CN {multiColl} | R Documentation |
This function returns the Condition Number (CN) of the independent variables in a multiple linear regression.
CN(X)
X |
A numeric design matrix that should contain more than one regressor (intercept included). |
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
.
The condition number of a matrix, that is, the maximum condition index.
Values of CN between 20 and 30 indicate near moderate multicollinearity while values higher than 30 indicate near worrying collinearity.
R. Salmeron (romansg@ugr.es) and C. Garcia (cbgarcia@ugr.es).
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.
# 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)