# [R] Loop through columns of outcomes

Kuma Raj pollaroid at gmail.com
Tue Nov 12 14:28:59 CET 2013

```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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>>
>

```