1\name{hunua}
2\alias{hunua}
3\docType{data}
4\title{Hunua Ranges Data}
5\description{
6  The \code{hunua} data frame has 392 rows and 18 columns.
7  Altitude is explanatory, and there are binary responses
8  (presence/absence = 1/0 respectively) for 17 plant species.
9
10
11}
12\usage{data(hunua)}
13\format{
14  This data frame contains the following columns:
15  \describe{
16    \item{agaaus}{Agathis australis, or Kauri}
17    \item{beitaw}{Beilschmiedia tawa, or Tawa}
18    \item{corlae}{Corynocarpus laevigatus}
19    \item{cyadea}{Cyathea dealbata}
20    \item{cyamed}{Cyathea medullaris}
21    \item{daccup}{Dacrydium cupressinum}
22    \item{dacdac}{Dacrycarpus dacrydioides}
23    \item{eladen}{Elaecarpus dentatus}
24    \item{hedarb}{Hedycarya arborea}
25    \item{hohpop}{Species name unknown}
26    \item{kniexc}{Knightia excelsa, or Rewarewa}
27    \item{kuneri}{Kunzea ericoides}
28    \item{lepsco}{Leptospermum scoparium}
29    \item{metrob}{Metrosideros robusta}
30    \item{neslan}{Nestegis lanceolata}
31    \item{rhosap}{Rhopalostylis sapida}
32    \item{vitluc}{Vitex lucens, or Puriri}
33    \item{altitude}{meters above sea level}
34  }
35}
36\details{
37  These were collected from the Hunua Ranges, a small forest in southern
38  Auckland, New Zealand. At 392 sites in the forest, the presence/absence
39  of 17 plant species was recorded, as well as the altitude.
40  Each site was of area size 200\eqn{m^2}{m^2}.
41
42
43}
44\source{
45  Dr Neil Mitchell, University of Auckland.
46
47
48}
49%\references{
50%  None.
51%}
52\seealso{
53  \code{\link{waitakere}}.
54
55
56}
57\examples{
58# Fit a GAM using vgam() and compare it with the Waitakere Ranges one
59fit.h <- vgam(agaaus ~ s(altitude, df = 2), binomialff, data = hunua)
60\dontrun{
61plot(fit.h, se = TRUE, lcol = "orange", scol = "orange",
62     llwd = 2, slwd = 2, main = "Orange is Hunua, Blue is Waitakere") }
63head(predict(fit.h, hunua, type = "response"))
64
65fit.w <- vgam(agaaus ~ s(altitude, df = 2), binomialff, data = waitakere)
66\dontrun{
67plot(fit.w, se = TRUE, lcol = "blue", scol = "blue", add = TRUE) }
68head(predict(fit.w, hunua, type = "response"))   # Same as above?
69}
70\keyword{datasets}
71