View source: R/daFitFunctions.r
da.glm.fit | R Documentation |
These functions are only available for logistic regression models and are based on the work of Azen and Traxel (2009).
da.glm.fit(original.model, newdata = NULL, ...)
original.model |
Original fitted model |
newdata |
Data used in update statement |
... |
ignored |
Check daRawResults.
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)
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.
Other fit indices:
da.betareg.fit()
,
da.clm.fit()
,
da.dynlm.fit()
,
da.lm.fit()
,
da.lmWithCov.fit()
,
da.lmerMod.fit()
,
da.mlmWithCov.fit()
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)
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