Very helpful, many thanks.
On 12 November 2013 16:09, Rui Barradas 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'
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 s
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 co
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 <-
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
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