Your example and explanation are not complete, but I have the gut feeling that
you could do all this both more efficiently *and* more R-ish.
First of all, why would you pass Y and X separately, to ultimately build the Y
~ X formula within the body of your function?
Secondly, it seems to me that subY and subY.val does something very similar to
the subset argument in, say, lm().
Personally, I would write it like this:
foo <- function(formula, data, subset) {
if (!missing(subset))
data <- data[subset, ]
fit <- some_regression_tool(formula, data = data)
## <more code>
data_after_processing
}
with subset = subY == subY.val.
Best,
Philippe
> On 02 Dec 2015, at 06:11, Brant Inman <brant.in...@me.com> wrote:
>
> I am trying to build a function that can accept variables for a regression.
> It would work something like this:
>
> ---
> # Y = my response variable (e.g. income)
> # X = my key predictor variable (e.g. education)
> # subY = a subsetting variable for Y (e.g. race)
> # subY.val = the value of the subsetting value that I want (e.g. ‘black’)
>
> foo <- function(Y, X, subY, subY.val, dataset){
>
> if(is.na(subY) == F) {
> Y <- paste(Y, ‘[‘, subY, ‘==‘, subY.val, ‘]’)
> }
> FORMULA <- paste(Y ~ X)
> fit <- some.regression.tool(FORMULA, data=dataset)
>
> return(some.data.after.processing)
> }
> ---
>
> If I call this function with, foo(income, education, race, “black”,
> my.dataset), I do not get the result that I need because the FORMULA is
> "income[race==black] ~ education” when what I need is “income[race==‘black’]
> ~ education”. How do I get the quotes to stay on ‘black’? Or, is there a
> better way?
>
> Help appreciated.
>
> --
> Brant
> [[alternative HTML version deleted]]
>
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