Re: [R] problem with 'svyby' function from SURVEY package

From: Thomas Lumley <>
Date: Thu, 03 Jun 2010 09:05:26 -0700 (PDT)

On Thu, 3 Jun 2010, Roni Kobrosly wrote:

> Hello,
> I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data):
> dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE)
> No problem there.
> Now I use the "svyby" function as follows:
> svyby(~outcome, ~covariate, design=dhanes, svymean, na.rm=T) -> haha
> print(haha)
> covariate outcome se.outcome
> 1 1 0.4961189 0.08828457
> 2 2 0.4474706 0.22214557
> 3 3 0.5157026 0.12076008
> 4 4 0.6773910 0.20605025
> NA NA 0.8728167 0.15622274
> ...and it works just fine. I get a nice table of the mean and standard error for each level of the covariate. I started writing a custom function to automate this and I had problems. Consider this really basic custom function (that does not seem very different from the above code):
> this_is_a_test <-function(outcome, covariate)
> {
> svyby(~outcome, ~covariate, design=dhanes, svymean, na.rm=T) -> haha
> print(hah)
> }

You are asking for the mean of a variable called 'outcome', divided up according to a variable called 'covariate'. Presumably you don't have variables with either of those names, so R is getting confused.

Formulas don't work the way you want them to. As a simpler example with nothing to do with the survey package



> this_is_a_simpler_example(test)


If you want to substitute a variable into a formula, you need to do it yourself. In your case, you probably want to use make.formula(), from the survey package

> make.formula("test")

> make.formula(c("fred","barney","wilma"))
~fred + barney + wilma

Presumably you want to do something like

approach_that_works <-function(outcome, covariate, design=dhanes,...) svyby(make.formula(outcome), make.formula(covariate), design,...)

some_outcomes <- colnames(dhanes)[47:63]

some_covariates <- colnames(dhanes)[83:95]

lapply( some_outcomes,

               function(an_outcome) lapply(some_covariates,  approach_that_works, outcome=an_outcome)

For another recent thread using another approach to a related question, see


Thomas Lumley			Assoc. Professor, Biostatistics	University of Washington, Seattle

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