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# data will need to be an unmarkedMultFrame
gpcount <- function(lambdaformula, phiformula, pformula, data,
mixture=c('P', 'NB', 'ZIP'), K, starts, method = "BFGS", se = TRUE,
engine=c('C', 'R'), threads=1, ...)
{
if(!is(data, "unmarkedFrameGPC"))
stop("Data is not of class unmarkedFrameGPC.")
mixture <- match.arg(mixture)
engine <- match.arg(engine)
formlist <- list(lambdaformula = lambdaformula, phiformula = phiformula,
pformula = pformula)
check_no_support(formlist)
form <- as.formula(paste(unlist(formlist), collapse=" "))
D <- getDesign(data, formula = form)
Xlam <- D$Xlam
Xphi <- D$Xphi
Xdet <- D$Xdet
ym <- D$y # MxJT
Xlam.offset <- D$Xlam.offset
Xphi.offset <- D$Xphi.offset
Xdet.offset <- D$Xdet.offset
if(is.null(Xlam.offset)) Xlam.offset <- rep(0, nrow(Xlam))
if(is.null(Xphi.offset)) Xphi.offset <- rep(0, nrow(Xphi))
if(is.null(Xdet.offset)) Xdet.offset <- rep(0, nrow(Xdet))
if(missing(K) || is.null(K)) {
K <- max(ym, na.rm=TRUE) + 100
warning("K was not specified, so was set to max(y)+100 =", K)
}
M <- N <- 0:K
lM <- length(M)
I <- nrow(ym)
T <- data@numPrimary
if(T==1)
stop("use pcount instead")
J <- numY(data) / T
y <- array(ym, c(I, J, T))
lamPars <- colnames(Xlam)
detPars <- colnames(Xdet)
nLP <- ncol(Xlam)
nPP <- ncol(Xphi)
phiPars <- colnames(Xphi)
nDP <- ncol(Xdet)
nP <- nLP + nPP + nDP + (mixture%in%c('NB','ZIP'))
if(!missing(starts) && length(starts) != nP)
stop("There should be", nP, "starting values, not", length(starts))
if(identical(engine, "R")) {
# Minus negative log-likelihood
nll <- function(pars) {
lam <- exp(Xlam %*% pars[1:nLP] + Xlam.offset)
phi <- plogis(Xphi %*% pars[(nLP+1):(nLP+nPP)] + Xphi.offset)
phi <- matrix(phi, I, T, byrow=TRUE)
p <- plogis(Xdet %*% pars[(nLP+nPP+1):(nLP+nPP+nDP)] + Xdet.offset)
p <- matrix(p, I, byrow=TRUE)
p <- array(p, c(I, J, T)) # byrow?
L <- rep(NA, I)
for(i in 1:I) {
f <- switch(mixture,
P = dpois(M, lam[i], log=TRUE),
NB = dnbinom(M, mu=lam[i], size=exp(pars[nP]), log=TRUE),
ZIP = log(dzip(M, lambda=lam[i], psi=plogis(pars[nP])))
)
ghi <- rep(0, lM)
for(t in 1:T) {
gh <- matrix(-Inf, lM, lM)
for(m in M) {
if(m < max(y[i,,], na.rm=TRUE)) {
gh[,m+1] <- -Inf
next
}
if(is.na(phi[i,t])) {
g <- rep(0, lM)
g[N>m] <- -Inf
}
else
g <- dbinom(N, m, phi[i,t], log=TRUE)
h <- rep(0, lM)
for(j in 1:J) {
if(is.na(y[i,j,t]))
next
h <- h + dbinom(y[i,j,t], N, p[i,j,t], log=TRUE)
}
gh[,m+1] <- g + h
}
ghi <- ghi + log(colSums(exp(gh))) # sum over N(t)
}
fgh <- f + ghi
L[i] <- sum(exp(fgh)) # sum over M
}
return(-sum(log(L)))
}
} else
if(identical(engine, "C")) {
nll <- function(pars) {
beta.lam <- pars[1:nLP]
beta.phi <- pars[(nLP+1):(nLP+nPP)]
beta.p <- pars[(nLP+nPP+1):(nLP+nPP+nDP)]
log.alpha <- 1
if(mixture %in% c("NB", "ZIP"))
log.alpha <- pars[nP]
nll_gpcount(ym, Xlam, Xphi, Xdet, beta.lam, beta.phi, beta.p,
log.alpha, Xlam.offset, Xphi.offset, Xdet.offset,
as.integer(K), mixture, T, threads)
}
}
if(missing(starts)) starts <- rep(0, nP)
fm <- optim(starts, nll, method = method, hessian = se, ...)
covMat <- invertHessian(fm, nP, se)
ests <- fm$par
fmAIC <- 2 * fm$value + 2 * nP
nbParm <- switch(mixture, P={character(0)}, NB={"alpha"}, ZIP={"psi"})
names(ests) <- c(lamPars, phiPars, detPars, nbParm)
lamEstimates <- unmarkedEstimate(name = "Abundance", short.name = "lambda",
estimates = ests[1:nLP],
covMat = as.matrix(covMat[1:nLP, 1:nLP]), invlink = "exp",
invlinkGrad = "exp")
phiEstimates <- unmarkedEstimate(name = "Availability",
short.name = "phi",
estimates = ests[(nLP+1):(nLP+nPP)],
covMat = as.matrix(covMat[(nLP+1) :
(nLP+nPP), (nLP+1):(nLP+nPP)]),
invlink = "logistic",
invlinkGrad = "logistic.grad")
detEstimates <- unmarkedEstimate(name = "Detection", short.name = "p",
estimates = ests[(nLP+nPP+1):(nLP+nPP+nDP)],
covMat = as.matrix(
covMat[(nLP+nPP+1):(nLP+nPP+nDP), (nLP+nPP+1):(nLP+nPP+nDP)]),
invlink = "logistic", invlinkGrad = "logistic.grad")
estimateList <- unmarkedEstimateList(list(lambda=lamEstimates, phi=phiEstimates, det=detEstimates))
if(identical(mixture,"NB"))
estimateList@estimates$alpha <- unmarkedEstimate(name = "Dispersion",
short.name = "alpha", estimates = ests[nP],
covMat = as.matrix(covMat[nP, nP]), invlink = "exp",
invlinkGrad = "exp")
if(identical(mixture,"ZIP")) {
estimateList@estimates$psi <- unmarkedEstimate(name="Zero-inflation",
short.name = "psi", estimates = ests[nP],
covMat=as.matrix(covMat[nP, nP]), invlink = "logistic",
invlinkGrad = "logistic.grad")
}
umfit <- new("unmarkedFitGPC", fitType = "gpcount",
call = match.call(), formula = form, formlist = formlist,
data = data, estimates = estimateList, sitesRemoved = D$removed.sites,
AIC = fmAIC, opt = fm, negLogLike = fm$value, nllFun = nll,
mixture=mixture, K=K)
return(umfit)
}
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