CODEWITHSUNDEEP
rsurveymultinomial
MDSF
MDSF

Reputation: 125

how to get global p for categorical variables in svy_vglm

I'm using the function svyVGAM::svy_vglm to run a multinomial model with survey weights:

mmodel <- svy_glm(y~x1+x2+x3+x4..., family=multinomial, design=w_data)

where x represent categorical variables, some with three or more levels. Through model summary, I can know the p-value for each coefficient, but I don't know how to get the p-value for the global variable.

In other contexts, anova(), waldtest(), lrtest(), ... could be used, but none of them seem to work with svy_vglm objects. tbl_regression does not work either: Error: No tidy method for objects of class svy_vglm.

Any help?

Thanks

Upvotes: 1

Views: 567

Answers (1)

Thomas Lumley
Thomas Lumley

Reputation: 2765

You can do this using the coef and vcov methods. There's probably a package, but it's not hard to program

Suppose that model is your model object, design is your survey design object and index is a vector with the positions of the coefficients you want to test. If you had ten coefficients and wanted to test all except the first two, you would have index<-3:10, for example.

beta<-coef(model)[index]
V<-vcov(model)[index,index]
teststat <- crossprod(beta, solve(V,beta))
pf(teststat, df1=length(beta), df2=degf(design), lower.tail=FALSE)

This doesn't give you a likelihood ratio test; you'd probably need to write to the package author and suggest that as a new feature.

Upvotes: 2

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