Build plots using the outputs stored in the extr object.
Usage
# S3 method for class 'extr'
plot(x, ..., category = "ppdensity")Arguments
- x
An object of class
extr.- ...
Passed to ggplot2::geom_histogram, when
category = histogram. Useful to change the number of bins.- category
A string indicating which plot to build. See options in the Details section.
Details
The available options are:
ppdensity: Density plots of the empirical and sampled data, useful to assess the model's convergence.density: Density plots of the model's effects.histogram: Histograms of the model's effects.traceplot: Trace plot showing the changes in the effects' values across iterations and chains.
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)
#> 542.553 seconds (Total)
#> Chain 1:
#> Warning: There were 2 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 3 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 1.14, 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
#> 2 of 4000 iterations ended with a divergence (0.05%).
#> 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 = 0.174
#> Chain 3: E-BFMI = 0.088
#> Chain 4: E-BFMI = 0.187
#> E-BFMI below 0.2 indicates you may need to reparameterize your model.
plot(outs, category = "ppdensity")
plot(outs, category = "density")
plot(outs, category = "histogram")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot(outs, category = "traceplot")
# }