Rename normalized_chi() to fei()

NAMESPACE

@@ -53,6 +53,7 @@ export(F_to_omega2)
 export(F_to_r)
 export(chisq_to_cohens_w)
 export(chisq_to_cramers_v)
+export(chisq_to_fei)
 export(chisq_to_normalized)
 export(chisq_to_pearsons_c)
 export(chisq_to_phi)
@@ -93,6 +94,7 @@ export(eta_squared)
 export(eta_squared_posterior)
 export(f2_to_eta2)
 export(f_to_eta2)
+export(fei)
 export(format_standardize)
 export(get_effectsize_label)
 export(get_effectsize_name)

NEWS.md

@@ -1,6 +1,9 @@
-
 # effectsize 0.7.0.9999
 
+## Breaking Changes
+
+- `normalized_chi()` has been renamed `fei()`.
+
 ## Bug fixes
 
 - Effect sizes for goodness-of-fit now work when passing a `p` that is a table.

R/convert_stat_chisq.R

@@ -1,8 +1,9 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
-#' Convert between Chi square (\eqn{\chi^2}), Cramer's V, phi (\eqn{\phi}),
-#' Cohen's *w*, normalized Chi (\eqn{\chi}) and Pearson's *C* for contingency
-#' tables or goodness of fit.
+#' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
+#' Cohen's *w*, Fei (\u05e4) and Pearson's *C* for contingency tables or
+#' goodness of fit.
+#'
 #'
 #' @param chisq The Chi-squared statistic.
 #' @param n Total sample size.
@@ -21,14 +22,14 @@
 #'
 #' @details These functions use the following formulae:
 #' \cr
-#' \deqn{\phi = \sqrt{\chi^2 / n}}{phi = sqrt(\chi^2 / n)}
+#' \deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\chi^2 / n)}
 #' \cr
-#' \deqn{Cramer's V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
+#' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 #' \cr
-#' \deqn{Pearson's C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+#' \deqn{Fei = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+#' Where `p_E` are the expected probabilities.
 #' \cr
-#' \deqn{\chi_{Normalized} = w \times \sqrt{\frac{q}{1-q}}}{Chi (Normalized) = w * sqrt(q/(1-q))}
-#' Where `q` is the smallest of the expected probabilities.
+#' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
 #' \cr\cr
 #' For adjusted versions of *phi* and *V*, see Bergsma, 2013.
 #'
@@ -65,7 +66,7 @@
 #' #> data:  Smoking_ASD
 #' #> X-squared = 7.8521, df = 2, p-value = 0.01972
 #'
-#' chisq_to_normalized(
+#' chisq_to_fei(
 #'   7.8521,
 #'   n = sum(Smoking_ASD),
 #'   nrow = 1,
@@ -229,7 +230,7 @@ chisq_to_cramers_v <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "g
 #' @rdname chisq_to_phi
 #' @export
 #' @param p Vector of expected values. See [stats::chisq.test()].
-chisq_to_normalized <- function(chisq, n, nrow, ncol, p,
+chisq_to_fei <- function(chisq, n, nrow, ncol, p,
                                 ci = 0.95, alternative = "greater", ...) {
   if (is.numeric(ci) || (!missing(nrow) && !missing(ncol))) {
     # This means that the user passed ncol/nrow
@@ -255,7 +256,7 @@ chisq_to_normalized <- function(chisq, n, nrow, ncol, p,
   N <- n * (1 - q) / q
 
   res <- chisq_to_phi(chisq, N, nrow, ncol, ci = ci, alternative = alternative, adjust = FALSE, dont_stop = TRUE)
-  colnames(res)[1] <- "normalized_chi"
+  colnames(res)[1] <- "Fei"
 
   if ("CI" %in% colnames(res)) {
     if ((alternative <- attr(res, "alternative")) == "less") {

R/effectsize.R

@@ -13,7 +13,7 @@
 #' - For an object of class `htest`, data is extracted via [insight::get_data()], and passed to the relevant function according to:
 #'   - A **t-test** depending on `type`: `"cohens_d"` (default), `"hedges_g"`, or `"cles"`.
 #'   - A **Chi-squared tests of independence**, depending on `type`: `"cramers_v"` (default), `"phi"`, `"cohens_w"`, `"pearsons_c"`, `"cohens_h"`, `"oddsratio"`, or `"riskratio"`.
-#'   - A **Chi-squared tests of goodness-of-fit**, depending on `type`: `"normalized_chi"` (default) `"cohens_w"`, `"pearsons_c"`
+#'   - A **Chi-squared tests of goodness-of-fit**, depending on `type`: `"fei"` (default) `"cohens_w"`, `"pearsons_c"`
 #'   - A **One-way ANOVA test**, depending on `type`: `"eta"` (default), `"omega"` or `"epsilon"` -squared, `"f"`, or `"f2"`.
 #'   - A **McNemar test** returns *Cohen's g*.
 #'   - A **Wilcoxon test** depending on `type`: returns "`rank_biserial`" correlation (default) or `"cles"`.

R/effectsize.htest.R

@@ -114,14 +114,14 @@ effectsize.htest <- function(model, type = NULL, verbose = TRUE, ...) {
 
   if (is.null(type)) {
     if (nr == 1 || nc == 1) {
-      type <- "normalized_chi"
+      type <- "fei"
     } else {
       type <- "cramers_v"
     }
   }
 
-  if (grepl("(c|v|w|phi)$", tolower(type)) || tolower(type) %in% c("normalized_chi", "chi")) {
-    if (tolower(type) %in% c("normalized_chi", "chi")) {
+  if (grepl("(c|v|w|phi|fei)$", tolower(type))) {
+    if (tolower(type) == "fei") {
       p <- Exp
     } else {
       p <- NULL
@@ -136,7 +136,8 @@ effectsize.htest <- function(model, type = NULL, verbose = TRUE, ...) {
       c = ,
       pearsons_c = chisq_to_pearsons_c,
       chi = ,
-      normalized_chi = chisq_to_normalized
+      normalized_chi = ,
+      fei = chisq_to_fei
     )
 
     out <- f(

R/interpret.R

@@ -172,6 +172,7 @@ interpret.effectsize_table <- function(x, rules, ...) {
     Cramers_v = ,
     Cramers_v_adjusted = ,
     normalized_chi = ,
+    fei = ,
     Cohens_w = ,
     phi = ,
     phi_adjusted = interpret_cramers_v(value, rules = rules),

R/is_effectsize_name.R

@@ -55,7 +55,7 @@ es_info <- matrix(
     "phi_adjusted", "Phi (adj.)", "onetail", 0, 1, 0,
     "Pearsons_c", "Pearson's C", "onetail", 0, 1, 0,
     "Cohens_w", "Cohen's w", "onetail", 0, Inf, 0,
-    "normalized_chi", "Norm. Chi", "onetail", 0, 1, 0,
+    "Fei", "Fei", "onetail", 0, 1, 0,
     "Cohens_g", "Cohen's g", "onetail", -0.5, 0.5, 0,
     "Cohens_h", "Cohen's h", "twotail", -pi, pi, 0,
     "Odds_ratio", "Odds ratio", "twotail", 0, Inf, 1,

R/xtab.R

@@ -1,9 +1,8 @@
 #' Effect size for contingency tables
 #'
-#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, normalized Chi
-#' (\eqn{\chi}), Pearson's contingency coefficient, Odds ratios, Risk ratios,
-#' Cohen's *h* and Cohen's *g* for contingency tables or goodness-of-fit. See
-#' details.
+#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, Fei (\eqn{XXX}), Pearson's
+#' contingency coefficient, Odds ratios, Risk ratios, Cohen's *h* and Cohen's
+#' *g* for contingency tables or goodness-of-fit. See details.
 #'
 #' @inheritParams stats::chisq.test
 #' @param ci Confidence Interval (CI) level
@@ -14,8 +13,8 @@
 #'   Partial matching is allowed (e.g., `"g"`, `"l"`, `"two"`...). See
 #'   *One-Sided CIs* in [effectsize_CIs].
 #' @param adjust Should the effect size be bias-corrected? Defaults to `FALSE`.
-#' @param ... Arguments passed to [stats::chisq.test()], such as `p` for
-#'   goodness-of-fit. Ignored for `cohens_g()`.
+#' @param ... For goodness-of-fit effect sizes, can pass `rescale.p` (see
+#'   [stats::chisq.test()]). Else, ignored.
 #'
 #' @details
 #'
@@ -30,17 +29,17 @@
 #' can also be used, but since it is not bounded at 1 (can be larger) its
 #' interpretation is more difficult.
 #' \cr \cr
-#' For goodness-of-fit in 1D tables Cohen's *W*, normalized Chi (\eqn{\chi}) or
-#' Pearson's *C* can be used. Cohen's *w* has no upper bound (can be arbitrarily
-#' large, depending on the expected distribution). Normalized Chi is an adjusted
-#' Cohen's *w*, accounting for the expected distribution, making it bounded
-#' between 0-1. Pearson's *C* is also bounded between 0-1.
+#' For goodness-of-fit in 1D tables Cohen's *W*, Fei (\eqn{XXX}) or Pearson's *C*
+#' can be used. Cohen's *w* has no upper bound (can be arbitrarily large,
+#' depending on the expected distribution). Fei is an adjusted Cohen's *w*,
+#' accounting for the expected distribution, making it bounded between 0-1.
+#' Pearson's *C* is also bounded between 0-1.
 #' \cr \cr
 #' To summarize, for correlation-like effect sizes, we recommend:
 #'
 #' - For a 2x2 table, use `phi()`
 #' - For larger tables, use `cramers_v()`
-#' - For goodness-of-fit, use `normalized_chi()`
+#' - For goodness-of-fit, use `fei()`
 #'
 #' ## Other Effect Sizes for 2-by-2 xtabs
 #'
@@ -69,15 +68,15 @@
 #' \cr \cr
 #' See *Confidence (Compatibility) Intervals (CIs)*, *CIs and Significance Tests*,
 #' and *One-Sided CIs* sections for *phi*, Cohen's *w*, Cramer's *V*,
-#' Pearson's *C*, and normalized Chi.
+#' Pearson's *C*, and *Fei*.
 #'
 #' @inheritSection effectsize_CIs Confidence (Compatibility) Intervals (CIs)
 #' @inheritSection effectsize_CIs CIs and Significance Tests
 #'
 #' @return A data frame with the effect size (`Cramers_v`, `phi` (possibly with
-#'   the suffix `_adjusted`), `Cohens_w`, `normalized_chi`, `Odds_ratio`,
-#'   `Risk_ratio` (possibly with the prefix `log_`), `Cohens_h`, or `Cohens_g`)
-#'   and its CIs (`CI_low` and `CI_high`).
+#'   the suffix `_adjusted`), `Cohens_w`, `Fei`, `Odds_ratio`, `Risk_ratio`
+#'   (possibly with the prefix `log_`), `Cohens_h`, or `Cohens_g`) and its CIs
+#'   (`CI_low` and `CI_high`).
 #'
 #' @seealso [chisq_to_phi()] for details regarding estimation and CIs.
 #' @family effect size indices
@@ -141,14 +140,14 @@
 #'
 #' Smoking_ASD <- as.table(c(ASD = 17, ASP = 11, TD = 640))
 #'
-#' normalized_chi(Smoking_ASD)
+#' fei(Smoking_ASD)
 #'
 #' cohens_w(Smoking_ASD)
 #'
 #' pearsons_c(Smoking_ASD)
 #'
 #' # Use custom expected values:
-#' normalized_chi(Smoking_ASD, p = c(0.015, 0.010, 0.975))
+#' fei(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 #'
 #' cohens_w(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 #'
@@ -205,50 +204,52 @@ phi <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE,
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y))
     x$data.name <- NULL
   }
 
   effectsize(x, type = "phi", adjust = adjust, ci = ci, alternative = alternative)
 }
 
 #' @rdname phi
+#' @importFrom stats chisq.test
 #' @export
-cohens_w <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
+cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "phi", ci = ci, ...))
+    return(effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, ...))
   }
 
 
   if (inherits(x, "htest")) {
     if (!(grepl("Pearson's Chi-squared", x$method) ||
-      grepl("Chi-squared test for given probabilities", x$method))) {
+          grepl("Chi-squared test for given probabilities", x$method))) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "cohens_w", ci = ci, alternative = alternative)
+  effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, alternative = alternative)
 }
 
 #' @rdname phi
 #' @importFrom stats chisq.test
 #' @export
-cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
+cohens_w <- function(x, y = NULL, p = rep(1/length(x), length(x)),
+                     ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, ...))
+    return(effectsize(x, type = "phi", ci = ci, ...))
   }
 
 
@@ -258,21 +259,22 @@ cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust =
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y, p = p, ...))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, alternative = alternative)
+  effectsize(x, type = "cohens_w", ci = ci, alternative = alternative)
 }
 
 
 #' @rdname phi
+#' @importFrom stats chisq.test
 #' @export
-normalized_chi <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
+fei <- function(x, p = rep(1/length(x), length(x)), ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
-    stop("Normalized Chi is only applicable to goodness of fit tests.", call. = FALSE)
+    stop("Fei is only applicable to goodness of fit tests.", call. = FALSE)
   }
 
 
@@ -282,24 +284,25 @@ normalized_chi <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...)
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y = NULL, p = p, ...))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "normalized_chi", ci = ci, alternative = alternative)
+  effectsize(x, type = "fei", ci = ci, alternative = alternative)
 }
 
 #' @rdname phi
 #' @importFrom stats chisq.test
 #' @export
-pearsons_c <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
+pearsons_c <- function(x, y = NULL, p = rep(1/length(x), length(x)),
+                     ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "pearsons_c", adjust = adjust, ci = ci, ...))
+    return(effectsize(x, type = "pearsons_c", ci = ci, ...))
   }
 
 
@@ -309,11 +312,11 @@ pearsons_c <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust =
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y, p = p, ...))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "pearsons_c", adjust = adjust, ci = ci, alternative = alternative)
+  effectsize(x, type = "pearsons_c", ci = ci, alternative = alternative)
 }
 
 
@@ -340,7 +343,7 @@ oddsratio <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", log = F
     return(effectsize(x, type = "or", log = log, ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {
@@ -416,7 +419,7 @@ riskratio <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", log = F
     return(effectsize(x, type = "rr", log = log, ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {
@@ -494,7 +497,7 @@ cohens_h <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", ...) {
     return(effectsize(x, type = "cohens_h", ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {