Here is an example with the dipper data using made up rain levels instead of flooding. I create a model in which rain is a design covariate and then I create 6 individual covariates specifically named r1,r2,...r6 to match the time values of 1 to 6. r1 differs from say r2 but all the values of r1 are the same. Evan has used the same example in the sidebar at the end of section 11.5 to show how the same thing can be done with the MARK interface. You get the same estimates if you treat the covariates as design or individual covariates but using as an individual covariates let's you get predictions easily.

- Code: Select all
`library(RMark)`

data(dipper)

# add a time varying covariate for Phi named to match beginning time of each time interval (default begin.time=1 and time.intervals=1)(r1,r2,..r6)

dipper$r1=rep(1,294)

dipper$r2=rep(10,294)

dipper$r3=rep(8,294)

dipper$r4=rep(15,294)

dipper$r5=rep(3,294)

dipper$r6=rep(6,294)

# process data

dp=process.data(dipper)

# create default design data

ddl=make.design.data(dp)

# add rain environmental covariate to design data for Phi; this matches r1 to r6.

ddl$Phi$rain=1

ddl$Phi$rain[ddl$Phi$time==2]=10

ddl$Phi$rain[ddl$Phi$time==3]=8

ddl$Phi$rain[ddl$Phi$time==4]=15

ddl$Phi$rain[ddl$Phi$time==5]=3

ddl$Phi$rain[ddl$Phi$time==6]=6

# fit model using design covariate

modcov=mark(dp,ddl,model.parameters=list(Phi=list(formula=~rain)))

# fit model using individual covariate

modicov=mark(dp,ddl,model.parameters=list(Phi=list(formula=~r)))

# get and plot predictions using individual covariates

predictions=covariate.predictions(modicov,data=data.frame(r1=0:20),indices=1)$estimates

with(predictions,

{

plot(0:20,estimate,xlab="Rain",ylab="Survival",ylim=c(0,1))

lines(0:20,lcl,lty=2)

lines(0:20,ucl,lty=2)

})