## Take an M x J matrix of detection probabilities and return a matrix
## of M x J observation probs
# Compute the cell probabilities for the observation classes
# in removal sampling.
#
# Both p and the returned matrix are M x J for M sites and J sampling occasions.
removalPiFun <- function(p){
M <- nrow(p)
J <- ncol(p)
pi <- matrix(NA, M, J)
pi[,1] <- p[,1]
for(i in seq(from = 2, length = J - 1)) {
pi[, i] <- pi[,i-1] / p[,i-1] * (1-p[,i-1]) * p[,i]
}
return(pi)
}
# p is an M x 2 matrix of detection probabilities (site x observer).
# returns an M x 3 matrix of row=(1 not 2, 2 not 1, 1 and 2).
# Compute the cell probabilities for the observation classes
# in double observer sampling.
doublePiFun <- function(p){
M <- nrow(p)
pi <- matrix(NA, M, 3)
pi[,1] <- p[,1] * (1 - p[,2])
pi[,2] <- p[,2] * (1 - p[,1])
pi[,3] <- p[,1] * p[,2]
return(pi)
}
# p is an M x 2 matrix of detection probabilities (site x observer).
# returns an M x 2 matrix of row=(1, 2 not 1).
# Compute the cell probabilities for the observation classes
# in double observer sampling.
depDoublePiFun <- function(p){
M <- nrow(p)
pi <- matrix(NA, M, 2)
pi[,1] <- p[,1]
pi[,2] <- p[,2]*(1-p[,1])
return(pi)
}
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