Very helpful, many thanks.
On 12 November 2013 16:09, Rui Barradas <ruipbarra...@sapo.pt> wrote:
> Hello,
>
> Once again, use lapply.
>
> mlist <- lapply(seq_along(m2), function(i) m2[[i]])
> names(mlist) <- paste0("mod", seq_along(mlist))
>
> slist <- lapply(mlist, summary)
>
>
> plist <- lapply(slist, `[[`, 'p.table')
>
>
> Hope this helps,
>
> Rui Barradas
>
> Em 12-11-2013 13:28, Kuma Raj escreveu:
>
>> Thanks for the script which works perfectly. I am interested to do
>> model checking and also interested to extract the coefficients for
>> linear and spline terms. For model checkup I could run this script
>> which will give different plots to test model fit: gam.check(m2[[1]]).
>> Thanks to mnel from SO I could also extract the linear terms with the
>> following script:
>>
>> m2 <- unlist(m1, recursive = FALSE) ## unlist
>>
>> First extract the model elements:
>>
>> mod1<-m2[[1]]
>> mod2<-m2[[2]]
>> mod3<-m2[[3]]
>> mod4<-m2[[4]]
>> mod5<-m2[[5]]
>> mod6<-m2[[6]]
>>
>> And run the following:
>>
>> mlist <- list(mod1, mod2, mod3,mod4,mod5,mod6) ## Creates a list of
>> models
>> names(mlist) <- list("mod1", "mod2", "mod3","mod4","mod5","mod6")
>>
>> slist <- lapply(mlist, summary) ## obtain summaries
>>
>> plist <- lapply(slist, `[[`, 'p.table') ## list of the coefficients
>> linear terms
>>
>> For 6 models this is relatively easy to do, but how could I shorten
>> the process if I have large number of models?
>>
>> Thanks
>>
>>
>> On 12 November 2013 12:32, Rui Barradas <ruipbarra...@sapo.pt> wrote:
>>>
>>> Hello,
>>>
>>> Use nested lapply(). Like this:
>>>
>>>
>>>
>>> m1 <- lapply(varlist0,function(v) {
>>> lapply(outcomes, function(o){
>>> f <- sprintf("%s~ s(time,bs='cr',k=200)+s(temp,bs='cr')
>>> +
>>> Lag(%s,0:6)", o, v)
>>>
>>> gam(as.formula(f),family=quasipoisson,na.action=na.omit,data=df)
>>> })})
>>>
>>> m1 <- unlist(m1, recursive = FALSE)
>>> m1
>>>
>>>
>>> Hope this helps,
>>>
>>> Rui Barradas
>>>
>>>
>>> Em 12-11-2013 09:53, Kuma Raj escreveu:
>>>>
>>>>
>>>> I have asked this question on SO, but it attracted no response, thus I
>>>> am
>>>> cross- posting it here with the hope that someone would help.
>>>>
>>>> I want to estimate the effect of pm10 and o3 on three outcome(death,
>>>> cvd
>>>> and resp). What I want to do is run one model for each of the main
>>>> predictors (pm10 and o3) and each outcome(death, cvd and resp). Thus I
>>>> expect to obtain 6 models. The script below works for one outcome
>>>> (death)
>>>> and I wish to use it for more dependent variables.
>>>>
>>>>
>>>>
>>>> library(quantmod)
>>>> library(mgcv)
>>>> library(dlnm)
>>>> df <- chicagoNMMAPS
>>>> outcomes<- c("death", "cvd", "resp ")
>>>> varlist0 <- c("pm10", "o3")
>>>>
>>>> m1 <- lapply(varlist0,function(v) {
>>>> f <- sprintf("death~ s(time,bs='cr',k=200)+s(temp,bs='cr') +
>>>> Lag(%s,0:6)",v)
>>>>
>>>> gam(as.formula(f),family=quasipoisson,na.action=na.omit,data=df)
>>>> })
>>>>
>>>> Thanks
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>
>
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
