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# data will need to be an unmarkedMultFrame
gmultmix <- 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, "unmarkedFrameGMM"))
stop("Data is not of class unmarkedFrameGMM.")
engine <- match.arg(engine, c("C", "R"))
mixture <- match.arg(mixture)
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$Xdet
y <- D$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))
K <- check_K_multinomial(K, K_adjust = 100, y, data@numPrimary)
k <- 0:K
lk <- length(k)
M <- nrow(y)
T <- data@numPrimary
R <- numY(data) / T
J <- obsNum(data) / T
y <- array(y, c(M, R, T))
y <- aperm(y, c(1,3,2))
yt <- apply(y, 1:2, function(x) {
if(all(is.na(x)))
return(NA)
else return(sum(x, na.rm=TRUE))
})
piFun <- data@piFun
lamPars <- colnames(Xlam)
detPars <- colnames(Xdet)
nLP <- ncol(Xlam)
if(T==1) {
nPP <- 0
phiPars <- character(0)
} else if(T>1) {
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(paste("The number of starting values should be", nP))
lfac.k <- lgamma(k+1)
kmyt <- array(NA, c(M, T, lk))
lfac.kmyt <- array(0, c(M, T, lk))
fin <- matrix(NA, M, lk)
naflag <- array(NA, c(M, T, R))
for(i in 1:M) {
fin[i, ] <- k - max(yt[i,], na.rm=TRUE) >= 0
for(t in 1:T) {
naflag[i,t,] <- is.na(y[i,t,])
if(!all(naflag[i,t,])) {
kmyt[i,t,] <- k - yt[i,t]
lfac.kmyt[i, t, fin[i,]] <- lgamma(kmyt[i, t, fin[i,]] + 1)
}
}
}
nll_R <- function(pars) {
lambda <- exp(Xlam %*% pars[1:nLP] + Xlam.offset)
if(T==1)
phi <- 1
else if(T>1)
phi <- drop(plogis(Xphi %*% pars[(nLP+1):(nLP+nPP)] + Xphi.offset))
p <- plogis(Xdet %*% pars[(nLP+nPP+1):(nLP+nPP+nDP)] + Xdet.offset)
phi.mat <- matrix(phi, M, T, byrow=TRUE)
phi <- as.numeric(phi.mat)
p <- matrix(p, nrow=M, byrow=TRUE)
p <- array(p, c(M, J, T))
p <- aperm(p, c(1,3,2))
cp <- array(as.numeric(NA), c(M, T, R+1))
for(t in 1:T) cp[,t,1:R] <- do.call(piFun, list(p[,t,]))
cp[,,1:R] <- cp[,,1:R] * phi
cp[,, 1:R][is.na(y)]<- NA # andy added 5/29
cp[,,R+1] <- 1 - apply(cp[,,1:R,drop=FALSE], 1:2, sum, na.rm=TRUE)
switch(mixture,
P = f <- sapply(k, function(x) dpois(x, lambda)),
NB = f <- sapply(k, function(x) dnbinom(x, mu=lambda, size=exp(pars[nP]))),
ZIP = f <- sapply(k, function(x) dzip(rep(x, length(lambda)), lambda=lambda, psi=plogis(pars[nP])))
)
g <- matrix(as.numeric(NA), M, lk)
for(i in 1:M) {
A <- matrix(0, lk, T)
for(t in 1:T) {
na <- naflag[i,t,]
if(!all(na))
A[, t] <- lfac.k - lfac.kmyt[i, t,] +
sum(y[i, t, !na] * log(cp[i, t, which(!na)])) +
kmyt[i, t,] * log(cp[i, t, R+1])
}
g[i,] <- exp(rowSums(A))
}
f[!fin] <- g[!fin] <- 0
ll <- rowSums(f*g)
-sum(log(ll))
}
if(engine=="R"){
nll <- nll_R
} else {
long_format <- function(x){
out <- matrix(aperm(x,c(1,3,2)),nrow=nrow(x),ncol=dim(x)[2]*dim(x)[3])
as.vector(t(out))
}
y_long <- long_format(y)
kmytC <- kmyt
kmytC[which(is.na(kmyt))] <- 0
mixture_code <- switch(mixture, P={1}, NB={2}, ZIP={3})
n_param <- c(nLP, nPP, nDP, mixture%in%c("NB","ZIP"))
Kmin <- apply(yt, 1, max, na.rm=TRUE)
nll <- function(params) {
nll_gmultmix(params, n_param, y_long, mixture_code, piFun, Xlam, Xlam.offset,
Xphi, Xphi.offset, Xdet, Xdet.offset, k, lfac.k, lfac.kmyt,
kmytC, Kmin, threads)
}
if(!piFun%in%c('doublePiFun','removalPiFun','depDoublePiFun')){
warning("Custom pi functions are not supported by C engine. Using R engine instead.")
nll <- nll_R
}
}
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$value + 2 * nP
nbParm <- switch(mixture, P={character(0)}, NB={"alpha"}, ZIP={"psi"})
names(ests) <- c(lamPars, phiPars, detPars, nbParm)
lamEstimates <- unmarkedEstimate(name = "Abundance", short.name = "lambda",
estimates = ests[1:nLP],
covMat = as.matrix(covMat[1:nLP, 1:nLP]), invlink = "exp",
invlinkGrad = "exp")
estimateList <- unmarkedEstimateList(list(lambda=lamEstimates))
if(T>1) {
phiEstimates <- unmarkedEstimate(name = "Availability",
short.name = "phi",
estimates = ests[(nLP+1):(nLP+nPP)],
covMat = as.matrix(covMat[(nLP+1) :
(nLP+nPP), (nLP+1):(nLP+nPP)]),
invlink = "logistic",
invlinkGrad = "logistic.grad")
estimateList@estimates$phi <- phiEstimates
}
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@estimates$det <- detEstimates
if(identical(mixture,"NB"))
estimateList@estimates$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$psi <- unmarkedEstimate(name="Zero-inflation",
short.name = "psi", estimates = ests[nP],
covMat=as.matrix(covMat[nP, nP]), invlink = "logistic",
invlinkGrad = "logistic.grad")
}
umfit <- new("unmarkedFitGMM", fitType = "gmn",
call = match.call(), formula = form, formlist = formlist,
data = data, estimates = estimateList, sitesRemoved = D$removed.sites,
AIC = fmAIC, opt = fm, negLogLike = fm$value, nllFun = nll,
mixture=mixture, K=K)
return(umfit)
}
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