For independent observers, probit.fct computes observer-specific detection functions, conditional detection functions, delta dependence function, duplicate detection function (seen by both), and pooled detection function (seen by at least one).
probit.fct(x, formula, beta, rho, ...)vector of perpendicular distances
linear probit formula for detection using distance and other covariates
parameter values
maximum correlation at largest distance
any number of named vectors of covariates used in the formula
dat dataframe with distance, observer, any covariates specified in ... and detection probability p, conditional detection probability pc, dupiicate detection dup, pooled detection pool and dependence pc/p=delta.
The vectors of covariate values can be of different lengths because expand.grid is used to create a dataframe of all unique combinations of the distances and covariate values and the detection and related values are computed for each combination. The covariate vector observer=1:2 is automatically included. The folowing is too long for the examples section: test=probit.fct(0:10,~distance,c(1,-.15),.8,size=1:3) par(mfrow=c(1,2)) with(test[test$observer==1,], plot(distance,p,ylim=c(0,1),xlab="Distance",ylab="Detection probability") points(distance,pc,pch=2) points(distance,dup,pch=3) points(distance,pool,pch=4) legend(1,.2,legend=c("Detection","Conditional detection","Duplicate detection","Pooled detection"),pch=1:4,bty="n") plot(distance,delta,xlab="Distance",ylab="Dependence") )