CN: Condition Number

In multiColl: Collinearity Detection in a Multiple Linear Regression Model

Condition Number

Description

This function returns the Condition Number (CN) of the independent variables in a multiple linear regression.

Usage

CN(X)

Arguments

X	A numeric design matrix that should contain more than one regressor (intercept included).

Details

Due to the CN takes into account the intercept, it allows to detect not only the essential but also the non-essential collinearity. It also allows to consider non-quantitative independent variables.

Its calculation is obtained from the function lu, contrary to the function kappa.

Value

The condition number of a matrix, that is, the maximum condition index.

Note

Values of CN between 20 and 30 indicate near moderate multicollinearity while values higher than 30 indicate near worrying collinearity.

Author(s)

R. Salmeron ([e-mail]) and C. Garcia ([e-mail]).

References

D. A. Belsley (1991). Conditioning diagnostics: collinearity and weak dara in regression. John Wiley & Sons, New York.

L. R. Klein and A.S. Goldberger (1964). An economic model of the United States, 1929-1952. North Holland Publishing Company, Amsterdan.

H. Theil (1971). Principles of Econometrics. John Wiley & Sons, New York.

See Also

lu, kappa, CNs.

Examples

# Henri Theil's textile consumption data modified
data(theil)
head(theil)
cte = array(1,length(theil[,2]))
theil.X = cbind(cte,theil[,-(1:2)])
CN(theil.X)

# Klein and Goldberger data on consumption and wage income
data(KG)
head(KG)
cte = array(1,length(KG[,1]))
KG.X = cbind(cte,KG[,-1])
CN(KG.X)

multiColl documentation built on July 21, 2022, 9:06 a.m.