CNs: Condition Number with and without intercept

In multiColl: Collinearity detection in a multiple linear regression model

Description

This function returns the Condition Number (CN) of the independent variables of a multiple linear model considering the intercept and without considering it. It also returns the increase produced by going from not taking into account the intercept to having it.

Usage

CNs(X)

Arguments

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

Value

CN1	Condition Number without intercept.
CN2	Condition Number with intercept.
increment	Increase (in percentage) in the CN from CN1 to CN2.

Author(s)

R. Salmer<f3>n ([e-mail]) and C. Garc<ed>a ([e-mail]).

References

D. A. Belsley (1991). Conditioning diagnostics: collinearity and weak data 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, CN.

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)])
CNs(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])
CNs(KG.X)

multiColl documentation built on May 2, 2019, 4:53 p.m.