Merge branch 'main' into report_participants_gender

DESCRIPTION

@@ -57,7 +57,7 @@ Depends:
 Imports:
     bayestestR (>= 0.13.0),
     effectsize (>= 0.8.5),
-    insight (>= 0.19.1),
+    insight (>= 0.19.3.2),
     parameters (>= 0.20.2),
     performance (>= 0.10.2),
     datawizard (>= 0.7.0),

R/report_htest_chi2.R

@@ -8,7 +8,7 @@
 
 # report_effectsize ---------------------
 
-.report_effectsize_chi2 <- function(x, table, dot_args, type, rules = "funder2019") {
+.report_effectsize_chi2 <- function(x, table, dot_args, rules = "funder2019") {
   if (grepl("Pearson", x$method, fixed = TRUE)) {
     args <- c(list(x), dot_args)
     table <- do.call(effectsize::effectsize, args)
@@ -31,10 +31,12 @@
     main <- paste0("Adjusted Cramer's v = ", insight::format_value(table[[estimate]]))
   } else if (estimate %in% "Tschuprows_t") {
     main <- paste0("Tschuprow's t = ", insight::format_value(table[[estimate]]))
+  } else if (estimate %in% "Tschuprows_t_adjusted") {
+    main <- paste0("Adjusted Tschuprow's t = ", insight::format_value(table[[estimate]]))
   } else if (estimate %in% "Pearsons_c") {
     main <- paste0("Pearson's c = ", insight::format_value(table[[estimate]]))
   } else if (estimate %in% "phi_adjusted") {
-    main <- paste0("Adjusted's Phi = ", insight::format_value(table[[estimate]]))
+    main <- paste0("Adjusted Phi = ", insight::format_value(table[[estimate]]))
   } else if (estimate %in% "Cohens_h") {
     main <- paste0("Cohen's h = ", insight::format_value(table[[estimate]]))
   } else if (estimate %in% "Odds_ratio") {

tests/testthat/_snaps/windows/report.htest-chi2.md

@@ -73,12 +73,21 @@
 ---
 
     Code
-      report_effectsize(x, type = "tschuprows_t")
+      report_effectsize(x, type = "tschuprows_t", adjust = FALSE)
     Output
       Effect sizes were labelled following Funder's (2019) recommendations. 
       
       very small (Tschuprows_t_adjusted = 0.08, 95% CI [0.06, 1.00])
 
+---
+
+    Code
+      report_effectsize(x, type = "tschuprows_t")
+    Output
+       
+      
+       (Adjusted Tschuprow's t = 0.08, 95% CI [0.06, 1.00])
+
 ---
 
     Code
@@ -95,7 +104,7 @@
     Output
       Effect sizes were labelled following Funder's (2019) recommendations. 
       
-      large (Adjusted's Phi = 0.36, 95% CI [0.25, 1.00])
+      large (Adjusted Phi = 0.36, 95% CI [0.25, 1.00])
 
 ---
 

tests/testthat/_snaps/windows/report.lm.md

@@ -32,3 +32,17 @@
       version of the dataset. 95% Confidence Intervals (CIs) and p-values were
       computed using a Wald z-distribution approximation.
 
+# report.lm - lm intercept-only
+
+    Code
+      out
+    Output
+      We fitted a constant (intercept-only) linear model (estimated using OLS) to
+      predict d_wide$group0 - d_wide$group1 (formula: d_wide$group0 - d_wide$group1 ~
+      1). The model's intercept is at -1.58 (95% CI [-2.46, -0.70], t(9) = -4.06, p =
+      0.003).
+      
+      Standardized parameters were obtained by fitting the model on a standardized
+      version of the dataset. 95% Confidence Intervals (CIs) and p-values were
+      computed using a Wald t-distribution approximation.
+

tests/testthat/_snaps/windows/report_sample.md

@@ -618,3 +618,35 @@
       Species [versicolor], % |        36.9
       Species [virginica], %  |        56.3
 
+# report_sample, with more than one grouping variable
+
+    Code
+      out
+    Output
+      # Descriptive Statistics
+      
+      Variable               | setosa, a (n=16) | versicolor, a (n=17) | virginica, a (n=9) | setosa, b (n=15) | versicolor, b (n=17) | virginica, b (n=22) | setosa, c (n=19) | versicolor, c (n=16) | virginica, c (n=19) | Total (n=150)
+      -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+      Mean Sepal.Length (SD) |      5.13 (0.36) |          6.08 (0.48) |        6.59 (0.44) |      4.91 (0.31) |          5.84 (0.54) |         6.77 (0.63) |      4.97 (0.36) |          5.88 (0.53) |         6.38 (0.69) |   5.84 (0.83)
+      Mean Sepal.Width (SD)  |      3.50 (0.42) |          2.76 (0.40) |        3.02 (0.25) |      3.34 (0.25) |          2.81 (0.25) |         3.05 (0.36) |      3.44 (0.43) |          2.74 (0.29) |         2.87 (0.30) |   3.06 (0.44)
+
+# report_sample, print vertical
+
+    Code
+      print(out, layout = "vertical")
+    Output
+      # Descriptive Statistics
+      
+      Groups               | Mean Sepal.Length (SD) | Mean Sepal.Width (SD) | Mean Petal.Length (SD)
+      ----------------------------------------------------------------------------------------------
+      setosa, a (n=16)     |            5.13 (0.36) |           3.50 (0.42) |            1.50 (0.12)
+      versicolor, a (n=17) |            6.08 (0.48) |           2.76 (0.40) |            4.37 (0.44)
+      virginica, a (n=9)   |            6.59 (0.44) |           3.02 (0.25) |            5.57 (0.52)
+      setosa, b (n=15)     |            4.91 (0.31) |           3.34 (0.25) |            1.47 (0.17)
+      versicolor, b (n=17) |            5.84 (0.54) |           2.81 (0.25) |            4.25 (0.44)
+      virginica, b (n=22)  |            6.77 (0.63) |           3.05 (0.36) |            5.73 (0.57)
+      setosa, c (n=19)     |            4.97 (0.36) |           3.44 (0.43) |            1.43 (0.21)
+      versicolor, c (n=16) |            5.88 (0.53) |           2.74 (0.29) |            4.16 (0.53)
+      virginica, c (n=19)  |            6.38 (0.69) |           2.87 (0.30) |            5.34 (0.49)
+      Total (n=150)        |            5.84 (0.83) |           3.06 (0.44) |            3.76 (1.77)
+

tests/testthat/test-report.htest-chi2.R

@@ -1,7 +1,7 @@
 test_that("report.htest-chi2", {
-  M <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
-  dimnames(M) <- list(gender = c("F", "M"), party = c("Democrat", "Independent", "Republican"))
-  x <- chisq.test(M)
+  m <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
+  dimnames(m) <- list(gender = c("F", "M"), party = c("Democrat", "Independent", "Republican"))
+  x <- chisq.test(m)
 
   expect_snapshot(
     variant = "windows",
@@ -45,6 +45,11 @@ test_that("report.htest-chi2", {
     report_effectsize(x, type = "pearsons_c")
   )
 
+  expect_snapshot(
+    variant = "windows",
+    report_effectsize(x, type = "tschuprows_t", adjust = FALSE)
+  )
+
   expect_snapshot(
     variant = "windows",
     report_effectsize(x, type = "tschuprows_t")

tests/testthat/test-report.lm.R

@@ -36,6 +36,6 @@ test_that("report.lm - lm intercept-only", {
   )
 
   model_io <- lm(d_wide$group0 - d_wide$group1 ~ 1)
-  out <- report(model_io)
-  expect_snapshot(out)
+  out <- suppressWarnings(report(model_io, verbose = FALSE))
+  expect_snapshot(variant = "windows", out)
 })

tests/testthat/test-report_sample.R

@@ -263,7 +263,7 @@ test_that("report_sample, with more than one grouping variable", {
   )
   # verified against
   expected <- aggregate(iris["Sepal.Length"], iris[c("Species", "grp")], mean)
-  expect_snapshot(out)
+  expect_snapshot(variant = "windows", out)
 })
 
 test_that("report_sample, numeric select", {
@@ -286,5 +286,5 @@ test_that("report_sample, print vertical", {
   iris$grp <- sample(letters[1:3], nrow(iris), TRUE)
   iris_grp <- datawizard::data_group(iris, c("Species", "grp"))
   out <- report_sample(iris_grp, select = 1:3)
-  expect_snapshot(print(out, layout = "vertical"))
+  expect_snapshot(variant = "windows", print(out, layout = "vertical"))
 })