check_model.fit_model_bh_GxER/plot.check_model_bh_GxE.R
plot.check_model_bh_GxE.Rdplot.check_model_bh_GxE returns ggplot to visualize outputs from check_model.fit_model_bh_GxE
# S3 method for check_model_bh_GxE plot(x, nb_parameters_per_plot = 8, ...)
| x | Output from |
|---|---|
| nb_parameters_per_plot | number of parameter per plot to display |
| ... | further arguments passed to or from other methods |
alpha_i : distribution of each alpha_i.
There are as many graph as needed with nb_parameters_per_plot alpha_i per graph.
beta_i : distribution of each beta_i.
There are as many graph as needed with nb_parameters_per_plot beta_i per graph.
theta_j : distribution of each theta_j.
There are as many graph as needed with nb_parameters_per_plot theta_j per graph.
epsilon_ij : standardised residuals distribution. If the model went well it should be between -2 and 2.
mcmc_not_converge_traceplot_density : a list with the plots of trace and density to check the convergence of the two MCMC only for chains that are not converging thanks to the Gelman-Rubin test. If all the chains converge, it is NULL.
S3 method.
For mcmc_not_converge_traceplot_density : If you wish exhaustive information, look at ggmcmc::ggmcmc with ggmcmc(out_model$MCMC).
But be careful with the size of your MCMC output which are often too big to be performed in R.
See example in the book: https://priviere.github.io/PPBstats_book/family-2.html#model-2