GEE on R-someone can explain to me the SFV code

Question

model0 <- glm(stable  ~ Sex+Age+Height+Weight+Surface+Vision,binomial,ctsib) 
summary(model) 

model1 <- glm(stable  ~ Sex+Age+Height+Weight+Surface+Vision+factor(Subject),binomial,ctsib) 
summary(model1)
anova(model0,model1,test="Chisq") 

I don’t understand why we compare these two models. stable is divided into 2 categories.

I also don’t understand what the "brglm" in the code means

modbr <- brglm (stable  ~ Sex+Age+Height+Weight+Surface+Vision+factor (Subject), data=ctsib) 
summary(modbr) 

Answer 1

The objects model0 and model1 are generalized linear model (MLG) adjustments to set data ctsib. The argument binomial assumes that the response variable in this case is binary, success and failure. Also, binomial determines the connection function to be used in this MLG.

model1 is the so-called complete model, which considers all predictive variables of interest. model0 is the reduced model, where one of the predictive variables is not considered. In this specific case, the variable Subject was out of tune on model0. All predictive variables of this problem were considered as fixed effects, although in general, Subject be considered as a random effect on the vast majority of these problems.

anova is the function that compares the models adjusted by model0 and model1, in order to verify whether there is a significant difference between them. The test used in this case is the likelihood ratio test. The result of this test will tell if the variable Subject is required to adjust the data in this example.

I never used the function brglm, but your help says the following:

Fits binomial-Response Glms using the bias-reduction method developed in Firth (1993) for the Removal of the Leading (O(n 1)) term from the Asymptotic Expansion of the bias of the Maximum likelihood Stimator. Fitting is performed using pseudo-data representations, as described in Kosmidis (2007, Chapter 5). For estimation in binomial-Sponse Glms, the bias-reduction method is an Improvement over Traditional Maximum likelihood because:

• the bias-Reduced Estimator is Second-order unbiased and has smaller Variance than the Maximum likelihood Estimator and
• the resultant estimates and their corresponding standard errors are Always Finite while the Maximum likelihood estimates can be Infinite (in situations Where complete or quasi Separation occurs).

That is, it is just another method of making an adjustment of a GLM to data with binomial response.