# P values from quasi-binomial generalised linear models in R

I am sorry if this has been asked elsewhere. I could find it when looking.

I am running a quasi-binomial generalised linear model (to correct for overdispersion) weighted by a count of sample size. I have simplified the model using F tests.

model2 <-update(model1,~.-A)
anova(model1,model2,test=”F”)


I now have a model with 3 interaction terms and 6 main effects (4 categorical and 2 continuous). All remaining interaction are significant using F tests as are the 2 fixed effects not bound up in interactions. No further model simplification is possible.

This is an example of how I have run my model;

  glm(X ~ (A+ B + C + D + E + F + B:F + B:C + B:D),family=quasibinomial(link=logit), weights=Y)


I understand that the R output for quasi-binomial generalised linear model gives t values and Pr(>|t|) however it seems to be convention to report p values.

I know that it is possible to get P values of terms by comparing models with and without the terms using F tests in the case of a quasi-binomial distribution (this is what I have done to simplify my model). However as I understand it, it doesn’t make sense to remove fixed terms which are bound up in the significant interactions. For example to assess the significance of fixed term F when B:F is a significant interaction.

Is it true that it’s not possible to get the p value for the original fixed terms that are bound up in the interactions, only the interaction itself?

Is there another way to test that the fixed term is significant? I understand that the t values reported are for each level of the terms rather than a single test of the term. There could be a problem with multiple testing?