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
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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