Parameters from Structural Models (PCA, EFA, ...)
Format structural models from the psych or FactoMineR packages.
## S3 method for class 'PCA' model_parameters( model, sort = FALSE, threshold = NULL, labels = NULL, verbose = TRUE, ... ) ## S3 method for class 'principal' model_parameters( model, sort = FALSE, threshold = NULL, labels = NULL, verbose = TRUE, ... ) ## S3 method for class 'omega' model_parameters(model, verbose = TRUE, ...)
model |
PCA or FA created by the psych or FactoMineR packages (e.g. through |
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 |
labels |
A character vector containing labels to be added to the loadings data. Usually, the question related to the item. |
verbose |
Toggle warnings and messages. |
... |
Arguments passed to or from other methods. |
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 – 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.
MSA represents the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (Kaiser and Rice, 1974) for each item. It indicates whether there is enough data for each factor give reliable results for the PCA. The value should be > 0.6, and desirable values are > 0.8 (Tabachnick and Fidell, 2013).
A data frame of loadings.
Kaiser, H.F. and Rice. J. (1974). Little jiffy, mark iv. Educational and Psychological Measurement, 34(1):111–117
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.
Tabachnick, B. G., and Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson Education.
library(parameters)
if (require("psych", quietly = TRUE)) {
# 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)
# Exploratory Factor Analysis (EFA) ---------
efa <- psych::fa(attitude, nfactors = 3)
model_parameters(efa, threshold = "max", sort = TRUE, labels = as.character(1:ncol(attitude)))
# Omega ---------
omega <- psych::omega(mtcars, nfactors = 3)
params <- model_parameters(omega)
params
summary(params)
}
# FactoMineR ---------
if (require("FactoMineR", quietly = TRUE)) {
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)
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