Extract the results for individuals/variables - PCA

Extract all the results (coordinates, squared cosines, and 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

Usage

get_pca(res.pca, element = c("var", "ind"))

get_pca_ind(res.pca, ...)

get_pca_var(res.pca)

Arguments

res.pca: an object of class PCA [FactoMineR]; prcomp or princomp [stats]; factoextra_pca; pca, dudi, between, or within [ade4]; or expoOutput/epPCA [ExPosition].
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. For an adapted recipe/workflow object this is NULL when the metric cannot be recovered. For a rank-truncated prcomp object, individual cos2 is the quality within the retained component subspace because the discarded row inertia is not stored in the fitted object.
contrib: contributions of the individuals/variables; contributions to each nonzero-inertia axis sum to 100 percent, while a zero-inertia axis contains zeros
cor: loading-times-component-standard-deviation coordinates for PCA objects from stats; these equal variable-component correlations when the input variables were standardized. Returned for variables; for an adapted recipe/workflow object it is NULL when correlations cannot be recovered.

References

https://www.datanovia.com/learn/

Author

Alboukadel Kassambara alboukadel.kassambara@gmail.com

Examples

# \donttest{
# Principal Component Analysis
# +++++++++++++++++++++++++++++
 data(iris)
 res.pca <- prcomp(iris[, -5],  scale = TRUE)
 # Extract the results for individuals
 ind <- get_pca_ind(res.pca)
 print(ind)
#> Principal Component Analysis Results for individuals
#>  ===================================================
#>   Name       Description                       
#> 1 "$coord"   "Coordinates for the individuals" 
#> 2 "$cos2"    "Cos2 for the individuals"        
#> 3 "$contrib" "contributions of the individuals"
 head(ind$coord) # coordinates of individuals
#>       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
 head(ind$cos2) # cos2 of individuals
#>       Dim.1      Dim.2        Dim.3        Dim.4
#> 1 0.9539975 0.04286032 0.0030335249 1.086460e-04
#> 2 0.8927725 0.09369248 0.0113475382 2.187482e-03
#> 3 0.9790410 0.02047578 0.0003422122 1.410446e-04
#> 4 0.9346682 0.06308947 0.0014732682 7.690193e-04
#> 5 0.9315095 0.06823959 0.0000403979 2.104697e-04
#> 6 0.6600989 0.33978301 0.0001114335 6.690714e-06
 head(ind$contrib) # contributions of individuals
#>       Dim.1      Dim.2       Dim.3       Dim.4
#> 1 1.1715796 0.16806554 0.074085470 0.018798188
#> 2 0.9891845 0.33146674 0.250034006 0.341474919
#> 3 1.2768164 0.08526419 0.008875320 0.025915633
#> 4 1.2077372 0.26029781 0.037858004 0.140000650
#> 5 1.3046313 0.30516562 0.001125175 0.041530572
#> 6 0.9841236 1.61748779 0.003303827 0.001405371
 
 # Extract the results for variables
 var <- get_pca_var(res.pca)
 print(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"               
 head(var$coord) # coordinates of variables
#>                   Dim.1       Dim.2       Dim.3       Dim.4
#> Sepal.Length  0.8901688 -0.36082989  0.27565767  0.03760602
#> Sepal.Width  -0.4601427 -0.88271627 -0.09361987 -0.01777631
#> Petal.Length  0.9915552 -0.02341519 -0.05444699 -0.11534978
#> Petal.Width   0.9649790 -0.06399985 -0.24298265  0.07535950
 head(var$cos2) # cos2 of variables
#>                  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
 head(var$contrib) # contributions of variables
#>                  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
 
 # 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"               
 # }