diff --git a/tests/testthat/_snaps/windows/report.brmsfit.md b/tests/testthat/_snaps/windows/report.brmsfit.md deleted file mode 100644 index 35e5ea49..00000000 --- a/tests/testthat/_snaps/windows/report.brmsfit.md +++ /dev/null @@ -1,124 +0,0 @@ -# report.brms - - Code - report(model, verbose = FALSE) - Message - Start sampling - 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 student_t - (location = 19.20, 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 = 19.23, 95% CI [6.80, 31.02]) has a 99.67% - probability of being positive (> 0), 99.67% of being significant (> 0.30), and - 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = - 0.999) but the indices are unreliable (ESS = 343) - - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% - probability of being positive (> 0), 99.17% 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 = 345) - - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% - probability of being negative (< 0), 100.00% of being significant (< -0.30), - and 100.00% of being large (< -1.81). The estimation successfully converged - (Rhat = 0.999) but the indices are unreliable (ESS = 586) - - 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)., 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: - - - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% - probability of being positive (> 0), 99.67% of being significant (> 0.30), and - 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = - 0.999) but the indices are unreliable (ESS = 343) - - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% - probability of being positive (> 0), 99.17% 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 = 345) - - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% - probability of being negative (< 0), 100.00% of being significant (< -0.30), - and 100.00% of being large (< -1.81). The estimation successfully converged - (Rhat = 0.999) but the indices are unreliable (ESS = 586) - - 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)., 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: - - - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% - probability of being positive (> 0), 99.67% of being significant (> 0.30), and - 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = - 0.999) but the indices are unreliable (ESS = 343) - - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% - probability of being positive (> 0), 99.17% 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 = 345) - - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% - probability of being negative (< 0), 100.00% of being significant (< -0.30), - and 100.00% of being large (< -1.81). The estimation successfully converged - (Rhat = 0.999) but the indices are unreliable (ESS = 586) - - 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 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 = 19.23, 95% CI [6.80, 31.02]) has a 99.67% - probability of being positive (> 0), 99.67% of being significant (> 0.30), and - 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = - 0.999) but the indices are unreliable (ESS = 343) - - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% - probability of being positive (> 0), 99.17% 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 = 345) - - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% - probability of being negative (< 0), 100.00% of being significant (< -0.30), - and 100.00% of being large (< -1.81). The estimation successfully converged - (Rhat = 0.999) but the indices are unreliable (ESS = 586) - - 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). - diff --git a/tests/testthat/test-report.brmsfit.R b/tests/testthat/test-report.brmsfit.R index 8b5a2f98..f646fac1 100644 --- a/tests/testthat/test-report.brmsfit.R +++ b/tests/testthat/test-report.brmsfit.R @@ -13,9 +13,6 @@ test_that("report.brms", { expect_s3_class(summary(r), "character") expect_s3_class(as.data.frame(r), "data.frame") - set.seed(333) - expect_snapshot(variant = "windows", report(model, verbose = FALSE)) - expect_identical( as.data.frame(r)$Parameter, c( @@ -33,4 +30,8 @@ test_that("report.brms", { c(rep(1, 4), rep(NA, 7)), tolerance = 1e-1 ) + + skip("Skipping because of a .01 decimal difference in snapshots") + set.seed(333) + expect_snapshot(variant = "windows", report(model, verbose = FALSE)) }) diff --git a/tests/testthat/test-report.lm.R b/tests/testthat/test-report.lm.R index 0b182fd9..12a64552 100644 --- a/tests/testthat/test-report.lm.R +++ b/tests/testthat/test-report.lm.R @@ -3,7 +3,7 @@ # Readding back because of a .1 decimal difference in snapshots test_that("report.lm - lm", { - skip("Skipping because of a .1 decimal difference in snapshots") + skip("Skipping because of a .01 decimal difference in snapshots") # lm ------- # simple effect diff --git a/tests/testthat/test-report_performance.R b/tests/testthat/test-report_performance.R index 4e842c32..bbc7616a 100644 --- a/tests/testthat/test-report_performance.R +++ b/tests/testthat/test-report_performance.R @@ -121,7 +121,7 @@ test_that("report_performance Bayesian 2)", { variant = "windows", summary(report_performance(x7)) ) - skip("Skipping because of a .1 decimal difference in snapshots") + skip("Skipping because of a .01 decimal difference in snapshots") expect_snapshot( variant = "windows", report_performance(x7)