|
| 1 | +# report.brms |
| 2 | + |
| 3 | + Code |
| 4 | + report(model, verbose = FALSE) |
| 5 | + Message |
| 6 | + Start sampling |
| 7 | + Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 8 | + 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) |
| 9 | + Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 10 | + Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 11 | + Chain 1 |
| 12 | + Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 13 | + 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) |
| 14 | + Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 15 | + Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 16 | + Chain 2 |
| 17 | + Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 18 | + 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) |
| 19 | + Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 20 | + Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 21 | + Chain 2 |
| 22 | + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 23 | + 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) |
| 24 | + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 25 | + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 26 | + Chain 3 |
| 27 | + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 28 | + 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) |
| 29 | + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 30 | + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 31 | + Chain 3 |
| 32 | + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 33 | + 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) |
| 34 | + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 35 | + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 36 | + Chain 3 |
| 37 | + Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: |
| 38 | + 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) |
| 39 | + Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, |
| 40 | + Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. |
| 41 | + Chain 3 |
| 42 | + Output |
| 43 | + We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains |
| 44 | + of 300 iterations and a warmup of 150) to predict mpg with qsec and wt |
| 45 | + (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t |
| 46 | + (location = 19.20, scale = 5.40) distributions. The model's explanatory power |
| 47 | + is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this |
| 48 | + model: |
| 49 | + |
| 50 | + - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% |
| 51 | + probability of being positive (> 0), 99.67% of being significant (> 0.30), and |
| 52 | + 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 53 | + 0.999) but the indices are unreliable (ESS = 343) |
| 54 | + - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% |
| 55 | + probability of being positive (> 0), 99.17% of being significant (> 0.30), and |
| 56 | + 0.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 57 | + 0.999) but the indices are unreliable (ESS = 345) |
| 58 | + - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% |
| 59 | + probability of being negative (< 0), 100.00% of being significant (< -0.30), |
| 60 | + and 100.00% of being large (< -1.81). The estimation successfully converged |
| 61 | + (Rhat = 0.999) but the indices are unreliable (ESS = 586) |
| 62 | + |
| 63 | + Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) |
| 64 | + framework, we report the median of the posterior distribution and its 95% CI |
| 65 | + (Highest Density Interval), along the probability of direction (pd), the |
| 66 | + probability of significance and the probability of being large. The thresholds |
| 67 | + beyond which the effect is considered as significant (i.e., non-negligible) and |
| 68 | + large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the |
| 69 | + outcome's SD). Convergence and stability of the Bayesian sampling has been |
| 70 | + assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and |
| 71 | + Effective Sample Size (ESS), which should be greater than 1000 (Burkner, |
| 72 | + 2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4 |
| 73 | + chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt |
| 74 | + (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform |
| 75 | + (location = , scale = ) distributions. The model's explanatory power is |
| 76 | + substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this |
| 77 | + model: |
| 78 | + |
| 79 | + - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% |
| 80 | + probability of being positive (> 0), 99.67% of being significant (> 0.30), and |
| 81 | + 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 82 | + 0.999) but the indices are unreliable (ESS = 343) |
| 83 | + - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% |
| 84 | + probability of being positive (> 0), 99.17% of being significant (> 0.30), and |
| 85 | + 0.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 86 | + 0.999) but the indices are unreliable (ESS = 345) |
| 87 | + - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% |
| 88 | + probability of being negative (< 0), 100.00% of being significant (< -0.30), |
| 89 | + and 100.00% of being large (< -1.81). The estimation successfully converged |
| 90 | + (Rhat = 0.999) but the indices are unreliable (ESS = 586) |
| 91 | + |
| 92 | + Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) |
| 93 | + framework, we report the median of the posterior distribution and its 95% CI |
| 94 | + (Highest Density Interval), along the probability of direction (pd), the |
| 95 | + probability of significance and the probability of being large. The thresholds |
| 96 | + beyond which the effect is considered as significant (i.e., non-negligible) and |
| 97 | + large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the |
| 98 | + outcome's SD). Convergence and stability of the Bayesian sampling has been |
| 99 | + assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and |
| 100 | + Effective Sample Size (ESS), which should be greater than 1000 (Burkner, |
| 101 | + 2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4 |
| 102 | + chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt |
| 103 | + (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform |
| 104 | + (location = , scale = ) distributions. The model's explanatory power is |
| 105 | + substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this |
| 106 | + model: |
| 107 | + |
| 108 | + - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% |
| 109 | + probability of being positive (> 0), 99.67% of being significant (> 0.30), and |
| 110 | + 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 111 | + 0.999) but the indices are unreliable (ESS = 343) |
| 112 | + - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% |
| 113 | + probability of being positive (> 0), 99.17% of being significant (> 0.30), and |
| 114 | + 0.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 115 | + 0.999) but the indices are unreliable (ESS = 345) |
| 116 | + - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% |
| 117 | + probability of being negative (< 0), 100.00% of being significant (< -0.30), |
| 118 | + and 100.00% of being large (< -1.81). The estimation successfully converged |
| 119 | + (Rhat = 0.999) but the indices are unreliable (ESS = 586) |
| 120 | + |
| 121 | + Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) |
| 122 | + framework, we report the median of the posterior distribution and its 95% CI |
| 123 | + (Highest Density Interval), along the probability of direction (pd), the |
| 124 | + probability of significance and the probability of being large. The thresholds |
| 125 | + beyond which the effect is considered as significant (i.e., non-negligible) and |
| 126 | + large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the |
| 127 | + outcome's SD). Convergence and stability of the Bayesian sampling has been |
| 128 | + assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and |
| 129 | + Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017). |
| 130 | + and We fitted a Bayesian linear model (estimated using MCMC sampling with 4 |
| 131 | + chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt |
| 132 | + (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t |
| 133 | + (location = 0.00, scale = 5.40) distributions. The model's explanatory power is |
| 134 | + substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this |
| 135 | + model: |
| 136 | + |
| 137 | + - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67% |
| 138 | + probability of being positive (> 0), 99.67% of being significant (> 0.30), and |
| 139 | + 99.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 140 | + 0.999) but the indices are unreliable (ESS = 343) |
| 141 | + - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00% |
| 142 | + probability of being positive (> 0), 99.17% of being significant (> 0.30), and |
| 143 | + 0.33% of being large (> 1.81). The estimation successfully converged (Rhat = |
| 144 | + 0.999) but the indices are unreliable (ESS = 345) |
| 145 | + - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00% |
| 146 | + probability of being negative (< 0), 100.00% of being significant (< -0.30), |
| 147 | + and 100.00% of being large (< -1.81). The estimation successfully converged |
| 148 | + (Rhat = 0.999) but the indices are unreliable (ESS = 586) |
| 149 | + |
| 150 | + Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) |
| 151 | + framework, we report the median of the posterior distribution and its 95% CI |
| 152 | + (Highest Density Interval), along the probability of direction (pd), the |
| 153 | + probability of significance and the probability of being large. The thresholds |
| 154 | + beyond which the effect is considered as significant (i.e., non-negligible) and |
| 155 | + large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the |
| 156 | + outcome's SD). Convergence and stability of the Bayesian sampling has been |
| 157 | + assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and |
| 158 | + Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017). |
| 159 | + |
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