Thanks! that works, more or less. Although the wonky behaviour of mapply that
David pointed out is irritating. I tried deleting the $call item from the
models produced and passing them to stargazer for reporting the results, but
stargazer won't recognize the results even though the class is explicitly "glm
lm".
Does anyone know why mapply produces such weird results?
On 2013-12-18, at 3:29 AM, Dennis Murphy <djmu...@gmail.com> wrote:
> Hi:
>
> Here's a way to generate a list of model objects. Once you have the
> list, you can write or call functions to extract useful pieces of
> information from each model object and use lapply() to call each list
> component recursively.
>
> sample.df<-data.frame(var1=rbinom(50, size=1, prob=0.5),
> var2=rbinom(50, size=2, prob=0.5),
> var3=rbinom(50, size=3, prob=0.5),
> var4=rbinom(50, size=2, prob=0.5),
> var5=rbinom(50, size=2, prob=0.5))
>
> # vector of x-variable names
> xvars <- names(sample.df)[-1]
>
> # function to paste a variable x into a formula object and
> # then pass it to glm()
> f <- function(x)
> {
> form <- as.formula(paste("var1", x, sep = " ~ "))
> glm(form, data = sample.df)
> }
>
> # Apply the function f to each variable in xvars
> modlist <- lapply(xvars, f)
>
> To give you an idea of some of the things you can do with the list:
>
> sapply(modlist, class) # return class of each component
> lapply(modlist, summary) # return the summary of each model
>
> # combine the model coefficients into a two-column matrix
> do.call(rbind, lapply(modlist, coef))
>
>
> You'd probably want to rename the second column since the slopes are
> associated with different x variables.
>
> Dennis
>
> On Tue, Dec 17, 2013 at 5:53 PM, Simon Kiss <sjk...@gmail.com> wrote:
>> I think I'm missing something. I have a data frame that looks below.
>> sample.df<-data.frame(var1=rbinom(50, size=1, prob=0.5), var2=rbinom(50,
>> size=2, prob=0.5), var3=rbinom(50, size=3, prob=0.5), var4=rbinom(50,
>> size=2, prob=0.5), var5=rbinom(50, size=2, prob=0.5))
>>
>> I'd like to run a series of univariate general linear models where var1 is
>> always the dependent variable and each of the other variables is the
>> independent. Then I'd like to summarize each in a table.
>> I've tried :
>>
>> sample.formula=list(var1~var2, var1 ~var3, var1 ~var4, var1~var5)
>> mapply(glm, formula=sample.formula, data=list(sample.df), family='binomial')
>>
>> And that works pretty well, except, I'm left with a matrix that contains all
>> the information I need. I can't figure out how to use summary() properly on
>> this information to usefully report that information.
>>
>> Thank you for any suggestions.
>>
>> *********************************
>> Simon J. Kiss, PhD
>> Assistant Professor, Wilfrid Laurier University
>> 73 George Street
>> Brantford, Ontario, Canada
>> N3T 2C9
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
Cell: +1 905 746 7606
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