parameters (version 0.3.0)

model_parameters.PCA: Structural Models (PCA, EFA, ...)

Description

Format structural models from the psych or FactoMineR packages.

Usage

# S3 method for PCA
model_parameters(model, sort = FALSE, threshold = NULL,
  labels = NULL, ...)
# S3 method for principal
model_parameters(model, sort = FALSE,
  threshold = NULL, labels = NULL, ...)
# S3 method for omega
model_parameters(model, sort = FALSE, threshold = NULL,
  labels = NULL, ...)

Arguments

model

PCA or FA created by the psych or FactoMineR packages (e.g. through psych::principal, psych::fa or psych::omega).

sort

Sort the loadings.

threshold

A value between 0 and 1 indicates which (absolute) values from the loadings should be removed. An integer higher than 1 indicates the n strongest loadings to retain. Can also be "max", in which case it will only display the maximum loading per variable (the most simple structure).

labels

A character vector containing labels to be added to the loadings data. Usually, the question related to the item.

...

Arguments passed to or from other methods.

Value

A data.frame of loadings.

Details

For the structural models obtained with psych, the following indices are present:

• Complexity (Hoffman's, 1978; Pettersson and Turkheimer, 2010) represents the number of latent components needed to account for the observed variables. Whereas a perfect simple structure solution has a complexity of 1 in that each item would only load on one factor, a solution with evenly distributed items has a complexity greater than 1.

• Uniqueness represents the variance that is 'unique' to the variable and not shared with other variables. It is equal to 1 <U+2013> communality (variance that is shared with other variables). A uniqueness of 0.20 suggests that 20% or that variable's variance is not shared with other variables in the overall factor model. The greater 'uniqueness' the lower the relevance of the variable in the factor model.

References

• Pettersson, E., \& Turkheimer, E. (2010). Item selection, evaluation, and simple structure in personality data. Journal of research in personality, 44(4), 407-420.

• Revelle, W. (2016). How To: Use the psych package for Factor Analysis and data reduction.

Examples

# NOT RUN {
library(parameters)
library(psych)

# Principal Component Analysis (PCA) ---------
pca <- psych::principal(attitude)
model_parameters(pca)

pca <- psych::principal(attitude, nfactors = 3, rotate = "none")
model_parameters(pca, sort = TRUE, threshold = 0.2)

principal_components(attitude, n = 3, sort = TRUE, threshold = 0.2)
# }
# NOT RUN {
# Exploratory Factor Analysis (EFA) ---------
efa <- psych::fa(attitude, nfactors = 3)
model_parameters(efa, threshold = "max", sort = TRUE, labels = as.character(1:ncol(attitude)))
# }
# NOT RUN {
# FactoMineR ---------
# }
# NOT RUN {
library(FactoMineR)

model <- FactoMineR::PCA(iris[, 1:4], ncp = 2)
model_parameters(model)
attributes(model_parameters(model))$scores

model <- FactoMineR::FAMD(iris, ncp = 2)
model_parameters(model)
# }