Create reports for Bayesian models. The description of the parameters follows the Sequential Effect eXistence and sIgnificance Testing framework (see SEXIT documentation).
Usage
# S3 method for class 'stanreg'
report(x, ...)Value
An object of class report().
See also
Specific components of reports (especially for stats models):
Other types of reports:
Methods:
Template file for supporting new models:
Examples
# \donttest{
# Bayesian models
library(rstanarm)
#> This is rstanarm version 2.32.1
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
#>
#> Attaching package: ‘rstanarm’
#> The following objects are masked from ‘package:brms’:
#>
#> dirichlet, exponential, get_y, lasso, ngrps
model <- suppressWarnings(stan_glm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 500))
r <- report(model)
r
#> We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
#> of 500 iterations and a warmup of 250) to predict mpg with qsec and wt
#> (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
#> = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
#> explanatory power is substantial (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 =
#> 0.79). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.94
#> (95% CI [8.47, 31.34]). Within this model:
#>
#> - The effect of qsec (Median = 0.93, 95% CI [0.31, 1.50]) has a 99.80%
#> probability of being positive (> 0), 97.80% of being significant (> 0.30), and
#> 0.00% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 0.998) and the indices are reliable (ESS = 1092)
#> - The effect of wt (Median = -5.03, 95% CI [-6.06, -3.99]) 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) and the indices are reliable (ESS = 1160)
#>
#> 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).
summary(r)
#> We fitted a Bayesian linear model to predict mpg with qsec and wt. Priors over
#> parameters were all set as normal (mean = 0.00, SD = 8.43; mean = 0.00, SD =
#> 15.40) distributions. The model's explanatory power is substantial (R2 = 0.81,
#> adj. R2 = 0.79). The model's intercept is at 19.94 (95% CI [8.47, 31.34]).
#> Within this model:
#>
#> - The effect of qsec (Median = 0.93, 95% CI [0.31, 1.50]) has 99.80%, 97.80%
#> and 0.00% probability of being positive (> 0), significant (> 0.30) and large
#> (> 1.81)
#> - The effect of wt (Median = -5.03, 95% CI [-6.06, -3.99]) has 100.00%, 100.00%
#> and 100.00% probability of being negative (< 0), significant (< -0.30) and
#> large (< -1.81)
as.data.frame(r)
#> Parameter | Median | 95% CI | pd | Rhat | ESS | Prior | Fit
#> ---------------------------------------------------------------------------------------------------
#> (Intercept) | 19.94 | [ 8.47, 31.34] | 100% | 0.998 | 1123.00 | Normal (20.09 +- 15.07) |
#> qsec | 0.93 | [ 0.31, 1.50] | 99.80% | 0.998 | 1092.00 | Normal (0.00 +- 8.43) |
#> wt | -5.03 | [-6.06, -3.99] | 100% | 0.997 | 1160.00 | Normal (0.00 +- 15.40) |
#> | | | | | | |
#> ELPD | | | | | | | -79.43
#> LOOIC | | | | | | | 158.87
#> WAIC | | | | | | | 158.61
#> R2 | | | | | | | 0.81
#> R2 (adj.) | | | | | | | 0.79
#> Sigma | | | | | | | 2.67
# }