https://github.com/cran/unmarked
Tip revision: 0e9915b1bbee346e4c283f39772af69032684e39 authored by Ken Kellner on 09 January 2024, 10:20:02 UTC
version 1.4.1
version 1.4.1
Tip revision: 0e9915b
unmarkedFrameDS.Rd
\name{unmarkedFrameDS}
\title{Organize data for the distance sampling model of Royle et al. (2004)
fit by distsamp}
\alias{unmarkedFrameDS}
\usage{unmarkedFrameDS(y, siteCovs=NULL, dist.breaks, tlength, survey,
unitsIn, mapInfo)}
\description{Organizes count data along with the covariates and metadata.
This S4 class is required by the data argument of \code{\link{distsamp}}}
\arguments{
\item{y}{An RxJ matrix of count data, where R is the
number of sites (transects) and J is the number of distance
classes.}
\item{siteCovs}{A \code{\link{data.frame}} of covariates that vary at the
site level. This should have R rows and one column per covariate}
\item{dist.breaks}{vector of distance cut-points delimiting the
distance classes. It must be of length J+1.}
\item{tlength}{A vector of length R containing the trasect lengths. This is
ignored when survey="point".}
\item{survey}{Either "point" or "line" for point- and line-transects.}
\item{unitsIn}{Either "m" or "km" defining the measurement units for
\emph{both} \code{dist.breaks} and \code{tlength}}.
\item{mapInfo}{Currently ignored}}
\details{
unmarkedFrameDS is the S4 class that holds data to be passed
to the \code{\link{distsamp}} model-fitting function.}
\value{an object of class unmarkedFrameDS}
\note{If you have continuous distance data, they must be "binned" into
discrete distance classes, which are delimited by dist.breaks.}
\references{
Royle, J. A., D. K. Dawson, and S. Bates (2004) Modeling
abundance effects in distance sampling. \emph{Ecology} 85, pp. 1591-1597.
}
\seealso{\code{\link{unmarkedFrame-class}}, \code{\link{unmarkedFrame}},
\code{\link{distsamp}}}
\examples{
# Fake data
R <- 4 # number of sites
J <- 3 # number of distance classes
db <- c(0, 10, 20, 30) # distance break points
y <- matrix(c(
5,4,3, # 5 detections in 0-10 distance class at this transect
0,0,0,
2,1,1,
1,1,0), nrow=R, ncol=J, byrow=TRUE)
y
site.covs <- data.frame(x1=1:4, x2=factor(c('A','B','A','B')))
site.covs
umf <- unmarkedFrameDS(y=y, siteCovs=site.covs, dist.breaks=db, survey="point",
unitsIn="m") # organize data
umf # look at data
summary(umf) # summarize
fm <- distsamp(~1 ~1, umf) # fit a model
}