% \VignetteIndexEntry{Exposure to Dust - Generalized Additive Models}
% \VignetteDepends{mgcv}
%\VignetteEngine{knitr::knitr}
%\VignetteEncoding{UTF-8}
\documentclass[a4paper]{article}

\title{Exposure to Dust - Generalized Additive Models}

\begin{document}

\maketitle


<<echo=FALSE,eval=FALSE>>=
options(width=80)
@

For GAMs the package "mgcv" is used.

<<eval=FALSE>>=
library(mgcv)
library(catdata)
data(dust)
@

The first model uses bivariate smoothing for the covariates "dust" and "years" from the "dust"--data. By the option "pers=TRUE" the
bivariate effect can be plotted three--dimensional.

<<eval=FALSE>>=
gamdust1 <- gam(bronch ~ s(dust,years), family=binomial, 
                data=dust[(dust$dust<10) & (dust$smoke==1),])

plot(gamdust1, pers=TRUE)
@

Now each covariate is fitted separately, afterwards both effects are plotted.

<<eval=FALSE>>=
gamdust2<- gam(bronch ~ s(dust) + s(years), family=binomial, 
               data=dust[(dust$dust<10) & (dust$smoke==1),])
@
<<fig.width=8,eval=FALSE>>=
plot(gamdust2, select=1)
@

<<fig.width=8,eval=FALSE>>=
plot(gamdust2, select=2)
@


\end{document}
