da.glm.fit {dominanceanalysis}

Provides fit indices for GLM models.

Description

These functions are only available for logistic regression models and are based on the work of Azen and Traxel (2009).

Usage

da.glm.fit(original.model, newdata = NULL, ...)

Arguments

original.model

Original fitted model

newdata

Data used in update statement

...

ignored

Details

Check daRawResults.

Value

A function described by using-fit-indices. You could retrieve the following indices:

r2.m

McFadden(1974)

r2.cs

Cox and Snell(1989). Use with caution, because don't have 1 as upper bound

r2.n

Nagelkerke(1991), that corrects the upper bound of Cox and Snell(1989) index

r2.e

Estrella(1998)

References

• Azen, R. and Traxel, N. (2009). Using Dominance Analysis to Determine Predictor Importance in Logistic Regression. Journal of Educational and Behavioral Statistics, 34 (3), 319-347. doi:10.3102/1076998609332754.

• Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692. doi:10.1093/biomet/78.3.691.

• Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London, UK: Chapman and Hall.

• Estrella, A. (1998). A new measure of fit for equations with dichotomous dependent variables. Journal of Business & Economic Statistics, 16(2), 198-205. doi: 10.1080/07350015.1998.10524753

• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 104-142). New York, NY: Academic Press.

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Examples

x1<-rnorm(1000)
x2<-rnorm(1000)
x3<-rnorm(1000)
y<-factor(runif(1000) > exp(x1+x2+x3)/(1+exp(x1+x2+x3)))
df.1=data.frame(x1,x2,x3,y)
glm.1<-glm(y~x1+x2+x3,data=df.1,family=binomial)
da.glm.fit(original.model=glm.1)("names")
da.glm.fit(original.model=glm.1)(y~x1)