gen.latent.datasets: Generate multiple datasets of latent underlying variables

In rosiezou/440proj: Analyze Continuous Latent Variables From Ordinal Survey Responses

Description

Given ordinal responses to survey questions, we generate multiple underlying latent variable datasets

Usage

gen.latent.datasets(M, data, grp.indicator, scores = "Bartlett",
  num.iter = 5)

Arguments

M	number of datasets to construct.
data	a (non-empty) numeric vector of data values.
grp.indicator	a (non-empty) numeric vector of data values.
scores	type of score to use.
num.iter	number of iterations to use in imputation step

Value

A list containing datasets with latent underlying variables

Examples

# Create indicators (a label indicating which latent variable the question corresponds to)
grp.indicator <- sapply(names(multiis), FUN =
                         function(x){strsplit(x, split = "_")[[1]][2]})

latent.datasets <- gen.latent.datasets(5, multiis, grp.indicator = grp.indicator, num.iter = 1)

head(latent.datasets[[1]])

head(latent.datasets[[2]])

rosiezou/440proj documentation built on May 12, 2019, 6:25 p.m.