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Extract outputs from stanfit objects obtained from bayes_met

Source: R/extr_outs.R
extr_outs.Rd

Extracts outputs of the Bayesian model fitted using bayes_met(), and provides some diagnostics.

Usage

extr_outs(model, probs = c(0.025, 0.975), verbose = FALSE)

Arguments

model

An object of class stanfit, obtained using bayes_met()

probs

A vector with two elements representing the probabilities (in decimal scale) that will be considered for computing the quantiles.

verbose

A logical value. If TRUE, the function will indicate the completed steps. Defaults to FALSE

Value

The function returns an object of class extr, which is a list with:

  • variances : a data frame containing the variance components of the model effects, their standard deviation, naive standard error and highest posterior density interval.

  • post : a list with the posterior of the effects, and the data generated by the model.

  • map : a list with the maximum posterior values of each effect

  • ppcheck : a matrix containing the p-values of maximum, minimum, median, mean and standard deviation; effective number of parameters, WAIC2 value, Rhat and effective sample size.

Details

More details about the usage of extr_outs and other functions of the ProbBreed package can be found at https://saulo-chaves.github.io/ProbBreed_site/.

See also

rstan::stan_diag, ggplot2::ggplot, rstan::check_hmc_diagnostics, plot.extr

Examples

# \donttest{
mod = bayes_met(data = maize,
                gen = "Hybrid",
                loc = "Location",
                repl = c("Rep","Block"),
                trait = "GY",
                reg = "Region",
                year = NULL,
                res.het = TRUE,
                iter = 2000, cores = 2, chain = 4)
#>                454.84 seconds (Total)
#> Chain 1: 
#> Warning: There were 1 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 2 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.26, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

outs = extr_outs(model = mod,
                 probs = c(0.05, 0.95),
                 verbose = TRUE)
#> -> Posterior effects extracted
#> -> Variances extracted
#> -> Maximum posterior values extracted
#> -> Posterior predictive checks computed
#> 1 of 4000 iterations ended with a divergence (0.025%).
#> Try increasing 'adapt_delta' to remove the divergences.
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> E-BFMI indicated possible pathological behavior:
#>   Chain 2: E-BFMI = 0.049
#>   Chain 4: E-BFMI = 0.188
#> E-BFMI below 0.2 indicates you may need to reparameterize your model.
# }