On Fri, 23 Feb 2007, Dimitris Rizopoulos wrote:
>
> ----- Original Message ----- From: "Prof Brian Ripley"
> <[EMAIL PROTECTED]>
> To: "Dimitris Rizopoulos" <[EMAIL PROTECTED]>
> Cc: <r-devel@r-project.org>
> Sent: Friday, February 23, 2007 1:40 PM
> Subject: Re: [Rd] Bootstrapping stepAIC() with glm.nb()
>
>
>> You did not say what the problem was!
>>
>> But you are asking that an object which is not in scope (index) be found a
>> few levels down. You should be able to fix this by substituting in the
>> values in fn. Here is one way:
>>
>> up.obj <- update(object, data = data[index[, i], ])
>> Call <- up.obj$call
>> Call$data <- data[index[, i], ]
>> up.obj$call <- Call
>>
>> (there are others).
>
>
> thanks very much for your reply and workaround. However, could you please
> explain me (or point me to the right direction) why it *does* work, without
> the workaround, for 'lm', 'glm' and 'aov' objects.
They have dropterm methods that make use of the model frame that is stored
with the objects (by default).
>
> Thanks in advance,
> Dimitris
>
>
>
>
>>
>> On Fri, 23 Feb 2007, Dimitris Rizopoulos wrote:
>>
>>> Dear all,
>>>
>>> I would like to Boostrap the stepAIC() procedure from package MASS for
>>> variety of model objects, i.e.,
>>>
>>> fn <- function(object, data, B = 2){
>>> n <- nrow(data)
>>> res <- vector(mode = "list", length = B)
>>> index <- sample(n, n * B, replace = TRUE)
>>> dim(index) <- c(n, B)
>>> for (i in 1:B) {
>>> up.obj <- update(object, data = data[index[, i], ])
>>> res[[i]] <- stepAIC(up.obj, trace = FALSE)
>>> }
>>> res
>>> }
>>>
>>> ####################
>>>
>>> library(MASS)
>>>
>>>
>>> # 'glm' objects
>>> x1 <- runif(100, -4, 4)
>>> x2 <- runif(100, -4, 4)
>>> y <- 1 + 2 * x1 + rnorm(100, sd = 3)
>>> dat <- data.frame(y, x1, x2)
>>> glmFit <- glm(y ~ x1 + x2, data = dat)
>>> fn(glmFit, data = dat)
>>>
>>> # 'aov' objects
>>> quine.hi <- aov(log(Days + 2.5) ~ .^4, quine)
>>> fn(quine.hi, data = quine)
>>>
>>>
>>> However, for "negbin" objects returned by glm.nb() the following
>>> problem occurs:
>>>
>>> quine.nb <- glm.nb(Days ~ .^4, data = quine)
>>> fn(quine.nb, data = quine)
>>>
>>>
>>> Any hints to overcome this are greatly appreciated.
>>>
>>> Thanks in advance,
>>> Dimitris
>>>
>>> ----
>>> Dimitris Rizopoulos
>>> Ph.D. Student
>>> Biostatistical Centre
>>> School of Public Health
>>> Catholic University of Leuven
>>>
>>> Address: Kapucijnenvoer 35, Leuven, Belgium
>>> Tel: +32/(0)16/336899
>>> Fax: +32/(0)16/337015
>>> Web: http://med.kuleuven.be/biostat/
>>> http://www.student.kuleuven.be/~m0390867/dimitris.htm
>>>
>>>
>>> Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
>>>
>>> ______________________________________________
>>> R-devel@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>
>>
>> --
>> Brian D. Ripley, [EMAIL PROTECTED]
>> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
>> University of Oxford, Tel: +44 1865 272861 (self)
>> 1 South Parks Road, +44 1865 272866 (PA)
>> Oxford OX1 3TG, UK Fax: +44 1865 272595
>>
>
>
> Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
>
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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