Merge pull request #57 from rcalinjageman/development

esci 1.0.3

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: esci
 Title: Estimation Statistics with Confidence Intervals
-Version: 1.0.2
+Version: 1.0.3
 Authors@R: person("Robert", "Calin-Jageman", email = "[email protected]",
   role = c("aut", "cre", "cph"))
 Description: A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'.  This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions.  Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>.
@@ -28,7 +28,7 @@ Imports:
     Rdpack,
     stringr,
     mathjaxr
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 RdMacros: Rdpack, mathjaxr
 Suggests: 

NAMESPACE

@@ -71,17 +71,13 @@ importFrom(rlang,enquo)
 importFrom(statpsych,ci.lc.mean.bs)
 importFrom(statpsych,ci.lc.median.bs)
 importFrom(statpsych,ci.lc.prop.bs)
-importFrom(statpsych,ci.mean1)
-importFrom(statpsych,ci.median1)
 importFrom(statpsych,ci.oddsratio)
 importFrom(statpsych,ci.phi)
-importFrom(statpsych,ci.prop1)
 importFrom(statpsych,ci.ratio.mean.ps)
 importFrom(statpsych,ci.ratio.mean2)
 importFrom(statpsych,ci.ratio.median.ps)
 importFrom(statpsych,ci.ratio.median2)
 importFrom(statpsych,ci.stdmean.ps)
-importFrom(statpsych,ci.stdmean1)
 importFrom(stats,aggregate)
 importFrom(stats,anova)
 importFrom(stats,complete.cases)
@@ -90,10 +86,12 @@ importFrom(stats,cor)
 importFrom(stats,line)
 importFrom(stats,median)
 importFrom(stats,na.omit)
+importFrom(stats,pbinom)
 importFrom(stats,pnorm)
 importFrom(stats,predict)
 importFrom(stats,pt)
 importFrom(stats,qnorm)
+importFrom(stats,qt)
 importFrom(stats,quantile)
 importFrom(stats,rnorm)
 importFrom(stats,sd)

NEWS.md

@@ -1,3 +1,12 @@
+esci version 1.0.3 (Release data: July 2024)
+===========
+
+Changes:
+
+* Tweaks to deal with changes in statpsych 1.6. No substantive updates.  
+
+
+
 esci version 1.0.2 (Release data: March 2024)
 ===========
 

R/CI_smd_ind_contrast.R

@@ -2,11 +2,11 @@
 #' contrast
 #'
 #'
-#' @description `CI_smd_ind_contrast` returns the point estimate
+#' @description \loadmathjax  `CI_smd_ind_contrast` returns the point estimate
 #'   and confidence interval for a standardized mean difference (smd aka Cohen's
 #' *d* aka Hedges *g*). A standardized mean difference is a difference in means standardized
 #'   to a standard deviation:
-#'   d = psi/s
+#'   \mjdeqn{d = \frac{ \psi }{s}}{d = psi/s}
 #'
 #'
 #' @param means A vector of 2 or more means
@@ -88,16 +88,16 @@
 #' When equal variance is assumed, the standardized mean difference is
 #' d_s, defined in Kline, p. 196:
 #'
-#'  d_s = psi / sd_s
+#'  \mjdeqn{ d_s = \frac{ \psi }{ sd_{pooled} } }{ d_s = psi / sd_s}
 #'
 #'
 #' where psi is defined in Kline, equation 7.8:
 #'
-#' psi = sum(contrasts*means)
+#' \mjdeqn{ \psi =\sum_{i=1}^{a}c_iM_i }{psi = sum(contrasts*means)}
 #'
 #'
 #' and where sd_pooled is defined in Kline, equation 3.11
-#' sqrt(sum(variances*dfs) / sum(dfs))
+#' \mjdeqn{sd_{pooled} = { \frac{ \sum_{i=1}^{a} (n_i -1) s_i^2 } { \sum_{i=1}^{a} (n_i-1) } }}{sqrt(sum(variances*dfs) / sum(dfs))}
 #'
 #'
 #' The CI for d_s is derived from lambda-prime transformation from Lecoutre,
@@ -115,12 +115,12 @@
 #' assumed, the standardized mean difference is d_avg, defined in Bonett,
 #' equation 6:
 #'
-#' d_avg = psi / sd_avg
+#' \mjdeqn{ d_{avg} = \frac{ \psi }{ sd_{avg} }}{ d_avg = psi / sd_avg}
 #'
 #' Where sd_avg is the square root of the average of the group variances, as
 #' given in Bonett, explanation of equation 6:
 #'
-#' sqrt(mean(variances))
+#' \mjdeqn{sd_{avg} = \sqrt{ \frac{ \sum_{i=1}^{a} s_i^2 }{ a } }}{sqrt(mean(variances))}
 #'
 #'
 #' #### If only 2 groups ####

R/esci-package.R

@@ -13,16 +13,12 @@
 #' @importFrom statpsych ci.lc.mean.bs
 #' @importFrom statpsych ci.lc.median.bs
 #' @importFrom statpsych ci.lc.prop.bs
-#' @importFrom statpsych ci.mean1
-#' @importFrom statpsych ci.median1
 #' @importFrom statpsych ci.oddsratio
 #' @importFrom statpsych ci.phi
-#' @importFrom statpsych ci.prop1
 #' @importFrom statpsych ci.ratio.mean2
 #' @importFrom statpsych ci.ratio.mean.ps
 #' @importFrom statpsych ci.ratio.median2
 #' @importFrom statpsych ci.ratio.median.ps
-#' @importFrom statpsych ci.stdmean1
 #' @importFrom statpsych ci.stdmean.ps
 #' @importFrom stats aggregate
 #' @importFrom stats anova
@@ -35,8 +31,10 @@
 #' @importFrom stats qnorm
 #' @importFrom stats predict
 #' @importFrom stats pt
+#' @importFrom stats pbinom
 #' @importFrom stats pnorm
 #' @importFrom stats qnorm
+#' @importFrom stats qt
 #' @importFrom stats quantile
 #' @importFrom stats rnorm
 #' @importFrom stats sd

R/estimate_magnitude.R

@@ -16,9 +16,11 @@
 #' If you want to compare your sample to a known value or reference, then
 #' use [esci::estimate_mdiff_one()].
 #'
-#' The estimated mean is from [statpsych::ci.mean1()].
+#' The estimated mean is from [statpsych::ci.mean1()] (renamed ci.mean as of
+#' statpsych 1.6).
 #'
-#' The estimated median is from [statpsych::ci.median1()]
+#' The estimated median is from [statpsych::ci.median1()] (renamed ci.median
+#' as of statpsych 1.6)
 #'
 #'
 #' @param data For raw data - A data frame or tibble
@@ -466,7 +468,7 @@ estimate_magnitude.vector <- function(
   estimate$properties$data_source <- NULL
 
   # 2 alpha CI for median
-  mdn_2a <- statpsych::ci.median1(
+  mdn_2a <- wrapper_ci.median(
     alpha = (1 - conf_level)*2,
     y =outcome_variable[!is.na(outcome_variable)]
   )

R/estimate_mdiff_one.R

@@ -16,11 +16,13 @@
 #' it with [esci::plot_mdiff()] and you can test hypotheses with
 #' [esci::test_mdiff()].
 #'
-#' The estimated mean differences are from [statpsych::ci.mean1()].
+#' The estimated mean differences are from [statpsych::ci.mean1()] (renamed
+#' ci.mean as of statpsych 1.6).
 #'
 #' The estimated SMDs are from [esci::CI_smd_one()].
 #'
-#' The estimated median differences are from [statpsych::ci.median1()]
+#' The estimated median differences are from [statpsych::ci.median1()] (renamed
+#' ci.median as of statpsych 1.6)
 #'
 #'
 #' @param data For raw data - a data frame or tibble

R/estimate_pdiff_one.R

@@ -15,7 +15,8 @@
 #' it with [esci::plot_pdiff()] and you can test hypotheses with
 #' [esci::test_pdiff()].
 #'
-#' The estimated proportion differences are from [statpsych::ci.prop1()].
+#' The estimated proportion differences are from [statpsych::ci.prop1()] (renamed
+#' ci.prop as of statpsych 1.6).
 #'
 #'
 #' @param data For raw data - a dataframe or tibble

R/estimate_proportion.R

@@ -14,7 +14,8 @@
 #' If you want to compare your estimate to a known value or reference, then
 #' use [esci::estimate_pdiff_one()].
 #'
-#' The estimated proportions are from [statpsych::ci.prop1()].
+#' The estimated proportions are from [statpsych::ci.prop1()] (renamed
+#' ci.prop as of statpsych 1.6).
 #'
 #'
 #' @param data For raw data - a data frame or tibble

R/overview.R

@@ -285,7 +285,7 @@ overview.base <- function(
   # Analysis ----------------------------------------
   if (n_means == 1) {
     res <- as.data.frame(
-        statpsych::ci.mean1(
+       wrapper_ci.mean (
         alpha = 1 - conf_level,
         m = overview_table$mean,
         sd = overview_table$sd,
@@ -725,7 +725,7 @@ wrap_ci_median1 <- function(
 
   if(length(na.omit(x)) < 2) return(c(median(x), NA, NA, NA))
 
-  res <- statpsych::ci.median1(
+  res <- wrapper_ci.median(
     alpha = 1 - conf_level,
     y = if(na.rm) x[!is.na(x)] else x
   )
@@ -743,7 +743,7 @@ wrap_ci_mean1 <- function(
 
   if(length(x) < 2) return(c(mean(x), NA, NA, NA))
 
-  res <- statpsych::ci.mean1(
+  res <- wrapper_ci.mean(
     alpha = 1 - conf_level,
     m = mean(x, na.rm = na.rm),
     sd = sd(x, na.rm = na.rm),

R/overview_nominal.R

@@ -244,15 +244,15 @@ overview_nominal.base <- function(
       res_ta <- res
     } else {
       res <- as.data.frame(
-        statpsych::ci.prop1(
+        wrapper_ci.prop(
           alpha = 1 - conf_level,
           f = overview_table$cases[x],
           n = overview_table$n[x]
         )
       )
 
       res_ta <- as.data.frame(
-        statpsych::ci.prop1(
+        wrapper_ci.prop(
           alpha = (1 - conf_level)*2,
           f = overview_table$cases[x],
           n = overview_table$n[x]

R/plot_aesthetics.R

@@ -197,6 +197,11 @@ esci_plot_mdiff_aesthetics <- function(
 esci_plot_simple_aesthetics <- function(myplot, use_ggdist = TRUE) {
   # Customize plot -------------------------------
   # No legend
+
+  ggplot_version <- as.numeric(gsub("\\.", "", utils::packageVersion("ggplot2")))
+
+
+
   myplot <- myplot + ggplot2::theme(legend.position = "none")
 
   # Points
@@ -214,24 +219,43 @@ esci_plot_simple_aesthetics <- function(myplot, use_ggdist = TRUE) {
 
   if (use_ggdist) {
     myplot <- myplot + ggplot2::discrete_scale(
-      c("size", "point_size"),
-      "point_size_d",
-      function(n) return(c("raw" = 1, "summary" = 3))
+      aesthetics = c("size", "point_size"),
+      scale_name = if (ggplot_version >= 350) NULL else "point_size_d",
+      palette = function(n) return(c("raw" = 1, "summary" = 3))
     )
 
   } else {
+
+    if (ggplot_version >= 350) {
+      myplot <- myplot + ggplot2::discrete_scale(
+        aesthetics = c("size"),
+        palette = function(n) return(c("raw" = 1, "summary" = 1))
+      )
+
+    } else {
+      myplot <- myplot + ggplot2::discrete_scale(
+        aesthetics = c("size"),
+        scale_name = "point_size_d",
+        palette = function(n) return(c("raw" = 1, "summary" = 1))
+      )
+
+    }
+
+  }
+
+  if (ggplot_version >= 350) {
     myplot <- myplot + ggplot2::discrete_scale(
-      c("size"),
-      "point_size_d",
-      function(n) return(c("raw" = 1, "summary" = 1))
+      aesthetics = c("alpha", "point_alpha"),
+      palette = function(n) return(c("raw" = 0.8, "summary" = 1))
+    )
+  } else {
+    myplot <- myplot + ggplot2::discrete_scale(
+      c("alpha", "point_alpha"),
+      "point_alpha_d",
+      function(n) return(c("raw" = 0.8, "summary" = 1))
     )
   }
 
-  myplot <- myplot + ggplot2::discrete_scale(
-    c("alpha", "point_alpha"),
-    "point_alpha_d",
-    function(n) return(c("raw" = 0.8, "summary" = 1))
-  )
 
   # Error bars
   myplot <- myplot + ggplot2::scale_linetype_manual(
@@ -241,28 +265,53 @@ esci_plot_simple_aesthetics <- function(myplot, use_ggdist = TRUE) {
     values = c("summary" = "black"),
     aesthetics = "interval_color"
   )
-  myplot <- myplot + ggplot2::discrete_scale(
-    "interval_alpha",
-    "interval_alpha_d",
-    function(n) return(c("summary" = 1))
-  )
-  myplot <- myplot + ggplot2::discrete_scale(
-    "interval_size",
-    "interval_size_d",
-    function(n) return(c("summary" = 3))
-  )
+
+  if (ggplot_version >= 350) {
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "interval_alpha",
+      palette = function(n) return(c("summary" = 1))
+    )
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "interval_size",
+      palette = function(n) return(c("summary" = 3))
+    )
+
+  } else {
+
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "interval_alpha",
+      scale_name = "interval_alpha_d",
+      palette = function(n) return(c("summary" = 1))
+    )
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "interval_size",
+      scale_name = "interval_size_d",
+      palette = function(n) return(c("summary" = 3))
+    )
+
+  }
 
 
   # Slab
   myplot <- myplot + ggplot2::scale_fill_manual(
     values = c("summary" = "gray"),
     aesthetics = "slab_fill"
   )
-  myplot <- myplot + ggplot2::discrete_scale(
-    "slab_alpha",
-    "slab_alpha_d",
-    function(n) return(c("summary" = 1))
-  )
+
+  if (ggplot_version >= 350) {
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "slab_alpha",
+      palette = function(n) return(c("summary" = 1))
+    )
+
+  } else {
+    myplot <- myplot + ggplot2::discrete_scale(
+      aesthetics = "slab_alpha",
+      scale_name = "slab_alpha_d",
+      palette = function(n) return(c("summary" = 1))
+    )
+
+  }
 
   return(myplot)
 }

R/statpsych_apply.R

@@ -222,7 +222,7 @@ apply_ci_prop1 <- function(
 ) {
 
   res <- as.data.frame(
-    statpsych::ci.prop1(
+    wrapper_ci.prop(
       alpha = 1 - conf_level,
       f = myrow[["cases"]],
       n = myrow[["N"]]
@@ -279,7 +279,7 @@ apply_ci_mean1 <- function(
 ) {
 
   res <- as.data.frame(
-    statpsych::ci.mean1(
+    wrapper_ci.mean(
       alpha = 1 - conf_level,
       m = myrow[["mean"]] - reference_mean,
       sd = myrow[["sd"]],