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occuMulti <- function(detformulas, stateformulas, data, maxOrder,
penalty=0, boot=30, starts, method='BFGS', se=TRUE,
engine=c("C","R"), silent=FALSE, ...){
#Format input data-----------------------------------------------------------
#Check data object
if(!inherits(data, "unmarkedFrameOccuMulti"))
stop("Data must be created with unmarkedFrameOccuMulti()")
#Check engine
engine <- match.arg(engine, c("C", "R"))
#Formula format
if(missing(detformulas)){
if(!silent){
warning("No detection formulas specified; set to intercept-only")
}
detformulas <- rep('~1',length(data@ylist))
}
all_forms <- c(detformulas, stateformulas)
all_forms <- all_forms[!all_forms %in% c("0","~0")]
check_no_support(lapply(all_forms, as.formula))
#Check penalty
if(penalty < 0) stop("Penalty term must be >= 0")
#Get design matrices and indices
designMats <- getDesign(data, detformulas, stateformulas, maxOrder, warn=!silent)
#Don't think there is a better way...
N <- designMats$N; S <- designMats$S; J <- designMats$J; M <- designMats$M
nF <- designMats$nF; nP <- designMats$nP; nOP <- designMats$nOP
fStart <- designMats$fStart; fStop <- designMats$fStop
dStart <- designMats$dStart; dStop <- designMats$dStop
yStart <- designMats$yStart; yStop <- designMats$yStop
dmF <- designMats$dmF; dmOcc <- designMats$dmOcc; dmDet <- designMats$dmDet
y <- designMats$y; z <- designMats$z; Iy0 <- designMats$Iy0
paramNames <- designMats$paramNames; fixed0 <- designMats$fixed0
dmF <- Matrix::Matrix(dmF, sparse=TRUE) # convert to sparse matrix
#Only transpose once
t_dmF <- Matrix::t(dmF)
#----------------------------------------------------------------------------
#Likelihood function in R----------------------------------------------------
nll_R <- function(params){
#psi
f <- matrix(NA,nrow=N,ncol=nF)
index <- 1
for (i in 1:nF){
if(fixed0[i]){
f[,i] <- 0
} else {
f[,i] <- dmOcc[[index]] %*% params[fStart[index]:fStop[index]]
index <- index + 1
}
}
psi <- exp(f %*% t_dmF)
psi <- psi/rowSums(psi)
#p
p <- matrix(NA,nrow=nrow(y),ncol=S)
for (i in 1:S){
p[,i] <- plogis(dmDet[[i]] %*% params[dStart[i]:dStop[i]])
}
prdProbY <- matrix(NA,nrow=N,ncol=M)
for (i in 1:N){
inds <- yStart[i]:yStop[i]
#column dot product of y and log(p) at site i
cdp <- exp(colSums(y[inds,,drop=F] * log(p[inds,,drop=F])) +
colSums((1-y[inds,,drop=F]) * log(1-p[inds,,drop=F])))
prbSeq <- z * tcrossprod(rep(1,M),cdp) +
(1-z) * tcrossprod(rep(1,M),Iy0[i,])
#prod of all p at site i given occupancy states m
prdProbY[i,] <- apply(prbSeq,1,prod)
}
#Penalty term (defaults to 0)
pen <- penalty * 0.5 * sum(params^2)
#neg log likelihood
-1 * (sum(log(rowSums(psi*prdProbY))) - pen)
}
#----------------------------------------------------------------------------
#Likelihood function in C----------------------------------------------------
nll_C <- function(params) {
.Call("nll_occuMulti",
fStart-1, fStop-1, t_dmF, dmOcc, params, dmDet, dStart-1, dStop-1,
y, yStart-1, yStop-1, Iy0, as.matrix(z), fixed0, penalty, 0,
PACKAGE = "unmarked")
}
#----------------------------------------------------------------------------
#Run optim()-----------------------------------------------------------------
if(engine=="R"){
nll <- nll_R
} else {
nll <- nll_C
}
if(missing(starts)) starts <- rep(0, nP)
fm <- optim(starts, nll, method = method, hessian = se, ...)
covMat <- invertHessian(fm, nP, se)
fmAIC <- 2 * fm$value + 2 * nP
#----------------------------------------------------------------------------
#Format output---------------------------------------------------------------
ests <- fm$par
names(ests) <- paramNames
state <- unmarkedEstimate(name = "Occupancy", short.name = "psi",
estimates = ests[1:nOP],
covMat = as.matrix(covMat[1:nOP,1:nOP]),
invlink = "logistic",
invlinkGrad = "logistic.grad")
det <- unmarkedEstimate(name = "Detection", short.name = "p",
estimates = ests[(nOP + 1) : nP],
covMat = as.matrix(covMat[(nOP + 1) : nP,
(nOP + 1) : nP]),
invlink = "logistic",
invlinkGrad = "logistic.grad")
estimateList <- unmarkedEstimateList(list(state=state, det=det))
umfit <- new("unmarkedFitOccuMulti", fitType = "occuMulti", call = match.call(),
detformulas = detformulas, stateformulas = stateformulas,
formula = ~1, data = data,
#sitesRemoved = designMats$removed.sites,
estimates = estimateList, AIC = fmAIC, opt = fm,
negLogLike = fm$value, nllFun = nll_R)
if(penalty > 0 & se){
cat("Bootstraping covariance matrix\n")
umfit <- nonparboot(umfit, B=boot, se=FALSE)
#Replace covMat with covMatBS
for (i in 1:length(umfit@estimates@estimates)){
umfit@estimates@estimates[[i]]@covMat <- umfit@estimates@estimates[[i]]@covMatBS
}
}
umfit
}
# Functions for optimizing penalty value
occuMultiLogLik <- function(fit, data){
if(!inherits(fit, "unmarkedFitOccuMulti"))
stop("Fit must be created with occuMulti()")
if(!inherits(data, "unmarkedFrameOccuMulti"))
stop("Data must be created with unmarkedFrameOccuMulti()")
maxOrder <- fit@call$maxOrder
if(is.null(maxOrder)) maxOrder <- length(fit@data@ylist)
dm <- getDesign(data, fit@detformulas, fit@stateformulas,
maxOrder=maxOrder, warn=FALSE)
dmF <- Matrix::Matrix(dm$dmF, sparse=TRUE)
t_dmF <- Matrix::t(dmF)
out <- .Call("nll_occuMulti",
dm$fStart-1, dm$fStop-1, t_dmF,
dm$dmOcc, coef(fit), dm$dmDet, dm$dStart-1, dm$dStop-1, dm$y,
dm$yStart-1, dm$yStop-1, dm$Iy0, as.matrix(dm$z), dm$fixed0, 0,
#return site likelihoods
1,
PACKAGE = "unmarked")
as.vector(out)
}
setGeneric("optimizePenalty",
function(object, penalties=c(0,2^seq(-4,4)), k = 5, boot = 30, ...)
standardGeneric("optimizePenalty"))
setMethod("optimizePenalty", "unmarkedFitOccuMulti",
function(object, penalties=c(0,2^seq(-4,4)), k = 5, boot = 30, ...){
folds <- partitionKfold(object, k)
cvp <- sapply(penalties, function(p, k, fit, folds){
cv <- sapply(1:k, function(k){
refit <- update(fit, data=folds[[k]]$trainData, penalty=p, se=FALSE)
sum(occuMultiLogLik(refit, folds[[k]]$testData))
})
sum(cv)
}, k=k, fit=object, folds=folds)
max_cvp <- penalties[which(cvp == max(cvp))]
cat("Optimal penalty is", max_cvp,"\n")
object@call$penalty <- max_cvp
update(object, data=object@data, boot=boot)
})
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