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#' @rdname dnegocc
dnegocc <- function(x, space, occupancy, prob = 1, approx = FALSE, log = FALSE) {
#Check that argument and parameters are appropriate type
if (!is.numeric(x)) stop('Error: Argument x is not numeric')
if (!is.numeric(space)) stop('Error: Space parameter is not numeric')
if (!is.numeric(occupancy)) stop('Error: Occupancy parameter is not numeric')
if (!is.numeric(prob)) stop('Error: Probability parameter is not numeric')
if (!is.logical(approx)) stop('Error: approx option is not a logical value')
if (!is.logical(log)) stop('Error: log option is not a logical value')
#Check that parameters are atomic
if (length(space) != 1) stop('Error: Space parameter should be a single number')
if (length(occupancy) != 1) stop('Error: Occupancy parameter should be a single number')
if (length(prob) != 1) stop('Error: Probability parameter should be a single number')
if (length(approx) != 1) stop('Error: approx option should be a single logical value')
if (length(log) != 1) stop('Error: log option should be a single logical value')
#Set parameters
if (space == Inf) { m <- Inf } else { m <- as.integer(space) }
k <- as.integer(occupancy)
#Check that parameters are in allowable range
if (space != m) stop('Error: Space parameter is not an integer')
if (m <= 0) stop('Error: Space parameter must be positive')
if (occupancy != k) stop('Error: Occupancy parameter is not an integer')
if (k < 0) stop('Error: Occupancy parameter must be non-negative')
if (k > m) stop('Error: Occupancy parameter is larger than space parameter')
if (prob < 0) stop('Error: Probability parameter must be between zero and one')
if (prob > 1) stop('Error: Probability parameter must be between zero and one')
#Create output vector
max.x <- floor(max(x))
NEGOCC <- rep(-Inf, length(x))
#Compute for trivial case where k = 0
if (k == 0) {
for (i in 1:length(x)) {
xx <- x[i]
if (xx == 0) { NEGOCC[i] <- 0 } }
if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } }
#Compute for trivial case where prob = 0
if (prob == 0) {
if (k > 0) {
for (i in 1:length(x)) {
xx <- x[i]
if (xx == Inf) { NEGOCC[i] <- 0 } }
if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } } }
#Compute for special case where m = Inf
if (m == Inf) {
NEGOCC <- dnbinom(x, size = k, prob = prob, log = TRUE)
if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } }
#Compute for non-trivial cases where k > 0 and prob > 0
#Compute log-probablities using recursion
if (!approx) {
#Create base vector for recursion
if(prob == 1) {
LOGS <- c(0, rep(-Inf, max.x)) } else {
LOGS <- log(prob) + (0:max.x)*log(1-prob) }
#Update via recursion
r <- 2
while (r <= k) {
NEWLOGS <- rep(-Inf, max.x+1)
LLL <- (0:max.x)*log(1-prob*(m-r+1)/m)
for (t in 0:max.x) {
TERMS <- LLL[1:(t+1)] + LOGS[(t+1):1]
NEWLOGS[t+1] <- log(prob*(m-r+1)/m) + matrixStats::logSumExp(TERMS) }
LOGS <- NEWLOGS
r <- r+1 }
#Generate output vector
for (i in 1:length(x)) {
xx <- x[i]
if ((as.integer(xx) == xx)&(xx >= 0)) {
NEGOCC[i] <- LOGS[xx+1] } } }
#Compute log-probabilities using approximation
if (approx) {
#Compute generalised harmonic numbers
H1 <- sum(1/((m-k+1):m))
H2 <- sum(1/((m-k+1):m)^2)
#Compute moments
MEAN <- max(0,(m/prob)*H1 - k)
VAR <- max(0,(m/prob)^2*H2 - (m/prob)*H1)
#Approximation using discretised gamma distribution
if (VAR == 0) {
APPROX <- c(0, rep(-Inf, max.x)) }
if (VAR > 0) {
SHAPE <- (MEAN + 1/2)^2/VAR
RATE <- m*(MEAN + 1/2)/VAR
LGA <- pgamma((0:(max.x+1))/m, shape = SHAPE, rate = RATE, log.p = TRUE)
LOWER <- LGA[1:(max.x+1)]
UPPER <- LGA[2:(max.x+2)]
APPROX <- UPPER + VGAM::log1mexp(UPPER-LOWER) }
#Generate output vector
for (i in 1:length(x)) {
xx <- x[i]
if ((as.integer(xx) == xx)&(xx >= 0)) {
NEGOCC[i] <- APPROX[xx+1] } } }
#Return output
if (log) { NEGOCC } else { exp(NEGOCC) } }
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