gvif.overglm {glmtoolbox}

Generalized Variance Inflation Factor for alternatives to the Poisson and Binomial Regression Models under the presence of Overdispersion

Description

Computes the generalized variance inflation factor (GVIF) for regression models based on the negative binomial, beta-binomial, and random-clumped binomial distributions, which are alternatives to the Poisson and binomial regression models under the presence of overdispersion. The GVIF is aimed to identify collinearity problems.

Usage

## S3 method for class 'overglm'
gvif(model, verbose = TRUE, ...)

Arguments

model

an object of class overglm.

verbose

an (optional) logical switch indicating if should the report of results be printed. As default, verbose is set to TRUE.

...

further arguments passed to or from other methods.

Details

If the number of degrees of freedom is 1 then the GVIF reduces to the Variance Inflation Factor (VIF).

Value

A matrix with so many rows as effects in the model and the following columns:

GVIF	the values of GVIF,
df	the number of degrees of freedom,
GVIF^(1/(2*df))	the values of GVIF^{1/2 df},

References

Fox J., Monette G. (1992) Generalized collinearity diagnostics, JASA 87, 178–183.

See Also

gvif.lm, gvif.glm

Examples

###### Example 1: Self diagnozed ear infections in swimmers
data(swimmers)
fit1 <- overglm(infections ~ frequency + location, family="nb1(log)", data=swimmers)
gvif(fit1)

###### Example 2: Article production by graduate students in biochemistry PhD programs
bioChemists <- pscl::bioChemists
fit2 <- overglm(art ~ fem + kid5 + ment, family="nb1(log)", data = bioChemists)
gvif(fit2)

###### Example 3: Agents to stimulate cellular differentiation
data(cellular)
fit3 <- overglm(cbind(cells,200-cells) ~ tnf + ifn, family="bb(logit)", data=cellular)
gvif(fit3)