I just noticed you actually added some support for svyglm today in the prediction package. That is a super odd coincidence.

Great minds think alike! Can you let me know if this is now working with the dev version of prediction? It probably won't (I fear), so I'll try to work on it soon. This is definitely a top priority for me.

Unfortunately it did not work with the dev version of prediction. The issue is with this code in prediction::find_data:

data <- eval(model[["call"]][["data"]], env) 

With svyglm there is no data parameter, instead there is a design parameter. However, you don't actually need to go into call because the svyglm object holds the data used to generate it. For example:

# from examples
library(survey)
data(scd)
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
                    c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights, combined.weights=FALSE)
x <- svyglm(arrests~alive,scdrep)

x$data # gives full scd data.frame
# also works for database backed survey objects
library(RSQLite)
dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc,
                   data="apiclus1",dbtype="SQLite", 
                   dbname=system.file("api.db",package="survey"))

y <-svyglm(meals~api00,dbclus1)

y$data # gives data.frame with columns used including weights

I was able to get things to work by making prediction::find_data generic, and creating a method for svyglm, but you may be opposed to making prediction::find_data generic. I can make a PR if you'd like.

Regards

Ooh, I like the idea of the making it generic, actually. That would likely simplify it moving forward as new model classes are supported. Send a PR?

This appears to be working with latest dev versions of prediction and margins.

I find that factor/character variables cause errors. MRE:

library(margins)
library(survey)

data(api)

dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

# works without svy
margins(glm(api00~api99*sch.wide,data=apistrat))

# doesn't work with svy
margins(svyglm(api00~api99*sch.wide,design=dstrat))

# works with svy
margins(svyglm(api00~api99,design=dstrat))

# doesn't work with svy
margins(svyglm(api00~sch.wide,design=dstrat))

This appears to be a survey package issue:

library(survey)

data(api)

dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

svymodel <- svyglm(api00~sch.wide,design=dstrat)
# errors
predict(svymodel, data.frame(sch.wide=rep("No",10)))

regmodel <- glm(api00~sch.wide,data=apistrat)
# works
predict(regmodel,data.frame(sch.wide=rep("No",10)))

Issue is that the factor in the newdata needs to have more than one level to work (the levels don't even have to correspond to the levels used in the original data, it just needs more than one level), so this works:

library(survey)

data(api)

dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

svymodel <- svyglm(api00~sch.wide,design=dstrat)

predict(svymodel, data.frame(sch.wide=factor(rep("No",10),
                                             levels = c("No","random phrase"))))

We could add a line here: https://github.com/leeper/prediction/blob/master/R/prediction_methods.R#L67 to check that all factors have levels with length at least 1, and if they don't just add a level, but that feels like a hack. I am going to think about this some more.

Version 3.31-5 of survey, now uploaded to r-forge, uses the $xlevels and $contrasts attributes of the objects so this should now all work.

Thank you, @tslumley! Really appreciate it.

@carlganz Can you retest if you get a chance? I'm not able to look at this at the moment.

I can confirm everything works with the dev versions of margins and prediction, and version 3.31-5 of survey.

Excellent! Just checked it, as well, and it seems to be working. I've added versioned dependencies accordingly. Thank you for this!

I wonder, weather design argument in svyglm method is needed. You can extract survey design from svyglm object itself (it's in "survey.design" element of a list that is svyglm object). Moreover survey design included in svyglm object is already restricted only to observations that were used in model estimation - this is important fact if there are missings in variables included in a model (and current implementation fails in such a situation).

@tzoltak Can you open a new issue with an reproducible example (or just some more detail)? Thanks.