[R] coef(summary) and plyr

[David Winsemius]

On Aug 9, 2010, at 10:11 AM, moleps wrote:

> ldply doesnt need a grouping variable as far as I understand the  
> command..

There is one further improvement to consider. When I tried using dlply  
to tackle a problem on which I had been bashing my head for the last  
three days and it gave just the results I had been looking for, I also  
noticed that the dlply function returns the grouping variable levels  
in an attribute, "split_labels", which could be unlisted to use as an  
argument to the rep() call I suggested earlier:

dfdl$group=rep(unlist(attr(dl, "split_labels")), each=4)

That might make the results more self-documenting in situations where  
the grouping levels were more involved than 0:2.

(Now, if I can get rms::Predict to behave as nicely with the plyr  
functions as did rms::cph, I will be home free.)
-- 
David.
>
> "Description
>
> For each element of a list, apply function then combine results into  
> a data frame
>
> Usage
>
> ldply(.data, .fun = NULL, ..., .progress = "none")"
>
>
> regards,
>
> M
>
>
> On 9. aug. 2010, at 15.33, David Winsemius wrote:
>
>>
>> On Aug 9, 2010, at 7:51 AM, moleps wrote:
>>
>>> Dear all,
>>>
>>> I´m having trouble getting a list of regression variables back  
>>> into a dataframe.
>>>
>>> mydf <- data.frame(x1=rnorm(100), x2=rnorm(100), x3=rnorm(100))
>>>
>>> mydf$fac<-factor(sample((0:2),replace=T,100))
>>>
>>> mydf$y<- mydf$x1+0.01+mydf$x2*3-mydf$x3*19+rnorm(100)
>>>
>>> dlply(mydf,.(fac),function(df) lm(y~x1+x2+x3,data=df))->dl
>>>
>>> here I´d like to use
>>>
>>> ldply(dl,coef(summary)) or something similar but I cant figure it  
>>> out...
>>
>> dfdl <- ldply(dl, function(x) coef(summary(x)) )
>>
>> Doesn't create a grouping variable, so:
>>
>> dfdl$group=rep(0:2, each=4)
>>
>>
>> David Winsemius, MD
>> West Hartford, CT
>>
>

David Winsemius, MD
West Hartford, CT