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 |
A data frame with subscales, which are average sum scores for all items from each component.
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.
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
}
#> [1] 63.230 63.510 55.805 64.720 96.010 62.610 130.420 41.000 58.950
#> [10] 70.650 70.950 98.700 98.800 99.000 111.490 116.410 123.710 42.735
#> [19] 35.260 42.450 58.505 83.435 83.650 130.205 96.025 42.450 53.850
#> [28] 64.950 139.250 95.250 174.800 63.800