Can you use VIF for logistic regression?

Can you use VIF for logistic regression?

Like tolerance there is no formal cutoff value to use with VIF for determining the presence of multicollinearity. Values of VIF exceeding 10 are often regarded as indicating multicollinearity, but in weaker models, which is often the case in logistic regression; values above 2.5 may be a cause for concern.

What is VIF in logistic regression?

One way to measure multicollinearity is the variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated. A VIF between 5 and 10 indicates high correlation that may be problematic.

How do you check for multicollinearity in logistic regression in SAS?

There are no such command in PROC LOGISTIC to check multicollinearity . 1) you can use CORRB option to check the correlation between two variables. 2) Change your binary variable Y into 0 1 (yes->1 , no->0) and use PROC REG + VIF/COLLIN .

Does multicollinearity effects logistic regression?

Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression model are highly correlated. Multicollinearity can cause unstable estimates and inac- curate variances which affects confidence intervals and hypothesis tests.

What is VIF in SAS?

vif stands for variance inflation factor. As a rule of thumb, a variable whose VIF values is greater than 10 may merit further investigation. Tolerance, defined as 1/VIF, is used by many researchers to check on the degree of collinearity. A tolerance value lower than 0.1 is comparable to a VIF of 10.

How do you predict values in SAS?

You can specify the predicted value either by using a SAS programming expression that involves the input data set variables and parameters or by using the keyword MEAN. If you specify the keyword MEAN, the predicted mean value for the distribution specified in the MODEL statement is used.

How much collinearity is too much?

A rule of thumb regarding multicollinearity is that you have too much when the VIF is greater than 10 (this is probably because we have 10 fingers, so take such rules of thumb for what they’re worth). The implication would be that you have too much collinearity between two variables if r≥. 95.

What does the VIF option in the model statement provide?

The VIF option in the MODEL statement provides the variance inflation factors (VIF). These factors measure the inflation in the variances of the parameter estimates due to collinearities that exist among the regressor (independent) variables. There are no formal criteria for deciding if a VIF is large enough to affect the predicted values.

How to check multicollinearity of two variables using proc logistic?

2) Change your binary variable Y into 0 1 (yes->1 , no->0) and use PROC REG + VIF/COLLIN . There are no such command in PROC LOGISTIC to check multicollinearity . 1) you can use CORRB option to check the correlation between two variables.

How to decide if a VIF is large enough to affect Tol?

There are no formal criteria for deciding if a VIF is large enough to affect the predicted values. The TOL option requests the tolerance values for the parameter estimates. The tolerance is defined as .