score calculates principal component or factor scores
based on the model generated by PCA and FA
functions
score(data, model)the data frame used to generate a PCA or FA model.
the model generated by the PCA or FA functions.
the data frame with component or factor scores appended.
The score function adds component or factor scores to a data frame. Scores
are calculated using the regression method.
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