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.