New CRAN release

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: effectsize
 Title: Indices of Effect Size
-Version: 0.8.8.2
+Version: 0.8.9
 Authors@R: 
     c(person(given = "Mattan S.",
              family = "Ben-Shachar",
@@ -72,10 +72,10 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.13.2),
-    insight (>= 0.19.10),
-    parameters (>= 0.21.7),
-    performance (>= 0.11.0),
-    datawizard (>= 0.10.0),
+    insight (>= 0.20.1),
+    parameters (>= 0.22.0),
+    performance (>= 0.12.0),
+    datawizard (>= 0.11.0),
     stats,
     utils
 Suggests:
@@ -112,4 +112,3 @@ Config/Needs/website:
     rstudio/bslib,
     r-lib/pkgdown,
     easystats/easystatstemplate
-Remotes: easystats/insight, easystats/datawizard, easystats/bayestestR, easystats/parameters, easystats/performance

NEWS.md

@@ -1,3 +1,9 @@
+# effectsize 0.8.9
+
+## Bug fixes
+
+- `interpret(<effectsize_table>)` no longer returns transformed effect sizes ( #640 )
+
 # effectsize 0.8.8
 
 ## Bug fixes

R/cohens_g.R

@@ -76,25 +76,23 @@ cohens_g <- function(x, y = NULL,
       insight::format_error("'x' and 'y' must have the same number of levels (minimum 2)")
     }
     x <- table(x, y)
-  } else {
-    if ((nrow(x) < 2) || (ncol(x) != nrow(x))) {
-      insight::format_error("'x' must be square with at least two rows and columns")
-    }
+  } else if ((nrow(x) < 2) || (ncol(x) != nrow(x))) {
+    insight::format_error("'x' must be square with at least two rows and columns")
   }
 
 
-  b <- x[upper.tri(x)]
-  c <- t(x)[upper.tri(x)]
+  a <- x[upper.tri(x)]
+  b <- t(x)[upper.tri(x)]
 
-  P <- sum(pmax(b, c)) / (sum(b) + sum(c))
+  P <- sum(pmax(a, b)) / (sum(a) + sum(b))
   g <- P - 0.5
 
   out <- data.frame(Cohens_g = g)
 
   if (.test_ci(ci)) {
     out$CI <- ci
 
-    n <- sum(b) + sum(c)
+    n <- sum(a) + sum(b)
     k <- P * n
 
     res <- stats::prop.test(k, n,

R/common_language.R

@@ -369,8 +369,8 @@ wmw_odds <- function(x, y = NULL, data = NULL,
     y <- data[data$g == "y", "r"]
 
     .foo <- function(p) {
-      diff <- stats::quantile(x, probs = c(p, 1 - p)) - stats::quantile(y, probs = c(1 - p, p))
-      min(abs(diff))
+      difference <- stats::quantile(x, probs = c(p, 1 - p)) - stats::quantile(y, probs = c(1 - p, p))
+      min(abs(difference))
     }
 
     stats::optim(

R/convert_between_odds_to_probs.R

@@ -78,10 +78,10 @@ probs_to_odds.data.frame <- function(probs, log = FALSE, select = NULL, exclude
 #' @keywords internal
 .odds_to_probs_df <- function(odds = NULL, probs = NULL, log = FALSE, select = NULL, exclude = NULL, ...) {
   # If vector
-  if (!is.null(odds)) {
-    df <- odds
+  if (is.null(odds)) {
+    mydata <- probs
   } else {
-    df <- probs
+    mydata <- odds
   }
 
   # check for formula notation, convert to character vector
@@ -93,55 +93,55 @@ probs_to_odds.data.frame <- function(probs, log = FALSE, select = NULL, exclude
   }
 
   # Variable order
-  var_order <- names(df)
+  var_order <- names(mydata)
 
   # Keep subset
-  if (!is.null(select) && select %in% names(df)) {
+  if (!is.null(select) && select %in% names(mydata)) {
     select <- as.vector(select)
-    to_keep <- as.data.frame(df[!names(df) %in% select])
-    df <- df[names(df) %in% select]
+    to_keep <- as.data.frame(mydata[!names(mydata) %in% select])
+    mydata <- mydata[names(mydata) %in% select]
   } else {
     to_keep <- NULL
   }
 
   # Remove exceptions
-  if (!is.null(exclude) && exclude %in% names(df)) {
+  if (!is.null(exclude) && exclude %in% names(mydata)) {
     exclude <- as.vector(exclude)
     if (is.null(to_keep)) {
-      to_keep <- as.data.frame(df[exclude])
+      to_keep <- as.data.frame(mydata[exclude])
     } else {
-      to_keep <- cbind(to_keep, as.data.frame(df[exclude]))
+      to_keep <- cbind(to_keep, as.data.frame(mydata[exclude]))
     }
 
-    df <- df[!names(df) %in% exclude]
+    mydata <- mydata[!names(mydata) %in% exclude]
   }
 
   # Remove non-numerics
-  is_num <- vapply(df, is.numeric, logical(1))
-  dfother <- df[!is_num]
-  dfnum <- df[is_num]
+  is_num <- vapply(mydata, is.numeric, logical(1))
+  dfother <- mydata[!is_num]
+  dfnum <- mydata[is_num]
 
   # Tranform
-  if (!is.null(odds)) {
-    dfnum <- data.frame(lapply(dfnum, odds_to_probs.numeric, log = log))
-  } else {
+  if (is.null(odds)) {
     dfnum <- data.frame(lapply(dfnum, probs_to_odds.numeric, log = log))
+  } else {
+    dfnum <- data.frame(lapply(dfnum, odds_to_probs.numeric, log = log))
   }
 
   # Add non-numerics
   if (is.null(ncol(dfother))) {
-    df <- dfnum
+    mydata <- dfnum
   } else {
-    df <- cbind(dfother, dfnum)
+    mydata <- cbind(dfother, dfnum)
   }
 
   # Add exceptions
   if (!is.null(select) || !is.null(exclude) && exists("to_keep")) {
-    df <- cbind(df, to_keep)
+    mydata <- cbind(mydata, to_keep)
   }
 
   # Reorder
-  df <- df[var_order]
+  mydata <- mydata[var_order]
 
-  return(df)
+  mydata
 }

R/effectsize.htest.R

@@ -100,7 +100,7 @@ effectsize.htest <- function(model, type = NULL, verbose = TRUE, ...) {
 
   cl <- match.call()
   cl <- cl[-which(names(cl) == "subset")]
-  dots <- list(eval(cl, parent.frame()))
+  dots <- insight::compact_list(list(eval(cl, parent.frame())))
 
   dots$alternative <- model$alternative
   dots$ci <- attr(model$conf.int, "conf.level")

R/eta_squared-main.R

@@ -23,7 +23,7 @@
 #' @param verbose Toggle warnings and messages on or off.
 #' @inheritParams chisq_to_phi
 #' @param ... Arguments passed to or from other methods.
 #'   - Can be `include_intercept = TRUE` to include the effect size for the intercept (when it is included in the ANOVA table).
 #'   - For Bayesian models, arguments passed to `ss_function`.
 #'
 #' @return
@@ -248,7 +248,7 @@
                             ci = 0.95, alternative = "greater",
                             verbose = TRUE, ...) {
   alternative <- .match.alt(alternative, FALSE)
   out <- .anova_es(model, type = "epsilon", partial = partial, ci = ci, alternative = alternative, verbose = verbose, ...)
   class(out) <- unique(c("effectsize_anova", "effectsize_table", "see_effectsize_table", class(out)))
   if ("CI" %in% colnames(out)) attr(out, "ci_method") <- list(method = "ncp", distribution = "F")
   attr(out, "approximate") <- isTRUE(attr(out, "approximate", exact = TRUE))
@@ -294,7 +294,7 @@
 
   es_name <- get_effectsize_name(colnames(res))
   res[[es_name]] <- res[[es_name]] / (1 - res[[es_name]])
-  if (grepl("_partial", es_name)) {
+  if (grepl("_partial", es_name, fixed = TRUE)) {
     colnames(res)[colnames(res) == es_name] <- "Cohens_f2_partial"
   } else {
     colnames(res)[colnames(res) == es_name] <- "Cohens_f2"
@@ -316,7 +316,7 @@
   if ("CI" %in% colnames(res)) attr(res, "ci_method") <- list(method = "ncp", distribution = "F")
   class(res) <- unique(c("effectsize_anova", "effectsize_table", "see_effectsize_table", class(res)))
   attr(res, "approximate") <- isTRUE(attr(res, "approximate", exact = TRUE))
   attr(res, "table_footer") <- if (method != "eta") sprintf("Based on %s squared.", paste0(toupper(substring(method, 1, 1)), substring(method, 2)))
   res
 }
 
@@ -430,7 +430,7 @@
 
 
   # Estimate effect size ---
-  if (type == "eta") {
+  if (type == "eta") { # nolint
     if (isTRUE(generalized) || is.character(generalized)) {
       ## copied from afex
       obs <- logical(nrow(aov_table))
@@ -448,44 +448,43 @@
 
       aov_table$Eta2_generalized <- aov_table$Sum_Squares /
         (aov_table$Sum_Squares + values$Sum_Squares_residuals + obs_SSn1 - obs_SSn2)
-    } else if (!isTRUE(partial)) {
-      aov_table$Eta2 <- aov_table$Sum_Squares /
-        values$Sum_Squares_total
-    } else {
+    } else if (isTRUE(partial)) {
       aov_table$Eta2_partial <-
         aov_table$Sum_Squares /
           (aov_table$Sum_Squares + values$Sum_Squares_residuals)
+    } else {
+      aov_table$Eta2 <- aov_table$Sum_Squares / values$Sum_Squares_total
     }
   } else if (type == "omega") {
-    if (!isTRUE(partial)) {
-      aov_table$Omega2 <-
-        (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
-          (values$Sum_Squares_total + values$Mean_Square_residuals)
-      aov_table$Omega2 <- pmax(0, aov_table$Omega2)
-    } else {
+    if (isTRUE(partial)) {
       aov_table$Omega2_partial <-
         (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
           (aov_table$Sum_Squares + (values$n - aov_table$df) * values$Mean_Square_residuals)
       aov_table$Omega2_partial <- pmax(0, aov_table$Omega2_partial)
+    } else {
+      aov_table$Omega2 <-
+        (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
+          (values$Sum_Squares_total + values$Mean_Square_residuals)
+      aov_table$Omega2 <- pmax(0, aov_table$Omega2)
     }
   } else if (type == "epsilon") {
-    if (!isTRUE(partial)) {
-      aov_table$Epsilon2 <-
-        (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
-          values$Sum_Squares_total
-      aov_table$Epsilon2 <- pmax(0, aov_table$Epsilon2)
-    } else {
+    if (isTRUE(partial)) {
       aov_table$Epsilon2_partial <-
         (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
           (aov_table$Sum_Squares + values$Sum_Squares_residuals)
       aov_table$Epsilon2_partial <- pmax(0, aov_table$Epsilon2_partial)
+    } else {
+      aov_table$Epsilon2 <-
+        (aov_table$Sum_Squares - aov_table$df * values$Mean_Square_residuals) /
+          values$Sum_Squares_total
+      aov_table$Epsilon2 <- pmax(0, aov_table$Epsilon2)
     }
   }
 
   out <- aov_table
 
   # Add CIs ---
   if (!is.null(ci)) {
     # based on MBESS::ci.R2
     ES <- pmax(0, out[[ncol(out)]])
     f <- (ES / out$df) / ((1 - ES) / df_error)
@@ -570,7 +569,7 @@
 
 
   # Estimate effect size ---
-  if (type == "eta") {
+  if (type == "eta") { # nolint
     if (isTRUE(generalized) || is.character(generalized)) {
       ## copied from afex
       obs <- logical(nrow(aov_table))
@@ -589,12 +588,12 @@
       aov_table$Eta2_generalized <- aov_table$Sum_Squares /
         (aov_table$Sum_Squares + sum(sapply(values, "[[", "Sum_Squares_residuals")) +
           obs_SSn1 - obs_SSn2)
-    } else if (!isTRUE(partial)) {
-      aov_table$Eta2 <- aov_table$Sum_Squares / Sum_Squares_total
-    } else {
+    } else if (isTRUE(partial)) {
       aov_table$Eta2_partial <-
         aov_table$Sum_Squares /
           (aov_table$Sum_Squares + Sum_Squares_residuals)
+    } else {
+      aov_table$Eta2 <- aov_table$Sum_Squares / Sum_Squares_total
     }
   } else if (type == "omega") {
     SSS_values <- values[[which(names(values) %in% DV_names)]]
@@ -603,29 +602,29 @@
     Mean_Squares_Subjects <- SSS_values$Mean_Square_residuals
 
     # implemented from https://www.jasonfinley.com/tools/OmegaSquaredQuickRef_JRF_3-31-13.pdf/
-    if (!isTRUE(partial)) {
-      aov_table$Omega2 <-
-        (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
-          (Sum_Squares_total + Mean_Squares_Subjects)
-      aov_table$Omega2 <- pmax(0, aov_table$Omega2)
-    } else {
+    if (isTRUE(partial)) {
       aov_table$Omega2_partial <-
         (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
           (aov_table$Sum_Squares + is_within * Sum_Squares_residuals +
             Sum_Squares_Subjects + Mean_Squares_Subjects)
       aov_table$Omega2_partial <- pmax(0, aov_table$Omega2_partial)
+    } else {
+      aov_table$Omega2 <-
+        (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
+          (Sum_Squares_total + Mean_Squares_Subjects)
+      aov_table$Omega2 <- pmax(0, aov_table$Omega2)
     }
   } else if (type == "epsilon") {
-    if (!isTRUE(partial)) {
-      aov_table$Epsilon2 <-
-        (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
-          Sum_Squares_total
-      aov_table$Epsilon2 <- pmax(0, aov_table$Epsilon2)
-    } else {
+    if (isTRUE(partial)) {
       aov_table$Epsilon2_partial <-
         (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
           (aov_table$Sum_Squares + Sum_Squares_residuals)
       aov_table$Epsilon2_partial <- pmax(0, aov_table$Epsilon2_partial)
+    } else {
+      aov_table$Epsilon2 <-
+        (aov_table$Sum_Squares - aov_table$df * Mean_Square_residuals) /
+          Sum_Squares_total
+      aov_table$Epsilon2 <- pmax(0, aov_table$Epsilon2)
     }
   }
 
@@ -633,7 +632,7 @@
 
 
   # Add CIs ---
   if (!is.null(ci)) {
     # based on MBESS::ci.R2
     ES <- pmax(0, out[[ncol(out)]])
     f <- (ES / out$df) / ((1 - ES) / df_residuals)
@@ -819,7 +818,7 @@
     df_error = model[, df_errori],
     stringsAsFactors = FALSE
   )
-  par_table <- par_table[!par_table[["Parameter"]] %in% "Residuals", ]
+  par_table <- par_table[par_table[["Parameter"]] != "Residuals", ]
 
   out <-
     .es_aov_table(
@@ -833,7 +832,8 @@
       include_intercept = include_intercept
     )
 
-  attr(out, "anova_type") <- tryCatch(attr(parameters::model_parameters(model, verbose = FALSE, effects = "fixed", es_type = NULL), "anova_type"),
+  ## TODO: add back `effects = "fixed"` once the deprecation warning in parameters is removed
+  attr(out, "anova_type") <- tryCatch(attr(parameters::model_parameters(model, verbose = FALSE, es_type = NULL), "anova_type"),
     error = function(...) 1
   )
   attr(out, "approximate") <- TRUE
@@ -864,7 +864,8 @@
 
   # TODO this should be in .anova_es.anvoa
   # TODO the aoc method should convert to an anova table, then pass to anova
-  params <- parameters::model_parameters(model, verbose = verbose, effects = "fixed", es_type = NULL)
+  ## TODO: add back `effects = "fixed"` once the deprecation warning in parameters is removed
+  params <- parameters::model_parameters(model, verbose = verbose, es_type = NULL)
   out <- .es_aov_simple(as.data.frame(params),
     type = type,
     partial = partial, generalized = generalized,
@@ -890,7 +891,8 @@
                               verbose = TRUE,
                               include_intercept = FALSE,
                               ...) {
-  params <- parameters::model_parameters(model, verbose = verbose, effects = "fixed", es_type = NULL)
+  ## TODO: add back `effects = "fixed"` once the deprecation warning in parameters is removed
+  params <- parameters::model_parameters(model, verbose = verbose, es_type = NULL)
   anova_type <- attr(params, "anova_type")
   params <- as.data.frame(params)
 
@@ -944,11 +946,11 @@
   df_residuals <- sum(params[iResid, "df"])
 
   list(
-    "Mean_Square_residuals" = Mean_Square_residuals,
-    "Sum_Squares_residuals" = Sum_Squares_residuals,
-    "Sum_Squares_total" = Sum_Squares_total,
-    "n_terms" = N_terms,
-    "n" = N,
-    "df_residuals" = df_residuals
+    Mean_Square_residuals = Mean_Square_residuals,
+    Sum_Squares_residuals = Sum_Squares_residuals,
+    Sum_Squares_total = Sum_Squares_total,
+    n_terms = N_terms,
+    n = N,
+    df_residuals = df_residuals
   )
 }