pglm package: fitted values and residuals

On Wed, 24 Apr 2013, Paul Johnson wrote:

On Wed, Apr 24, 2013 at 3:11 AM,  <alfonso.carfora at 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?"  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

pglm package: fitted values and residuals

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