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 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.
Details
get_scores() takes the results from principal_components() or
factor_analysis() 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
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