Re: [R] cv.glm {boot}

From: Dimitris Rizopoulos <dimitris.rizopoulos_at_med.kuleuven.ac.be>
Date: Wed 16 Mar 2005 - 03:12:37 EST

you could also take a look at function `?errortest' from package `ipred' and V&R's S programming, pp.175

I hope it helps.

Best,
Dimitris



Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium

Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat/
     http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm


> On Tue, 15 Mar 2005 07:05:49 +0000 (GMT)
> Prof Brian Ripley <ripley@stats.ox.ac.uk> wrote:
>
>>
>> Cross-validation assumes exchangeability of units. You can easily
>> write
>> your own code (lots of examples in MASS), but first you would need
>> to
>> prove the validity of what you are attempting. For example,
>> dropping
>> chunks in the middle of a time series is not valid unless your
>> prediction
>> somehow takes the temporal structure into account (and glm does
>> not).
>>
>
> Yes, I'm aware of that and I do have a number of predictors which
> vary with time (from year to year such as precipitation or properly
> timed vegetation indices from each year....) so that isn't my
> problem. Also my spatial blocking is also valid (distinct partitions
> of the study area). I'm also aware of the problems of spatial
> autocorrelation and have taken some measures to deal with that. I am
> however rather new at R and not a statistician, so I am heavily
> reliant on books such as Hosmer and Lemeshow or Manley(Resource
> selection by Animals) on procedure. Unforunately, they are not
> S-plus or R oriented so I have some difficulty translating those
> ideas to R.
>
> You mention lots of examples in MASS regarding cross-validation, but
> I can't find them. Perhaps I'm looking in the wrong spot. I've done
> help.search('validation'), .... and found nothing that seemed
> obviously applicable to my problem. I suppose I should pick up a
> copy of your books which would probably be very helpful. However, if
> it isn't too much trouble. I would really appreciate a bit more
> direct help.
>
> This is what I assumed I would do somethink like this (in this
> example basp = Baird's Sparrow presence or absence)
>
> train <- birddata[birddata$recordyear != 2000]
> test <- birddata[birddata$recordyear == 2000]
> train.glm <- glm(basp ~ elev + slope + precip + precip_1 ...,
> data=birddata, family=binomial)
> pred <- predict(train.glm, newdata=test, type='response')
> actual <- test$basp
> what happens next??
>
> Thanks in advance.
>
> T
> --
> Trevor Wiens
> twiens@interbaun.com
>
> The significant problems that we face cannot be solved at the same
> level of thinking we were at when we created them.
> (Albert Einstein)
>
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>



R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Mar 16 03:15:50 2005

This archive was generated by hypermail 2.1.8 : Fri 03 Mar 2006 - 03:30:47 EST