[R] cv.glm {boot}

[Dimitris Rizopoulos]

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


----- Original Message ----- 
From: "Trevor Wiens" <twiens at interbaun.com>
To: "Prof Brian Ripley" <ripley at stats.ox.ac.uk>
Cc: <r-help at stat.math.ethz.ch>
Sent: Tuesday, March 15, 2005 4:59 PM
Subject: Re: [R] cv.glm {boot}


> On Tue, 15 Mar 2005 07:05:49 +0000 (GMT)
> Prof Brian Ripley <ripley at 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 at 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)
>
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