Report text duplicated many times for brms model using in a tidymodels workflow #418
Description
Describe the bug
The report text for report(fit) is repeated several times for {brms} models.
To Reproduce
Here is a reprex:
I'm using a tidymodels workflow in this reprex, it appears on brms models fit outside of tidymodels, too.
library(tidymodels)
library(bayesian)
#> Loading required package: brms
#> Loading required package: Rcpp
#>
#> Attaching package: 'Rcpp'
#> The following object is masked from 'package:rsample':
#>
#> populate
#> Loading 'brms' package (version 2.21.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#>
#> Attaching package: 'brms'
#> The following object is masked from 'package:dials':
#>
#> mixture
#> The following object is masked from 'package:stats':
#>
#> ar
library(brms)
library(easystats)
#> # Attaching packages: easystats 0.7.0.3
#> ✔ bayestestR 0.13.2 ✔ correlation 0.8.4.2
#> ✔ datawizard 0.9.1.8 ✔ effectsize 0.8.6.6
#> ✔ insight 0.19.10 ✔ modelbased 0.8.7
#> ✔ performance 0.11.0 ✔ parameters 0.21.6.1
#> ✔ report 0.5.8.1 ✔ see 0.8.3.1
rec <- recipe(mpg ~ wt + cyl + drat, data = mtcars) |>
step_scale(all_predictors()) |>
step_center(all_predictors())
mod <- bayesian() |>
set_engine("brms")
wflow <- workflow() |>
add_recipe(rec) |>
add_model(mod) |>
fit(data = mtcars)
#> Compiling Stan program...
#> Trying to compile a simple C file
#> Running /opt/homebrew/Cellar/r/4.3.3/lib/R/bin/R CMD SHLIB foo.c
#> using C compiler: ‘Apple clang version 15.0.0 (clang-1500.1.0.2.5)’
#> using SDK: ‘MacOSX14.2.sdk’
#> clang -I"/opt/homebrew/Cellar/r/4.3.3/lib/R/include" -DNDEBUG -I"/opt/homebrew/lib/R/4.3/site-library/Rcpp/include/" -I"/opt/homebrew/lib/R/4.3/site-library/RcppEigen/include/" -I"/opt/homebrew/lib/R/4.3/site-library/RcppEigen/include/unsupported" -I"/opt/homebrew/lib/R/4.3/site-library/BH/include" -I"/opt/homebrew/lib/R/4.3/site-library/StanHeaders/include/src/" -I"/opt/homebrew/lib/R/4.3/site-library/StanHeaders/include/" -I"/opt/homebrew/lib/R/4.3/site-library/RcppParallel/include/" -I"/opt/homebrew/lib/R/4.3/site-library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DUSE_STANC3 -DSTRICT_R_HEADERS -DBOOST_PHOENIX_NO_VARIADIC_EXPRESSION -D_HAS_AUTO_PTR_ETC=0 -include '/opt/homebrew/lib/R/4.3/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/opt/homebrew/opt/gettext/include -I/opt/homebrew/opt/readline/include -I/opt/homebrew/opt/xz/include -I/opt/homebrew/include -fPIC -g -O2 -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /opt/homebrew/lib/R/4.3/site-library/StanHeaders/include/stan/math/prim/fun/Eigen.hpp:22:
#> In file included from /opt/homebrew/lib/R/4.3/site-library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /opt/homebrew/lib/R/4.3/site-library/RcppEigen/include/Eigen/Core:19:
#> /opt/homebrew/lib/R/4.3/site-library/RcppEigen/include/Eigen/src/Core/util/Macros.h:679:10: fatal error: 'cmath' file not found
#> #include <cmath>
#> ^~~~~~~
#> 1 error generated.
#> make: *** [foo.o] Error 1
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
#> Chain 1: 0.017 seconds (Sampling)
#> Chain 1: 0.035 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.017 seconds (Warm-up)
#> Chain 2: 0.016 seconds (Sampling)
#> Chain 2: 0.033 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.019 seconds (Warm-up)
#> Chain 3: 0.016 seconds (Sampling)
#> Chain 3: 0.035 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.018 seconds (Warm-up)
#> Chain 4: 0.016 seconds (Sampling)
#> Chain 4: 0.034 seconds (Total)
#> Chain 4:
mt_fit <- wflow |> extract_fit_engine()
report(mt_fit)
#> Response residuals not available to calculate mean square error. (R)MSE
#> is probably not reliable.
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
#> Chain 1: 0.014 seconds (Sampling)
#> Chain 1: 0.029 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.015 seconds (Warm-up)
#> Chain 2: 0.016 seconds (Sampling)
#> Chain 2: 0.031 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.015 seconds (Warm-up)
#> Chain 3: 0.015 seconds (Sampling)
#> Chain 3: 0.03 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.016 seconds (Warm-up)
#> Chain 4: 0.016 seconds (Sampling)
#> Chain 4: 0.032 seconds (Total)
#> Chain 4:
#> Response residuals not available to calculate mean square error. (R)MSE
#> is probably not reliable.
#> We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
#> of 2000 iterations and a warmup of 1000) to predict ..y with wt, cyl and drat
#> (formula: ..y ~ wt + cyl + drat). 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 [0.76, 0.85], adj. R2 = 0.79). Within this
#> model:
#>
#> - The effect of b Intercept (Median = 20.07, 95% CI [19.10, 20.98]) has a
#> 100.00% probability of being positive (> 0), 100.00% of being significant (>
#> 0.30), and 100.00% of being large (> 1.81). The estimation successfully
#> converged (Rhat = 1.001) and the indices are reliable (ESS = 2523)
#> - The effect of b wt (Median = -3.10, 95% CI [-4.78, -1.51]) has a 99.98%
#> probability of being negative (< 0), 99.98% of being significant (< -0.30), and
#> 94.27% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.000) and the indices are reliable (ESS = 3146)
#> - The effect of b cyl (Median = -2.72, 95% CI [-4.27, -1.04]) has a 99.88%
#> probability of being negative (< 0), 99.80% of being significant (< -0.30), and
#> 85.78% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 3801)
#> - The effect of b drat (Median = 0.02, 95% CI [-1.42, 1.40]) has a 51.20%
#> probability of being positive (> 0), 34.17% of being significant (> 0.30), and
#> 0.75% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 2431)
#>
#> 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 2000 iterations and a warmup of 1000) to predict ..y with wt, cyl and
#> drat (formula: ..y ~ wt + cyl + drat). Priors over parameters were set as
#> uniform (location = , scale = ) distributions. The model's explanatory power is
#> substantial (R2 = 0.82, 95% CI [0.76, 0.85], adj. R2 = 0.79). Within this
#> model:
#>
#> - The effect of b Intercept (Median = 20.07, 95% CI [19.10, 20.98]) has a
#> 100.00% probability of being positive (> 0), 100.00% of being significant (>
#> 0.30), and 100.00% of being large (> 1.81). The estimation successfully
#> converged (Rhat = 1.001) and the indices are reliable (ESS = 2523)
#> - The effect of b wt (Median = -3.10, 95% CI [-4.78, -1.51]) has a 99.98%
#> probability of being negative (< 0), 99.98% of being significant (< -0.30), and
#> 94.27% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.000) and the indices are reliable (ESS = 3146)
#> - The effect of b cyl (Median = -2.72, 95% CI [-4.27, -1.04]) has a 99.88%
#> probability of being negative (< 0), 99.80% of being significant (< -0.30), and
#> 85.78% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 3801)
#> - The effect of b drat (Median = 0.02, 95% CI [-1.42, 1.40]) has a 51.20%
#> probability of being positive (> 0), 34.17% of being significant (> 0.30), and
#> 0.75% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 2431)
#>
#> 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 2000 iterations and a warmup of 1000) to predict ..y with wt, cyl and
#> drat (formula: ..y ~ wt + cyl + drat). Priors over parameters were set as
#> uniform (location = , scale = ) distributions. The model's explanatory power is
#> substantial (R2 = 0.82, 95% CI [0.76, 0.85], adj. R2 = 0.79). Within this
#> model:
#>
#> - The effect of b Intercept (Median = 20.07, 95% CI [19.10, 20.98]) has a
#> 100.00% probability of being positive (> 0), 100.00% of being significant (>
#> 0.30), and 100.00% of being large (> 1.81). The estimation successfully
#> converged (Rhat = 1.001) and the indices are reliable (ESS = 2523)
#> - The effect of b wt (Median = -3.10, 95% CI [-4.78, -1.51]) has a 99.98%
#> probability of being negative (< 0), 99.98% of being significant (< -0.30), and
#> 94.27% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.000) and the indices are reliable (ESS = 3146)
#> - The effect of b cyl (Median = -2.72, 95% CI [-4.27, -1.04]) has a 99.88%
#> probability of being negative (< 0), 99.80% of being significant (< -0.30), and
#> 85.78% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 3801)
#> - The effect of b drat (Median = 0.02, 95% CI [-1.42, 1.40]) has a 51.20%
#> probability of being positive (> 0), 34.17% of being significant (> 0.30), and
#> 0.75% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 2431)
#>
#> 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 2000 iterations and a warmup of 1000) to predict ..y with wt, cyl and
#> drat (formula: ..y ~ wt + cyl + drat). Priors over parameters were set as
#> uniform (location = , scale = ) distributions. The model's explanatory power is
#> substantial (R2 = 0.82, 95% CI [0.76, 0.85], adj. R2 = 0.79). Within this
#> model:
#>
#> - The effect of b Intercept (Median = 20.07, 95% CI [19.10, 20.98]) has a
#> 100.00% probability of being positive (> 0), 100.00% of being significant (>
#> 0.30), and 100.00% of being large (> 1.81). The estimation successfully
#> converged (Rhat = 1.001) and the indices are reliable (ESS = 2523)
#> - The effect of b wt (Median = -3.10, 95% CI [-4.78, -1.51]) has a 99.98%
#> probability of being negative (< 0), 99.98% of being significant (< -0.30), and
#> 94.27% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.000) and the indices are reliable (ESS = 3146)
#> - The effect of b cyl (Median = -2.72, 95% CI [-4.27, -1.04]) has a 99.88%
#> probability of being negative (< 0), 99.80% of being significant (< -0.30), and
#> 85.78% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 3801)
#> - The effect of b drat (Median = 0.02, 95% CI [-1.42, 1.40]) has a 51.20%
#> probability of being positive (> 0), 34.17% of being significant (> 0.30), and
#> 0.75% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 2431)
#>
#> 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 2000 iterations and a warmup of 1000) to predict ..y with wt, cyl and
#> drat (formula: ..y ~ wt + cyl + drat). 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 [0.76, 0.85], adj. R2 =
#> 0.79). Within this model:
#>
#> - The effect of b Intercept (Median = 20.07, 95% CI [19.10, 20.98]) has a
#> 100.00% probability of being positive (> 0), 100.00% of being significant (>
#> 0.30), and 100.00% of being large (> 1.81). The estimation successfully
#> converged (Rhat = 1.001) and the indices are reliable (ESS = 2523)
#> - The effect of b wt (Median = -3.10, 95% CI [-4.78, -1.51]) has a 99.98%
#> probability of being negative (< 0), 99.98% of being significant (< -0.30), and
#> 94.27% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.000) and the indices are reliable (ESS = 3146)
#> - The effect of b cyl (Median = -2.72, 95% CI [-4.27, -1.04]) has a 99.88%
#> probability of being negative (< 0), 99.80% of being significant (< -0.30), and
#> 85.78% of being large (< -1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 3801)
#> - The effect of b drat (Median = 0.02, 95% CI [-1.42, 1.40]) has a 51.20%
#> probability of being positive (> 0), 34.17% of being significant (> 0.30), and
#> 0.75% of being large (> 1.81). The estimation successfully converged (Rhat =
#> 1.001) and the indices are reliable (ESS = 2431)
#>
#> 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).Created on 2024-03-24 with reprex v2.1.0
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- Package Version 0.5.8.1