brms: wrong parameters info

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.24.0.6
+Version: 0.24.0.7
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -5,6 +5,9 @@
 * `model_parameters()` now gives informative error messages for more model
   classes than before when the function fails to extract model parameters.
 
+* Improved information for credible intervals and sampling method from output
+  of `model_parameters()` for Bayesian models.
+
 ## Bug fixes
 
 * Fixed issue when printing `model_parameters()` with models from `mgcv::gam()`.

R/extract_parameters.R

@@ -2,7 +2,7 @@
 
 
 #' @keywords internal
 .extract_parameters_generic <- function(model,
                                         ci,
                                         component,
                                         merge_by = c("Parameter", "Component"),
@@ -839,6 +839,9 @@
   }
 
   rownames(parameters) <- NULL
+  # indicate it's a Bayesian model
+  attr(parameters, "is_bayesian") <- TRUE
+
   parameters
 }
 

R/format.R

@@ -3,7 +3,7 @@
 #' @inheritParams print.parameters_model
 #' @rdname print.parameters_model
 #' @export
 format.parameters_model <- function(x,
                                     pretty_names = TRUE,
                                     split_components = TRUE,
                                     select = NULL,
@@ -240,7 +240,7 @@
 #' @rdname print.compare_parameters
 #' @inheritParams print.parameters_model
 #' @export
 format.compare_parameters <- function(x,
                                       split_components = TRUE,
                                       select = NULL,
                                       digits = 2,
@@ -741,12 +741,13 @@
 
 
 # footer: type of uncertainty interval
 .print_footer_cimethod <- function(x) {
   if (isTRUE(getOption("parameters_cimethod", TRUE))) {
     # get attributes
     ci_method <- .additional_arguments(x, "ci_method", NULL)
     test_statistic <- .additional_arguments(x, "test_statistic", NULL)
     bootstrap <- .additional_arguments(x, "bootstrap", FALSE)
+    is_bayesian <- .additional_arguments(x, "is_bayesian", FALSE)
     simulated <- .additional_arguments(x, "simulated", FALSE)
     residual_df <- .additional_arguments(x, "residual_df", NULL)
     random_variances <- .additional_arguments(x, "ran_pars", FALSE)
@@ -825,6 +826,8 @@
         # bootstrapped intervals
         if (isTRUE(bootstrap)) {
           msg <- paste0("\nUncertainty intervals (", string_tailed, ") are ", string_method, "intervals.")
+        } else if (isTRUE(is_bayesian)) {
+          msg <- paste0("\nUncertainty intervals (", string_tailed, ") computed using a ", string_method, "distribution ", string_approx, "approximation.") # nolint
         } else {
           msg <- paste0("\nUncertainty intervals (", string_tailed, ") and p-values (two-tailed) computed using a ", string_method, "distribution ", string_approx, "approximation.") # nolint
         }

R/p_function.R

@@ -61,7 +61,7 @@
 #' null hypothesis is correct (Wikipedia)._
 #'
 #' However, this definition or interpretation is inadequate because it only
-#' referes to the test hypothesis (often the null hypothesis), which is only
+#' refers to the test hypothesis (often the null hypothesis), which is only
 #' one component of the entire model that is being tested. Thus,
 #' _Greenland et al. 2022_ suggest an "unconditional" interpretation.
 #'
@@ -77,7 +77,7 @@
 #' A non-significant p-value could occur because the test hypothesis is false,
 #' but could also be the result of any of the model assumptions being incorrect.
 #'
 #' \if{html}{\cr \figure{unconditional_interpretation.png}{options: alt="Conditional versus unconditional interpretations of P-values"} \cr}
 #'
 #' "Emphasizing unconditional interpretations helps avoid overconfident and
 #' misleading inferences in light of uncertainties about the assumptions used

tests/testthat/_snaps/brms.md

@@ -14,8 +14,8 @@
       var_binom1:groupsb |   0.53 | [-1.70, 2.69] | 69.25% | 1.000 | 2699.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
       The model has a log- or logit-link. Consider using `exponentiate =
         TRUE` to interpret coefficients as ratios.
@@ -36,6 +36,6 @@
       var_binom1:groupsb |   1.69 | [0.18, 14.80] | 69.25% | 1.000 | 2699.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 

tests/testthat/_snaps/printing-stan.md

@@ -18,8 +18,8 @@
       sigma     | 2.67 | [2.06, 3.51] | 100% | 1.000 | 2390.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -49,8 +49,8 @@
       Cor (Intercept~wt: gear) | -0.25 | [-0.99,  0.83] | 62.48% | 1.106 |  36.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -79,8 +79,8 @@
       Cor (Intercept~Days: Subject) |  0.09 | [-0.47,  0.67] | 60.42% | 1.003 |  899.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -109,8 +109,8 @@
       SD (Intercept: Subject)    | 38.51 | [26.89, 55.98] | 100% | 1.003 | 1254.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -137,8 +137,8 @@
       SD (Intercept: Species) | 1.68 | [0.64, 3.64] | 100% | 1.003 | 796.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -168,8 +168,8 @@
       Cor (Intercept~wt: gear) | -0.38 | [-0.99,  0.82] | 76.85% | 1.003 | 854.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
 
 ---
 
@@ -238,6 +238,52 @@
       mpg          |  -1.80 | [ -3.20,  -0.90] | 100% | 1.002 | 1021.00
     Message
 
-      Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
-        using a MCMC distribution approximation.
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
+
+# print-information
+
+    Code
+      out
+    Output
+      # Fixed Effects
+
+      Parameter   | Median |         95% CI |     pd |  Rhat |     ESS
+      ----------------------------------------------------------------
+      (Intercept) |  39.68 | [36.12, 43.27] |   100% | 1.000 | 5242.00
+      wt          |  -3.20 | [-4.79, -1.65] | 99.95% | 1.000 | 2071.00
+      cyl         |  -1.49 | [-2.36, -0.64] | 99.95% | 1.000 | 1951.00
+
+      # Sigma
+
+      Parameter | Median |       95% CI |   pd |  Rhat |     ESS
+      ----------------------------------------------------------
+      sigma     |   2.63 | [2.06, 3.51] | 100% | 1.000 | 2390.00
+    Message
+
+      Uncertainty intervals (equal-tailed) computed using a MCMC distribution
+        approximation.
+
+---
+
+    Code
+      out
+    Output
+      # Fixed Effects
+
+      Parameter   | Median |         95% CI |     pd |  Rhat |     ESS
+      ----------------------------------------------------------------
+      (Intercept) |  39.68 | [36.27, 43.34] |   100% | 1.000 | 5242.00
+      wt          |  -3.20 | [-4.70, -1.57] | 99.95% | 1.000 | 2071.00
+      cyl         |  -1.49 | [-2.38, -0.68] | 99.95% | 1.000 | 1951.00
+
+      # Sigma
+
+      Parameter | Median |       95% CI |   pd |  Rhat |     ESS
+      ----------------------------------------------------------
+      sigma     |   2.63 | [1.99, 3.39] | 100% | 1.000 | 2390.00
+    Message
+
+      Uncertainty intervals (highest-density) computed using a MCMC
+        distribution approximation.
 

tests/testthat/test-printing-stan.R

@@ -45,5 +45,18 @@ withr::with_options(
       mp9 <- model_parameters(m9, effects = "all", component = "all", centrality = "mean")
       expect_snapshot(mp9)
     })
+
+    test_that("print-information", {
+      skip_if_offline()
+      skip_if_not_installed("httr2")
+
+      m <- insight::download_model("brms_1")
+      out <- model_parameters(m)
+      expect_snapshot(out)
+      out <- model_parameters(m, ci_method = "HDI")
+      expect_snapshot(out)
+      m <- insight::download_model("stanreg_glm_1")
+      out <- model_parameters(m)
+    })
   }
 )