Get Scores from Principal Component or Factor Analysis (PCA/FA)
Source:R/utils_pca_efa.R
get_scores.Rdget_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. This results in a sum score for each component from the PCA/FA,
which is on the same scale as the original, single items that were used to
compute the PCA/FA.
Arguments
- x
An object returned by
principal_components()orfactor_analysis().- n_items
Number of required (i.e. non-missing) items to build the sum score for an observation. If an observation has more missing values than
n_itemsin all items of a (sub) scale,NAis returned for that observation, else, the sum score of all (sub) items is calculated. IfNULL, the value is chosen to match half of the number of columns in a data frame, i.e. no more than 50% missing values are allowed.
Value
A data frame with subscales, which are average sum scores for all items from each component or factor.
Details
get_scores() takes the results from principal_components() or
factor_analysis() and extracts the variables for each component found by
the PCA/FA. 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/FA, which
is on the same scale as the original, single items that were used to compute
the PCA/FA.
See also
Functions to carry out a PCA (principal_components()) or
a FA (factor_analysis()). factor_scores() extracts factor scores
from an FA object.
Examples
pca <- principal_components(mtcars[, 1:7], n = 2, rotation = "varimax")
# PCA extracted two components
pca
#> # Rotated loadings from Principal Component Analysis (varimax-rotation)
#>
#> Variable | RC1 | RC2 | Complexity | Uniqueness | MSA
#> ---------------------------------------------------------
#> mpg | -0.84 | -0.42 | 1.47 | 0.13 | 0.87
#> cyl | 0.77 | 0.58 | 1.86 | 0.08 | 0.87
#> disp | 0.85 | 0.43 | 1.48 | 0.08 | 0.85
#> hp | 0.55 | 0.77 | 1.81 | 0.10 | 0.90
#> drat | -0.89 | 0.01 | 1.00 | 0.21 | 0.85
#> wt | 0.93 | 0.17 | 1.07 | 0.10 | 0.77
#> qsec | -0.03 | -0.97 | 1.00 | 0.06 | 0.61
#>
#> The 2 principal components (varimax rotation) accounted for 89.18% of the total variance of the original data (RC1 = 56.82%, RC2 = 32.36%).
#>
# assignment of items to each component
closest_component(pca)
#> mpg cyl disp hp drat wt qsec
#> 1 1 1 2 1 1 2
# now we want to have sum scores for each component
get_scores(pca)
#> Component_1 Component_2
#> Mazda RX4 38.7040 63.230
#> Mazda RX4 Wag 38.7550 63.510
#> Datsun 710 28.1940 55.805
#> Hornet 4 Drive 58.3390 64.720
#> Hornet Sportabout 78.6580 96.010
#> Valiant 51.0640 62.610
#> Duster 360 77.8160 130.420
#> Merc 240D 36.3960 41.000
#> Merc 230 34.9340 58.950
#> Merc 280 40.0320 70.650
#> Merc 280C 39.7520 70.950
#> Merc 450SE 61.4680 98.700
#> Merc 450SL 61.5800 98.800
#> Merc 450SLC 61.1700 99.000
#> Cadillac Fleetwood 99.7160 111.490
#> Lincoln Continental 97.3648 116.410
#> Chrysler Imperial 94.2550 123.710
#> Fiat 128 24.2760 42.735
#> Honda Civic 23.3290 35.260
#> Toyota Corolla 23.0110 42.450
#> Toyota Corona 30.3530 58.505
#> Dodge Challenger 69.5560 83.435
#> AMC Javelin 66.7570 83.650
#> Camaro Z28 75.7740 130.205
#> Pontiac Firebird 86.8250 96.025
#> Fiat X1-9 23.2630 42.450
#> Porsche 914-2 31.3740 53.850
#> Lotus Europa 26.9566 64.950
#> Ford Pantera L 76.4380 139.250
#> Ferrari Dino 35.4180 95.250
#> Maserati Bora 66.2220 174.800
#> Volvo 142E 30.6580 63.800
# 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