# Study design and data structure:
# two sessions (i.e., seasons) and 346 sampling sites
# up to three secondary sampling periods per season
# up to three survey devices per sampling site

# import the sample data, RDMultScalOcc.sampledata.csv
pathtodata=paste(path.package("RMark"),"extdata",sep="/")
dt=read.csv(paste(pathtodata,"RDMultScalOcc.sampledata.csv",sep="/"))
dt[is.na(dt)]=0 # replace NAs with 0
dt$ch=as.character(dt$ch) # encounter histories (dt$ch) must be characters

# note: habitat variables (amount of open forest and average slope) were collected at
# two spatial scales
# 'sOpen' and 'sSlope' represent the entire sampling site
# 'pOpen##' and 'pSlope##' represent conditions relevant to individual devices 
# at each sampling period
# the p-scale variables are coded [name][session][primary]; 
# dt.ddl$p$primary indicates how this should be entered
# in this case the values for p$primary varied among devices and between sessions, 
# but were constant between secondary sampling periods

# create the Process Data MARK object
dt.pr=process.data(dt,model="RDMultScalOcc",
  time.intervals=c(0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0),mixtures=3)
# note: time.intervals refers to seasons, not secondary sampling periods (K)
# note: mixtures refers to the number of devices (L)

# create the design data object
dt.ddl=make.design.data(dt.pr)

fit.models=function()
{
# Models for p 
p.open=list(formula=~sOpen)
p.popen=list(formula=~pOpen)
p.slope=list(formula=~sSlope)
p.pslope=list(formula=~pSlope)
# Models for Psi
Psi.open=list(formula=~sOpen)
Psi.Slope=list(formula=~sSlope)
#Model for Gamma
Gamma.open=list(formula=~sOpen)
# Model for Epsilon 
Epsilon.slope=list(formula=~sSlope)
# Model for Theta
# note: 'time' is defined in dt.ddl$Theta (use str(dt) to see all predefined variables)
Theta.time=list(formula=~time)
# create all combinations of these sub-models
cml=create.model.list("RDMultScalOcc")
results=mark.wrapper(cml,data=dt.pr,ddl=dt.ddl,output=FALSE) 
return(results)
}
# fit model collections and view models sorted by AICc
fit.models()