BMSC_pareto_k_table | R Documentation |
bmscstan wrapper for diagnostics for Pareto smoothed importance sampling (PSIS)
BMSC_pareto_k_table(x) BMSC_pareto_k_ids(x, threshold = 0.5) BMSC_mcse_loo(x, threshold = 0.7) BMSC_pareto_k_values(x) BMSC_pareto_k_influence_values(x) BMSC_psis_n_eff_values(x) ## S3 method for class 'pareto_k_table_BMSC' print(x, ...) ## S3 method for class 'pareto_k_ids_BMSC' print(x, ...) ## S3 method for class 'pareto_k_values_BMSC' print(x, ...) ## S3 method for class 'pareto_k_influence_values_BMSC' print(x, ...) ## S3 method for class 'psis_n_eff_values_BMSC' print(x, ...) ## S3 method for class 'mcse_loo_BMSC' print(x, ...)
x |
An object of class |
threshold |
for the 'pareto_k_ids' method is the minimum $k$ value to flag. for the 'mcse_loo' method all the $k$ values greater than the 'threshold' will be returned as NA. |
... |
further arguments passed to the 'print' function. |
pareto_k_table returns an object of class "pareto_k_table_BMSC", which is a matrix with columns "Count", "Proportion", and "Min. n_eff"
pareto_k_ids returns an integer vector indicating which observations have Pareto k estimates above threshold
mcse_loo returns the Monte Carlo standard error (MCSE) estimate for PSIS-LOO. MCSE will be NA if any Pareto kk values are above threshold.
pareto_k_values returns a vector of the estimated Pareto k parameters. These represent the reliability of sampling.
pareto_k_influence_values returns a vector of the estimated Pareto k parameters. These represent influence of the observations on the model posterior distribution.
psis_k_influence_table returns a vector of the estimated PSIS effective sample sizes.
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