Get Scores from Principal Component Analysis (PCA)
get_scores() takes n_items amount of items that load the most
(either by loading cutoff or number) on a component, and then computes their
average.
get_scores(x, n_items = NULL)
x |
An object returned by |
n_items |
Number of required (i.e. non-missing) items to build the sum
score. If |
get_scores() takes the results from
principal_components and extracts the variables for each
component found by the PCA. Then, for each of these "subscales", row means
are calculated (which equals adding up the single items and dividing by the
number of items). This results in a sum score for each component from the
PCA, which is on the same scale as the original, single items that were
used to compute the PCA.
A data frame with subscales, which are average sum scores for all items from each component.
if (require("psych")) {
pca <- principal_components(mtcars[, 1:7], n = 2, rotation = "varimax")
# PCA extracted two components
pca
# assignment of items to each component
closest_component(pca)
# now we want to have sum scores for each component
get_scores(pca)
# compare to manually computed sum score for 2nd component, which
# consists of items "hp" and "qsec"
(mtcars$hp + mtcars$qsec) / 2
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