Nothing
R/Lnegbin.R
In phenology: Tools to Manage a Parametric Function that Describes Phenology and More
# .Lnegbin estimate a negative binomial likelihood.
# @title The function ".Lnegbin"
# @author Marc Girondot
# @return Return the likelihood
# @param x Set of parameters
# @param pt Transfer parameters
# pt=list(data=data, fixed=fixed.parameters,
# model_before=model_before,
# parallel=parallel,
# out=out,
# namespar=names(fitted.parameters),
# zero=zero, cofactors=cofactors,
# add.cofactors=add.cofactors)
# @description Function of the package phenology
.Lnegbin <- function (x, pt) {
if (!is.null(pt$parallel)) {
if (pt$parallel) {
mc.cores <- getOption("mc.cores", detectCores())
forking <- getOption("forking", ifelse(.Platform$OS.type == "windows", FALSE, TRUE))
} else {
mc.cores <- 1
forking <- FALSE
}
} else {
mc.cores <- getOption("mc.cores", detectCores())
forking <- getOption("forking", ifelse(.Platform$OS.type == "windows", FALSE, TRUE))
}
# 2/10/2019: A priori ca sert en méthode Brent qui ne transmet pas les noms. Oui c'est ça
if (is.null(names(x))) {
if (!is.null(pt$namespar)) {
names(x) <- pt$namespar
}
}
# 19/3/2016: je rajoute cofactors et add.cofactors dans pt
# if (length(pt)==1 & names(pt)[1]=="pt") pt <- pt$pt
# .phenology.env<- NULL
# rm(.phenology.env)
# pt=list(data=data, fixed=fixed.parameters)
sum = 0
# je mets tous les paramètres dans xpar
xpar <- c(x, pt$fixed)
# Si out vaut TRUE, renvoie la somme des vraisemblances
# Sinon la vraisemblance de chaque série
if (is.null(pt$out)) {
out <- TRUE
} else {
out <- pt$out
}
if (is.null(pt$zero)) {
zero <- 1E-9
} else {
zero <- pt$zero
}
# deb <- ifelse(zero, 0, 1)
model_before <- pt$model_before
datatot <- pt$data
add.cofactors <- pt$add.cofactors
cofactors <- pt$cofactors
daily_count <- getFromNamespace(".daily_count", ns = "phenology")
format_par <- getFromNamespace(".format_par", ns = "phenology")
rg <- try(universalmclapply(X = seq_along(datatot), mc.cores = mc.cores,
FUN = function(k) {
# for (k in seq_along(datatot)) {
#
# print(k)
# k <- 1
deb <- 0
data <- datatot[[k]]
nmser <- names(datatot)[k]
xparec <- format_par(xpar=xpar, serie=nmser, model_before=model_before)
th <- xparec["Theta"]
if (th < 1E-6) th <- 1E-6
# Il faut que je fasse daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero) une seule fois
# Le premier jour de la série est 0 et donc il est à l'indice 1
# data$ordinal[i]
# data$ordinal2[i]
j <- unname(unlist(apply(X = data, MARGIN = 1, FUN=function(x) seq(from=x["ordinal"], to=ifelse(is.na(x["ordinal2"]), x["ordinal"], x["ordinal2"]), by=1))))
nbperday <- rep(NA, max(366, j+1)-deb)
# Je dois envoyer les données de cofactors avec seulement les dates
# à analyser
cof <- NULL
if ((!is.null(add.cofactors)) & (!is.null(cofactors))) {
cof <- cofactors[cofactors$Date %in% (data[1, "Date"]+j), ]
cof <- cof[, -1, drop=FALSE]
cof <- as.data.frame(cbind(Date=j, cof))
}
nbperday[j+1] <- daily_count(d=unname(j), xpar=xparec,
cofactors=cof,
add.cofactors=add.cofactors,
print = FALSE, zero = zero)
for (i in 1:nrow(data)) {
if (data$ZeroCounts[i]) {
if (data$CountTypes[i] == "exact") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
lnli2 <- -dnbinom(data$nombre[i], size = th,
mu = sumnbcount, log = TRUE)
} else {
# break
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
lnli2 <- -dSnbinom(x=data$nombre[i], size = th, mu = nbcount,
log = TRUE, method = method_Snbinom, normalize = FALSE)
}
} else {
# je suis en minimum
if (data$CountTypes[i] == "minimum") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
# Par exemple j'ai un minimum de 1
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)
# C'est bon puisque
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)+dnbinom(0, size = 3, mu = 10, log=FALSE) = 1
if (data$nombre[i]-1 >= 0) {
lnli2 <- -pnbinom(q=data$nombre[i]-1, size = th,
mu = sumnbcount, log.p = TRUE,
lower.tail = TRUE)
} else {
# si j'ai 0 observation forcément likelihood=1
lnli2 <- 0
}
} else {
# j'ai une range de dates
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
# -log((1-sum(dSnbinom(x=0:(data$nombre[i]-1), size = th, mu = nbcount, log=FALSE, method = method_Snbinom))))
lnli2 <- -pSnbinom(q=data$nombre[i]-1, size = th, mu = nbcount, log.p = TRUE,
lower.tail = FALSE, method = method_Snbinom, tol=1E-6, normalize = FALSE)
} else {
lnli2 <- 0
}
}
} else {
# Je suis en nombre max
nbmin <- data$nombre[i]
nbmax <- as.numeric(data$CountTypes[i])
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
# Par exemple j'ai un minimum de 1
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)
# C'est bon puisque
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)+dnbinom(0, size = 3, mu = 10, log=FALSE) = 1
lnli2 <- -sum(dnbinom(x=nbmin:nbmax, size = th,
mu = sumnbcount, log = TRUE))
} else {
# j'ai une range de dates
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
# -log((1-sum(dSnbinom(x=0:(data$nombre[i]-1), size = th, mu = nbcount, log=FALSE, method = method_Snbinom))))
lnli2 <- -sum(dSnbinom(x=nbmin:nbmax, size = th, mu = nbcount, log = TRUE,
method = method_Snbinom, tol=1E-6, normalize = FALSE))
} else {
lnli2 <- 0
}
}
}
}
} else {
# Là je suis en ZeroCounts == FALSE
if (data$CountTypes[i] == "exact") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
lnli2 <- (- dnbinom(data$nombre[i], size = th, mu = sumnbcount, log = TRUE)+
log(1 - dnbinom(0, size = th, mu = sumnbcount, log = FALSE)))
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "furman"
if ((th > 10) | (any(nbcount <1E-5))) {
if (length(nbcount) <= 5) {
method_Snbinom <- "Vellaisamy&Upadhye"
} else {
method_Snbinom <- "approximate.negativebinomial"
}
}
if ((length(nbcount) > 5) & (method_Snbinom == "furman")) {
method_Snbinom <- "saddlepoint"
}
}
lnli2 <- (- dSnbinom(data$nombre[i], size = th, mu = nbcount, log = TRUE, method = method_Snbinom, normalize = FALSE) +
log(1 - dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)))
}
} else {
# Je suis en minimum
if (data$CountTypes[i] == "minimum") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
if (data$nombre[i]-1 >= 0) {
prob.sup.egal.obs <- pnbinom(q=data$nombre[i]-1, size = th, mu = sumnbcount, log.p = TRUE, lower.tail = FALSE)
prob0 <- dnbinom(0, size = th, mu = sumnbcount, log = FALSE)
lnli2 <- (- prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
prob.sup.egal.obs <- pSnbinom(q=data$nombre[i]-1, size = th, mu = nbcount, log.p = TRUE,
lower.tail = FALSE, method = method_Snbinom, tol=1E-6, normalize = FALSE)
prob0 <- dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)
lnli2 <- (- prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
}
} else {
# Je suis en nombre max
nbmin <- data$nombre[i]
nbmax <- as.numeric(data$CountTypes[i])
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
if (data$nombre[i]-1 >= 0) {
prob.sup.egal.obs <- -sum(dnbinom(x=nbmin:nbmax, size = th,
mu = sumnbcount, log = TRUE))
prob0 <- dnbinom(0, size = th, mu = sumnbcount, log = FALSE)
lnli2 <- (prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
prob.sup.egal.obs <- -sum(dSnbinom(x=nbmin:nbmax, size = th, mu = nbcount, log = TRUE,
method = method_Snbinom, tol=1E-6, normalize = FALSE))
prob0 <- dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)
lnli2 <- (prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
}
}
}
}
# print(paste0(as.character(i), " ", lnli2))
if (is.na(lnli2)) lnli2 <- 1E9
data$LnL[i] <- lnli2
data$Modeled[i] <- sumnbcount
}
data$LnL[is.infinite(data$LnL)] <- 115.1293 # -log(1e-50)
return(data)
},
clusterExport = list(varlist=c("xpar", "datatot",
"daily_count", "format_par",
"zero",
"model_before",
"add.cofactors", "cofactors"),
envir=environment()),
clusterEvalQ=list(expr=expression(library(phenology))
),
forking = forking), silent = FALSE)
# }
if (inherits(rg, "try-error")) {
save(x, file = "x.Rdata")
save(pt, file = "pt.Rdata")
stop("Error during likelihood estimation; look at x.Rdata and pt.Rdata")
}
sum <- sum(sapply(X = rg, function(x) sum(x$LnL, na.rm = TRUE)))
if (is.na(sum)) sum <- 1E9
if (!is.null(pt$store.intermediate)) {
if (pt$store.intermediate) {
load(file=pt$file.intermediate)
store <- c(store, iteration=list(list(fitted=x, fixed=pt$fixed, loglik=sum)))
save(store, file=pt$file.intermediate)
}
}
if (out) {
return(sum)
} else {
names(rg) <- names(datatot)
return(rg)
}
}
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# .Lnegbin estimate a negative binomial likelihood.
# @title The function ".Lnegbin"
# @author Marc Girondot
# @return Return the likelihood
# @param x Set of parameters
# @param pt Transfer parameters
# pt=list(data=data, fixed=fixed.parameters,
# model_before=model_before,
# parallel=parallel,
# out=out,
# namespar=names(fitted.parameters),
# zero=zero, cofactors=cofactors,
# add.cofactors=add.cofactors)
# @description Function of the package phenology
.Lnegbin <- function (x, pt) {
if (!is.null(pt$parallel)) {
if (pt$parallel) {
mc.cores <- getOption("mc.cores", detectCores())
forking <- getOption("forking", ifelse(.Platform$OS.type == "windows", FALSE, TRUE))
} else {
mc.cores <- 1
forking <- FALSE
}
} else {
mc.cores <- getOption("mc.cores", detectCores())
forking <- getOption("forking", ifelse(.Platform$OS.type == "windows", FALSE, TRUE))
}
# 2/10/2019: A priori ca sert en méthode Brent qui ne transmet pas les noms. Oui c'est ça
if (is.null(names(x))) {
if (!is.null(pt$namespar)) {
names(x) <- pt$namespar
}
}
# 19/3/2016: je rajoute cofactors et add.cofactors dans pt
# if (length(pt)==1 & names(pt)[1]=="pt") pt <- pt$pt
# .phenology.env<- NULL
# rm(.phenology.env)
# pt=list(data=data, fixed=fixed.parameters)
sum = 0
# je mets tous les paramètres dans xpar
xpar <- c(x, pt$fixed)
# Si out vaut TRUE, renvoie la somme des vraisemblances
# Sinon la vraisemblance de chaque série
if (is.null(pt$out)) {
out <- TRUE
} else {
out <- pt$out
}
if (is.null(pt$zero)) {
zero <- 1E-9
} else {
zero <- pt$zero
}
# deb <- ifelse(zero, 0, 1)
model_before <- pt$model_before
datatot <- pt$data
add.cofactors <- pt$add.cofactors
cofactors <- pt$cofactors
daily_count <- getFromNamespace(".daily_count", ns = "phenology")
format_par <- getFromNamespace(".format_par", ns = "phenology")
rg <- try(universalmclapply(X = seq_along(datatot), mc.cores = mc.cores,
FUN = function(k) {
# for (k in seq_along(datatot)) {
#
# print(k)
# k <- 1
deb <- 0
data <- datatot[[k]]
nmser <- names(datatot)[k]
xparec <- format_par(xpar=xpar, serie=nmser, model_before=model_before)
th <- xparec["Theta"]
if (th < 1E-6) th <- 1E-6
# Il faut que je fasse daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero) une seule fois
# Le premier jour de la série est 0 et donc il est à l'indice 1
# data$ordinal[i]
# data$ordinal2[i]
j <- unname(unlist(apply(X = data, MARGIN = 1, FUN=function(x) seq(from=x["ordinal"], to=ifelse(is.na(x["ordinal2"]), x["ordinal"], x["ordinal2"]), by=1))))
nbperday <- rep(NA, max(366, j+1)-deb)
# Je dois envoyer les données de cofactors avec seulement les dates
# à analyser
cof <- NULL
if ((!is.null(add.cofactors)) & (!is.null(cofactors))) {
cof <- cofactors[cofactors$Date %in% (data[1, "Date"]+j), ]
cof <- cof[, -1, drop=FALSE]
cof <- as.data.frame(cbind(Date=j, cof))
}
nbperday[j+1] <- daily_count(d=unname(j), xpar=xparec,
cofactors=cof,
add.cofactors=add.cofactors,
print = FALSE, zero = zero)
for (i in 1:nrow(data)) {
if (data$ZeroCounts[i]) {
if (data$CountTypes[i] == "exact") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
lnli2 <- -dnbinom(data$nombre[i], size = th,
mu = sumnbcount, log = TRUE)
} else {
# break
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
lnli2 <- -dSnbinom(x=data$nombre[i], size = th, mu = nbcount,
log = TRUE, method = method_Snbinom, normalize = FALSE)
}
} else {
# je suis en minimum
if (data$CountTypes[i] == "minimum") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
# Par exemple j'ai un minimum de 1
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)
# C'est bon puisque
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)+dnbinom(0, size = 3, mu = 10, log=FALSE) = 1
if (data$nombre[i]-1 >= 0) {
lnli2 <- -pnbinom(q=data$nombre[i]-1, size = th,
mu = sumnbcount, log.p = TRUE,
lower.tail = TRUE)
} else {
# si j'ai 0 observation forcément likelihood=1
lnli2 <- 0
}
} else {
# j'ai une range de dates
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
# -log((1-sum(dSnbinom(x=0:(data$nombre[i]-1), size = th, mu = nbcount, log=FALSE, method = method_Snbinom))))
lnli2 <- -pSnbinom(q=data$nombre[i]-1, size = th, mu = nbcount, log.p = TRUE,
lower.tail = FALSE, method = method_Snbinom, tol=1E-6, normalize = FALSE)
} else {
lnli2 <- 0
}
}
} else {
# Je suis en nombre max
nbmin <- data$nombre[i]
nbmax <- as.numeric(data$CountTypes[i])
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
# Par exemple j'ai un minimum de 1
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)
# C'est bon puisque
# pnbinom(q=1-1, size = 3, mu = 10, log.p = FALSE, lower.tail = FALSE)+dnbinom(0, size = 3, mu = 10, log=FALSE) = 1
lnli2 <- -sum(dnbinom(x=nbmin:nbmax, size = th,
mu = sumnbcount, log = TRUE))
} else {
# j'ai une range de dates
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
# -log((1-sum(dSnbinom(x=0:(data$nombre[i]-1), size = th, mu = nbcount, log=FALSE, method = method_Snbinom))))
lnli2 <- -sum(dSnbinom(x=nbmin:nbmax, size = th, mu = nbcount, log = TRUE,
method = method_Snbinom, tol=1E-6, normalize = FALSE))
} else {
lnli2 <- 0
}
}
}
}
} else {
# Là je suis en ZeroCounts == FALSE
if (data$CountTypes[i] == "exact") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
lnli2 <- (- dnbinom(data$nombre[i], size = th, mu = sumnbcount, log = TRUE)+
log(1 - dnbinom(0, size = th, mu = sumnbcount, log = FALSE)))
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "furman"
if ((th > 10) | (any(nbcount <1E-5))) {
if (length(nbcount) <= 5) {
method_Snbinom <- "Vellaisamy&Upadhye"
} else {
method_Snbinom <- "approximate.negativebinomial"
}
}
if ((length(nbcount) > 5) & (method_Snbinom == "furman")) {
method_Snbinom <- "saddlepoint"
}
}
lnli2 <- (- dSnbinom(data$nombre[i], size = th, mu = nbcount, log = TRUE, method = method_Snbinom, normalize = FALSE) +
log(1 - dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)))
}
} else {
# Je suis en minimum
if (data$CountTypes[i] == "minimum") {
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
if (data$nombre[i]-1 >= 0) {
prob.sup.egal.obs <- pnbinom(q=data$nombre[i]-1, size = th, mu = sumnbcount, log.p = TRUE, lower.tail = FALSE)
prob0 <- dnbinom(0, size = th, mu = sumnbcount, log = FALSE)
lnli2 <- (- prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
prob.sup.egal.obs <- pSnbinom(q=data$nombre[i]-1, size = th, mu = nbcount, log.p = TRUE,
lower.tail = FALSE, method = method_Snbinom, tol=1E-6, normalize = FALSE)
prob0 <- dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)
lnli2 <- (- prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
}
} else {
# Je suis en nombre max
nbmin <- data$nombre[i]
nbmax <- as.numeric(data$CountTypes[i])
if (is.na(data$Date2[i])) {
# sumnbcount <- daily_count(data$ordinal[i], xparec, print = FALSE, zero = zero)
# Le premier jour de la série est 0 et donc il est à l'indice 1
sumnbcount <- nbperday[data$ordinal[i]+1]
if (data$nombre[i]-1 >= 0) {
prob.sup.egal.obs <- -sum(dnbinom(x=nbmin:nbmax, size = th,
mu = sumnbcount, log = TRUE))
prob0 <- dnbinom(0, size = th, mu = sumnbcount, log = FALSE)
lnli2 <- (prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
} else {
nbjour <- data$ordinal2[i] - data$ordinal[i] + 1
# nbcount <- daily_count((1:nbjour) + data$ordinal[i] - 1, xparec, print = FALSE)
# Le premier jour de la série est 0 et donc il est à l'indice 1
nbcount <- nbperday[((1:nbjour) + data$ordinal[i] - 1)+1]
sumnbcount <- sum(nbcount)
# Est ce que les 0 ont été comptés
if (data$nombre[i]-1 >= 0) {
if (!is.null(pt$method_Snbinom)) {
method_Snbinom <- pt$method_Snbinom
} else {
method_Snbinom <- "saddlepoint"
}
prob.sup.egal.obs <- -sum(dSnbinom(x=nbmin:nbmax, size = th, mu = nbcount, log = TRUE,
method = method_Snbinom, tol=1E-6, normalize = FALSE))
prob0 <- dSnbinom(0, size = th, mu = nbcount, log = FALSE, method = method_Snbinom, normalize = FALSE)
lnli2 <- (prob.sup.egal.obs + log(1 - prob0))
} else {
lnli2 <- 0
}
}
}
}
}
# print(paste0(as.character(i), " ", lnli2))
if (is.na(lnli2)) lnli2 <- 1E9
data$LnL[i] <- lnli2
data$Modeled[i] <- sumnbcount
}
data$LnL[is.infinite(data$LnL)] <- 115.1293 # -log(1e-50)
return(data)
},
clusterExport = list(varlist=c("xpar", "datatot",
"daily_count", "format_par",
"zero",
"model_before",
"add.cofactors", "cofactors"),
envir=environment()),
clusterEvalQ=list(expr=expression(library(phenology))
),
forking = forking), silent = FALSE)
# }
if (inherits(rg, "try-error")) {
save(x, file = "x.Rdata")
save(pt, file = "pt.Rdata")
stop("Error during likelihood estimation; look at x.Rdata and pt.Rdata")
}
sum <- sum(sapply(X = rg, function(x) sum(x$LnL, na.rm = TRUE)))
if (is.na(sum)) sum <- 1E9
if (!is.null(pt$store.intermediate)) {
if (pt$store.intermediate) {
load(file=pt$file.intermediate)
store <- c(store, iteration=list(list(fitted=x, fixed=pt$fixed, loglik=sum)))
save(store, file=pt$file.intermediate)
}
}
if (out) {
return(sum)
} else {
names(rg) <- names(datatot)
return(rg)
}
}
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