auto.pca: Automatic Variable Reduction Using Principal Component...

In auto.pca: Automatic Variable Reduction Using Principal Component Analysis

Description

Prints the uncorrelated variables from the input dataframe

Usage

auto.pca(input_data)

Arguments

input_data

dataframe without ID Variables & Categorical Variables

Examples

auto.pca(attitude)

Example output

[1] "Number of Factors identified based on eigen Value greater than 1 : 2 Factors"
Principal Components Analysis
Call: principal(r = input_data, nfactors = nfactors, rotate = "varimax", 
    scores = T)
Standardized loadings (pattern matrix) based upon correlation matrix
            RC1   RC2   h2   u2 com
rating     0.90 -0.01 0.81 0.19 1.0
complaints 0.92  0.07 0.85 0.15 1.0
privileges 0.65  0.22 0.48 0.52 1.2
learning   0.76  0.34 0.68 0.32 1.4
raises     0.68  0.57 0.78 0.22 1.9
critical   0.02  0.74 0.54 0.46 1.0
advance    0.24  0.81 0.71 0.29 1.2

                       RC1  RC2
SS loadings           3.17 1.69
Proportion Var        0.45 0.24
Cumulative Var        0.45 0.69
Proportion Explained  0.65 0.35
Cumulative Proportion 0.65 1.00

Mean item complexity =  1.2
Test of the hypothesis that 2 components are sufficient.

The root mean square of the residuals (RMSR) is  0.11 
 with the empirical chi square  14.83  with prob <  0.063 

Fit based upon off diagonal values = 0.95
 ################################################################
 #############  Uncorrelated Variables Identified  ##############
 ################################################################
                                                                  
[1] "complaints" "advance"   

auto.pca documentation built on May 2, 2019, 3:29 p.m.