rc CRAN 0.8.5 (#595)

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: effectsize
 Title: Indices of Effect Size
-Version: 0.8.3.11
+Version: 0.8.5
 Authors@R: 
     c(person(given = "Mattan S.",
              family = "Ben-Shachar",

NEWS.md

@@ -2,6 +2,7 @@
 
 ## New features
 
+- `interpret_cfi()` gains a new rule option: `"hu&bentler1999"` ( #538 ).
 - `cohens_f()` added option to return unbiased estimators (based on Omega- or Epsilon-squared).
 - `tschuprows_t()` now returns an effect size corrected for small-sample bias. Set `adjust = FALSE` to preserve old behavior.
 - `w_to_v()` and others for converting between effect sizes of Chi-square tests.
@@ -18,6 +19,7 @@
 ## Breaking Changes
 
 - `*_to_riskratio()` and `riskratio_to_*()` argument `log` not longer converts RR to/from log(RR).
+- `interpret_gfi()` and friends: some previously named `"default"` rules have been re-labelled as `"byrne1994"`.
 
 ## Bug fixes
 

R/convert_between_xtabcorr.R

@@ -1,4 +1,4 @@
-#' Convert Between *d*, *r*, and Odds Ratio
+#' Convert Between Effect Sizes for Contingency Tables Correlations
 #'
 #' Enables a conversion between different indices of effect size, such as
 #' Cohen's *w* to \ifelse{latex}{\eqn{Fei}}{פ (Fei)}, and Cramer's *V* to

R/eta_squared-main.R

@@ -59,7 +59,7 @@
 #' `afex` package.
 #' \cr\cr
 #' For type 3 sum of squares, it is generally recommended to fit models with
-#' *orthoginal factor weights* (e.g., `contr.sum`) and *centered covariates*,
+#' *orthogonal factor weights* (e.g., `contr.sum`) and *centered covariates*,
 #' for sensible results. See examples and the `afex` package.
 #'
 #' ## Un-Biased Estimate of Eta
@@ -316,19 +316,25 @@ cohens_f <- function(model,
   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(methed, 1, 1)), substring(methed, 2)))
+  attr(res, "table_footer") <- if (method != "eta") sprintf("Based on %s squared.", paste0(toupper(substring(method, 1, 1)), substring(method, 2)))
   res
 }
 
 #' @rdname eta_squared
 #' @export
 cohens_f_squared <- function(model,
-                             partial = TRUE, squared = TRUE, model2 = NULL,
+                             partial = TRUE, generalized = FALSE,
+                             squared = TRUE,
+                             method = c("eta", "omega", "epsilon"),
+                             model2 = NULL,
                              ci = 0.95, alternative = "greater",
                              verbose = TRUE, ...) {
   cohens_f(
     model,
-    partial = partial, squared = squared, model2 = model2,
+    partial = partial, generalized = generalized,
+    squared = squared,
+    method = method,
+    model2 = model2,
     ci = ci, alternative = alternative,
     verbose = verbose, ...
   )

R/eta_squared-methods.R

@@ -33,6 +33,9 @@
 
 #' @keywords internal
 .anova_es.mixed <- function(model,
+                            type = c("eta", "omega", "epsilon"),
+                            partial = TRUE, generalized = FALSE,
+                            ci = 0.95, alternative = "greater",
                             verbose = TRUE,
                             include_intercept = FALSE,
                             ...) {
@@ -52,7 +55,12 @@
   aov_tab$df_error <- aov_tab[["den Df"]]
   aov_tab <- aov_tab[, c("Parameter", "df", "df_error", "F")]
 
-  out <- .es_aov_table(aov_tab, verbose = verbose, include_intercept = include_intercept, ...)
+  out <- .es_aov_table(aov_tab, type = type,
+    partial = partial, generalized = generalized,
+    ci = ci, alternative = alternative,
+    verbose = verbose,
+    include_intercept = include_intercept, ...
+  )
 
   attr(out, "anova_type") <- attr(model, "type")
   attr(out, "approximate") <- TRUE

R/interpret_cfa_fit.R

@@ -4,7 +4,8 @@
 #' equation modelling, such as RMSEA, CFI, NFI, IFI, etc.
 #'
 #' @param x vector of values, or an object of class `lavaan`.
-#' @param rules Can be `"default"` or custom set of [rules()].
+#' @param rules Can be the name of a set of rules (see below) or custom set of
+#'   [rules()].
 #' @inheritParams interpret
 #'
 #' @inherit performance::model_performance.lavaan details
@@ -19,29 +20,19 @@
 #'
 #' - **GFI/AGFI**: The (Adjusted) Goodness of Fit is the proportion of variance
 #' accounted for by the estimated population covariance. Analogous to R2. The
-#' GFI and the AGFI should be > .95 and > .90, respectively.
+#' GFI and the AGFI should be > .95 and > .90, respectively (Byrne, 1994;
+#' `"byrne1994"`).
 #'
 #' - **NFI/NNFI/TLI**: The (Non) Normed Fit Index. An NFI of 0.95, indicates the
 #' model of interest improves the fit by 95\% relative to the null model. The
 #' NNFI (also called the Tucker Lewis index; TLI) is preferable for smaller
-#' samples. They should be > .90 (Byrne, 1994) or > .95 (Schumacker & Lomax,
-#' 2004).
+#' samples. They should be > .90 (Byrne, 1994; `"byrne1994"`) or > .95
+#' (Schumacker & Lomax, 2004; `"schumacker2004"`).
 #'
 #' - **CFI**: The Comparative Fit Index is a revised form of NFI. Not very
 #' sensitive to sample size (Fan, Thompson, & Wang, 1999). Compares the fit of a
-#' target model to the fit of an independent, or null, model. It should be >
-#' .90.
-#'
-#' - **RMSEA**: The Root Mean Square Error of Approximation is a
-#' parsimony-adjusted index. Values closer to 0 represent a good fit. It should
-#' be < .08 or < .05. The p-value printed with it tests the hypothesis that
-#' RMSEA is less than or equal to .05 (a cutoff sometimes used for good fit),
-#' and thus should be not significant.
-#'
-#' - **RMR/SRMR**: the (Standardized) Root Mean Square Residual represents the
-#' square-root of the difference between the residuals of the sample covariance
-#' matrix and the hypothesized model. As the RMR can be sometimes hard to
-#' interpret, better to use SRMR. Should be < .08.
+#' target model to the fit of an independent, or null, model. It should be > .96
+#' (Hu & Bentler, 1999; `"hu&bentler1999"`) or .90 (Byrne, 1994; `"byrne1994"`).
 #'
 #' - **RFI**: the Relative Fit Index, also known as RHO1, is not guaranteed to
 #' vary from 0 to 1. However, RFI close to 1 indicates a good fit.
@@ -54,6 +45,18 @@
 #' agreed-upon cutoff value for an acceptable model for this index. Should be >
 #' 0.50.
 #'
+#' - **RMSEA**: The Root Mean Square Error of Approximation is a
+#' parsimony-adjusted index. Values closer to 0 represent a good fit. It should
+#' be < .08 (Awang, 2012; `"awang2012"`) or < .05 (Byrne, 1994; `"byrne1994"`).
+#' The p-value printed with it tests the hypothesis that RMSEA is less than or
+#' equal to .05 (a cutoff sometimes used for good fit), and thus should be not
+#' significant.
+#'
+#' - **RMR/SRMR**: the (Standardized) Root Mean Square Residual represents the
+#' square-root of the difference between the residuals of the sample covariance
+#' matrix and the hypothesized model. As the RMR can be sometimes hard to
+#' interpret, better to use SRMR. Should be < .08 (Byrne, 1994; `"byrne1994"`).
+#'
 #' See the documentation for \code{\link[lavaan:fitmeasures]{fitmeasures()}}.
 #'
 #'
@@ -92,29 +95,35 @@
 #' - Awang, Z. (2012). A handbook on SEM. Structural equation modeling.
 #'
 #' - Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/Windows.
-#' Thousand Oaks, CA: Sage Publications.
-#'
-#' - Tucker, L. R., and Lewis, C. (1973). The reliability coefficient for maximum
-#' likelihood factor analysis. Psychometrika, 38, 1-10.
+#'   Thousand Oaks, CA: Sage Publications.
 #'
-#' - Schumacker, R. E., and Lomax, R. G. (2004). A beginner's guide to structural
-#' equation modeling, Second edition. Mahwah, NJ: Lawrence Erlbaum Associates.
+#' - Fan, X., B. Thompson, and L. Wang (1999). Effects of sample size,
+#'   estimation method, and model specification on structural equation modeling
+#'   fit indexes. Structural Equation Modeling, 6, 56-83.
 #'
-#' - Fan, X., B. Thompson, and L. Wang (1999). Effects of sample size, estimation
-#' method, and model specification on structural equation modeling fit indexes.
-#' Structural Equation Modeling, 6, 56-83.
+#' - Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
+#'   covariance structure analysis: Conventional criteria versus new
+#'   alternatives. Structural equation modeling: a multidisciplinary journal,
+#'   6(1), 1-55.
 #'
 #' - Kline, R. B. (2015). Principles and practice of structural equation
-#' modeling. Guilford publications.
+#'   modeling. Guilford publications.
+#'
+#' - Schumacker, R. E., and Lomax, R. G. (2004). A beginner's guide to
+#'   structural equation modeling, Second edition. Mahwah, NJ: Lawrence Erlbaum
+#'   Associates.
+#'
+#' - Tucker, L. R., and Lewis, C. (1973). The reliability coefficient for
+#'   maximum likelihood factor analysis. Psychometrika, 38, 1-10.
 #'
 #'
 #' @keywords interpreters
 #' @export
-interpret_gfi <- function(x, rules = "default") {
+interpret_gfi <- function(x, rules = "byrne1994") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.95), c("poor", "satisfactory"), name = "default", right = FALSE)
+      byrne1994 = rules(c(0.95), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
     )
   )
 
@@ -124,11 +133,11 @@ interpret_gfi <- function(x, rules = "default") {
 
 #' @rdname interpret_gfi
 #' @export
-interpret_agfi <- function(x, rules = "default") {
+interpret_agfi <- function(x, rules = "byrne1994") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
+      byrne1994 = rules(c(0.90), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
     )
   )
 
@@ -157,42 +166,38 @@ interpret_nnfi <- interpret_nfi
 
 #' @rdname interpret_gfi
 #' @export
-interpret_cfi <- function(x, rules = "default") {
+interpret_cfi <- function(x, rules = "byrne1994") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
+      "hu&bentler1999" = rules(c(0.96), c("poor", "satisfactory"), name = "hu&bentler1999", right = FALSE),
+      "byrne1994" = rules(c(0.90), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
     )
   )
 
   interpret(x, rules)
 }
 
-
-
-
 #' @rdname interpret_gfi
 #' @export
-interpret_rmsea <- function(x, rules = "default") {
+interpret_rfi <- function(x, rules = "default") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.05), c("satisfactory", "poor"), name = "default"),
-      awang2012 = rules(c(0.05, 0.08), c("good", "satisfactory", "poor"), name = "awang2012")
+      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
     )
   )
 
   interpret(x, rules)
 }
 
-
 #' @rdname interpret_gfi
 #' @export
-interpret_srmr <- function(x, rules = "default") {
+interpret_ifi <- function(x, rules = "default") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.08), c("satisfactory", "poor"), name = "default")
+      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
     )
   )
 
@@ -201,11 +206,11 @@ interpret_srmr <- function(x, rules = "default") {
 
 #' @rdname interpret_gfi
 #' @export
-interpret_rfi <- function(x, rules = "default") {
+interpret_pnfi <- function(x, rules = "default") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
+      default = rules(c(0.50), c("poor", "satisfactory"), name = "default")
     )
   )
 
@@ -214,31 +219,32 @@ interpret_rfi <- function(x, rules = "default") {
 
 #' @rdname interpret_gfi
 #' @export
-interpret_ifi <- function(x, rules = "default") {
+interpret_rmsea <- function(x, rules = "byrne1994") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
+      byrne1994 = rules(c(0.05), c("satisfactory", "poor"), name = "byrne1994"),
+      awang2012 = rules(c(0.05, 0.08), c("good", "satisfactory", "poor"), name = "awang2012")
     )
   )
 
   interpret(x, rules)
 }
 
+
 #' @rdname interpret_gfi
 #' @export
-interpret_pnfi <- function(x, rules = "default") {
+interpret_srmr <- function(x, rules = "byrne1994") {
   rules <- .match.rules(
     rules,
     list(
-      default = rules(c(0.50), c("poor", "satisfactory"), name = "default")
+      byrne1994 = rules(c(0.08), c("satisfactory", "poor"), name = "byrne1994")
     )
   )
 
   interpret(x, rules)
 }
 
-
 # lavaan ------------------------------------------------------------------
 
 #' @rdname interpret_gfi

cran-comments.md

@@ -6,7 +6,7 @@
 
 ## Test environments
 
-* local installation: R 4.2.2 on Windows
+* local installation: R 4.3.1 on Windows
 * GitHub Actions
     - Windows:        release
     - macOS:          release
@@ -21,7 +21,7 @@
 
 ## revdepcheck results
 
-We checked 19 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 20 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

inst/WORDLIST

@@ -13,6 +13,7 @@ BMC
 Baptista
 BayestestR's
 Behaviour
+Bentler
 Bergsma
 Biometrics
 Biometrika
@@ -66,6 +67,7 @@ Hedges’
 Hjort
 Hoekstra
 Hotelling's
+Hu
 IFI
 IRRs
 Intraclass