I have created two generalised linear models as follows:
glm1 <-glm(Y ~ X1 + X2 + X3, family=binomial(link=logit))
glm2 <-glm(Y ~ X1 + X2, family=bino
The solution is to use:
glm1 <-glm(Y ~ X1 + X2 + X3, family = binomial(link = logit), na.action = na.exclude)
glm2 <-glm(Y ~ X1 + X2, family = binomial(link = logit), na.action = na.exclude)
anova(glm2,glm1)
This will make R include the cases with missing data (NA) in the fitted model. This ensures that datasets are identical across different fit models no matter how missing data is distributed.