mixedresponse_posterior_prediction: Posterior Predictions of Item Factor Analysis with Mixed...

In bayesefa: Bayesian Exploratory Factor

Description

Generate posterior predictions for new variables using posterior samples.

Usage

mixedresponse_posterior_prediction(
  OUTPUT,
  Y,
  Ms,
  variable_predict_flag,
  bounds,
  n_mcmc_iterations = 10L
)

Arguments

OUTPUT	A list of output from IFA_Mode_Jumper_MixedResponses.
Y	A N by J matrix of item responses for predictions. Variables to predict are indicated in Y by NAs.
Ms	model indicator where 0 = "bounded", 1 = "continuous", 2 = "binary", >2 = "ordinal".
variable_predict_flag	A J vector. 0 = do not predict the variable; 1 = predict the variable.
bounds	A J by 2 matrix denoting the min and max variable values. Note that bounds are only used for variable j if element j of Ms is zero.
n_mcmc_iterations	The number of Gibbs iterations for sampling posterior predictions for factor scores and missing data. The default is 10 iterations.

Value

array of predictions for all posterior samples provided in OUTPUT.

Author(s)

Steven Andrew Culpepper

bayesefa documentation built on Feb. 10, 2021, 5:10 p.m.