# [R] Loop through columns of outcomes

Tue Nov 12 16:09:02 CET 2013

```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,

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 <ruipbarradas at 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,
>>
>>
>>
>> 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]]
>>>
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