blcInitializeSplitDichotomizeUsingMean: Initialize Gaussian Latent Class via Mean Dichotomization

In RPMM: Recursively Partitioned Mixture Model

Description

Creates a function for initializing latent class model by dichotomizing via mean over all responses

Usage

blcInitializeSplitDichotomizeUsingMean(threshold = 0.5, fuzz = 0.95)

Arguments

threshold	Mean threshold for determining class
fuzz	“fuzz” factor for producing imperfectly clustered subjects

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, a simple threshold will be applied to the mean over all item responses. See blcTree for example of using “blcInitializeSplit...” to create starting values.

Value

A function f(x) (see Details.)

See Also

glcInitializeSplitFanny, glcInitializeSplitHClust

RPMM documentation built on May 2, 2019, 2:52 p.m.