tests for stats::anova.glm() and car::Anova.glm()

R/apa_print_anova.R

@@ -416,11 +416,14 @@ apa_print.anova <- function(
         , simplify = TRUE
       )
     )
+  # stats::anova.glm() and car::Anova.glm
   } else if(any(grepl("Deviance", object_heading))) {
     x$Term <-rownames(x)
     y <- canonize(x)
     y <- remove_residuals_row(y)
-    y$df.residual <- NULL
+    if(all(colnames(x) != "F values")) y$df.residual <- NULL
+    if(any(colnames(x) == "Cp")) y$df <- NULL
+
     if(is.null(y$statistic)) {
       y$statistic <- y$deviance
       variable_label(y$statistic) <- "$\\chi^2$"
@@ -503,7 +506,7 @@ apa_print.anova <- function(
   } else if(identical(object_heading[1], "ANOVA-like table for random-effects: Single term deletions")) {
     stop("Single-term deletions are not supported, yet.\nVisit https://github.com/crsh/papaja/issues to request support.")
   }
-  # anova::lm (single model) ----
+  # anova.lm() (single model) ----
   # Canonize, beautify, glue ----
   y <- as.data.frame(x, stringsAsFactors = FALSE)
   y$Effect <- trimws(rownames(y))

R/lookup_tables.R

@@ -83,6 +83,7 @@ lookup_names <- c(
   , "F.value"   = "statistic"
   , "F"         = "statistic"
   , "F.ratio"   = "statistic"
+  , "F.values"  = "statistic"
   , "LRT"       = "statistic"
   , "LR.Chisq"  = "statistic"
   , "Chisq"     = "statistic"
@@ -208,6 +209,7 @@ lookup_labels <- c(
   , "F.value"   = "$F$"
   , "F"         = "$F$"
   , "F.ratio"   = "$F$"
+  , "F.values"  = "$F$"
   , "approx.F"  = "$F$"
   , "LRT"       = "$\\chi^2$"
   , "LR.Chisq"  = "$\\chi^2$"

tests/testthat/test_apa_print_anova.R

@@ -545,7 +545,7 @@ test_that(
 )
 
 test_that(
-  "Analysis of deviance from the stats package"
+  "Analysis of deviance from the stats and car packages"
   , {
     # From stats::glm() examples section:
     ## Dobson (1990) Page 93: Randomized Controlled Trial :
@@ -554,11 +554,64 @@ test_that(
     treatment <- gl(3,3)
     data.frame(treatment, outcome, counts) # showing data
     glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
-    apa_print(anova(glm.D93, test = "Chisq"))
-    apa_print(anova(glm.D93, test = "Cp"))
-    apa_print(anova(glm.D93, test = "LRT"))
-    apa_print(car::Anova(glm.D93, test.statistic = "LR"))
-    apa_print(anova(glm.D93, test = "Rao"))
+
+    chisq_out <- apa_print(anova(glm.D93, test = "Chisq"))
+    cp_out    <- apa_print(anova(glm.D93, test = "Cp"))
+    lrt_out   <- apa_print(anova(glm.D93, test = "LRT"))
+    rao_out   <- apa_print(anova(glm.D93, test = "Rao"))
+
+    expect_identical(
+      chisq_out$full_result
+      , list(
+        outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
+        , treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
+      )
+    )
+    expect_identical(
+      cp_out$full_result
+      , list(
+        outcome = "$C_p = 11.13$"
+        , treatment = "$C_p = 15.13$"
+      )
+    )
+    expect_identical(
+      lrt_out
+      , chisq_out
+    )
+    expect_identical(
+      rao_out$full_result
+      , list(
+        outcome = "$\\mathit{RS}(2) = 5.56$, $p = .062$"
+        , treatment = "$\\mathit{RS}(2) = 0.00$, $p > .999$"
+      )
+    )
+
+    car_lr_out   <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "LR"))
+    car_wald_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "Wald"))
+    car_f_out    <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "F"))
+
+    expect_identical(
+      car_lr_out$full_result
+      , list(
+        outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
+        , treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
+      )
+    )
+    expect_identical(
+      car_wald_out$full_result
+      , list(
+        Intercept = "$\\chi^2(1) = 317.37$, $p < .001$"
+        , outcome = "$\\chi^2(2) = 5.49$, $p = .064$"
+        , treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
+      )
+    )
+    expect_identical(
+      car_f_out$full_result
+      , list(
+        outcome     = "$F(2, 4) = 2.11$, $p = .237$"
+        , treatment = "$F(2, 4) = 0.00$, $p > .999$"
+      )
+    )
   }
 )