dominanceanalysis (version 2.1.0)

da.glm.fit: 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, ...)

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)

Arguments

original.model

Original fitted model

newdata

Data used in update statement

...

ignored

Details

Check daRawResults.

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)