n <- 50 # number of sites
T <- 4 # number of primary periods
J <- 3 # number of secondary periods
site <- 1:50
years <- data.frame(matrix(rep(2010:2013, each=n), n, T))
years <- data.frame(lapply(years, as.factor))
occasions <- data.frame(matrix(rep(1:(J*T), each=n), n, J*T))
y <- matrix(0:1, n, J*T)
umf <- unmarkedMultFrame(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=list(year=years),
numPrimary=T)
umfGMM1 <- unmarkedFrameGMM(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=data.frame(year=c(t(years))),
# or: yearlySiteCovs=list(year=years),
numPrimary=T, type="removal")
# A user-defined piFun calculating removal probs when time intervals differ.
instRemPiFun <- function(p) {
M <- nrow(p)
J <- ncol(p)
pi <- matrix(NA, M, J)
p[,1] <- pi[,1] <- 1 - (1 - p[,1])^2
p[,2] <- 1 - (1 - p[,2])^3
p[,3] <- 1 - (1 - p[,3])^5
for(i in 2:J) {
pi[,i] <- pi[, i - 1]/p[, i - 1] * (1 - p[, i - 1]) * p[, i]
}
return(pi)
}
# Associated obsToY matrix required by unmarkedFrameMPois
o2y <- diag(ncol(y))
o2y[upper.tri(o2y)] <- 1
o2y
umfGMM2 <- unmarkedFrameGMM(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=data.frame(year=c(t(years))),
numPrimary=T, obsToY=o2y, piFun="instRemPiFun")
str(umfGMM2)
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