I ran into exactly the same question and tried to work my way through. See my detailed answer below.
First of all, I found 4 options producing similar VIF values in R:
• corvif command from the AED package,
• vif command from the car package,
• vif command from the rms package,
• vif command from the DAAG package.
Using these commands on a set of predictors not including any factors / categorical variables or polynomial terms is strait forward. All three commands produce the same numerical output even though the corvif command from the AED package labels the results as GVIF.
However, typically, GVIF only comes into play for factors and polynomial variables. Variables which require more than 1 coefficient and thus more than 1 degree of freedom are typically evaluated using the GVIF. For one-coefficient terms VIF equals GVIF.
Thus, you may apply standard rules of thumb on whether collinearity may be a problem, such as a 3,5 or 10 threshold. However, some caution could (should) be applied (see: http://www.nkd-group.com/ghdash/mba555/PDF/VIF%20article.pdf).
In case of multi-coefficient terms, as for eg. categorical predictors the 4 packages produce different outputs. The vif command from the rms and DAAG packages produces VIF values, whereas the other 2 produce GVIF values.
Let us have a look at VIF values from the rms and DAAG package first:
TNAP ICE RegB RegC RegD RegE
1.994 2.195 3.074 3.435 2.907 2.680
TNAP and ICE are continuous predictors and Reg is a categorical variable presented by the dummies RegB to RegE. In this case RegA is the baseline. All VIF values are rather moderate and usually nothing to worry about. The problem with this result is, that it is affected by the baseline of the categorical variable. In order to be sure of not having a VIF value above an acceptable level, it would be necessary to redo this analysis for every level of the categorical variable being the baseline. In this case 5 times.
Applying the corvif command from the AED package or vif command from the car package, GVIF values are produced:
| GVIF | Df | GVIF^(1/2Df) |
TNAP | 1.993964 | 1 | 1.412078 |
ICE | 2.195035 | 1 | 1.481565 |
Reg | 55.511089 | 5 | 1.494301 |
The GVIF is calculated for sets of related regressors such as a for a set of dummy regressors. For the 2 continuous variables TNAP and ICE this is the same as the VIF values before. For the categorical variable Reg, we now get one very high GVIF value, even though the VIF values for the single levels of the categorical variable were all moderate (as shown above).
However, the interpretation is different. For the 2 continuous variables, GVIFˆ(1/(2*Df)) (which is basically the square root of the VIF/GVIF value as DF=1) is the proportional change of the standard error and confidence interval of their coefficients due to the level of collinearity. The GVIFˆ(1/(2*Df)) value of the categorical variable is a similar measure for the reduction in precision of the coefficients' estimation due to collinearity (even though not ready for quoting also look at http://socserv2.socsci.mcmaster.ca/jfox/papers/linear-models-problems.pdf).
If we then simply apply the same standard rules of thumb for GVIFˆ(1/(2*Df)) values as recommended in literature for the VIF, we simply need to square GVIFˆ(1/(2*Df)).
Reading through all the forum posts, short notes in the web and scientific papers, it seems to me that there is quite some confusion going on. In peer reviewed papers, I found the values for GVIFˆ(1/(2*Df)) ignored and the same standard rules suggested for the VIF are applied to the GVIF values. In another paper, GVIF values of close to 100 are excepted because of a reasonably small GVIFˆ(1/(2*Df)) (due to a high DF). The rule of GVIF^2(1/(2*Df)) <2 is applied in some publications, which would equal a VIF of 4 for one-coefficient variables.