Initialize Gaussian Latent Class via Mean Dichotomization

Description

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

Usage

1
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

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