README.md

kfa: K-Fold Cross Validation For Factor Analysis

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kfa provides utilities for examining the dimensionality of a set of variables to foster scale development. Harnessing a k-fold cross validation approach, kfa helps researchers compare possible factor structures and identify which structures are plausible and replicable across samples.

Installation

# From CRAN
install.packages("kfa")

# Development version
install.packages("remotes")
remotes::install_github("knickodem/kfa")

library(kfa)

Workflow

The two primary functions are kfa() and kfa_report(). When the set of potential variables and (optionally) the maximum number of factors, m, are supplied to kfa(), the function:

Then for each fold:

The factor analyses are run using the lavaan package with many of the lavaan estimation and missing data options available for use in kfa(). kfa() returns a list of lists with k outer elements for each fold and m inner elements for each replicable factor model, each containing a lavaan object. To expedite running k x m x 2 (EFA and CFA) models, the function utilizes the parallel and foreach packages for parallel processing.

library(kfa)
# simulate data based on a 3-factor model with standardized loadings
sim.mod <- "f1 =~ .7*x1 + .8*x2 + .3*x3 + .7*x4 + .6*x5 + .8*x6 + .4*x7
                f2 =~ .8*x8 + .7*x9 + .6*x10 + .5*x11 + .5*x12 + .7*x13 + .6*x14
                f3 =~ .6*x15 + .5*x16 + .9*x17 + .4*x18 + .7*x19 + .5*x20
                f1 ~~ .2*f2
                f2 ~~ .2*f3
                f1 ~~ .2*f3
                x9 ~~ .2*x10"
set.seed(1161)
sim.data <- simstandard::sim_standardized(sim.mod,
                                          n = 900,
                                          latent = FALSE,
                                          errors = FALSE)[c(2:9,1,10:20)]

# include a custom 2-factor model
custom2f <- paste0("f1 =~ ", paste(colnames(sim.data)[1:10], collapse = " + "),
                   "\nf2 =~ ",paste(colnames(sim.data)[11:20], collapse = " + "))

mods <- kfa(variables = sim.data,
            k = NULL, # prompts power analysis to determine number of folds
            custom.cfas = custom2f)

kfa_report() then aggregates the CFA model fit, parameter estimates, and model-based reliability across folds for each factor structure extracted in kfa(). The results are then organized and exported via rmarkdown, such as the example report run below.

# Run report
kfa_report(mods, file.name = "example_sim_kfa_report",
           report.title = "K-fold Factor Analysis - Example Sim"
           report.format = "html_document")

Under Development and Consideration



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kfa documentation built on March 18, 2022, 5:26 p.m.