Model_factors: Latent Factors Recovery from Variables Loadings

In JobNmadu/Dyn4cast: Dynamic Modeling and Machine Learning Environment

Latent Factors Recovery from Variables Loadings

Description

This function retrieves the latent factors and their variable loadings which can be used as R objects to perform other analysis.

Arguments

data	An ⁠R object⁠ obtained from exploratory factor analysis (EFA) using the fa function in psych package.
DATA	A data.frame, the raw data used to carry out the parallel analysis to obtain data object.

Value

A list with the following components:

Latent_frame	data.frame of latent factors based on the variables loadings.
Latent_1	data.frame of variables in Latent factor 1 with their loadings.
Latent_2	data.frame of variables in Latent factor 2 with their loadings.
Latent_3	data.frame of variables in Latent factor 3 with their loadings.
Latent_4	data.frame of variables in Latent factor 3 with their loadings.
Latent_5	data.frame of variables in Latent factor 5 with their loadings.
Latent_6	data.frame of variables in Latent factor 6 with their loadings.
Latent_7	data.frame of variables in Latent factor 7 with their loadings.
Latent_8	data.frame of variables in Latent factor 8 with their loadings.
Latent_9	data.frame of variables in Latent factor 9 with their loadings.

Examples

library(psych)
Data <- Quicksummary
GGn <- names(Data)
GG <- ncol(Data)
GGx <- c(paste0('x0', 1:9), paste("x", 10:ncol(Data), sep = ""))
names(Data) <- GGx
lll <- fa.parallel(Data, fm = 'minres', fa = 'fa')
dat <- fa(Data, nfactors = lll[["nfact"]], rotate = "varimax",fm="minres")

model_factors(data = dat, DATA = Data)

JobNmadu/Dyn4cast documentation built on March 5, 2025, 9:56 p.m.