Collinearity detection in a linear regression model
The function collects all existing measures to detect worrying multicollinearity in the package multiCol.
multiCol(X, dummy = FALSE, pos = NULL)
X |
A numeric design matrix that should contain more than one regressor (intercept included). |
dummy |
A logical value that indicates if there are dummy variables in the design matrix |
pos |
A numeric vector that indicates the position of the dummy variables, if these exist, in the design matrix |
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 |
Prop |
Proportion of ones in the dummy variables. |
R |
Matrix correlation of the quantitative variables in |
detR |
Determinant of the matrix correlation of the quantitative variables in |
VIF |
Variance Inflation Factors of the quantitative variables in |
CN |
Condition Number of |
ki |
Stewart's index of the quantitative variables in |
For more detail, see the help of the functions in See Also.
R. Salmerón (romansg@ugr.es) and C. García (cbgarcia@ugr.es).
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)]) multiCol(theil.X, TRUE, pos = 4) # 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]) multiCol(KG.X) # random x1 = array(1,25) x2 = rnorm(25,100,1) x = cbind(x1,x2) head(x) multiCol(x) # random x1 = array(1,25) x2 = sample(cbind(array(1,25),array(0,25)),25) x = cbind(x1,x2) head(x) multiCol(x, TRUE)
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