BMSC_pareto_k_table {bmscstan}

bmscstan wrapper for diagnostics for Pareto smoothed importance sampling (PSIS)

Description

bmscstan wrapper for diagnostics for Pareto smoothed importance sampling (PSIS)

Usage

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, ...)

Arguments

x

An object of class loo_BMSC

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.

Value

• 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.