Function estimates Variance Inflation Factors (VIFs), measures of collinearity in a linear model. The VIF provides a measure of how much the variance of an estimated regression coefficient is increased because of collinearity. Collinearity is present when there is a high correlation between the independent, predictor, variables in a model, i.e. they tell the same ‘story’. Where collinearity exists it is often best to remove predictor variables with high VIFs from the model.
any additional parameters.
A (structure) table of Variable Inflation Factors for the predictor variables.
VIFs >5 are indicative of collinearity, and the information conveyed in that variable is also in the subset of the remaining variables.
W.N. Venables,function shared on S-News, October 21, 2002
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## Make test data available data(sind) attach(sind) ## Model 1 sind.1 <- lm(log(Zn) ~ Fe + log(Mn) + log(Cu) + log(Cd)) summary(sind.1) gx.lm.vif(sind.1) ## Model 2 sind.2 <- lm(log(Zn) ~ Fe + log(Mn)) summary(sind.2) gx.lm.vif(sind.2) AIC(sind.1, sind.2) ## Model 3 sind.3 <- lm(log(Zn) ~ log(Mn) + log(Cu)) summary(sind.3) gx.lm.vif(sind.3) AIC(sind.1, sind.2, sind.3) ## Clean-up and detach test data rm(sind.1) rm(sind.2) rm(sind.3) detach(sind)
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