Description Usage Arguments Examples
Prints the uncorrelated variables from the input dataframe
1 | auto.pca(input_data)
|
input_data |
dataframe without ID Variables & Categorical Variables |
1 |
[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
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############# Uncorrelated Variables Identified ##############
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[1] "complaints" "advance"
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