Vif for logistic regression in r

It is a "pseudo" R-square because it is unlike the R-square found in OLS regression, where R-square measures the proportion of variance explained by the model. The pseudo R-square is not measured in terms of variance, since in logistic regression the variance is fixed as the variance of the standard logistic distribution.

Second, sample size requirements for logistic regression are complex –Peduzzi et al.’s Monte Carlo study of events per variable (EPV) for binary logistic regression found 10 EPV to be the point at which few adverse statistical effects would be observed (Peduzzi et al., 1996). Our model for naloxone dispensing had slightly lower EPV than.

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To calculate the VIFs, all independent variables become a dependent variable. Each model produces an R-squared value indicating the percentage of the variance in the individual IV that the set of IVs explains. Consequently, higher R-squared values indicate higher degrees of multicollinearity. VIF calculations use these R-squared values.

The answer a statistician would give to this question is "logistic regression *is not* a linear model. "A statistician calls a model "linear" if the mean of the response is a linear function of the parameter, and this is clearly violated for logistic regression. Logistic regression is a *generalized linear model*.

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.