\Header{Orange}{Growth of orange trees}
\keyword{datasets}{Orange}
\begin{Description}\relax
The \code{Orange} data frame has 35 rows and 3 columns of records of
the growth of orange trees.
\end{Description}
\begin{Usage}
\begin{verbatim}data(Orange)\end{verbatim}
\end{Usage}
\begin{Format}\relax
This data frame contains the following columns:
\describe{
\item[Tree] an ordered factor indicating the tree on which the measurement is
made.  The ordering is according to increasing maximum diameter.

\item[age] a numeric vector giving the age of the tree (days since 1968/12/31)

\item[circumference] a numeric vector of trunk circumferences (mm).  This is probably
\dQuote{circumference at breast height}, a standard measurement in
forestry.

}
\end{Format}
\begin{Source}\relax
Draper, N. R. and Smith, H. (1998), \emph{Applied Regression Analysis
(3rd ed)}, Wiley (exercise 24.N).

Pinheiro, J. C. and Bates, D. M. (2000) \emph{Mixed-effects Models
in S and S-PLUS}, Springer.
\end{Source}
\begin{Examples}
\begin{ExampleCode}
data(Orange)
coplot(circumference ~ age | Tree, data = Orange, show = FALSE)
fm1 <- nls(circumference ~ SSlogis(age, Asym, xmid, scal),
           data = Orange, subset = Tree == 3)
plot(circumference ~ age, data = Orange, subset = Tree == 3,
     xlab = "Tree age (days since 1968/12/31)",
     ylab = "Tree circumference (mm)", las = 1,
     main = "Orange tree data and fitted model (Tree 3 only)")
age <- seq(0, 1600, len = 101)
lines(age, predict(fm1, list(age = age)))
\end{ExampleCode}
\end{Examples}

