multiColLM: All detection measures

In multiColl: Collinearity Detection in a Multiple Linear Regression Model

All detection measures

Description

The functions collects all the measure to detect near worrying multicollinearity existing in the package multiCol. In adddition, it provides the estimations by ordinary least squares (OLS) of the multiple linear regession model and the variations in the estimations of the coefficients as a consequence of changes in the observed data.

Usage

multiColLM(y, X, dummy=FALSE, pos1=NULL, n, mu, dv, tol=0.01, pos2=NULL, graf=TRUE)

Arguments

y	Observations of the dependent variable of the model.
X	Observations of the independent variables of the model (intercept included).
dummy	A logical value that indicates if there are dummy variables in the design matrix X. By default dummy=FALSE.
pos1	A numeric vector that indicates the position of the dummy variables, if these exist, in the design matrix X. By default pos=NULL.
n	Number of times that the perturbation is performed.
mu	Any real number.
dv	Any real positive number.
tol	A value between 0 and 1. By default tol=0.01.
pos2	A numeric vector that indicates the position of the independent variables to disturb once you eliminate in data the dependent variable and the intercept. By default pos=NULL.
graf	A logical value that indicates if the dispersion diagram of the variation coefficients of the independent variables is represented against its variance inflation factor. By default graf=TRUE.

Value

The estimation by OLS of the linear regression model.

Percentiles 2.5 and 97.5 of the proportion of the variations in the estimations of the coefficients obtained from a perturbation of tol% in the quantitative variables of X.

If X contains two independent variables (intercept included) see SLM function.

If X contains more than two independent variables (intercept included):

CV	Coeficients of variation of quantitative variables in X.
Prop	Proportion of ones in the dummy variables.
R	Matrix correlation of the quantitative variables in X.
detR	Determinant of the matrix correlation of the quantitative variables in X.
VIF	Variance Inflation Factors of the quantitative variables in X.
CN	Condition Number of X.
ki	Stewart's index of the quantitative variables in X.

Note

For more detail, see the help of the functions in See Also.

Author(s)

R. Salmerón ([e-mail]) and C. García ([e-mail]).

References

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

SLM, CV, PROPs, RdetR, VIF, CN, ki, multiCol, perturb, perturb.n.

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)])
head(theil.X)
multiColLM(theil[,2], theil.X, dummy = TRUE, pos1 = 4, 5, 5, 5, tol=0.01, pos2 = 1:2)

# 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])
head(KG.X)
multiColLM(KG[,1], KG.X, n = 500, mu = 5, dv = 5, tol=0.01, pos2 = 1:3)

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