the match between the distributions. See Linnenbrink et al (2023) for further details. #' @note See Meyer and Pebesma (2022) for an application of this plotting function #' @seealso \code{\link{nndm}} \code{\link{knndm}} #' @import ggplot2 -#' @author Hanna Meyer, Edzer Pebesma, Marvin Ludwig +#' @author Hanna Meyer, Edzer Pebesma, Marvin Ludwig, Jan Linnenbrink #' @examples #' \dontrun{ #' library(CAST) @@ -187,6 +187,17 @@ geodist <- function(x, preddata <- sf::st_transform(preddata,4326) } } + # get names of categorical variables + catVars <- names(x[,variables])[which(sapply(x[,variables], class)%in%c("factor","character"))] + if(length(catVars)==0) { + catVars <- NULL + } + if(!is.null(catVars)) { + message(paste0("variable(s) '", catVars, "' is (are) treated as categorical variables")) + } + } + if(type != "feature") { + catVars <- NULL } if (type=="time" & is.null(timevar)){ timevar <- names(which(sapply(x, lubridate::is.Date))) @@ -203,28 +214,28 @@ geodist <- function(x, ## Sample prediction location from the study area if preddata not available: if(is.null(preddata)){ - modeldomain <- sampleFromArea(modeldomain, samplesize, type,variables,sampling) + modeldomain <- sampleFromArea(modeldomain, samplesize, type,variables,sampling, catVars) } else{ modeldomain <- preddata } # always do sample-to-sample and sample-to-prediction - s2s <- sample2sample(x, type,variables,time_unit,timevar) - s2p <- sample2prediction(x, modeldomain, type, samplesize,variables,time_unit,timevar) + s2s <- sample2sample(x, type,variables,time_unit,timevar, catVars) + s2p <- sample2prediction(x, modeldomain, type, samplesize,variables,time_unit,timevar, catVars) dists <- rbind(s2s, s2p) # optional steps ---- ##### Distance to test data: if(!is.null(testdata)){ - s2t <- sample2test(x, testdata, type,variables,time_unit,timevar) + s2t <- sample2test(x, testdata, type,variables,time_unit,timevar, catVars) dists <- rbind(dists, s2t) } ##### Distance to CV data: if(!is.null(cvfolds)){ - cvd <- cvdistance(x, cvfolds, cvtrain, type, variables,time_unit,timevar) + cvd <- cvdistance(x, cvfolds, cvtrain, type, variables,time_unit,timevar, catVars) dists <- rbind(dists, cvd) } class(dists) <- c("geodist", class(dists)) @@ -258,7 +269,7 @@ geodist <- function(x, # Sample to Sample Distance -sample2sample <- function(x, type,variables,time_unit,timevar){ +sample2sample <- function(x, type,variables,time_unit,timevar, catVars){ if(type == "geo"){ sf::sf_use_s2(TRUE) d <- sf::st_distance(x) @@ -270,14 +281,29 @@ sample2sample <- function(x, type,variables,time_unit,timevar){ }else if(type == "feature"){ x <- x[,variables] x <- sf::st_drop_geometry(x) - scaleparam <- attributes(scale(x)) - x <- data.frame(scale(x)) - x_clean <- data.frame(x[complete.cases(x),]) + + if(!is.null(catVars)) { + x_cat <- x[,catVars,drop=FALSE] + x_num <- x[,-which(names(x)%in%catVars),drop=FALSE] + scaleparam <- attributes(scale(x_num)) + x_num <- data.frame(scale(x_num)) + x <- as.data.frame(cbind(x_num, lapply(x_cat, as.factor))) + x_clean <- x[complete.cases(x),] + } else { + scaleparam <- attributes(scale(x)) + x <- data.frame(scale(x)) + x_clean <- data.frame(x[complete.cases(x),]) + } + # sample to sample feature distance d <- c() for (i in 1:nrow(x_clean)){ - trainDist <- FNN::knnx.dist(x_clean[i,],x_clean,k=1) + if(is.null(catVars)) { + trainDist <- FNN::knnx.dist(x_clean[i,],x_clean,k=1) + } else { + trainDist <- gower::gower_dist(x_clean[i,],x_clean) + } trainDist[i] <- NA d <- c(d,min(trainDist,na.rm=T)) @@ -304,7 +330,7 @@ sample2sample <- function(x, type,variables,time_unit,timevar){ # Sample to Prediction -sample2prediction = function(x, modeldomain, type, samplesize,variables,time_unit,timevar){ +sample2prediction = function(x, modeldomain, type, samplesize,variables,time_unit,timevar, catVars){ if(type == "geo"){ modeldomain <- sf::st_transform(modeldomain, sf::st_crs(x)) @@ -318,19 +344,44 @@ sample2prediction = function(x, modeldomain, type, samplesize,variables,time_uni }else if(type == "feature"){ x <- x[,variables] x <- sf::st_drop_geometry(x) - scaleparam <- attributes(scale(x)) - x <- data.frame(scale(x)) - x_clean <- x[complete.cases(x),] - modeldomain <- modeldomain[,variables] modeldomain <- sf::st_drop_geometry(modeldomain) - modeldomain <- data.frame(scale(modeldomain,center=scaleparam$`scaled:center`, - scale=scaleparam$`scaled:scale`)) + + if(!is.null(catVars)) { + + x_cat <- x[,catVars,drop=FALSE] + x_num <- x[,-which(names(x)%in%catVars),drop=FALSE] + scaleparam <- attributes(scale(x_num)) + x_num <- data.frame(scale(x_num)) + + modeldomain_num <- modeldomain[,-which(names(modeldomain)%in%catVars),drop=FALSE] + modeldomain_cat <- modeldomain[,catVars,drop=FALSE] + modeldomain_num <- data.frame(scale(modeldomain_num,center=scaleparam$`scaled:center`, + scale=scaleparam$`scaled:scale`)) + + x <- as.data.frame(cbind(x_num, lapply(x_cat, as.factor))) + x_clean <- x[complete.cases(x),] + modeldomain <- as.data.frame(cbind(modeldomain_num, lapply(modeldomain_cat, as.factor))) + + } else { + scaleparam <- attributes(scale(x)) + x <- data.frame(scale(x)) + x_clean <- x[complete.cases(x),] + + modeldomain <- data.frame(scale(modeldomain,center=scaleparam$`scaled:center`, + scale=scaleparam$`scaled:scale`)) + } + target_dist_feature <- c() for (i in 1:nrow(modeldomain)){ - trainDist <- FNN::knnx.dist(modeldomain[i,],x_clean,k=1) + if(is.null(catVars)) { + trainDist <- FNN::knnx.dist(modeldomain[i,],x_clean,k=1) + } else { + trainDist <- gower::gower_dist(modeldomain[i,], x_clean) + } + target_dist_feature <- c(target_dist_feature,min(trainDist,na.rm=T)) } sampletoprediction <- data.frame(dist = target_dist_feature, @@ -358,7 +409,7 @@ sample2prediction = function(x, modeldomain, type, samplesize,variables,time_uni # sample to test -sample2test <- function(x, testdata, type,variables,time_unit,timevar){ +sample2test <- function(x, testdata, type,variables,time_unit,timevar, catVars){ if(type == "geo"){ testdata <- sf::st_transform(testdata,4326) @@ -371,21 +422,47 @@ sample2test <- function(x, testdata, type,variables,time_unit,timevar){ }else if(type == "feature"){ + + x <- x[,variables] x <- sf::st_drop_geometry(x) - scaleparam <- attributes(scale(x)) - x <- data.frame(scale(x)) - x_clean <- x[complete.cases(x),] testdata <- testdata[,variables] testdata <- sf::st_drop_geometry(testdata) - testdata <- data.frame(scale(testdata,center=scaleparam$`scaled:center`, - scale=scaleparam$`scaled:scale`)) + + if(!is.null(catVars)) { + + x_cat <- x[,catVars,drop=FALSE] + x_num <- x[,-which(names(x)%in%catVars),drop=FALSE] + scaleparam <- attributes(scale(x_num)) + x_num <- data.frame(scale(x_num)) + + testdata_num <- testdata[,-which(names(testdata)%in%catVars),drop=FALSE] + testdata_cat <- testdata[,catVars,drop=FALSE] + testdata_num <- data.frame(scale(testdata_num,center=scaleparam$`scaled:center`, + scale=scaleparam$`scaled:scale`)) + + x <- as.data.frame(cbind(x_num, lapply(x_cat, as.factor))) + x_clean <- x[complete.cases(x),] + testdata <- as.data.frame(cbind(testdata_num, lapply(testdata_cat, as.factor))) + + } else { + scaleparam <- attributes(scale(x)) + x <- data.frame(scale(x)) + x_clean <- x[complete.cases(x),] + + testdata <- data.frame(scale(testdata,center=scaleparam$`scaled:center`, + scale=scaleparam$`scaled:scale`)) + } test_dist_feature <- c() for (i in 1:nrow(testdata)){ - testDist <- FNN::knnx.dist(testdata[i,],x_clean,k=1) + if(is.null(catVars)) { + testDist <- FNN::knnx.dist(testdata[i,],x_clean,k=1) + } else { + testDist <- gower::gower_dist(testdata[i,], x_clean) + } test_dist_feature <- c(test_dist_feature,min(testDist,na.rm=T)) } dists_test <- data.frame(dist = test_dist_feature, @@ -413,7 +490,7 @@ sample2test <- function(x, testdata, type,variables,time_unit,timevar){ # between folds -cvdistance <- function(x, cvfolds, cvtrain, type, variables,time_unit,timevar){ +cvdistance <- function(x, cvfolds, cvtrain, type, variables,time_unit,timevar, catVars){ if(!is.null(cvfolds)&!is.list(cvfolds)){ # restructure input if CVtest only contains the fold ID tmp <- list() @@ -444,8 +521,16 @@ cvdistance <- function(x, cvfolds, cvtrain, type, variables,time_unit,timevar){ }else if(type == "feature"){ x <- x[,variables] x <- sf::st_drop_geometry(x) - x <- data.frame(scale(x)) + if(is.null(catVars)) { + x <- data.frame(scale(x)) + } else { + x_cat <- x[,catVars,drop=FALSE] + x_num <- x[,-which(names(x)%in%catVars),drop=FALSE] + scaleparam <- attributes(scale(x_num)) + x_num <- data.frame(scale(x_num)) + x <- as.data.frame(cbind(x_num, lapply(x_cat, as.factor))) + } d_cv <- c() for(i in 1:length(cvfolds)){ @@ -463,14 +548,25 @@ cvdistance <- function(x, cvfolds, cvtrain, type, variables,time_unit,timevar){ for (k in 1:nrow(testdata_i)){ - trainDist <- tryCatch(FNN::knnx.dist(testdata_i[k,],traindata_i,k=1), - error = function(e)e) - if(inherits(trainDist, "error")){ - trainDist <- NA - message("warning: no distance could be calculated for a fold. + if(is.null(catVars)) { + trainDist <- tryCatch(FNN::knnx.dist(testdata_i[k,],traindata_i,k=1), + error = function(e)e) + if(inherits(trainDist, "error")){ + trainDist <- NA + message("warning: no distance could be calculated for a fold. + Possibly because predictor values are NA") + } + } else { + trainDist <- tryCatch(gower::gower_dist(testdata_i[i,], traindata_i), + error = function(e)e) + if(inherits(trainDist, "error")){ + trainDist <- NA + message("warning: no distance could be calculated for a fold. Possibly because predictor values are NA") + } } + trainDist[k] <- NA d_cv <- c(d_cv,min(trainDist,na.rm=T)) } @@ -514,7 +610,7 @@ cvdistance <- function(x, cvfolds, cvtrain, type, variables,time_unit,timevar){ -sampleFromArea <- function(modeldomain, samplesize, type,variables,sampling){ +sampleFromArea <- function(modeldomain, samplesize, type,variables,sampling, catVars){ ##### Distance to prediction locations: # regularly spread points (prediction locations): @@ -552,7 +648,13 @@ sampleFromArea <- function(modeldomain, samplesize, type,variables,sampling){ if(type == "feature"){ - modeldomain <- terra::project(modeldomain, "epsg:4326") + + if(is.null(catVars)) { + modeldomain <- terra::project(modeldomain, "epsg:4326") + } else { + modeldomain <- terra::project(modeldomain, "epsg:4326", method="near") + } + predictionloc <- sf::st_as_sf(terra::extract(modeldomain,terra::vect(predictionloc),bind=TRUE)) predictionloc <- na.omit(predictionloc) } diff --git a/tests/testthat/test-geodist.R b/tests/testthat/test-geodist.R index 3762ac8d..a06fce18 100644 --- a/tests/testthat/test-geodist.R +++ b/tests/testthat/test-geodist.R @@ -13,7 +13,6 @@ test_that("geodist works with points and polygon in geographic space", { mean_sample2sample <- round(mean(dist_geo[dist_geo$what=="sample-to-sample","dist"])) mean_CV_distances <- round(mean(dist_geo[dist_geo$what=="CV-distances","dist"])) - # can't be tested for prediction-to-sample, which are sampled slightly different in each run nrow_dist <- nrow(dist_geo) expect_equal(mean_sample2sample, 20321) @@ -40,7 +39,6 @@ test_that("geodist works with points and polygon in feature space", { mean_sample2sample <- round(mean(dist_fspace[dist_fspace$what=="sample-to-sample","dist"]), 4) mean_CV_distances <- round(mean(dist_fspace[dist_fspace$what=="CV-distances","dist"]), 4) - # can't be tested for prediction-to-sample, which are sampled slightly different in each run expect_equal(mean_sample2sample, 0.0843) expect_equal(mean_CV_distances, 0.1036) @@ -217,6 +215,51 @@ test_that("geodist works with points and test data in feature space", { }) +test_that("geodist works with categorical variables in feature space", { + + set.seed(1234) + predictor_stack <- terra::rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST")) + predictors <- c("DEM","TWI", "NDRE.M", "Easting", "Northing", "fct") + predictor_stack$fct <- factor(c(rep(LETTERS[1], terra::ncell(predictor_stack)/2), + rep(LETTERS[2], terra::ncell(predictor_stack)/2))) + + predictor_stack <- predictor_stack[[predictors]] + studyArea <- predictor_stack + studyArea[!is.na(studyArea)] <- 1 + studyArea <- terra::as.polygons(studyArea, values = FALSE, na.all = TRUE) |> + sf::st_as_sf() |> + sf::st_union() + + pts <- clustered_sample(studyArea, 30, 5, 60) + pts <- sf::st_transform(pts, crs = sf::st_crs(studyArea)) + pts <- terra::extract(predictor_stack, terra::vect(pts), ID=FALSE, bind=TRUE) |> + sf::st_as_sf() + + test_pts <- clustered_sample(studyArea, 50, 5, 20) + + folds <- data.frame("folds"=sample(1:3, nrow(pts), replace=TRUE)) + folds <- CreateSpacetimeFolds(folds, spacevar="folds", k=3) + + sf::st_as_sf(terra::extract(predictor_stack,terra::vect(pts$geometry),bind=TRUE)) + + dist <- geodist(x=pts, + modeldomain=predictor_stack, + type = "feature", + testdata = test_pts, + cvfolds = folds$indexOut) + + mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) + mean_prediction2sample <- round(mean(dist[dist$what=="prediction-to-sample","dist"]), 4) + mean_test2sample <- round(mean(dist[dist$what=="test-to-sample","dist"]), 4) + mean_CV_distance <- round(mean(dist[dist$what=="CV-distances","dist"]), 4) + + expect_equal(mean_sample2sample, 0.0459) + expect_equal(mean_prediction2sample, 0.1625) + expect_equal(mean_test2sample, 0.2358) + expect_equal(mean_CV_distance, 0.0663) +}) + + test_that("geodist works in temporal space", { dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) diff --git a/tests/testthat/test-knndm.R b/tests/testthat/test-knndm.R index 42edae69..616f665d 100644 --- a/tests/testthat/test-knndm.R +++ b/tests/testthat/test-knndm.R @@ -269,8 +269,6 @@ test_that("kNNDM works in feature space with clustered training points", { trainDat <- sf::st_drop_geometry(splotdata) predictors_sp <- terra::rast(system.file("extdata", "predictors_chile.tif",package="CAST")) - - knndm_folds <- knndm(trainDat[,predictors], modeldomain = predictors_sp, space = "feature", clustering="kmeans", k=4, maxp=0.8) @@ -319,7 +317,6 @@ test_that("kNNDM works in feature space with clustered training points, categori predictor_stack$fct <- factor(c(rep(LETTERS[1], terra::ncell(predictor_stack)/2), rep(LETTERS[2], terra::ncell(predictor_stack)/2))) - predictor_stack <- predictor_stack[[predictors]] studyArea <- predictor_stack studyArea[!is.na(studyArea)] <- 1 From d6ddf489b33ca2728d54095fca0adc80c3af2d1e Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Wed, 13 Mar 2024 12:06:03 +0100 Subject: [PATCH 07/11] update Intro to CAST vignette --- vignettes/cast01-CAST-intro-cookfarm.R | 107 ------- vignettes/cast01-CAST-intro-cookfarm.Rmd | 279 ------------------- vignettes/cast01-CAST-intro.Rmd | 339 +++++++++++++++++++++++ 3 files changed, 339 insertions(+), 386 deletions(-) delete mode 100644 vignettes/cast01-CAST-intro-cookfarm.R delete mode 100644 vignettes/cast01-CAST-intro-cookfarm.Rmd create mode 100644 vignettes/cast01-CAST-intro.Rmd diff --git a/vignettes/cast01-CAST-intro-cookfarm.R b/vignettes/cast01-CAST-intro-cookfarm.R deleted file mode 100644 index 7ad54682..00000000 --- a/vignettes/cast01-CAST-intro-cookfarm.R +++ /dev/null @@ -1,107 +0,0 @@ -## ----setup, echo=FALSE-------------------------------------------------------- -knitr::opts_chunk$set(fig.width = 8.83,cache = FALSE) -user_hanna <- Sys.getenv("USER") %in% c("hanna") - -## ----c1, message = FALSE, warning=FALSE--------------------------------------- -#install.packages("CAST") -library(CAST) - -## ----c2, message = FALSE, warning=FALSE--------------------------------------- -help(CAST) - -## ----c3, message = FALSE, warning=FALSE--------------------------------------- -data <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -head(data) - -## ----c4, message = FALSE, warning=FALSE--------------------------------------- - -library(sf) -data_sp <- unique(data[,c("SOURCEID","Easting","Northing")]) -data_sp <- st_as_sf(data_sp,coords=c("Easting","Northing"),crs=26911) -plot(data_sp,axes=T,col="black") - -## ----c5, message = FALSE, warning=FALSE, eval=user_hanna---------------------- -#...or plot the data with mapview: -library(mapview) -mapviewOptions(basemaps = c("Esri.WorldImagery")) -mapview(data_sp) - -## ----c6, message = FALSE, warning=FALSE--------------------------------------- -library(lubridate) -library(ggplot2) -trainDat <- data[data$altitude==-0.3& - year(data$Date)==2012& - week(data$Date)%in%c(10:12),] -ggplot(data = trainDat, aes(x=Date, y=VW)) + - geom_line(aes(colour=SOURCEID)) - -## ----c7, message = FALSE, warning=FALSE--------------------------------------- -library(caret) -predictors <- c("DEM","TWI","Precip_cum","cday", - "MaxT_wrcc","Precip_wrcc","BLD", - "Northing","Easting","NDRE.M") -set.seed(10) -model <- train(trainDat[,predictors],trainDat$VW, - method="rf",tuneGrid=data.frame("mtry"=2), - importance=TRUE,ntree=50, - trControl=trainControl(method="cv",number=3)) - -## ----c8, message = FALSE, warning=FALSE--------------------------------------- -library(terra) -predictors_sp <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST")) -prediction <- predict(predictors_sp,model,na.rm=TRUE) -plot(prediction) - -## ----c9, message = FALSE, warning=FALSE--------------------------------------- -model - -## ----c10, message = FALSE, warning=FALSE-------------------------------------- -set.seed(10) -indices <- CreateSpacetimeFolds(trainDat,spacevar = "SOURCEID", - k=3) -set.seed(10) -model_LLO <- train(trainDat[,predictors],trainDat$VW, - method="rf",tuneGrid=data.frame("mtry"=2), importance=TRUE, - trControl=trainControl(method="cv", - index = indices$index)) -model_LLO - -## ----c11, message = FALSE, warning=FALSE-------------------------------------- -plot(varImp(model_LLO)) - -## ----c12, message = FALSE, warning=FALSE-------------------------------------- -set.seed(10) -ffsmodel_LLO <- ffs(trainDat[,predictors],trainDat$VW,metric="Rsquared", - method="rf", tuneGrid=data.frame("mtry"=2), - verbose=FALSE,ntree=50, - trControl=trainControl(method="cv", - index = indices$index)) -ffsmodel_LLO -ffsmodel_LLO$selectedvars - -## ----c13, message = FALSE, warning=FALSE-------------------------------------- -plot(ffsmodel_LLO) - -## ----c14, message = FALSE, warning=FALSE-------------------------------------- -prediction_ffs <- predict(predictors_sp,ffsmodel_LLO,na.rm=TRUE) -plot(prediction_ffs) - -## ----c15, message = FALSE, warning=FALSE-------------------------------------- -### AOA for which the spatial CV error applies: -AOA <- aoa(predictors_sp,ffsmodel_LLO) - -plot(prediction_ffs,main="prediction for the AOA \n(spatial CV error applied)") -plot(AOA$AOA,col=c("grey","transparent"),add=T) - -#spplot(prediction_ffs,main="prediction for the AOA \n(spatial CV error applied)")+ -#spplot(AOA$AOA,col.regions=c("grey","transparent")) - -### AOA for which the random CV error applies: -AOA_random <- aoa(predictors_sp,model) -plot(prediction,main="prediction for the AOA \n(random CV error applied)") -plot(AOA_random$AOA,col=c("grey","transparent"),add=T) - -#spplot(prediction,main="prediction for the AOA \n(random CV error applied)")+ -#spplot(AOA_random$AOA,col.regions=c("grey","transparent")) - - diff --git a/vignettes/cast01-CAST-intro-cookfarm.Rmd b/vignettes/cast01-CAST-intro-cookfarm.Rmd deleted file mode 100644 index 96ebea02..00000000 --- a/vignettes/cast01-CAST-intro-cookfarm.Rmd +++ /dev/null @@ -1,279 +0,0 @@ ---- -title: "1. Introduction to CAST" -author: "Hanna Meyer" -date: "`r Sys.Date()`" -output: - rmarkdown::html_vignette: - toc: true -vignette: > - %\VignetteIndexEntry{Introduction to CAST} - %\VignetteEncoding{UTF-8} - %\VignetteEngine{knitr::rmarkdown} -editor_options: - chunk_output_type: console ---- - -```{r setup, echo=FALSE} -knitr::opts_chunk$set(fig.width = 8.83,cache = FALSE) -user_hanna <- Sys.getenv("USER") %in% c("hanna") -``` - - - -## Introduction -!!Note: Some recent developments of CAST are not yet fully documented in this tutorial. A major update can be expected for Apr 2024!! - -### Background -One key task in environmental science is obtaining information of environmental variables continuously in space or in space and time, usually based on remote sensing and limited field data. In that respect, machine learning algorithms have been proven to be an important tool to learn patterns in nonlinear and complex systems. -However, standard machine learning applications are not suitable for spatio-temporal data, as they usually ignore the spatio-temporal dependencies in the data. This becomes problematic in (at least) two aspects of predictive modelling: Overfitted models as well as overly optimistic error assessment (see [Meyer et al 2018](https://www.sciencedirect.com/science/article/pii/S1364815217310976) or [Meyer et al 2019](https://www.sciencedirect.com/science/article/abs/pii/S0304380019303230) ). To approach these problems, CAST supports the well-known caret package ([Kuhn 2018](https://topepo.github.io/caret/index.html) to provide methods designed for spatio-temporal data. - -This tutorial shows how to set up a spatio-temporal prediction model that includes objective and reliable error estimation. It further shows how spatio-temporal overfitting can be detected by comparison between validation strategies. It will be shown that certain variables are responsible for the problem of overfitting due to spatio-temporal autocorrelation patterns. Therefore, this tutorial also shows how to automatically exclude variables that lead to overfitting with the aim to improve the spatio-temporal prediction model. - -In order to follow this tutorial, I assume that the reader is familiar with the basics of predictive modelling nicely explained in [Kuhn and Johnson 2013](https://doi.org/10.1007/978-1-4614-6849-3) as well as machine learning applications via the caret package. - - -### How to start - -To work with the tutorial, first install the CAST package and load the library: - -```{r c1, message = FALSE, warning=FALSE} -#install.packages("CAST") -library(CAST) -``` - -If you need help, see - -```{r c2, message = FALSE, warning=FALSE} -help(CAST) -``` - -## Example of a typical spatio-temporal prediction task -The example prediction task for this tutorial is the following: we have a set of data loggers distributed over a farm, and we want to map soil moisture, based on a set of spatial and temporal predictor variables. We will use Random Forests as a machine learning algorithm in this tutorial. - -### Description of the example dataset -To do so, we will work with the cookfarm dataset, described in e.g. [Gasch et al 2015](https://www.sciencedirect.com/science/article/pii/S2211675315000251/) and available via the GSIF package ([Hengl 2017](https://CRAN.R-project.org/package=GSIF)). The dataset included in the CAST package is a re-structured dataset which was used for the analysis in [Meyer et al 2018](https://www.sciencedirect.com/science/article/pii/S1364815217310976). - - -```{r c3, message = FALSE, warning=FALSE} -data <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -head(data) -``` - -I want to point out on the following information of this dataset: The "SOURCEID" represents the ID for the data logger, "VW" is soil moisture which is our response variable, "Easting" and "Northing" are the coordinates of the data loggers, "altitude" indicates the depth of the soil in which VW was measured, and the remaining columns represent different potential predictor variables which are terrain related (e.g. "DEM", "TWI"), vegetation indices (e.g. "NDRE"), soil properties (e.g. "BLD") or climate-related predictors (e.g. "Precip_wrcc"). -See [Gasch et al 2015](https://www.sciencedirect.com/science/article/pii/S2211675315000251) for further description on the dataset. - -To get an impression on the spatial properties of the dataset, let's have a look on the spatial distribution of the data loggers on the cookfarm: - -```{r c4, message = FALSE, warning=FALSE} - -library(sf) -data_sp <- unique(data[,c("SOURCEID","Easting","Northing")]) -data_sp <- st_as_sf(data_sp,coords=c("Easting","Northing"),crs=26911) -plot(data_sp,axes=T,col="black") -``` - -```{r c5, message = FALSE, warning=FALSE, eval=user_hanna} -#...or plot the data with mapview: -library(mapview) -mapviewOptions(basemaps = c("Esri.WorldImagery")) -mapview(data_sp) -``` - -We see that the data are taken at 42 locations (SOURCEID) over the field. The loggers recorded data between 2007 and 2013 (the dataset here only contains the data from 2010 on). The VW data are given here on a daily basis. - - -### Data subsetting -To reduce the data to an amount that can be handled in a tutorial, let's restrict the data to the depth of -0.3 and to two weeks of the year 2012. After subsetting let's have an overview on the soil moisture time series measured by the data loggers. - - -```{r c6, message = FALSE, warning=FALSE} -library(lubridate) -library(ggplot2) -trainDat <- data[data$altitude==-0.3& - year(data$Date)==2012& - week(data$Date)%in%c(10:12),] -ggplot(data = trainDat, aes(x=Date, y=VW)) + - geom_line(aes(colour=SOURCEID)) -``` - -What we can see is that (as expected) each logger location has a unique time series of soil moisture. - - -## Model training and prediction -In the following we will use this subset of the cookfarm data as an example to spatially predict soil moisture (i.e. to map soil moisture) with (and without) consideration of the spatio-temporal dependencies. -To start with, lets use this dataset to create a "default" Random Forest model that predicts soil moisture based on some predictor variables. -To keep computation time at a minimum, we don't include hyperparameter tuning (hence mtry was set to 2) which is reasonable as Random Forests are comparably insensitive to tuning. - -```{r c7, message = FALSE, warning=FALSE} -library(caret) -predictors <- c("DEM","TWI","Precip_cum","cday", - "MaxT_wrcc","Precip_wrcc","BLD", - "Northing","Easting","NDRE.M") -set.seed(10) -model <- train(trainDat[,predictors],trainDat$VW, - method="rf",tuneGrid=data.frame("mtry"=2), - importance=TRUE,ntree=50, - trControl=trainControl(method="cv",number=3)) -``` - - -Based on the trained model we can make spatial predictions of soil moisture. To do this we load a multiband raster that contains spatial data of all predictor variables for the 25th of March 2012 (as an example). We then apply the trained model on this data set. - - -```{r c8, message = FALSE, warning=FALSE} -library(terra) -predictors_sp <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST")) -prediction <- predict(predictors_sp,model,na.rm=TRUE) -plot(prediction) -``` - -The result is a spatially comprehensive map of soil moisture for this day. -We see that simply creating a map using machine learning and caret is an easy task, however accurately measuring its performance is less simple. Though the map looks good on a first sight we now have to follow up with the question of how accurate this map is, hence we need to ask how well the model is able to map soil moisture. - -From a visible inspection it is noticeable that the model produces a strange linear features at the eastern side of the farm which looks suspicious. But let's come back to this later and first focus on a statistical validation of the model. - -## Cross validation strategies for spatio-temporal data -Among validation strategies, k-fold cross validation (CV) is popular to estimate the performance of the model in view to data that have not been used for model training. During CV, models are repeatedly trained (k models) and in each model run, the data of one fold are put to the side and are not used for model training but for model validation. In this way, the performance of the model can be estimated using data that have not been included in the model training. - -### The Standard approach: Random k-fold CV - -In the example above we used a random k-fold CV that we defined in caret's trainControl argument. More specifically, we used a random 3-fold CV. Hence, the data points in our dataset were RANDOMLY split into 3 folds. -To assess the performance of the model let's have a look on the output of the Random CV: - - -```{r c9, message = FALSE, warning=FALSE} -model -``` - -We see that soil moisture could be modelled with a high R² (0.90) which indicates a nearly perfect fit of the data. Sounds good, but unfortunately, the random k fold CV does not give us a good indication for the map accuracy. Random k-fold CV means that each of the three folds (with the highest certainty) contains data points from each data logger. Therefore, a random CV cannot indicate the ability of the model to make predictions beyond the location of the training data (i.e. to map soil moisture). Since our aim is to map soil moisture, we rather need to perform a target-oriented validation which validates the model in view to spatial mapping. - - -### Target-oriented validation - -We are not interested in the model performance in view to random subsets of our data loggers, but we need to know how well the model is able to make predictions for areas without data loggers. -To find this out, we need to repeatedly leave the complete time series of one or more data loggers out and use them as test data during CV. - -To do this we first need to create meaningful folds rather than random folds. CAST's function "CreateSpaceTimeFolds" is designed to provide index arguments used by caret's trainControl. The index defines which data points are used for model training during each model run and reversely defines which data points are held back. Hence, using the index argument we can account for the dependencies in the data by leaving the complete data from one or more data loggers out (LLO CV), from one or more time steps out (LTO CV) or from data loggers and time steps out (LLTO CV). In this example we're focusing on LLO CV, therefore we use the column "SOURCEID" to define the location of a data logger and split the data into folds using this information. -Analog to the random CV we split the data into five folds, hence five model runs are performed each leaving one fifth of all data loggers out for validation. - -Note that several suggestions of spatial CV exist. What we call LLO here is just a simple example. See references in [Meyer and Pebesma 2022](https://www.nature.com/articles/s41467-022-29838-9) for some examples and have a look at [Mila et al 2022](https://doi.org/10.1111/2041-210X.13851) for the methodology implemented in the CAST function nndm. - - - -```{r c10, message = FALSE, warning=FALSE} -set.seed(10) -indices <- CreateSpacetimeFolds(trainDat,spacevar = "SOURCEID", - k=3) -set.seed(10) -model_LLO <- train(trainDat[,predictors],trainDat$VW, - method="rf",tuneGrid=data.frame("mtry"=2), importance=TRUE, - trControl=trainControl(method="cv", - index = indices$index)) -model_LLO -``` - - -By inspecting the output of the model, we see that in view to new locations, the R² is only 0.16 so the performance is much lower than expected from the random CV (R² = 0.90). - -Apparently, there is considerable overfitting in the model, causing a good random performance but a poor performance in view to new locations. This might partly be attributed to the choice of variables where we must suspect that certain variables are misinterpreted by the model (see [Meyer et al 2018](https://www.sciencedirect.com/science/article/pii/S1364815217310976) or [talk at the OpenGeoHub summer school 2019] (https://www.youtube.com/watch?v=mkHlmYEzsVQ)). - -Let's have a look at the variable importance ranking of Random Forest and see if we find something suspicious: - -```{r c11, message = FALSE, warning=FALSE} -plot(varImp(model_LLO)) -``` - -The importance ranking indicates that among others, "Easting" is an important variable. This fits to the observation of an inappropriate linear features in the predicted map. Apparently the model assigns a high importance to this variable which causes a high random CV performance. But at the same time the model fails in the prediction on new locations because the variable is unsuitable for predictions beyond the locations of the data loggers used for model training. - -Assuming that certain variables are misinterpreted by the algorithm we should be able to produce a higher LLO performance when such variables are removed. -Let's see if this is true... - - -## Removing variables that cause overfitting -CAST's forward feature selection (ffs) selects variables that make sense in view to the selected CV method and excludes those which are counterproductive (or meaningless) in view to the selected CV method. -When we use LLO as CV method, ffs selects variables that lead in combination to the highest LLO performance (i.e. the best spatial model). All variables that have no spatial meaning or are even counterproductive won't improve or even reduce the LLO performance and are therefore excluded from the model by the ffs. - -ffs is doing this job by first training models using all possible pairs of two predictor variables. The best model of these initial models is kept. On the basis of this best model the predictor variables are iterativly increased and each of the remaining variables is tested for its improvement of the currently best model. The process stops if none of the remaining variables increases the model performance when added to the current best model. - -So let's run the ffs on our case study using R² as a metric to select the optimal variables. This process will take 1-2 minutes... - -```{r c12, message = FALSE, warning=FALSE} -set.seed(10) -ffsmodel_LLO <- ffs(trainDat[,predictors],trainDat$VW,metric="Rsquared", - method="rf", tuneGrid=data.frame("mtry"=2), - verbose=FALSE,ntree=50, - trControl=trainControl(method="cv", - index = indices$index)) -ffsmodel_LLO -ffsmodel_LLO$selectedvars -``` - - -Using the ffs with LLO CV, the R² could be increased from 0.16 to 0.28. The variables that are used for this model are "DEM","NDRE.M" and "Northing". All others are removed because they have (at least in this small example) no spatial meaning or are even counterproductive. - -Using the plot$\_$ffs function we can visualize how the performance of the model changed depending on the variables being used: - - -```{r c13, message = FALSE, warning=FALSE} -plot(ffsmodel_LLO) -``` - -See that the best model using two variables led to an R² of slightly above 0.2. Using the third variable could slightly increase the R². Any further variable could not improve the LLO performance. -Note that the R² features a high standard deviation regardless of the variables being used. This is due to the small dataset that was used which cannot lead to robust results. - -What effect does the new model has on the spatial representation of soil moisture? - -```{r c14, message = FALSE, warning=FALSE} -prediction_ffs <- predict(predictors_sp,ffsmodel_LLO,na.rm=TRUE) -plot(prediction_ffs) -``` - -We see that the variable selection does not only have an effect on the statistical performance but also the predicted spatial patterns change considerably. It is of note that the linear feature is not any more in the resulting soil moisture map most likely because "Easting" was removed from the set of predictor variables by ffs. - -## Area of Applicability -Still it is required to analyse if the model can be applied to the entire study area of if there are locations that are very different in their predictor properties to what the model has learned from. See more details in the vignette on the Area of applicability and -[Meyer and Pebesma 2021](https://doi.org/10.1111/2041-210X.13650). - -```{r c15, message = FALSE, warning=FALSE} -### AOA for which the spatial CV error applies: -AOA <- aoa(predictors_sp,ffsmodel_LLO) - -plot(prediction_ffs,main="prediction for the AOA \n(spatial CV error applied)") -plot(AOA$AOA,col=c("grey","transparent"),add=T) - -#spplot(prediction_ffs,main="prediction for the AOA \n(spatial CV error applied)")+ -#spplot(AOA$AOA,col.regions=c("grey","transparent")) - -### AOA for which the random CV error applies: -AOA_random <- aoa(predictors_sp,model) -plot(prediction,main="prediction for the AOA \n(random CV error applied)") -plot(AOA_random$AOA,col=c("grey","transparent"),add=T) - -#spplot(prediction,main="prediction for the AOA \n(random CV error applied)")+ -#spplot(AOA_random$AOA,col.regions=c("grey","transparent")) - -``` - -The figure shows in grey areas that are outside the area of applicability, hence predictions should not be considered for these locations. See tutorial on the AOA in this package for more information. - -## Conclusions -To conclude, the tutorial has shown how CAST can be used to facilitate target-oriented (here: spatial) CV on spatial and spatio-temporal data which is crucial to obtain meaningful validation results. Using the ffs in conjunction with target-oriented validation, variables can be excluded that are counterproductive in view to the target-oriented performance due to misinterpretations by the algorithm. ffs therefore helps to select the ideal set of predictor variables for spatio-temporal prediction tasks and gives objective error estimates. - -## Final notes -The intention of this tutorial is to describe the motivation that led to the development of CAST as well as its functionality. Priority is not on modelling soil moisture of the cookfarm in the best possible way but to provide an example for the motivation and functionality of CAST that can run within a few minutes. Hence, only a very small subset of the entire cookfarm dataset was used. Keep in mind that due to the small subset the example is not robust and quite different results might be obtained depending on small changes in the settings. - -The intention of showing the motivation of CAST is also the reason why the coordinates are used here as predictor variables. Though coordinates are used as predictors in quite some scientific studies they rather provide here an extreme example of how misleading variables can lead to overfitting. - -## Further reading - -* Meyer, H., & Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications. Accepted. - -* Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. [https://doi.org/10.1111/2041-210X.13650] - -* Meyer H, Reudenbach C, Wöllauer S,Nauss T (2019) Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction. Ecological Modelling 411: 108815 [https://doi.org/10.1016/j.ecolmodel.2019.108815] - -* Meyer H, Reudenbach C, Hengl T, Katurij M, Nauss T (2018) Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software 101: 1–9 [https://doi.org/10.1016/j.envsoft.2017.12.001] - -* Talk from the OpenGeoHub summer school 2019 on spatial validation and variable selection: https://www.youtube.com/watch?v=mkHlmYEzsVQ. - -* Tutorial (https://youtu.be/EyP04zLe9qo) and Lecture (https://youtu.be/OoNH6Nl-X2s) recording from OpenGeoHub summer school 2020 on the area of applicability. As well as talk at the OpenGeoHub summer school 2021: https://av.tib.eu/media/54879 diff --git a/vignettes/cast01-CAST-intro.Rmd b/vignettes/cast01-CAST-intro.Rmd new file mode 100644 index 00000000..6c4affed --- /dev/null +++ b/vignettes/cast01-CAST-intro.Rmd @@ -0,0 +1,339 @@ +--- +title: "1. Introduction to CAST 1.0.0" +author: "Hanna Meyer, Marvin Ludwig, Carles Milà, Jan Linnenbrink, Fabian Schumacher" +date: "`r Sys.Date()`" +output: + rmarkdown::html_vignette: + toc: true +vignette: > + %\VignetteIndexEntry{Introduction to CAST 1.0.0} + %\VignetteEncoding{UTF-8} + %\VignetteEngine{knitr::rmarkdown} +editor_options: + chunk_output_type: console +--- + +```{r setup, echo=FALSE} +knitr::opts_chunk$set(fig.width = 8.83,cache = TRUE) +``` + + + +## Introduction +### Background +One key task in environmental science is obtaining information of environmental variables continuously in space or in space and time, usually based on remote sensing and limited field data. +In that respect, machine learning algorithms have been proven to be an important tool to learn patterns in nonlinear and complex systems. + + +However, the field data (or reference data in general) are often extremely clustered in space (and time) and rarely present an independent and representative sample of the prediction area. In this case, standard machine learning strategies are not suitable, as they usually ignore the spatio-temporal dependencies. This becomes problematic in (at least) two aspects of predictive modelling: Overfitted models that are hardly able to make predictions beyond the location of the reference data, as well as overly optimistic error assessment. To approach these problems, CAST supports the well-known caret package ([Kuhn 2018](https://topepo.github.io/caret/index.html) to provide machine learning strategies that we designed for spatio-temporal data. + +In this tutorial, we will guide through the package and show how CAST can be used to train spatial prediction models including objective error estimation, detection of spatial overfitting and assessment of the area of applicability of the prediction models. +In order to follow this tutorial, we assume that the reader is familiar with the basics of predictive modelling nicely explained in [Kuhn and Johnson 2013](https://doi.org/10.1007/978-1-4614-6849-3) as well as machine learning applications via the caret package. + + + +### How to start + +To work with the tutorial, first install the CAST package and load the library: + +```{r, message = FALSE, warning=FALSE} +#install.packages("CAST") +library(CAST) +``` + +If you need help, see + +```{r, message = FALSE, warning=FALSE} +help(CAST) +``` + +For this tutorial, we need a few additional packages: +```{r, message = FALSE, warning=FALSE} +library(geodata) +library(terra) +library(sf) +library(caret) +library(tmap) +library(viridis) +``` + +## Example of a typical spatio-temporal prediction task +For demonstration of the CAST functionalities, we will go through a typical spatial prediction task, which aims here at producing a spatially-continuous map of plant species richness for South America. As reference data, we use plant species richness data from plot-based vegetation surveys that are compiled in the sPlotOpen database described in [Sabatini et al. 2021](https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13346) and made available in the CAST package. We will use WorldClim climatic variables and elevation as predictors, assuming that +they are relevant drivers of species richness. We will use Random Forests as a machine learning algorithm in this tutorial. + +### Description of the example dataset + +```{r, message = FALSE, warning=FALSE} +data(splotdata) +head(splotdata) +``` + +The data include the extracted WorldClim and elevation information as well as the sampled species richness. Since we aim at making predictions for entire South America, we further need the WorldClimdata for this area. + +```{r, message = FALSE, warning=FALSE} + +wc <- worldclim_global(var="bio",res = 10,path=tempdir()) +elev <- elevation_global(res = 10, path=tempdir()) +predictors_sp <- crop(c(wc,elev),st_bbox(splotdata)) +names(predictors_sp) <- c(paste0("bio_",1:19),"elev") + +#note: if you prefer to work on a smaller dataset and not download any data, +#here is a subset of Chile: +#predictors_sp <- rast(system.file("extdata", "predictors_chile.tif",package="CAST")) +``` + + +To get an impression on the spatial properties of the dataset, let's have a look on the spatial distribution of the vegetation plots in South America, plotted on top of a elevation model: + +```{r, message = FALSE, warning=FALSE} +plot(predictors_sp$elev) +plot(splotdata[,"Species_richness"],add=T) +``` + + +## Model training and prediction +To start with, lets use this dataset to create a "default" Random Forest model that predicts species richness based on some predictor variables. For performance assessment we use default 3-fold random cross-validation. To keep computation time at a minimum, we don't include hyperparameter tuning (hence mtry was set to 2) which is reasonable as Random Forests are comparably insensitive to tuning. + +```{r, message = FALSE, warning=FALSE} +predictors <- c("bio_1", "bio_4", "bio_5", "bio_6", + "bio_8", "bio_9", "bio_12", "bio_13", + "bio_14", "bio_15", "elev") + +# note that to use the data for model training we have to get rid of the +# geometry column of the sf object: st_drop_geometry(splotdata) + +set.seed(10) # set seed to reproduce the model +model_default <- train(st_drop_geometry(splotdata)[,predictors], + st_drop_geometry(splotdata)$Species_richness, + method="rf",tuneGrid=data.frame("mtry"=2), + importance=TRUE, ntree=50, + trControl=trainControl(method="cv",number=3, savePredictions = "final")) +``` + + +Based on the trained model we can make spatial predictions of species richness. To do this we load a multiband raster that contains spatial data of all predictor variables for South America. We then apply the trained model on this data set. + + +```{r, message = FALSE, warning=FALSE} +prediction <- predict(predictors_sp,model_default,na.rm=TRUE) +plot(prediction) +``` + +The result is a spatially comprehensive map of the species richness of South America. +We see that simply creating a map using machine learning and caret is an easy task, however accurately measuring its performance is less simple. Though the map looks good on a first sight we now have to follow up with the question of how accurate this map is, hence we need to ask how well the model is able to map species richness. + + +## Cross validation strategies for spatio-temporal data +Among validation strategies, k-fold cross validation (CV) is popular to estimate the performance of the model in view to data that have not been used for model training. During CV, models are repeatedly trained (k models) and in each model run, the data of one fold are put to the side and are not used for model training but for model validation. In this way, the performance of the model can be estimated using data that have not been included in the model training. +Summary statistics can be calculated either by averaging the performance per fold (default in caret) or by calculating the statistics over all held-back data at once (CAST::global_validation). + +### The Standard approach: Random k-fold CV + +In the example above we used a random k-fold CV that we defined in caret's trainControl argument. More specifically, we used a random 3-fold CV. Hence, the data points in our dataset were RANDOMLY split into 3 folds. +To assess the performance of the model let's have a look on the output of the Random CV: + + +```{r, message = FALSE, warning=FALSE} +model_default +global_validation(model_default) +``` + +We see that species richness could be modelled with a R² of 0.66 which indicates a good fit of the data. Sounds good, but unfortunately, the random k fold CV does not give us a good indication for the map accuracy if the reference data are not a random sample of the prediction area. Random k-fold CV means that each of the three folds (with the highest certainty) contains data points from each spatial cluster. Therefore, a random CV cannot indicate the ability of the model to make predictions beyond the location of the training data (i.e. to map species richness). Since our aim is to map species richness, we rather need to perform a target-oriented validation which validates the model in view to spatial mapping. + + +### Target-oriented validation + +We are not interested in the model performance in view to random subsets of our vegetation plots, but we need to know how well the model is able to make predictions for areas without reference samples. +To find this out, we need to repeatedly leave larger spatial regions of one or more vegetation plots out and use them as test data during CV. Several suggestions of spatial CV exist. CAST implements a straightforward way (CAST::createSpaceTimeFolds) and the sophisticated method of nearest neighbor distance matching ([Mila et al 2022](https://doi.org/10.1111/2041-210X.13851), [Linnenbrink et al 2023](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1308/)) as either leave-one-out CV (CAST::nndm) or as k-fold CV (CAST::knndm). + +CAST's function "CreateSpaceTimeFolds" is designed to provide index arguments used by caret's trainControl. The index defines which data points are used for model training during each model run and reversely defines which data points are held back. Hence, using the index argument we can account for the dependencies in the data by leaving the complete data from one or more regions out (LLO CV), from one or more time steps out (LTO CV) or from locations and time steps out (LLTO CV). In this example we're focusing on LLO CV. We use the column "Country" to define the location of the samples and split the data into folds using this information, i.e. we avoid that data from the same country are located in both, training and test data. Analog to the random CV we split the data into three folds, hence three model runs are performed each leaving one third of all data out for validation. + +Knndm tries to find a k-fold configuration such that the integral of the absolute differences (Wasserstein W statistic) between the empirical nearest neighbour distance distribution function between the test and training data during CV, and the empirical nearest neighbour distance distribution function between the prediction and training points, is minimised. In other words, we try to split the data into folds so that the difficulty of the prediction during cross-validation is comparable to the difficulty when deploying the model for entire South America, where the difficulty is defined by spatial distances. Note, that feature space distances are also an implemented option. + +See tutorial on the cross-validation in this package for more information. + +Example for CreateSpacetimeFolds: +```{r, message = FALSE, warning=FALSE} +set.seed(10) +indices_LLO <- CreateSpacetimeFolds(splotdata,spacevar = "Country", + k=3) +``` + +Example for knndm: +```{r, message = FALSE, warning=FALSE} +set.seed(10) +indices_knndm <- knndm(splotdata,predictors_sp,k=3) +``` + +Let's compare how well these strategies fit the prediction task, especially in comparison to a random cross-validation. We will use CAST's geodist function for that. Geodist calculates nearest neighbor distances in geographic space or feature space between training data as well as between training data and prediction locations. Optional, an that is what we want to focus on here, the nearest neighbor distances between training data and CV folds is computed. +See tutorial on geodistance visualizations in this package for more information. + + + +```{r, message = FALSE, warning=FALSE} +plot(geodist(splotdata,predictors_sp,cvfolds =model_default$control$indexOut))+ + scale_x_log10(labels=round) +plot(geodist(splotdata,predictors_sp,cvfolds =indices_LLO$indexOut))+ + scale_x_log10(labels=round) +plot(geodist(splotdata,predictors_sp,cvfolds =indices_knndm$indx_test))+ + scale_x_log10(labels=round) +``` + +We see that using random folds, we're only testing how well the model can make predictions for new areas that are a few hundreds of meters away from the training data. Using createSpaceTimeFolds with the Country as spatial unit (i.e. leave-country-out CV), we produce prediction situations that are even harder than the prediction task. Knndm provides the best option, since the CV distances that we create during cross-validation are comparable to what is required when we predict for the entire area. + + +```{r, message = FALSE, warning=FALSE} +model <- train(st_drop_geometry(splotdata)[,predictors], + st_drop_geometry(splotdata)$Species_richness, + method="rf", + tuneGrid=data.frame("mtry"=2), + importance=TRUE, + trControl=trainControl(method="cv", + index = indices_knndm$indx_train, + savePredictions = "final")) +model +global_validation(model) +``` + + +By inspecting the output of the model, we see that in view to new locations, the R² is much lower and the RMSE much higher compared to what was expected from the random CV. + +Apparently, there is considerable overfitting in the model, causing a good random performance but a poor performance in view to new locations. This might partly be attributed to the choice of variables where we must suspect that certain variables are misinterpreted by the model (see [Meyer et al 2018](https://www.sciencedirect.com/science/article/pii/S1364815217310976) or [talk at the OpenGeoHub summer school 2019](https://www.youtube.com/watch?v=mkHlmYEzsVQ)). + +Let's have a look at the variable importance ranking of Random Forest. Assuming that certain variables are misinterpreted by the algorithm we should be able to produce a higher LLO performance when such variables are removed. Let's see if this is true... + +```{r, message = FALSE, warning=FALSE} +plot(varImp(model)) +``` + +## Removing variables that cause overfitting +CAST's forward feature selection (ffs) selects variables that make sense in view to the selected CV method and excludes those which are counterproductive (or meaningless) in view to the selected CV method. +When we use LLO as CV method, ffs selects variables that lead in combination to the highest LLO performance (i.e. the best spatial model). All variables that have no spatial meaning or are even counterproductive won't improve or even reduce the LLO performance and are therefore excluded from the model by the ffs. + +ffs is doing this job by first training models using all possible pairs of two predictor variables. The best model of these initial models is kept. On the basis of this best model the predictor variables are iterativly increased and each of the remaining variables is tested for its improvement of the currently best model. The process stops if none of the remaining variables increases the model performance when added to the current best model. + +So let's run the ffs on our case study. This process will take 1-2 minutes... + +```{r, message = FALSE, warning=FALSE} +set.seed(10) +ffsmodel <- ffs(st_drop_geometry(splotdata)[,predictors], + st_drop_geometry(splotdata)$Species_richness, + method="rf", + tuneGrid=data.frame("mtry"=2), + verbose=FALSE, + ntree=50, + trControl=trainControl(method="cv", + index = indices_knndm$indx_train, + savePredictions = "final")) +ffsmodel +global_validation(ffsmodel) +ffsmodel$selectedvars +``` + + +Using the ffs with LLO CV, the RMSE could be decreased. Only few variables have been selected for the final model. All others are removed because they have (at least in this small example) no spatial meaning or are even counterproductive. + +By plotting the results of ffs, we can visualize how the performance of the model changed depending on the variables being used: + + +```{r, message = FALSE, warning=FALSE} +plot(ffsmodel) +``` + +See that the best model using all combinations of two variables. Based on the best performing twi variables, using any third variable could slightly increase the R². Any further variable could not improve the LLO performance. +Note that the R² features a high standard deviation regardless of the variables being used. This is due to the small dataset that was used which cannot lead to robust results here. + +What effect does the new model has on the spatial representation of species richness? + +```{r, message = FALSE, warning=FALSE} +prediction_ffs <- predict(predictors_sp, ffsmodel, na.rm=TRUE) +plot(prediction_ffs) +``` + + +## Area of Applicability +Still it is required to analyse if the model can be applied to the entire study area of if there are locations that are very different in their predictor properties to what the model has learned from. See more details in the vignette on the Area of applicability and +[Meyer and Pebesma 2021](https://doi.org/10.1111/2041-210X.13650). + +```{r, message = FALSE, warning=FALSE} +### AOA for which the spatial CV error applies: +AOA <- aoa(predictors_sp,ffsmodel,LPD = TRUE,verbose=FALSE) + +tm_shape(prediction)+ + tm_raster(title="Species \nrichness",palette=viridis(50),style="cont")+ + tm_shape(AOA$AOA)+ + tm_raster(palette=c("1"=NA,"0"="grey"),style="cat",legend.show = FALSE)+ + tm_layout(frame=FALSE,legend.outside = TRUE)+ + tm_add_legend(type="fill",col="grey",border.lwd=0, labels="Outside \nAOA") + + +``` + +The figure shows in grey areas that are outside the area of applicability, hence predictions should not be considered for these locations. See tutorial on the AOA in this package for more information. +We also see that the area to which the low random CV error applies, is comparably small. + +### Error profiles +The aoa function returned, in addition to the AOA also the dissimilarity index (DI) and the local point density (LPD). The DI indicates the distance in feature space to a nearest training data point and the LPD indicates the data point density in feature space. + +We expect, that there is a relationship between DI/LPD and the prediction performance, i.e. we expect that areas that have a low DI and a high LPD (well covered by reference data) have a lower error. We are going to analyze this using CAST::errorProfiles. + +```{r, message = FALSE, warning=FALSE} +plot(c(AOA$DI,AOA$LPD)) + +errormodel_DI <- errorProfiles(model_default,AOA,variable="DI") +errormodel_LPD <- errorProfiles(model_default,AOA,variable="LPD") + +plot(errormodel_DI) +plot(errormodel_LPD) + +``` + +Since the relationship between LPD and the RMSE seems to be comparably high, we're going to use this to model the error for the entire prediction area. + +```{r, message = FALSE, warning=FALSE} +expected_error_LPD = terra::predict(AOA$LPD, errormodel_LPD) +plot(expected_error_LPD) +``` + +## Conclusions +To conclude, the tutorial has shown how CAST can be used to facilitate target-oriented (here: spatial) CV on spatial and spatio-temporal data which is crucial to obtain meaningful validation results. Using the ffs in conjunction with target-oriented validation, variables can be excluded that are counterproductive in view to the target-oriented performance due to misinterpretations by the algorithm. ffs therefore helps to select the ideal set of predictor variables for spatio-temporal prediction tasks and gives objective error estimates. + +## Final notes +The intention of this tutorial is to describe the motivation that led to the development of CAST as well as its functionality. Priority is not on modelling species richnessin the best possible way but to provide an example for the motivation and functionality of CAST that can run within a few minutes. Hence, only a very small subset of the entire sPlotOpen dataset was used. Keep in mind that due to the small subset the example is not robust and quite different results might be obtained depending on small changes in the settings. + +## Further Reading + +### Tutorials + +* [Tutorials for this package](https://hannameyer.github.io/CAST/) + +* The talk from the OpenGeoHub summer school 2019 on spatial validation and variable selection: +https://www.youtube.com/watch?v=mkHlmYEzsVQ. + +* Tutorial (https://youtu.be/EyP04zLe9qo) and Lecture (https://youtu.be/OoNH6Nl-X2s) recording from OpenGeoHub summer school 2020 on the area of applicability. As well as talk at the OpenGeoHub summer school 2021: https://av.tib.eu/media/54879 + +* Talk and tutorial from the OpenGeoHub 2022 summer school on Machine learning-based maps of the environment - challenges of extrapolation and overfitting, including discussions on the area of applicability and the nearest neighbor distance matching cross-validation (https://doi.org/10.5446/59412). + +### Scientific documentation of the methods + +#### Spatial cross-validation +* Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13. +https://doi.org/10.1111/2041-210X.13851 + +* Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2023): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. EGUsphere [preprint]. +https://doi.org/10.5194/egusphere-2023-1308 + +* Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001 + +#### Spatial variable selection +* Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001 + +* Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411. https://doi.org/10.1016/j.ecolmodel.2019.108815 + +#### Area of applicability +* Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. https://doi.org/10.1111/2041-210X.13650 + +#### Applications and use cases +* Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications, 13. https://www.nature.com/articles/s41467-022-29838-9 + +* Ludwig, M., Moreno-Martinez, A., Hoelzel, N., Pebesma, E., Meyer, H. (2023): Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13635. From 975d94b94df847437a332a86ef37d2475289c057 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Wed, 13 Mar 2024 12:06:34 +0100 Subject: [PATCH 08/11] update documentation --- man/geodist.Rd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/man/geodist.Rd b/man/geodist.Rd index 8838741e..ab4f71d1 100644 --- a/man/geodist.Rd +++ b/man/geodist.Rd @@ -56,7 +56,7 @@ Optional, the nearest neighbor distances between training data and test data or \details{ The modeldomain is a sf polygon or a raster that defines the prediction area. The function takes a regular point sample (amount defined by samplesize) from the spatial extent. If type = "feature", the argument modeldomain (and if provided then also the testdata and/or preddata) has to include predictors. Predictor values for x, testdata and preddata are optional if modeldomain is a raster. - If not provided they are extracted from the modeldomain rasterStack. + If not provided they are extracted from the modeldomain rasterStack. If some predictors are categorical (i.e., of class factor or character), gower distances will be used. W statistic describes the match between the distributions. See Linnenbrink et al (2023) for further details. } \note{ @@ -157,5 +157,5 @@ plot(dist) + scale_x_log10(labels=round) \code{\link{nndm}} \code{\link{knndm}} } \author{ -Hanna Meyer, Edzer Pebesma, Marvin Ludwig +Hanna Meyer, Edzer Pebesma, Marvin Ludwig, Jan Linnenbrink } From 1149f7ab9e79cc3eee75615a25b2504ac45cbc73 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Wed, 13 Mar 2024 12:06:57 +0100 Subject: [PATCH 09/11] change between imports and suggest --- DESCRIPTION | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6489880e..e22627d0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -20,7 +20,7 @@ URL: https://github.com/HannaMeyer/CAST, Encoding: UTF-8 LazyData: false Depends: R (>= 4.1.0) -Imports: caret, stats, utils, ggplot2, graphics, FNN, plyr, zoo, methods, grDevices, data.table, lattice, sf, forcats, PCAmixdata, gower, clustMixType +Imports: caret, stats, utils, ggplot2, graphics, FNN, plyr, zoo, methods, grDevices, data.table, lattice, sf, forcats Suggests: doParallel, randomForest, @@ -40,6 +40,9 @@ Suggests: MASS, twosamples, RColorBrewer, + PCAmixdata, + gower, + clustMixType, testthat (>= 3.0.0) RoxygenNote: 7.2.3 VignetteBuilder: knitr From 0466a02955a4a9c235d82825e425e4afb21d8c31 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Wed, 13 Mar 2024 12:07:08 +0100 Subject: [PATCH 10/11] news updated --- NEWS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/NEWS.md b/NEWS.md index 245e8dfc..79383971 100644 --- a/NEWS.md +++ b/NEWS.md @@ -8,6 +8,7 @@ * function DItoErrormetric renamed to errorProfiles and allows for other dissimilarity measures * Improvement and homogenization of plotting methods for nndm, knndm and geodist objects * aoa and trainDI `weight` now allows list input + * vignette on Introduction to CAST updated * deprecated: *plot_geodist (replaced by plot.geodist) *plot_ffs (replaced by plot.ffs) From 7572bc656a2a55a5ca56f68c72164bf4ad1695b4 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Wed, 13 Mar 2024 12:11:50 +0100 Subject: [PATCH 11/11] minor updates --- CAST.Rproj | 1 + DESCRIPTION | 2 ++ NAMESPACE | 1 + R/CAST-package.R | 2 +- 4 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CAST.Rproj b/CAST.Rproj index 42b95bd6..25b93899 100755 --- a/CAST.Rproj +++ b/CAST.Rproj @@ -18,5 +18,6 @@ StripTrailingWhitespace: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source +PackageBuildArgs: --resave-data PackageCheckArgs: --as-cran PackageRoxygenize: rd,collate,namespace,vignette diff --git a/DESCRIPTION b/DESCRIPTION index e22627d0..c95e0e75 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -40,6 +40,8 @@ Suggests: MASS, twosamples, RColorBrewer, + geodata, + tmap, PCAmixdata, gower, clustMixType, diff --git a/NAMESPACE b/NAMESPACE index 8fb1ffd2..27691118 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -42,6 +42,7 @@ importFrom(graphics,segments) importFrom(stats,complete.cases) importFrom(stats,dist) importFrom(stats,lm) +importFrom(stats,median) importFrom(stats,na.exclude) importFrom(stats,na.omit) importFrom(stats,predict) diff --git a/R/CAST-package.R b/R/CAST-package.R index 8a4fcaf1..cf052907 100644 --- a/R/CAST-package.R +++ b/R/CAST-package.R @@ -23,7 +23,7 @@ #' } #' #' @import caret -#' @importFrom stats sd dist na.omit lm predict quantile na.exclude complete.cases +#' @importFrom stats sd dist na.omit lm predict quantile na.exclude complete.cases median #' @importFrom utils combn txtProgressBar setTxtProgressBar #' @importFrom grDevices rainbow #' @importFrom graphics axis plot segments

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