exametrika (version 1.1.0)

LCA: Latent Class Analysis

Description

A function for estimating LCA using the EM algorithm.

Usage

LCA(U, ncls = 2, na = NULL, Z = NULL, w = NULL, maxiter = 100)

Value

nobs

Sample size. The number of rows in the dataset.

testlength

Length of the test. The number of items included in the test.

Nclass

number of classes you set

TRP

Test Reference Profile matrix. The TRP is the column sum vector of estimated class reference matrix, \(\hat{\Pi}_c\)

LCD

Latent Class Distribution table.see also plot.exametrika

CMD

Class Membership Distribution table. see also plot.exametrika

Students

Class Membership Profile matrix.The s-th row vector of \(\hat{M}_c\), \(\hat{m}_c\), is the class membership profile of Student s, namely the posterior probability distribution representing the student's belonging to the respective latent classes. The last column indicates the latent class estimate.

IRP

Item Reference Profile matrix.The IRP of item j is the j-th row vector in the class reference matrix, \(\hat{\pi}_c\)

ItemFitIndices

Fit index for each item.See also ItemFit

TestFitIndices

Overall fit index for the test.See also TestFit

Arguments

U

U is either a data class of exametrika, or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function.

ncls

number of latent class

na

na argument specifies the numbers or characters to be treated as missing values.

Z

Z is a missing indicator matrix of the type matrix or data.frame

w

w is item weight vector

maxiter

Maximum number of iterations.

Examples

# \donttest{
# Fit a Latent Class Analysis model with 5 classes to the sample dataset
result.LCA <- LCA(J15S500, ncls = 5)

# Display the first few rows of student class membership probabilities
head(result.LCA$Students)

# Plot Item Response Profiles (IRP) for items 1-6 in a 2x3 grid
plot(result.LCA, type = "IRP", items = 1:6, nc = 2, nr = 3)

# Plot Class Membership Probabilities (CMP) for students 1-9 in a 3x3 grid
plot(result.LCA, type = "CMP", students = 1:9, nc = 3, nr = 3)

# Plot Test Response Profile (TRP) showing response patterns across all classes
plot(result.LCA, type = "TRP")

# Plot Latent Class Distribution (LCD) showing the size of each latent class
plot(result.LCA, type = "LCD")
# }