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#' Perform \code{scan.test} on simulated data
#'
#' \code{scan.sim} efficiently performs
#' \code{\link{scan.test}} on a simulated data set. The
#' function is meant to be used internally by the
#' \code{\link{scan.test}} function, but is informative for
#' better understanding the implementation of the test.
#'
#' @inheritParams flex.sim
#' @inheritParams scan.test
#' @param nn A list of nearest neighbors produced by \code{\link{nnpop}}.
#'
#' @return A vector with the maximum test statistic for each
#' simulated data set.
#' @export
#'
#' @examples
#' data(nydf)
#' coords <- with(nydf, cbind(longitude, latitude))
#' d <- sp::spDists(as.matrix(coords), longlat = TRUE)
#' nn <- scan.nn(d, pop = nydf$pop, ubpop = 0.1)
#' cases <- floor(nydf$cases)
#' ty <- sum(cases)
#' ex <- ty / sum(nydf$pop) * nydf$pop
#' yin <- nn.cumsum(nn, cases)
#' ein <- nn.cumsum(nn, ex)
#' tsim <- scan.sim(nsim = 1, nn, ty, ex, ein = ein, eout = sum(ex) - ein)
scan.sim <- function(nsim = 1, nn, ty, ex, type = "poisson",
ein = NULL, eout = NULL,
tpop = NULL, popin = NULL, popout = NULL,
cl = NULL,
simdist = "multinomial",
pop = NULL,
min.cases = 2) {
# match simdist with options
simdist <- match.arg(simdist, c("multinomial", "poisson", "binomial"))
arg_check_sim(
nsim = nsim, ty = ty, ex = ex, type = type,
nn = nn, ein = ein, eout = eout, tpop = tpop,
popin = popin, popout = popout, static = TRUE,
simdist = simdist, pop = pop,
w = diag(length(ex))
)
# compute max test stat for nsim simulated data sets
tsim <- pbapply::pblapply(seq_len(nsim), function(i) {
# simulate new data
if (simdist == "multinomial") {
ysim <- stats::rmultinom(1, size = ty, prob = ex)
} else if (simdist == "poisson") {
ysim <- stats::rpois(length(ex), lambda = ex)
ty <- sum(ysim)
mult <- ty / sum(ex)
ein <- ein * mult
eout <- eout * mult
} else if (simdist == "binomial") {
ysim <- stats::rbinom(
n = length(ex), size = pop,
prob = ex / pop
)
ty <- sum(ysim)
}
# compute test statistics for each zone
yin <- nn.cumsum(nn, ysim)
if (type == "poisson") {
tall <- stat_poisson(yin, ty - yin, ein, eout)
} else if (type == "binomial") {
tall <- stat_binom(yin, ty - yin, ty, popin, popout, tpop)
}
# make sure minimum number of cases satisfied
max(tall[yin >= min.cases])
}, cl = cl)
unlist(tsim, use.names = FALSE)
}
#' Argument checking for *.sim functions
#'
#' Check the arguments of the \code{*.sim} functions.
#'
#' @param nsim Number of simulations
#' @param ty Total number of cases
#' @param ex Expected counts
#' @param type Type of statistic
#' @param nn List of nn (e.g., nnpop function)
#' @param zones List of zones (e.g., scan.zones)
#' @param ein List of expected in each zone
#' @param eout List of expected out of each zone
#' @param tpop Total population
#' @param popin Population in each zone
#' @param popout Population outside of each zone
#' @param w Spatial adjacency matrix
#' @param pop Vector of populations
#' @param ubpop Population upperbound
#' @param static Static zones. Logical. TRUE for scan.test.
#' FALSE for uls.test.
#' @param simdist Simulation distribution.
#' @return NULL
#' @noRd
arg_check_sim <- function(nsim, ty, ex, type,
nn = NULL, zones = NULL,
ein = NULL, eout = NULL,
tpop = NULL, popin = NULL,
popout = NULL, w = NULL,
pop = NULL, ubpop = NULL,
static = FALSE,
simdist = "multinomial") {
arg_check_nsim(nsim)
arg_check_ty(ty)
N <- length(ex)
arg_check_ex(ex, N)
arg_check_type(type)
if (!is.null(nn)) {
if (!is.list(nn)) stop("nn must be a list")
nz <- sum(sapply(nn, length))
}
if (!is.null(zones)) {
if (!is.list(zones)) stop("zones must be a list")
}
arg_check_w(w, N)
if (!is.null(ubpop)) {
arg_check_ubpop
}
if (!is.null(pop)) {
arg_check_pop(pop, N)
}
arg_check_simdist(simdist)
if (simdist == "binomial" & is.null(pop)) {
stop("pop must be specified when simdist == 'binomial'")
}
}
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