Bump insight version for .get_dep_version, etc.

DESCRIPTION

@@ -57,7 +57,7 @@ Depends:
 Imports:
     bayestestR (>= 0.13.0),
     effectsize (> 0.8.2),
-    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 (Tschuprow's t = 0.09, 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])
 
 ---
 
@@ -122,5 +131,5 @@
     Output
 
 
-       (Risk_ratio = 2.54, 95% CI [1.87, 3.45])
+       (Risk_ratio = 2.54, 95% CI [1.80, 3.60])
 

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

@@ -1,58 +1,3 @@
-# report.lm - lm
-
-    Code
-      report(lm(Sepal.Width ~ Species, data = iris))
-    Output
-      We fitted a linear model (estimated using OLS) to predict Sepal.Width with
-      Species (formula: Sepal.Width ~ Species). The model explains a statistically
-      significant and substantial proportion of variance (R2 = 0.40, F(2, 147) =
-      49.16, p < .001, adj. R2 = 0.39). The model's intercept, corresponding to
-      Species = setosa, is at 3.43 (95% CI [3.33, 3.52], t(147) = 71.36, p < .001).
-      Within this model:
-
-        - The effect of Species [versicolor] is statistically significant and negative
-      (beta = -0.66, 95% CI [-0.79, -0.52], t(147) = -9.69, p < .001; Std. beta =
-      -1.51, 95% CI [-1.82, -1.20])
-        - The effect of Species [virginica] is statistically significant and negative
-      (beta = -0.45, 95% CI [-0.59, -0.32], t(147) = -6.68, p < .001; Std. beta =
-      -1.04, 95% CI [-1.35, -0.73])
-
-      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.
-
----
-
-    Code
-      report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))
-    Output
-      We fitted a linear model (estimated using OLS) to predict wt with am and cyl
-      (formula: wt ~ as.factor(am) * as.factor(cyl)). The model explains a
-      statistically significant and substantial proportion of variance (R2 = 0.73,
-      F(5, 26) = 13.73, p < .001, adj. R2 = 0.67). The model's intercept,
-      corresponding to am = 0 and cyl = 0, is at 2.94 (95% CI [2.27, 3.60], t(26) =
-      9.08, p < .001). Within this model:
-
-        - The effect of am [1] is statistically significant and negative (beta = -0.89,
-      95% CI [-1.67, -0.11], t(26) = -2.36, p = 0.026; Std. beta = -0.91, 95% CI
-      [-1.71, -0.12])
-        - The effect of cyl [6] is statistically non-significant and positive (beta =
-      0.45, 95% CI [-0.43, 1.33], t(26) = 1.06, p = 0.298; Std. beta = 0.46, 95% CI
-      [-0.43, 1.36])
-        - The effect of cyl [8] is statistically significant and positive (beta = 1.17,
-      95% CI [0.43, 1.91], t(26) = 3.23, p = 0.003; Std. beta = 1.19, 95% CI [0.44,
-      1.95])
-        - The effect of am [1] × as cyl6 is statistically non-significant and positive
-      (beta = 0.26, 95% CI [-0.92, 1.43], t(26) = 0.45, p = 0.654; Std. beta = 0.26,
-      95% CI [-0.94, 1.47])
-        - The effect of am [1] × as cyl8 is statistically non-significant and positive
-      (beta = 0.16, 95% CI [-1.02, 1.33], t(26) = 0.28, p = 0.783; Std. beta = 0.16,
-      95% CI [-1.04, 1.36])
-
-      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.
-
 # report.lm - glm
 
     Code
@@ -87,3 +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_performance.md

@@ -27,12 +27,12 @@
     Output
       [1] "The model's explanatory power is substantial (R2 = 0.54)"
 
----
+# report_performance Bayesian 2)
 
     Code
       report_performance(x7)
     Output
-      The model's explanatory power is substantial (R2 = 0.83, 95% CI [0.79, 0.87],
+      The model's explanatory power is substantial (R2 = 0.83, 95% CI [0.79, 0.86],
       adj. R2 = 0.83) and the part related to the fixed effects alone (marginal R2)
       is of 0.95 (95% CI [0.93, 0.97])
 

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"))
 })