get_pca: Extract the results for individuals/variables in Principal Component Analysis - R software and data mining

Description
Install and load factoextra
Usage
Arguments
Value
Examples
Infos

Description

Extract all the results (coordinates, squared cosine, contributions) for the active individuals/variables from Principal Component Analysis (PCA) outputs.

get_pca(): Extract the results for variables and individuals
get_pca_ind(): Extract the results for individuals only
get_pca_var(): Extract the results for variables only

These functions are included in factoextra package.

Install and load factoextra

The package devtools is required for the installation as factoextra is hosted on github.

# install.packages("devtools")
library("devtools")
install_github("kassambara/factoextra")

Load factoextra :

library("factoextra")

Usage

# Extract the results for variables and individuals
get_pca(res.pca, element = c("var", "ind"))
# Extract the results for individuals only
get_pca_ind(res.pca, ...)
# Extract the results for variables only
get_pca_var(res.pca)

Arguments

Argument	Description
res.pca	an object of class PCA [FactoMineR]; prcomp and princomp [stats]; pca, dudi [adea4].
element	the element to subset from the output. Allowed values are “var” (for active variables) or “ind” (for active individuals).
…	not used

Value

A list of matrices containing all the results for the active individuals/variables including:

coord: coordinates for the individuals/variables
cos2: cos2 for the individuals/variables
contrib: contributions of the individuals/variables

Examples

Principal component analysis

A principal component analysis (PCA) is performed using the built-in R function prcomp() and iris data:

data(iris)
head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

# The variable Species (index = 5) is removed
# before PCA analysis
res.pca <- prcomp(iris[, -5],  scale = TRUE)

Extract the results for variables

var <- get_pca_var(res.pca)
var

Principal Component Analysis Results for variables
 ===================================================
  Name       Description                                    
1 "$coord"   "Coordinates for the variables"                
2 "$cor"     "Correlations between variables and dimensions"
3 "$cos2"    "Cos2 for the variables"                       
4 "$contrib" "contributions of the variables"

# Coordinates of variables
head(var$coord)

                  Dim.1       Dim.2       Dim.3       Dim.4
Sepal.Length  0.8901688 -0.36082989  0.27565767  0.03760602
Sepal.Width  -0.4601427 -0.88271627 -0.09361987 -0.01777631
Petal.Length  0.9915552 -0.02341519 -0.05444699 -0.11534978
Petal.Width   0.9649790 -0.06399985 -0.24298265  0.07535950

# Cos2 of variables
head(var$cos2)

                 Dim.1       Dim.2       Dim.3        Dim.4
Sepal.Length 0.7924004 0.130198208 0.075987149 0.0014142127
Sepal.Width  0.2117313 0.779188012 0.008764681 0.0003159971
Petal.Length 0.9831817 0.000548271 0.002964475 0.0133055723
Petal.Width  0.9311844 0.004095980 0.059040571 0.0056790544

# Contribution of variables
head(var$contrib)

                 Dim.1       Dim.2     Dim.3     Dim.4
Sepal.Length 27.150969 14.24440565 51.777574  6.827052
Sepal.Width   7.254804 85.24748749  5.972245  1.525463
Petal.Length 33.687936  0.05998389  2.019990 64.232089
Petal.Width  31.906291  0.44812296 40.230191 27.415396

Extract the results for individuals

ind <- get_pca_ind(res.pca)
ind

Principal Component Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"

# Coordinates of individuals
head(ind$coord)

      Dim.1      Dim.2       Dim.3        Dim.4
1 -2.257141 -0.4784238  0.12727962  0.024087508
2 -2.074013  0.6718827  0.23382552  0.102662845
3 -2.356335  0.3407664 -0.04405390  0.028282305
4 -2.291707  0.5953999 -0.09098530 -0.065735340
5 -2.381863 -0.6446757 -0.01568565 -0.035802870
6 -2.068701 -1.4842053 -0.02687825  0.006586116

# Cos2 of individuals
head(ind$cos2)

      Dim.1      Dim.2        Dim.3        Dim.4
1 0.9539975 0.04286032 0.0030335249 1.086460e-04
2 0.8927725 0.09369248 0.0113475382 2.187482e-03
3 0.9790410 0.02047578 0.0003422122 1.410446e-04
4 0.9346682 0.06308947 0.0014732682 7.690193e-04
5 0.9315095 0.06823959 0.0000403979 2.104697e-04
6 0.6600989 0.33978301 0.0001114335 6.690714e-06

# Contribution of individuals
head(ind$contrib)

      Dim.1      Dim.2       Dim.3       Dim.4
1 1.1637691 0.16694510 0.073591567 0.018672867
2 0.9825900 0.32925696 0.248367113 0.339198420
3 1.2683043 0.08469576 0.008816151 0.025742863
4 1.1996857 0.25856249 0.037605617 0.139067312
5 1.2959338 0.30313118 0.001117674 0.041253702
6 0.9775628 1.60670454 0.003281801 0.001396002

get_pca

# You can also use the function get_pca()
get_pca(res.pca, "ind") # Results for individuals

Principal Component Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"

get_pca(res.pca, "var") # Results for variable categories

Principal Component Analysis Results for variables
 ===================================================
  Name       Description                                    
1 "$coord"   "Coordinates for the variables"                
2 "$cor"     "Correlations between variables and dimensions"
3 "$cos2"    "Cos2 for the variables"                       
4 "$contrib" "contributions of the variables"

Infos

This analysis has been performed using R software (ver. 3.1.2) and factoextra (ver. 1.0.2)

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