`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.

## Arguments

- x
An object returned by

`principal_components()`

.- n_items
Number of required (i.e. non-missing) items to build the sum score. If

`NULL`

, the value is chosen to match half of the number of columns in a data frame.

## Details

`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.

## Examples

```
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
```