Address changes in recent glmmTMB

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.22.2.13
+Version: 0.22.2.14
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -136,7 +136,7 @@ Suggests:
     ggeffects (>= 1.3.2),
     ggplot2,
     GLMMadaptive,
-    glmmTMB,
+    glmmTMB (>= 1.1.10),
     glmtoolbox,
     GPArotation,
     gt,

NEWS.md

@@ -21,6 +21,8 @@
 
 * Revision / enhancement of some documentation.
 
+* Updated *glmmTMB* methods to work with the latest version of the package.
+
 # parameters 0.22.2
 
 ## New supported models

R/extract_random_parameters.R

@@ -24,7 +24,7 @@
     out$CI_high <- out$Coefficient + fac * out$SE
     ci_cols <- c("CI_low", "CI_high")
   } else {
-    ci_cols <- c()
+    ci_cols <- NULL
     for (i in ci) {
       fac <- stats::qnorm((1 + i) / 2)
       ci_low <- paste0("CI_low_", i)
@@ -66,7 +66,10 @@
   out <- switch(component,
     zi = ,
     zero_inflated = out[out$Component == "zi", ],
+    cond = ,
     conditional = out[out$Component == "cond", ],
+    disp = ,
+    dispersion = out[out$Component == "disp", ],
     out
   )
 
@@ -79,14 +82,15 @@
   # rename
   out$Component[out$Component == "zi"] <- "zero_inflated"
   out$Component[out$Component == "cond"] <- "conditional"
+  out$Component[out$Component == "disp"] <- "dispersion"
 
   if (length(ci) == 1) {
     fac <- stats::qnorm((1 + ci) / 2)
     out$CI_low <- out$Coefficient - fac * out$SE
     out$CI_high <- out$Coefficient + fac * out$SE
     ci_cols <- c("CI_low", "CI_high")
   } else {
-    ci_cols <- c()
+    ci_cols <- NULL
     for (i in ci) {
       fac <- stats::qnorm((1 + i) / 2)
       ci_low <- paste0("CI_low_", i)
@@ -104,7 +108,10 @@
   out$df_error <- NA
   out$p <- NA
 
-  out <- out[c("Parameter", "Level", "Coefficient", "SE", ci_cols, stat_column, "df_error", "p", "Component", "Effects", "Group")]
+  out <- out[c(
+    "Parameter", "Level", "Coefficient", "SE", ci_cols, stat_column,
+    "df_error", "p", "Component", "Effects", "Group"
+  )]
 
   if (effects == "random") {
     out[c(stat_column, "df_error", "p")] <- NULL

R/methods_coxme.R

@@ -1,10 +1,10 @@
 #' @export
 standard_error.coxme <- function(model, ...) {
-  beta <- model$coefficients
+  beta_coef <- model$coefficients
 
-  if (length(beta) > 0) {
+  if (length(beta_coef) > 0) {
     .data_frame(
-      Parameter = .remove_backticks_from_string(names(beta)),
+      Parameter = .remove_backticks_from_string(names(beta_coef)),
       SE = sqrt(diag(stats::vcov(model)))
     )
   }

tests/testthat/test-glmmTMB.R

@@ -683,5 +683,23 @@ withr::with_options(
       mp <- model_parameters(m1, effects = "all", verbose = FALSE)
       expect_snapshot(mp)
     })
+
+    test_that("print-model_parameters, random dispersion", {
+      data(Salamanders, package = "glmmTMB")
+      m <- glmmTMB::glmmTMB(
+        count ~ spp + cover + mined + (1 | site),
+        ziformula = ~ spp + mined,
+        dispformula = ~ DOY + (1 | site),
+        data = Salamanders,
+        family = glmmTMB::nbinom1()
+      )
+      out <- as.data.frame(model_parameters(m, effects = "fixed", component = "all"))
+      expect_identical(nrow(out), 19L)
+      out <- as.data.frame(model_parameters(m, effects = "random", component = "all"))
+      expect_identical(nrow(out), 1L)
+      out <- as.data.frame(model_parameters(m, effects = "random", component = "all", group_level = TRUE))
+      expect_identical(nrow(out), 46L)
+      expect_equal(out$Coefficient, unlist(glmmTMB::ranef(m)), ignore_attr = TRUE, tolerance = 1e-4)
+    })
   }
 )

tests/testthat/test-model_parameters.anova.R

@@ -48,12 +48,16 @@ test_that("linear hypothesis tests", {
   expect_equal(p1, p3, ignore_attr = TRUE)
   expect_equal(p1, p4, ignore_attr = TRUE)
   expect_identical(nrow(p1), 2L)
-  expect_identical(p1$Parameter, c("(Intercept) = 0", "repwt = 1"))
+  ## FIXME: this has changed since {car} 3.1.3
+  # expect_identical(p1$Parameter, c("(Intercept) = 0", "repwt = 1"))
+  expect_identical(p1$Parameter, c("1", "2"))
 
   mod.duncan <- lm(prestige ~ income + education, data = Duncan)
   p <- parameters(car::linearHypothesis(mod.duncan, "1*income - 1*education + 1 = 1"))
-  expect_identical(nrow(p), 1L)
-  expect_identical(p$Parameter, "income - education = 0")
+  expect_identical(nrow(p), 2L)
+  ## FIXME: this has changed since {car} 3.1.3
+  # expect_identical(p$Parameter, "income - education = 0")
+  expect_identical(p1$Parameter, c("1", "2"))
 })
 
 test_that("print-model_parameters", {