R: Calculation of Condition Indices for Linear Regression

cond.index {klaR} | R Documentation

## Calculation of Condition Indices for Linear Regression

### Description

Diagnosis of collinearity in X

### Usage

    cond.index(formula, data, ...)

### Arguments

`formula` | formula of the form ‘`groups ~ x1 + x2 + ...`’
`data` | data frame (or matrix) containing the explanatory variables
`...` | further arguments to be passed to `lm`

### Details

Collinearities can inflate the variance of the estimated regression coefficients and numerical stability. The condition indices are calculated by the eigenvalues of the crossproduct matrix of the scaled but uncentered explanatory variables. Indices > 30 may indicate collinearity.

### Value

A vector of the condition indices.

### Author(s)

Andrea Preusser, Karsten Luebke (karsten.luebke@fom.de)

### References

Belsley, D. , Kuh, E. and Welsch, R. E. (1979), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley (New York)

### See Also

`stepclass`, `manova`

### Examples

    data(Boston)
    condition_medv <- cond.index(medv ~ ., data = Boston)
    condition_medv

[Package klaR version 0.6-12 Index]