latentFactoR (version 0.0.4)

factor_forest: Estimate Number of Dimensions using Factor Forest

Description

Estimates the number of dimensions in data using the pre-trained Random Forest model from Goretzko and Buhner (2020, 2022). See examples to get started

Usage

factor_forest(data, sample_size, maximum_factors = 8)

Value

Returns a list containing:

dimensions

Number of dimensions identified

probabilities

Probability that the number of dimensions is most likely

Arguments

data

Matrix or data frame. Either a dataset with all numeric values (rows = cases, columns = variables) or a symmetric correlation matrix

sample_size

Numeric (length = 1). If input into data is a correlation matrix, then specifying the sample size is required

maximum_factors

Numeric (length = 1). Maximum number of factors to search over. Defaults to 8

Author

# Authors of Factor Forest
David Goretzko and Markus Buhner

# Authors of latentFactoR
Alexander P. Christensen <[e-mail]>, Hudson Golino <[e-mail]>, Luis Eduardo Garrido <[e-mail]>

References

Goretzko, D., & Buhner, M. (2022). Factor retention using machine learning with ordinal data. Applied Psychological Measurement, 01466216221089345.

Goretzko, D., & Buhner, M. (2020). One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. Psychological Methods, 25(6), 776-786.

Examples

# Generate factor data
two_factor <- simulate_factors(
  factors = 2, # factors = 2
  variables = 6, # variables per factor = 6
  loadings = 0.55, # loadings between = 0.45 to 0.65
  cross_loadings = 0.05, # cross-loadings N(0, 0.05)
  correlations = 0.30, # correlation between factors = 0.30
  sample_size = 1000 # number of cases = 1000
)

if (FALSE) {
# Perform Factor Forest
factor_forest(two_factor$data)}