After performing a GLM in R, I get back a set of p-values. Does anybody know whether these p-values still need to be adjusted using `p.adjust`

or similar to account for repeated hypothesis testing or is this already accounted for by the GLM? If the values need to be corrected, which adjustment method(s) are suitable/recommended?

Thanks, that's good information! But if I only have a

singleglm() call for a model like Y ~ X1 + X2 + X3, then would the resulting p-values for X1, X2, X3 also have to be adjusted?No. What you would get then is a regression table containing the different p-values for your X1 to X3 factors. A standard approach is to construct a big model, containing all the factors of interest and their interactions. Then, from the table, you spot the biggest p-value. If it is above your criterion (eg: 0.05), you remove that factor or interaction. You repeat the process until all factors are significant. What you have then is the minimal model that explains the variation found in your data. Cheers!

What about having several

`glm()`

calls for models like`Y ~ X1 + X2+ X3`

, then I have to adjust the p-values for the joint model of`X1+X2+X3`

for the number of multiple models tested?