On Thu, 25 Apr 2013, Ista Zahn wrote:
On Thu, Apr 25, 2013 at 3:14 PM, Paul Johnson <pauljoh...@gmail.com> wrote:
On Wed, Apr 24, 2013 at 4:37 PM, Achim Zeileis <achim.zeil...@uibk.ac.at> wrote:
On Wed, 24 Apr 2013, Paul Johnson wrote:
On Wed, Apr 24, 2013 at 3:11 AM, <alfonso.carf...@uniparthenope.it>
wrote:
I'm using the package pglm and I'have estimated a "random probit model".
I need to save in a vector the fitted values and the residuals of the model
but I can not do it.
I tried with the command fitted.values using the following procedure
without results:
This is one of those "ask the pglm authors" questions. You should take it
up with the authors of the package. There is a specialized email list
R-sig-mixed where you will find more people working on this exact same
thing.
pglm looks like fun to me, but it is not quite done, so far as I can tell.
I'm sure that there are many. One of my attempts to write up a list is in
Table 1 of vignette("betareg", package = "betareg").
Yes! That's exactly the list I was thinking of. It was driving me
crazy I could not find it.
Thanks for the explanation. I don't think I should have implied that
the pglm author must actually implement all the methods, it is
certainly acceptable to leverage the methods that exist. It just
happened that the ones I tested were not implemented by any of the
affiliated packages.
But this thread leads me to one question I've wondered about recently.
Suppose I run somebody's regression function and out comes an object.
Do we have a way to ask that object "what are all of the methods that
might apply to you?"
Yes, minus the "might":
library(pglm)
example(pglm) # produces an object named "la"
sapply(class(la), function(x) methods(class=x)) # lists functions with
methods for objects of this class
Well, this shows you the methods that are available for the class but not
necessarily what arguments are supported. And even if the arguments are
available they do not necessarily mean the same thing. And some things may
or may not work via inheritance...
So coming back to Paul's question: Yes, I think it would be nice to have
support for this and in fact I have thought about similar infrastructure.
But so far I didn't have a good idea for a sufficiently robust/reliable
implementation. There are just so many details in the different model
objects that can be handled differently.
Best,
Z
Best,
Ista
Here's why I wondered. You've noticed that
predict.lm has the interval="confidence" argument, but predict.glm
does not. So if I receive a regression model, I'd like to say to it
"do you have a predict method" and if I could get that predict method,
I could check to see if there is a formal argument interval. If it
does not, maybe I'd craft one for them.
pj
Personally, I don't write anova() methods for my model objects because I can
leverage lrtest() and waldtest() from "lmtest" and linearHypothesis() and
deltaMethod() from "car" as long as certain standard methods are available,
including coef(), vcov(), logLik(), etc.
Similarly, an AIC() method is typically not needed as long as logLik() is
available. And BIC() works if nobs() is available in addition.
Best,
Z
pj
library(pglm)
m1_S<-pglm(Feed ~ Cons_PC_1 + imp_gen_1 + LGDP_PC_1 + lnEI_1 +
SH_Ren_1,data,family=binomial(probit),model="random",method="bfgs",index=c("Year","IDCountry"))
m1_S$fitted.values
residuals(m1)
Can someone help me about it?
Thanks
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--
Paul E. Johnson
Professor, Political Science Assoc. Director
1541 Lilac Lane, Room 504 Center for Research Methods
University of Kansas University of Kansas
http://pj.freefaculty.org http://quant.ku.edu
______________________________________________
R-help@r-project.org mailing list
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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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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
