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| # report.brms | ||
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| 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). | ||
|
|