From a53f206b4dfed6f7dbce6c53a01b057a5c4acdcb Mon Sep 17 00:00:00 2001 From: Indrajeet Patil Date: Thu, 29 Feb 2024 12:37:28 +0200 Subject: [PATCH] accept snapshot --- .../testthat/_snaps/windows/report.brmsfit.md | 115 ++++++++----- .../_snaps/windows/report.brmsfit.new.md | 159 ------------------ 2 files changed, 75 insertions(+), 199 deletions(-) delete mode 100644 tests/testthat/_snaps/windows/report.brmsfit.new.md diff --git a/tests/testthat/_snaps/windows/report.brmsfit.md b/tests/testthat/_snaps/windows/report.brmsfit.md index 6f1f6348..4f8894dd 100644 --- a/tests/testthat/_snaps/windows/report.brmsfit.md +++ b/tests/testthat/_snaps/windows/report.brmsfit.md @@ -4,6 +4,41 @@ report(model, verbose = FALSE) Message Start sampling + Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 1 Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 1 + Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 2 + Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 2 + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 3 + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 3 + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 3 + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: + Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. + Chain 3 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 @@ -12,18 +47,18 @@ 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 - 99.67% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.000) but the indices are unreliable (ESS = 522) - - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83% - probability of being positive (> 0), 98.17% of being significant (> 0.30), and - 0.17% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.002) but the indices are unreliable (ESS = 521) - - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00% + - 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 = 0.997) but the indices are unreliable (ESS = 543) + (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 @@ -41,18 +76,18 @@ 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 - 99.67% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.000) but the indices are unreliable (ESS = 522) - - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83% - probability of being positive (> 0), 98.17% of being significant (> 0.30), and - 0.17% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.002) but the indices are unreliable (ESS = 521) - - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00% + - 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 = 0.997) but the indices are unreliable (ESS = 543) + (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 @@ -70,18 +105,18 @@ 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 - 99.67% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.000) but the indices are unreliable (ESS = 522) - - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83% - probability of being positive (> 0), 98.17% of being significant (> 0.30), and - 0.17% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.002) but the indices are unreliable (ESS = 521) - - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00% + - 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 = 0.997) but the indices are unreliable (ESS = 543) + (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 @@ -99,18 +134,18 @@ 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 - 99.67% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.000) but the indices are unreliable (ESS = 522) - - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83% - probability of being positive (> 0), 98.17% of being significant (> 0.30), and - 0.17% of being large (> 1.81). The estimation successfully converged (Rhat = - 1.002) but the indices are unreliable (ESS = 521) - - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00% + - 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 = 0.997) but the indices are unreliable (ESS = 543) + (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 diff --git a/tests/testthat/_snaps/windows/report.brmsfit.new.md b/tests/testthat/_snaps/windows/report.brmsfit.new.md deleted file mode 100644 index 4f8894dd..00000000 --- a/tests/testthat/_snaps/windows/report.brmsfit.new.md +++ /dev/null @@ -1,159 +0,0 @@ -# report.brms - - Code - report(model, verbose = FALSE) - Message - Start sampling - Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 1 Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 1 - Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 2 - Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 2 - Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 3 - Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 3 - Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 3 - Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: - Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62) - Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, - Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. - Chain 3 - 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). -