update snapshots

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

@@ -2,7 +2,7 @@
 
     Code
       report(model, verbose = FALSE)
-    Message <simpleMessage>
+    Message
       Start sampling
     Output
       We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
@@ -30,13 +30,14 @@
       (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|. 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)., 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
-      uniform (location = , scale = ) distributions. The model's explanatory power is
+      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)., 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 uniform
+      (location = , scale = ) distributions. The model's explanatory power is
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
@@ -58,13 +59,14 @@
       (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|. 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)., 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
-      uniform (location = , scale = ) distributions. The model's explanatory power is
+      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)., 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 uniform
+      (location = , scale = ) distributions. The model's explanatory power is
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
@@ -86,15 +88,16 @@
       (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|. 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
-      student_t (location = 0.00, scale = 5.40) distributions. The model's
-      explanatory power is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 =
-      0.79).  Within this model:
+      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 student_t
+      (location = 0.00, scale = 5.40) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
       probability of being positive (> 0), 99.83% of being significant (> 0.30), and
@@ -114,8 +117,8 @@
       (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|. 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).
+      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).
 

tests/testthat/_snaps/windows/report.htest-correlation.md

@@ -13,7 +13,8 @@
 
     Code
       report(cor.test(mtcars$wt, mtcars$mpg, method = "spearman"))
-    Warning <simpleWarning>
+    Condition
+      Warning in `cor.test.default()`:
       Cannot compute exact p-value with ties
     Output
       Effect sizes were labelled following Funder's (2019) recommendations.
@@ -26,7 +27,8 @@
 
     Code
       report(cor.test(mtcars$wt, mtcars$mpg, method = "kendall"))
-    Warning <simpleWarning>
+    Condition
+      Warning in `cor.test.default()`:
       Cannot compute exact p-value with ties
     Output
       Effect sizes were labelled following Funder's (2019) recommendations.

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

@@ -2,7 +2,7 @@
 
     Code
       report(ivr)
-    Message <simpleMessage>
+    Message
       Formula contains log- or sqrt-terms.
         See help("standardize") for how such terms are standardized.
       Formula contains log- or sqrt-terms.

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

@@ -23,11 +23,11 @@
 
     Code
       report(m2)
-    Message <simpleMessage>
+    Message
       boundary (singular) fit: see help('isSingular')
     Output
       Random effect variances not available. Returned R2 does not account for random effects.
-    Message <simpleMessage>
+    Message
       boundary (singular) fit: see help('isSingular')
     Output
       Random effect variances not available. Returned R2 does not account for random effects.

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

@@ -25,8 +25,8 @@
       (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|. 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).
+      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).