RMark (version 3.0.0)

RDMultScalOcc: Multi-scale dynamic occupancy models in RMark

Description

Multi-scale dynamic occupancy models in RMark

Author

Connor M. Wood, University of Wisconsin-Madison <cwood9 at wisc.edu>

Examples

# \donttest{
## Multi-scale dynamic occupancy models in RMark: a worked example ##

# 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
# note: mixtures refers to the number of devices

# create the design data object
dt.ddl=make.design.data(dt.pr)
# Examine the built-in covariates for each parameter
str(dt.ddl)

### Approach 1: Manually create (a somewhat arbitrary) set of models #######################
# Name variables for simplicity 
fit.models=function()
{
Open=list(formula=~sOpen)
pOpen=list(formula=~pOpen)
Slope=list(formula=~sSlope)
pSlope=list(formula=~pSlope)
Time=list(formula=~Time) #use carefully because different covariates use 'Time' in different ways

null.model=mark(dt.pr,dt.ddl,delete=TRUE)
mod1=mark(dt.pr,dt.ddl,model.parameters=list(p=Open),delete=TRUE)
#'mod2=mark(dt.pr,dt.ddl,model.parameters=list(p=pOpen),delete=TRUE)
mod3=mark(dt.pr,dt.ddl,model.parameters=list(p=Slope),delete=TRUE)
mod4=mark(dt.pr,dt.ddl,model.parameters=list(p=pSlope),delete=TRUE)
mod5=mark(dt.pr,dt.ddl,model.parameters=list(Psi=Open),delete=TRUE)
mod6=mark(dt.pr,dt.ddl,model.parameters=list(Psi=Slope),delete=TRUE)
mod7=mark(dt.pr,dt.ddl,model.parameters=list(Psi=Time),delete=TRUE)
mod8=mark(dt.pr,dt.ddl,model.parameters=list(Gamma=Open),delete=TRUE)
mod9=mark(dt.pr,dt.ddl,model.parameters=list(Epsilon=Slope),delete=TRUE)
mod10=mark(dt.pr,dt.ddl,model.parameters=list(Theta=list(formula=~time)),delete=TRUE) 

return(collect.models()) # View results sorted by AICc
}
results=fit.models()
###########################################################################################

### Approach 2: use mark.wrapper to create your models ####################################
fit.models=function()
{
 # Models for p 
 p.null=list(formula=~1)
 p.open=list(formula=~sOpen)
 p.popen=list(formula=~pOpen)
 p.slope=list(formula=~sSlope)
 p.pslope=list(formula=~pSlope)
 # Models for Psi
 Psi.null=list(formula=~1)
 Psi.open=list(formula=~sOpen)
 Psi.Slope=list(formula=~sSlope)
 #Model for Gamma
 Gamma.null=list(formula=~1)
 Gamma.open=list(formula=~sOpen)
 # Model for Epsilon 
 Epsilon.null=list(formula=~1)
 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.null=list(formula=~1)
 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,delete=TRUE) 
 return(results)
}

fit.models() # creates all combinations of variables and prints AICc table
results
# }