fix #286: stan_glm output not duplicated

R/report.stanreg.R

@@ -80,6 +80,7 @@ report_priors.stanreg <- function(x, ...) {
     )
   )
 
+  values <- paste0(values, collapse = "; ")
   values <- paste0(params$Prior_Distribution, " (", values, ")")
 
   if (length(unique(values)) == 1 && nrow(params) > 1) {

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

@@ -5,37 +5,11 @@
     Output
       We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
       of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
-      (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-      0.00, SD = 8.43) distributions. The model's explanatory power is substantial
-      (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 = 0.80). The model's intercept,
-      corresponding to qsec = 0 and wt = 0, is at 19.71 (95% CI [9.04, 30.18]).
-      Within this model:
-
-        - The effect of qsec (Median = 0.92, 95% CI [0.40, 1.47]) has a 99.83%
-      probability of being positive (> 0), 99.00% of being significant (> 0.30), and
-      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
-      0.999) but the indices are unreliable (ESS = 451)
-        - The effect of wt (Median = -5.06, 95% CI [-6.02, -4.18]) has a 100.00%
-      probability of being negative (< 0), 100.00% of being significant (< -0.30),
-      and 100.00% of being large (< -1.81). However, the estimation might not have
-      successfuly converged (Rhat = 1.013) and the indices are unreliable (ESS = 478)
-
-      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
-      framework, we report the median of the posterior distribution and its 95% CI
-      (Highest Density Interval), along the probability of direction (pd), the
-      probability of significance and the probability of being large. The thresholds
-      beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
-      outcome's SD). Convergence and stability of the Bayesian sampling has been
-      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
-      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
-      and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
-      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
-      (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-      0.00, SD = 15.40) distributions. The model's explanatory power is substantial
-      (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 = 0.80). The model's intercept,
-      corresponding to qsec = 0 and wt = 0, is at 19.71 (95% CI [9.04, 30.18]).
-      Within this model:
+      (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
+      = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
+      explanatory power is substantial (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 =
+      0.80). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.71
+      (95% CI [9.04, 30.18]). Within this model:
 
         - The effect of qsec (Median = 0.92, 95% CI [0.40, 1.47]) has a 99.83%
       probability of being positive (> 0), 99.00% of being significant (> 0.30), and