setPosteriorPredictiveCheckOptions {blatent}

Posterior Predictive Model Checking Options

Description

Provides a list of posterior predictive model checks to be run following estimation of a blatent model. Currently six types of posterior predictive model checks (PPMCs) are available: univarate: mean and univariate Chi-square statistic, bivariate: covariance, tetrachoric correlation, pearson correlation, and bivariate Chi-square statistic.

Usage

setPosteriorPredictiveCheckOptions(
  estimatePPMC = TRUE,
  PPMCsamples = 1000,
  PPMCtypes = c("mean", "covariance", "univariate", "bivariate", "tetrachoric",
    "pearson"),
  lowPPMCpercentile = c(0.025, 0.025, 0, 0, 0.025, 0.025),
  highPPMCpercentile = c(0.975, 0.975, 1, 1, 0.975, 0.975)
)

Arguments

estimatePPMC

If TRUE, runs all PPMCs listed in PPMCtypes. Defaults to TRUE.

PPMCsamples

The number of samples from the posterior distribution and simulated PPMC data sets.

PPMCtypes

The type of PPMC tests to conduct. For each test, the statistic listed is calculated on each PPMC-based simulated data set. Comparisons are made with the values of the statistics calculated on the original data set. Currently six PPMC statistics are available:

• mean Calculates the mean of each variable

• univariate Calculates the Pearson Chi-Square for each variable using simulated data as frequency expected and original data as frequency observed

• covariance Calculates the covariance of every pair of variables

• pearson Calculates the Pearson correlation of every pair of variables

• tetrachoric Calculates the tetrachoric correlation of every pair of variables

• bivariate Calculates the Pearson Chi-Square for each pair of variables using simulated data as frequency expected and original data as frequency observed

lowPPMCpercentile

A vector of length equal to the length and number of PPMCtypes listing the lower percentile limit for the statistic in the observed data to be considered extreme. Defaults to .025 for non-Chi-Square based statistics and 0 for the Chi-Square statistics

highPPMCpercentile

A vector of length equal to the length and number of PPMCtypes listing the upper percentile limit for the statistic in the observed data to be considered extreme. Defaults to .975 for non-Chi-Square based statistics and 1 for the Chi-Square statistics

Value

A list of named values containing a logical value for each parameter above.