Factor and component scores

Source: R/score.R

score calculates principal component or factor scores based on the model generated by PCA and FA functions

score(data, model)

Arguments

data

the data frame used to generate a PCA or FA model.
model

the model generated by the PCA or FA functions.

Value

the data frame with component or factor scores appended.

Details

The score function adds component or factor scores to a data frame. Scores are calculated using the regression method.

See also

PCA, FA.

Examples

ratings <- read.csv("https://www.promptcloud.com/wp-content/uploads/2017/02/EFA.csv")
fit.pca <- PCA(ratings, nfactors=2, rotate="varimax")

#> 
#> Principal Components Analysis
#> Number of Factors: 2 Rotation: varimax 
#> 
#> Component Structure
#>                       PC1   PC2   h2
#> Price                0.54  0.08 0.30
#> Safety              -0.33  0.32 0.21
#> Exterior_Looks      -0.25  0.27 0.13
#> Space_comfort       -0.08  0.80 0.65
#> Technology           0.07  0.48 0.24
#> After_Sales_Service  0.24  0.60 0.42
#> Resale_Value         0.67 -0.24 0.51
#> Fuel_Type           -0.01  0.61 0.37
#> Fuel_Efficiency      0.69  0.30 0.56
#> Color                0.56 -0.04 0.32
#> Maintenance          0.74  0.05 0.55
#> Test_drive           0.19  0.41 0.20
#> Product_reviews      0.48  0.34 0.34
#> Testimonials        -0.06  0.33 0.11
#> 
#>                    PC1  PC2
#> Variance          2.59 2.33
#> Var Explained     0.19 0.17
#> Cum Var Explained 0.19 0.35
newdf <- score(ratings, fit.pca)
head(newdf)

#>   Price Safety Exterior_Looks Space_comfort Technology After_Sales_Service
#> 1     4      4              5             4          3                   4
#> 2     3      5              3             3          4                   4
#> 3     4      4              3             4          5                   5
#> 4     4      4              4             3          3                   4
#> 5     5      5              4             4          5                   4
#> 6     4      4              5             3          4                   5
#>   Resale_Value Fuel_Type Fuel_Efficiency Color Maintenance Test_drive
#> 1            5         4               4     2           4          2
#> 2            3         4               3     4           3          2
#> 3            5         4               5     4           5          4
#> 4            5         5               4     4           4          2
#> 5            5         3               4     5           5          5
#> 6            3         4               3     2           3          2
#>   Product_reviews Testimonials        PC1         PC2
#> 1               4            3 -0.3494735 -0.76706234
#> 2               2            2 -1.5464964 -1.58854139
#> 3               4            3  1.2949130  0.09537139
#> 4               5            3  0.5124876 -1.00007813
#> 5               5            2  1.4584077 -0.31851462
#> 6               2            3 -1.6599795 -0.85914096