greybox (version 2.0.2)

determination: Coefficients of determination

Description

Function produces coefficients of determination for the provided data

Usage

determination(xreg, bruteforce = TRUE, ...)
determ(object, ...)

Value

Function returns the vector of determination coefficients.

Arguments

xreg

Data frame or a matrix, containing the exogenous variables.

bruteforce

If TRUE, then all the variables will be used for the regression construction (sink regression). If the number of observations is smaller than the number of series, the function will use stepwise function and select only meaningful variables. So the reported values will be based on stepwise regressions for each variable.

...

Other values passed to cor function.

object

The object, for which to calculate the coefficients of determination.

Author

Ivan Svetunkov,

Details

The function calculates coefficients of determination (aka R^2) between all the provided variables. The higher the coefficient for a variable is, the higher the potential multicollinearity effect in the model with the variable will be. Coefficients of determination are connected directly to Variance Inflation Factor (VIF): VIF = 1 / (1 - determination). Arguably it is easier to interpret, because it is restricted with (0, 1) bounds. The multicollinearity can be considered as serious, when determination > 0.9 (which corresponds to VIF > 10).

The method determ can be applied to wide variety of classes, including lm, glm and alm.

See details in the vignette "Marketing analytics with greybox": vignette("maUsingGreybox","greybox")

See Also

cor, mcor, stepwise

Examples

### Simple example
xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
colnames(xreg) <- c("x1","x2","x3","Noise")
determination(xreg)