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75 lines
3.3 KiB
R
75 lines
3.3 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/cal_owl.R
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\docType{data}
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\name{cal_owl_multistate_data.csv}
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\alias{cal_owl_multistate_data.csv}
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\title{Occupancy detection data for California Spotted Owls (single-season, multi-state analysis)}
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\format{csv file with 54 rows and 5 columns}
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\description{
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One of the motivating examples for Nichols et al. (2007a) was estimation of the
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reproductive rate of California spotted owls (Strix occidentalis) in the central
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Sierra Nevada (California, USA), while allowing for state classification uncertainty
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(e.g., whether territories are unoccupied, occupied without breeding, or
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occupied with breeding) and variable sampling protocols.
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}
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\details{
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Survey data collected during April to mid-August 2004 from 54 potential
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territories in the Eldorado study area were analyzed using a multi-state occupancy
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modeling framework.
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\itemize{
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\item 54 sites
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\item 5 surveys
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\item 1 season
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\item 3 occupancy states ("0"=unoccupied, "1"=occupied, "2"=occupied with breeding)
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\item contains missing data (denoted by ".")
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}
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}
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\examples{
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# Single-season multi-state example (2 occupancy states, psi-R parameteization) (pg 231 in book)
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# read detection history data from csv file...
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csv<-read.csv(system.file("extdata/cal_owl_multistate_data.csv",package="RPresence"),as.is=T)
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csv[csv=="."]=NA
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sitenames=csv[,1] # sitenames in 1st column
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dethist=csv[,-1]; # get rid of 1st column (site name)
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nsites=nrow(dethist) # set number of sites,surveys from det. history data
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nsrvys=ncol(dethist)
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# create survey covariate to categorize surveys 1-2 as 1 period
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# and surveys 3-5 as another period
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# Since it's a survey covariate, it will be a NxT matrix (N=nsites, T=nsurveys)
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# The covariate matrix would be:
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# 1 2 3 4 5
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# site1 [1 1 2 2 2]
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# site2 [1 1 2 2 2]
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# : [: : : : :]
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# siteN [1 1 2 2 2]
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# Filling in by cols means we repeat "1" N times, then "1" N times,
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# then "2" N times, then "2" N times, then "2" N times.
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# We save it as a data frame (without the matrix dimensions).
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cov2=data.frame(PER=as.factor(c(rep(1,2*nsites),rep(2,3*nsites))))
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# create input "pao" object, for use with occMod function
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data=createPao(dethist,survcov=cov2,title="Cal Owl example")
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xmods=list(); i=1 # run each model and save in list variable, "xmods"
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~1, delta~1),data=data,type="do.ms.2");i=i+1
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~SURVEY,delta~1),data=data,type="do.ms.2");i=i+1
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~STATE, delta~1),data=data,type="do.ms.2");i=i+1
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~1, delta~PER),data=data,type="do.ms.2");i=i+1
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~SURVEY,delta~PER),data=data,type="do.ms.2");i=i+1
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xmods[[i]]=occMod(model=list(psi~1,r~1,p~STATE, delta~PER),data=data,type="do.ms.2");i=i+1
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# create AIC table of model results and print
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results2<-createAicTable(xmods); cat('Cal Owl example\\n'); print(results2$table)
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# print table 5.1 from book...
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cat('CA spotted owl reproduction Table 5.1 (pg 234 in book)\\n')
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estmts=xmods[[6]]$real
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estimate_table=rbind(estmts$psi[1,],estmts$r[1,],estmts$p1[1,],estmts$p2[1,],estmts$delta[1,],estmts$delta[109,])
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rownames(estimate_table)=c('psi','R','p1','p2','delta1','delta2'); print(estimate_table)
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}
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\keyword{datasets}
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