This article talks about how we can correct multicollinearity problem with correlation matrix.
In caret package, there is a function called findCorrelation that helps to identify correlated variables.
How it works -
The absolute values of pair-wise correlations are considered. If some variables have a high correlation, the function looks at the mean absolute correlation of each variable and keeps only the variable with the smallest mean absolute correlation and remove the larger absolute correlation.Example - Correlation Matrix
| X1 | X2 | X3 | X4 | X5 | |
|---|---|---|---|---|---|
| X1 | 1.00 | 0.95 | 0.89 | 0.85 | 0.10 |
| X2 | 0.95 | 1.00 | 0.85 | 0.81 | 0.09 |
| X3 | 0.89 | 0.85 | 1.00 | 0.78 | 0.10 |
| X4 | 0.85 | 0.81 | 0.78 | 1.00 | 0.09 |
| X5 | 0.10 | 0.09 | 0.10 | 0.09 | 1.00 |
Variables to remove from X1 to X4 cluster - "X1" "X2" "X3" as they have larger mean absolute correlation than X4.
R Code -
# Identifying numeric variables
numericData <- dat2[sapply(dat2, is.numeric)]
# Calculate correlation matrix
descrCor <- cor(numericData)
# find attributes that are highly corrected
highlyCorrelated <- findCorrelation(descrCor, cutoff=0.7)
Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.
HI Deepanshu, I was trying to understand your explanation and I wanted to ask you what do you mean with mean absolute correlation?
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