Issue with the tabmeans.survey multi-categorical variables. Not recognising variables in the design - r

Question

I have had an issue with analysing survey data on r using the survey and tab packages.
I think I am setting up the survey design object correctly, but when i try to run the tabmean.survey function comparing the means across more than 2 categories, the function does not recognise the variable in the design.
Here's the example using my data:
svyd<-svydesign(id=~psu, #PSU variable
strata=~strata, #Strata variable
weights=~ca_betaindin_xw, #Weight variable
data=usds)
svyd_emp<-subset(svyd, usds$samp_employ==1) #subset the data to required analytic sample
t1<-tabmeans.svy(age~ethnicity,
design = svyd_emp) #Run tabmeans.svy comparing means of age by ethnicity
Which produces this error:
Error in svyglm.survey.design(Age ~ 1, design = design) :
all variables must be in design= argument
When I try the same function with a binary variable the function works
t2<-tabmeans.svy(age~sex,
design = svyd_emp) #Run tabmeans.svy comparing means of age by sex
#WORKS
Comparing means across multi categorical variables using this function has previously worked. I can't figure out why the function is throwing up an error now. The survey.design object had the variables listed in the object.
I cannot share my data but I have reproduced the same issue using the 'api' dataset in the survey package.
data(api)
sdesign<-svydesign(id=~dnum+snum,
strata=~stype,
weights=~pw,
data=apistrat,
nest = TRUE)
t3<-tabmeans.svy(api00~stype, # stype has 3 categories = DOESNT WORK
design=sdesign)
t4<-tabmeans.svy(api00~sch.wide,
design=sdesign) # sch.wide has 2 categories = WORKS
Appreciate any thoughts or suggestions on how to get around this issue.
Many thanks

Answer 1

Thanks for the reproducible example. When I run it, I get
> t3<-tabmeans.svy(api00~stype, # stype has 3 categories = DOESNT WORK
+ design=sdesign)
Error in svyglm.survey.design(Age ~ 1, design = design) :
all variables must be in design= argument
> traceback()
4: stop("all variables must be in design= argument")
3: svyglm.survey.design(Age ~ 1, design = design)
2: svyglm(Age ~ 1, design = design)
1: tabmeans.svy(api00 ~ stype, design = sdesign)
which is disconcerting, because why is it trying to find an Age variable? (This was masked a little in your example, because you have an age variable).
Looking at the code for tabmeans.svy I see
if (num.groups == 2) {
fit <- svyttest(formula, design = design)
diffmeans <- -fit$estimate
diffmeans.ci <- -rev(as.numeric(fit$conf.int))
p <- fit$p.value
}
else {
fit1 <- svyglm(Age ~ 1, design = design)
fit2 <- svyglm(Age ~ Sex, design = design)
fit <- do.call(anova, c(list(object = fit1, object2 = fit2),
anova.svyglm.list))
p <- as.numeric(fit$p)
}
which explains the problem: if there are more than two groups it ignores your variables and instead tests for an effect of Sex on Age.
I suspect a cut-and-paste error by the maintainer. I have filed a GitHub issue. Unfortunately, I can't see a simple work-around.