% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cal_owl.R
\docType{data}
\name{cal_owl_multistate_data.csv}
\alias{cal_owl_multistate_data.csv}
\title{Occupancy detection data for California Spotted Owls (single-season, multi-state analysis)}
\format{csv file with 54 rows and 5 columns}
\description{
One of the motivating examples for Nichols et al. (2007a) was estimation of the
reproductive rate of California spotted owls (Strix occidentalis) in the central
Sierra Nevada (California, USA), while allowing for state classification uncertainty
(e.g., whether territories are unoccupied, occupied without breeding, or
  occupied with breeding) and variable sampling protocols.
}
\details{
Survey data collected during April to mid-August 2004 from 54 potential
territories in the Eldorado study area were analyzed using a multi-state occupancy
modeling framework.

\itemize{
\item 54 sites
\item 5 surveys
\item 1 season
\item 3 occupancy states ("0"=unoccupied, "1"=occupied, "2"=occupied with breeding)
\item contains missing data (denoted by ".")
}
}
\examples{
#  Single-season multi-state example (2 occupancy states, psi-R parameteization) (pg 231 in book)

#    read detection history data from csv file...
csv<-read.csv(system.file("extdata/cal_owl_multistate_data.csv",package="RPresence"),as.is=T)
csv[csv=="."]=NA

sitenames=csv[,1]  #  sitenames in 1st column
dethist=csv[,-1];  #  get rid of 1st column (site name)
nsites=nrow(dethist) #  set number of sites,surveys from det. history data
nsrvys=ncol(dethist)

#  create survey covariate to categorize surveys 1-2 as 1 period
#                                    and surveys 3-5 as another period
#    Since it's a survey covariate, it will be a NxT matrix (N=nsites, T=nsurveys)
#    The covariate matrix would be:
#         1 2 3 4 5
#  site1 [1 1 2 2 2]
#  site2 [1 1 2 2 2]
#   :    [: : : : :]
#  siteN [1 1 2 2 2]
#    Filling in by cols means we repeat "1" N times, then "1" N times,
#                  then "2" N times, then "2" N times, then "2" N times.
#    We save it as a data frame (without the matrix dimensions).
cov2=data.frame(PER=as.factor(c(rep(1,2*nsites),rep(2,3*nsites))))

#          create input "pao" object, for use with occMod function
data=createPao(dethist,survcov=cov2,title="Cal Owl example")

xmods=list(); i=1       #  run each model and save in list variable, "xmods"
xmods[[i]]=occMod(model=list(psi~1,r~1,p~1,     delta~1),data=data,type="do.ms.2");i=i+1
xmods[[i]]=occMod(model=list(psi~1,r~1,p~SURVEY,delta~1),data=data,type="do.ms.2");i=i+1
xmods[[i]]=occMod(model=list(psi~1,r~1,p~STATE, delta~1),data=data,type="do.ms.2");i=i+1
xmods[[i]]=occMod(model=list(psi~1,r~1,p~1,     delta~PER),data=data,type="do.ms.2");i=i+1
xmods[[i]]=occMod(model=list(psi~1,r~1,p~SURVEY,delta~PER),data=data,type="do.ms.2");i=i+1
xmods[[i]]=occMod(model=list(psi~1,r~1,p~STATE, delta~PER),data=data,type="do.ms.2");i=i+1

#     create AIC table of model results and print
results2<-createAicTable(xmods); cat('Cal Owl example\\n'); print(results2$table)

#     print table 5.1 from book...
cat('CA spotted owl reproduction Table 5.1 (pg 234 in book)\\n')
estmts=xmods[[6]]$real
estimate_table=rbind(estmts$psi[1,],estmts$r[1,],estmts$p1[1,],estmts$p2[1,],estmts$delta[1,],estmts$delta[109,])
rownames(estimate_table)=c('psi','R','p1','p2','delta1','delta2'); print(estimate_table)
}
\keyword{datasets}