Check the degree of multicollinearity present in the dataset

Description

Degree of multicollinearity present in the dataset can be determined by using two type of indicators, called VIF and Condition Number.

Usage

1

Arguments

formula

in this section interested model should be given. This should be given as a formula.

data

an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.

na.action

if the dataset contain NA values, then na.action indicate what should happen to those NA values.

...

currently disregarded.

Details

If all the values of VIF > 10 implies that multicollinearity present.
If condition number < 10 ; There is not multicollinearity.
30 < condition number < 100 ; There is a multicollinearity.
condition number >100 ; Severe multicollinearity.

Value

checkm returns the values of two multicllinearity indicators VIF and Condition Number.

Author(s)

P.Wijekoon, A.Dissanayake

Examples

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## Portland cement data set is used.
data(pcd)
checkm(Y~X1+X2+X3+X4,data=pcd)

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