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<-function(outcome){

~outcome

}

> this_is_a_simpler_example(test)

~outcome

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")

~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

http://tolstoy.newcastle.edu.au/R/e10/help/10/05/5676.html -thomas

Thomas Lumley Assoc. Professor, Biostatistics

[hidden email] University of Washington, Seattle

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