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latentFactoR (version 0.0.4)

Data Simulation Based on Latent Factors

Description

Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) .

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Install

install.packages('latentFactoR')

Monthly Downloads

260

Version

0.0.4

License

GPL (>= 3.0)

Maintainer

Alexander Christensen

Last Published

November 22nd, 2022

Functions in latentFactoR (0.0.4)

obtain_zipfs_parameters

Obtain Zipf's Distribution Parameters from Data
simulate_factors

Simulates Latent Factor Data
categorize

Categorize Continuous Data
add_local_dependence

Adds Local Dependence to simulate_factors Data
skew_tables

Skew Tables
factor_forest

Estimate Number of Dimensions using Factor Forest
EKC

Estimate Number of Dimensions using Empirical Kaiser Criterion
add_population_error

Adds Population Error to simulate_factors Data
NEST

Estimate Number of Dimensions using Next Eigenvalue Sufficiency Test
data_to_zipfs

Transforms simulate_factors Data to Zipf's Distribution
add_cross_loadings

Adds (Substantial) Cross-loadings to simulate_factors Data
latentFactoR-package

latentFactoR--package
estimate_dimensions

Estimates Dimensions using Several State-of-the-art Methods