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. Well, the authors have not gone the "extra step" to make their
regression objects behave like other R regression objects. In case you
need alternative software, ask in R-sig-mixed. You'll learn that most of
these can be estimated with other packages. But I really like the
homogeneous user interface that is spelled out in pglm, and I expect my
students will run into the same questions that you have..
I just downloaded their source code, you probably ought to do that so
you can understand what they are doing. They provide the fitting
functions, but they do not do any of the other work necessary to make
these functions fit together with the R class framework. There are no
methods for "predict", anova, and so forth.
This is only partially true. In fact, "pglm" employs the framework
provided by the "maxLik" (by Ott Toomet and Arne Henningsen) and hence it
inherits some of the methods that "maxLik" provides for all of its fitted
model objects. So there is summary(), coef(), vcov(), AIC() work and you
can leverage tools like coeftest() from "lmtest", linearHypothesis() from
"car" or sandwich() from "sandwich" do work.
But it is certainly true that even more features would be desirable and I
think that Yves always planned on enhancing "pglm" at some point. (After
all it's still the initial version 0.1-0 on CRAN...)
I'm in their R folder looking for implementations:
pauljohn@pols110:/tmp/pglm/R$ grep summary *
pauljohn@pols110:/tmp/pglm/R$ grep predict *
pauljohn@pols110:/tmp/pglm/R$ grep class *
pauljohn@pols110:/tmp/pglm/R$ grep fitted *
pglm.R: # glm models can be fitted
Run
example(pglm)
what can we do after that?
plot(anb)
Error in xy.coords(x, y, xlabel, ylabel, log) :
'x' is a list, but does not have components 'x' and 'y'
## Nothing.
## We do get a regression summary object, that's better than some
packages provide:
anbsum <- summary(anb)
## And a coefficient table
coef(anbsum)
Estimate Std. error t value Pr(> t)
(Intercept) -6.933764e-01 0.061391429 -11.294351205 1.399336e-29
wage 1.517009e-02 0.006375966 2.379261231 1.734738e-02
exper 1.314229e-03 0.007400129 0.177595444 8.590407e-01
ruralyes -8.594328e-05 0.051334716 -0.001674175 9.986642e-01
model.matrix(anb)
Error in terms.default(object) : no terms component nor attribute
anova(anb)
Error in UseMethod("anova") :
no applicable method for 'anova' applied to an object of class
"c('maxLik', 'maxim')"
predict(anb)
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class
"c('maxLik', 'maxim')"
So, if you want those features with these models, you'll have to get
busy and do a lot of coding!
While working on regression support lately, I've reached the conclusion
that if an R package that claims to "do regression" but does not provide
methods for summary, predict, anova, nobs, fitted, logLik, AIC, and so
forth, then it is not done yet. Otherwise, users like you who expect to
be able to run methods like fitted or such have a bad experience, as you
are having now.
Maybe somebody reading this will remind us where the common list of R
regression methods is listed. I know for sure I've seen a document
about these things, but I'm baffled now trying to find it. But I'm
sure there is one.
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").
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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