report.brmsfit.md
In report: Automated Reporting of Results and Statistical Models

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).