[R] turn regression coefficients into matrix or...

[Jun Xu]

Stephen, Norbert, Anup, and Mark, Thank you all for your help. With these 
tips, I now have a much better understanding of the structure in R. Another 
related question that has been haunting me is whether it is necessary to be 
a convert from other statistical packages to R. Obviously, the great 
advantage that R has is it's free and usually the newest stats technique is 
first implemented in R. I had extensive experience with SAS and Stata, but I 
really hate to keep investing int the never-satiated big financial abyss 
there (e.g., various editions of manuals. However, I have some reservation 
to  be a convert as inertia always plays a big role if the utility is not 
that big by switching from one software to another. What do you think?


>From: Stephen Tucker <brown_emu at yahoo.com>
>To: Norbert NEUWIRTH <norbertneuwirth at gmx.at>, Jun Xu <mystata at hotmail.com>
>CC: r-help at stat.math.ethz.ch
>Subject: Re: [R] turn regression coefficients into matrix or...
>Date: Tue, 13 Mar 2007 07:07:53 -0700 (PDT)
>
>I believe the short answer to your question lies in
>
>      summary(ans.reg[[i]])$coefficients
>
>which will give you a matrix with coefficients and standard errors (and
>more). You can also find out what other information you can get from the
>regressions if you type
>
>      attributes(ans.reg[[i]])
>      attributes(summary(ans.reg[[i]]))
>
>and then see the names of the list elements (the regression functions and
>summary() return objects of mode "list") that might correspond to the
>information you are looking for.
>
>Good luck,
>
>ST
>
>
>
>
>--- Norbert NEUWIRTH <norbertneuwirth at gmx.at> wrote:
>
> > jun,
> >
> > im am also quite new to R. so i think, this is a question all we 
>R-newbees
> >
> > ask  ;-). having had the same "problem" the other day, i "solved" it the
> > following way:
> >
> > ####  Multiple Regressions - Tables
> >
> > data(anscombe)                                      # load anscombe
> > dataset (implemented somwhere in R)
> > x5 <- rnorm(11,14,1)                                # create an 
>additional
> >
> > variable (at random)
> > anscombe.nn <- cbind(anscombe,x5)                   # attach the 
>variable
> > to the dataset
> > attach(anscombe.nn)                                 # attach the dataset
> > to the searchpath (just for convenience)
> > anscombe.nn                                         # have a look on the
> > data
> >
> > ans.reg <- vector(4, mode = "list")                 # create empty list
> > (just  for speeding up)
> >
> > for(i in 1:4){                                      # now the 4
> > regressions are stored to the list
> >      j <- i+1
> >      x1 <- get(paste("x", i, sep = ""))              # exogenous var. #1
> >      x2 <- get(paste("x", j, sep = ""))              # exogenous var. #2
> >      y <- get(paste("y", i, sep = ""))               # endogenous
> >      ans.reg[[i]] <- glm(y ~ x1+x2,family=gaussian)  # do the regression
> > (out of 4)
> >      print(summary(ans.reg[[i]],cor=FALSE))
> > }
> >
> > detach(anscombe)                                    # detach dataset 
>from
> > search path
> > lapply(ans.reg, coef)                               # see each 
>regression
> > in one line
> > sapply(ans.reg, coef)                               # have the
> > coefficients in a table
> > x<-as.matrix(sapply(ans.reg, coef))                 # convert table to
> > matrix
> > x
> >
> > this solution is quite comparable to Ligges(2007) [published in german 
>and
> >
> > japanese, i think]
> >
> > i have an additional question to list-members, that have left the
> > newbee-status yet: how can i get R to hand over the standard errors,
> > significance levels etc, so that i can create a table with  coeff and SE
> > and sig.?
> >
> > norbert
> >
> >
> > Am 13.03.2007, 02:58 Uhr, schrieb Jun Xu <mystata at hotmail.com>:
> >
> > > I don't have much experience with r. What I am trying to do is to turn
> > > regression coefficients (after I run a lm or glm model) into some 
>matrix
> > > such that I can do some post-estimation calculation, for example
> > > predicted
> > > probabilities in glm model, etc.. Or, is there any function in r that 
>I
> > > can
> > > use to do something along that line? thanks.
> > >
> > > Jun Xu, Ph.D.
> > > Department of Sociology
> > > Ball State University
> > >
> > > _________________________________________________________________
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> > > month.
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> >
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> > >
> > > ______________________________________________
> > > R-help at stat.math.ethz.ch 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.
> > >
> >
> >
> >
> > --
> > -------------------------------
> > Mag. Norbert NEUWIRTH
> >
> > Roubiczekgasse 2/23
> > A-1100  WIEN
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> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch 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.
> >
>
>
>
>
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