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# Fit the occupancy model of MacKenzie et al (2002).
occu <- function(formula, data, knownOcc = numeric(0),
linkPsi = c("logit", "cloglog"), starts, method = "BFGS",
se = TRUE, engine = c("C", "R", "TMB"), threads=1, ...) {
# Check arguments------------------------------------------------------------
if(!is(data, "unmarkedFrameOccu"))
stop("Data is not an unmarkedFrameOccu object.")
engine <- match.arg(engine, c("C", "R", "TMB"))
if(any(sapply(split_formula(formula), has_random))) engine <- "TMB"
if(length(knownOcc)>0 & engine == "TMB"){
stop("TMB engine does not support knownOcc argument", call.=FALSE)
}
linkPsi <- match.arg(linkPsi, c("logit","cloglog"))
psiLinkFunc <- ifelse(linkPsi=="cloglog", cloglog, plogis)
psiInvLink <- ifelse(linkPsi=="cloglog", "cloglog", "logistic")
psiLinkGrad <- ifelse(linkPsi=="cloglog", "cloglog.grad", "logistic.grad")
# Format input data----------------------------------------------------------
designMats <- getDesign(data, formula)
y <- truncateToBinary(designMats$y)
X <- designMats$X; V <- designMats$V;
Z_state <- designMats$Z_state; Z_det <- designMats$Z_det
removed <- designMats$removed.sites
X.offset <- designMats$X.offset; V.offset <- designMats$V.offset
# Re-format some variables for C and R engines
yvec <- as.numeric(t(y))
navec <- is.na(yvec)
nd <- ifelse(rowSums(y,na.rm=TRUE) == 0, 1, 0) # no det at site i
# convert knownOcc to logical so we can correctly to handle NAs.
knownOccLog <- rep(FALSE, numSites(data))
knownOccLog[knownOcc] <- TRUE
if(length(removed)>0) knownOccLog <- knownOccLog[-removed]
# Set up parameter names and indices-----------------------------------------
occParms <- colnames(X)
detParms <- colnames(V)
nDP <- ncol(V)
nOP <- ncol(X)
nP <- nDP + nOP
psiIdx <- 1:nOP
pIdx <- (nOP+1):nP
# Set up negative log likelihood functions for C++ and R engines-----_-------
if(identical(engine, "C")) {
nll <- function(params) {
beta.psi <- params[1:nOP]
beta.p <- params[(nOP+1):nP]
nll_occu(
yvec, X, V, beta.psi, beta.p, nd, knownOccLog, navec,
X.offset, V.offset, linkPsi
)
}
} else if (identical(engine, "R")){
J <- ncol(y)
M <- nrow(y)
nll <- function(params) {
psi <- psiLinkFunc(X %*% params[1 : nOP] + X.offset)
psi[knownOccLog] <- 1
pvec <- plogis(V %*% params[(nOP + 1) : nP] + V.offset)
cp <- (pvec^yvec) * ((1 - pvec)^(1 - yvec))
cp[navec] <- 1 # so that NA's don't modify likelihood
cpmat <- matrix(cp, M, J, byrow = TRUE) #
loglik <- log(rowProds(cpmat) * psi + nd * (1 - psi))
-sum(loglik)
}
}
# Fit model with C++ and R engines-------------------------------------------
if(engine %in% c("C", "R")){
if(missing(starts)) starts <- rep(0, nP)
if(length(starts) != nP){
stop(paste("The number of starting values should be", nP))
}
fm <- optim(starts, nll, method = method, hessian = se, ...)
covMat <- invertHessian(fm, nP, se)
ests <- fm$par
tmb_mod <- NULL
fmAIC <- 2 * fm$value + 2 * nP #+ 2*nP*(nP + 1)/(M - nP - 1)
# Organize effect estimates
names(ests) <- c(occParms, detParms)
state_coef <- list(ests=ests[1:nOP], cov=as.matrix(covMat[1:nOP,1:nOP]))
det_coef <- list(ests=ests[(nOP+1):nP],
cov=as.matrix(covMat[(nOP+1):nP, (nOP+1):nP]))
# No random effects for C++ and R engines
state_rand_info <- det_rand_info <- list()
# Fit model with TMB engine--------------------------------------------------
} else if(identical(engine, "TMB")){
# Set up TMB input data
forms <- split_formula(formula)
obs_all <- add_covariates(obsCovs(data), siteCovs(data), length(getY(data)))
inps <- get_ranef_inputs(forms, list(det=obs_all, state=siteCovs(data)),
list(V, X), designMats[c("Z_det","Z_state")])
tmb_dat <- c(list(y=y, no_detect=nd, link=ifelse(linkPsi=="cloglog",1,0),
offset_state=X.offset, offset_det=V.offset), inps$data)
# Fit model with TMB
if(missing(starts)) starts <- NULL
tmb_out <- fit_TMB("tmb_occu", tmb_dat, inps$pars, inps$rand_ef,
starts=starts, method, ...)
tmb_mod <- tmb_out$TMB
fm <- tmb_out$opt
fmAIC <- tmb_out$AIC
nll <- tmb_mod$fn
# Organize fixed effect estimates
state_coef <- get_coef_info(tmb_out$sdr, "state", occParms, 1:nOP)
det_coef <- get_coef_info(tmb_out$sdr, "det", detParms, (nOP+1):nP)
# Organize random effect estimates
state_rand_info <- get_randvar_info(tmb_out$sdr, "state", forms[[2]],
siteCovs(data))
det_rand_info <- get_randvar_info(tmb_out$sdr, "det", forms[[1]],
obs_all)
}
# Create unmarkedEstimates---------------------------------------------------
state <- unmarkedEstimate(name = "Occupancy", short.name = "psi",
estimates = state_coef$est,
covMat = state_coef$cov,
fixed = 1:nOP,
invlink = psiInvLink,
invlinkGrad = psiLinkGrad,
randomVarInfo=state_rand_info
)
det <- unmarkedEstimate(name = "Detection", short.name = "p",
estimates =det_coef$est,
covMat = det_coef$cov,
fixed = 1:nDP,
invlink = "logistic",
invlinkGrad = "logistic.grad",
randomVarInfo=det_rand_info
)
estimateList <- unmarkedEstimateList(list(state=state, det=det))
# Create unmarkedFit object--------------------------------------------------
umfit <- new("unmarkedFitOccu", fitType = "occu", call = match.call(),
formula = formula, data = data,
sitesRemoved = designMats$removed.sites,
estimates = estimateList, AIC = fmAIC, opt = fm,
negLogLike = fm$value,
nllFun = nll, knownOcc = knownOccLog, TMB=tmb_mod)
return(umfit)
}
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