mctest (version 1.0)

omcdiag: Overall multicollinearity diagnostics

Description

Computes different overall measures of multicollinearit diagnostics for X matrix of regressors. Overall measures of collinearity detection includes Determinent of the correlation matrix (det), Farrar test of chi-square for presence of multicollinearity (Fchi), Red Indicator (Red), Sum of lambda inverse values (slambda), Theil's indicator and condition number with or without intercept term.

Usage

omcdiag(x, y, na.rm = TRUE, Inter = TRUE, detr=0.01, red=0.5, conf=0.95, theil=0.5,cn=30,...)

Arguments

x

a numeric design matrix and should contain more than one regressor

y

a numeric vector of response variable

na.rm

whether to remove missing observations

Inter

whether to include or exclude Intercept term

detr

determinant default threshold

red

red indicator default threshold

conf

confidence level of Farrar Chi-Square test

theil

Theil indicator default threshold

cn

condition number default threshold

...

extra argument(s) if used will be ignored

Value

idiags

listing of overall diagnostic measures. If method is not used all individual diagnostics will be dispalyed.

Inter

logical, if TRUE (the default value) a eigenvalues are returned with intercept term included.

x

matrix of regressors.

Details

This function detectes the existance of multicollinearity, by using different available diagnostic measure availble in literature such as Determinent of correlation matrix, Farrar test of chi-square, Red Indicator, Sum of lambda inverse values, Theil's Indicator and Condition Number.

Function also displays diagnostic measures value with the decision of either multicollinearity is detected by the diagnostics or not. The Value of 1 indicate that multicollineairty is detected and 0 indicate measure could not detect by the certain diagnostic measure.

References

Belsely, D.A., A Guide to Using the Collinearity Diagnostics. Computer science in Economics and Management, 1991. 4(1): 33--50.

Catterjee, S. and A. Hadi, Regression Analysis by Example. 4 ed. 2006, Hoboken, New York; John Willey and Sons Inc.

Belsley, David. A., Edwin. Kuh, and Roy. E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley and Sons.

Greene, William H. 2000. Econometric Analysis. 4th Ed. Upper Saddle River, NJ: Prentice--Hall.

Imdadullah, M. Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R, Unpublished Ph.D thesis, Department of Statistics, Bahauddin Zakriya University, Multan, Pakistan

See Also

imcdiag

Examples

## Hald Cement data
data(Hald)
x<-Hald[,-1]
y<-Hald[,1]

## all oveall diagnostic measures and eigenvalues with intercept
od<-omcdiag(x,y)

## all oveall diagnostic measures and eigenvalues without intercept
omcdiag(x,y, Inter=FALSE)

## all oveall diagnostic measures and eigenvalues with intercept
## with different determinant and confidence level threshold

omcdiag(x,y, detr=0.001, conf=0.99)

## returns the determinant of correlation matrix |X'X|
omcdiag(x,y)[1]