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################################################################################
### Simulate from a HHH4 model
###
### Copyright (C) 2012 Michaela Paul, 2013-2016,2018,2021 Sebastian Meyer
### (except where otherwise noted)
###
### This file is part of the R package "surveillance",
### free software under the terms of the GNU General Public License, version 2,
### a copy of which is available at https://www.R-project.org/Licenses/.
################################################################################
### Simulate-method for hhh4-objects
simulate.hhh4 <- function (object, # result from a call to hhh4
nsim=1, # number of replicates to simulate
seed=NULL,
y.start=NULL, # initial counts for epidemic components
subset=1:nrow(object$stsObj),
coefs=coef(object), # coefficients used for simulation
components=c("ar","ne","end"), # which comp to include
simplify=nsim>1, # counts array only (no full sts)
...)
{
## Determine seed (this part is copied from stats:::simulate.lm with
## Copyright (C) 1995-2012 The R Core Team)
if(!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
runif(1) # initialize the RNG if necessary
if(is.null(seed))
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
else {
R.seed <- get(".Random.seed", envir = .GlobalEnv)
set.seed(seed)
RNGstate <- structure(seed, kind = as.list(RNGkind()))
on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
}
## END seed
cl <- match.call()
theta <- if (missing(coefs)) coefs else checkCoefs(object, coefs)
stopifnot(subset >= 1, subset <= nrow(object$stsObj))
## lags
lag.ar <- object$control$ar$lag
lag.ne <- object$control$ne$lag
maxlag <- max(lag.ar, lag.ne)
## initial counts
nUnits <- object$nUnit
if (is.null(y.start)) { # set starting value to mean observed (in subset!)
y.means <- ceiling(colMeans(observed(object$stsObj)[subset,,drop=FALSE]))
y.start <- matrix(y.means, maxlag, nUnits, byrow=TRUE)
} else {
if (is.vector(y.start)) y.start <- t(y.start)
if (ncol(y.start) != nUnits)
stop(sQuote("y.start"), " must have nUnits=", nUnits, " columns")
if (nrow(y.start) < maxlag)
stop("need 'y.start' values for lag=", maxlag, " initial time points")
}
## store model terms in the hhh4 object because we request them repeatedly
## (within get_exppreds_with_offsets() and directly afterwards)
## CAVE: for an ri()-model, building the terms affects the .Random.seed,
## so doing that twice would yield different simulations than pre-1.16.2
if (is.null(object$terms))
object$terms <- terms(object)
## get fitted exppreds nu_it, phi_it, lambda_it (incl. offsets, t in subset)
exppreds <- get_exppreds_with_offsets(object, subset = subset, theta = theta)
## extract overdispersion parameters (simHHH4 assumes psi->0 means Poisson)
model <- terms(object)
psi <- splitParams(theta,model)$overdisp
if (length(psi) > 1) # "NegBinM" or shared overdispersion parameters
psi <- psi[model$indexPsi]
## weight matrix/array of the ne component
neweights <- getNEweights(object, coefW(theta))
## set predictor to zero if not included ('components' argument)
stopifnot(length(components) > 0, components %in% c("ar", "ne", "end"))
getComp <- function (comp) {
exppred <- exppreds[[comp]]
if (comp %in% components) exppred else "[<-"(exppred, value = 0)
}
ar <- getComp("ar")
ne <- getComp("ne")
end <- getComp("end")
## simulate
simcall <- quote(
simHHH4(ar, ne, end, psi, neweights, y.start, lag.ar, lag.ne)
)
if (!simplify) {
## result template
res0 <- object$stsObj[subset,]
setObserved <- function (observed) {
res0@observed[] <- observed
res0
}
simcall <- call("setObserved", simcall)
}
res <- if (nsim==1 && !simplify) eval(simcall) else
replicate(nsim, eval(simcall),
simplify=if (simplify) "array" else FALSE)
if (simplify) {
dimnames(res)[1:2] <- list(subset, colnames(model$response))
attr(res, "initial") <- y.start
attr(res, "stsObserved") <- object$stsObj[subset,]
class(res) <- "hhh4sims"
}
## Done
attr(res, "call") <- cl
attr(res, "seed") <- RNGstate
res
}
### Internal auxiliary function, which performs the actual simulation
simHHH4 <- function(ar, # lambda_it (nTime x nUnits matrix)
ne, # phi_it (nTime x nUnits matrix)
end, # nu_it (nTime x nUnits matrix, offset included)
psi, # overdisp param(s) or numeric(0) (psi->0 = Poisson)
neW, # weight matrix/array for neighbourhood component
start, # starting counts (vector of length nUnits, or
# matrix with nUnits columns if lag > 1)
lag.ar = 1,
lag.ne = lag.ar
)
{
nTime <- nrow(end)
nUnits <- ncol(end)
## check and invert psi since rnbinom() uses different parametrization
size <- if (length(psi) == 0 ||
isTRUE(all.equal(psi, 0, check.attributes=FALSE))) {
NULL # Poisson
} else {
if (!length(psi) %in% c(1, nUnits))
stop("'length(psi)' must be ",
paste(unique(c(1, nUnits)), collapse = " or "),
" (number of units)")
1/psi
}
## simulate from Poisson or NegBin model
rdistr <- if (is.null(size)) {
rpois
} else {
## unit-specific 'mean's and variance = mean + psi*mean^2
## where 'size'=1/psi and length(psi) == 1 or length(mean)
function(n, mean) rnbinom(n, mu = mean, size = size)
}
## if only endemic component -> simulate independently
if (all(ar + ne == 0)) {
if (!is.null(size))
size <- matrix(size, nTime, nUnits, byrow = TRUE)
return(matrix(rdistr(length(end), end), nTime, nUnits))
}
## weighted sum of counts of other (neighbouring) regions
## params: y - vector with (lagged) counts of regions
## W - nUnits x nUnits adjacency/weight matrix (0=no neighbour)
wSumNE <- if (is.null(neW) || all(neW == 0)) { # includes the case nUnits==1
function (y, W) numeric(nUnits)
} else function (y, W) .colSums(W * y, nUnits, nUnits)
## initialize matrices for means mu_i,t and simulated data y_i,t
mu <- y <- matrix(0, nTime, nUnits)
y <- rbind(start, y)
nStart <- nrow(y) - nrow(mu) # usually just 1 for lag=1
## simulate
timeDependentWeights <- length(dim(neW)) == 3
if (!timeDependentWeights) neWt <- neW
for(t in seq_len(nTime)){
if (timeDependentWeights) neWt <- neW[,,t]
## mean mu_i,t = lambda*y_i,t-1 + phi*sum_j wji*y_j,t-1 + nu_i,t
mu[t,] <-
ar[t,] * y[nStart+t-lag.ar,] +
ne[t,] * wSumNE(y[nStart+t-lag.ne,], neWt) +
end[t,]
## Sample from Poisson/NegBin with that mean
y[nStart+t,] <- rdistr(nUnits, mu[t,])
}
## return simulated data without initial counts
y[-seq_len(nStart),,drop=FALSE]
}
### check compatibility of a user-specified coefficient vector with model
checkCoefs <- function (object, coefs, reparamPsi=TRUE)
{
theta <- coef(object, reparamPsi=reparamPsi)
if (length(coefs) != length(theta))
stop(sQuote("coefs"), " must be of length ", length(theta))
names(coefs) <- names(theta)
coefs
}
### subset simulations and keep attributes in sync
"[.hhh4sims" <- function (x, i, j, ..., drop = FALSE)
{
xx <- NextMethod("[", drop = drop)
if (nargs() == 2L) # x[i] call -> hhh4sims class is lost
return(xx)
## otherwise we were subsetting the array and attributes are lost
attributes(xx) <- c(attributes(xx),
attributes(x)[c("initial", "stsObserved", "class")])
subset_hhh4sims_attributes(xx, i, j)
}
subset_hhh4sims_attributes <- function (x, i, j)
{
if (!missing(i))
attr(x, "stsObserved") <- attr(x, "stsObserved")[i,]
if (!missing(j)) {
attr(x, "stsObserved") <- suppressMessages(attr(x, "stsObserved")[, j])
is.na(attr(x, "stsObserved")@neighbourhood) <- TRUE
attr(x, "initial") <- attr(x, "initial")[, j, drop = FALSE]
}
x
}
### aggregate predictions over time and/or (groups of) units
aggregate.hhh4sims <- function (x, units = TRUE, time = FALSE, ..., drop = FALSE)
{
ax <- attributes(x)
if (time) {
## sum counts over the whole simulation period
res <- colSums(x)
## -> a nUnits x nsim matrix -> will no longer be "hhh4sims"
if (isTRUE(units)) { # sum over all units
res <- colSums(res) # now a vector of length nsim
} else if (!identical(FALSE, units)) { # sum over groups of units
stopifnot(length(units) == dim(x)[2])
res <- t(rowSumsBy.matrix(t(res), units))
}
} else {
if (isTRUE(units)) { # sum over all units
res <- apply(X = x, MARGIN = c(1L, 3L), FUN = sum)
if (!drop) {
## restore unit dimension conforming to "hhh4sims" class
dim(res) <- replace(ax$dim, 2L, 1L)
dimnames(res) <- replace(ax$dimnames, 2L, list(NULL))
## restore attributes
attr(res, "initial") <- as.matrix(rowSums(ax$initial))
attr(res, "stsObserved") <- aggregate(ax$stsObserved, by = "unit")
class(res) <- "hhh4sims"
}
} else if (!identical(FALSE, units)) { # sum over groups of units
stopifnot(length(units) == dim(x)[2])
groupnames <- names(split.default(seq_along(units), units))
res <- apply(X = x, MARGIN = 3L, FUN = rowSumsBy.matrix, by = units)
dim(res) <- replace(ax$dim, 2L, length(groupnames))
dimnames(res) <- replace(ax$dimnames, 2L, list(groupnames))
if (!drop) {
## restore attributes
attr(res, "initial") <- rowSumsBy.matrix(ax$initial, units)
attr(res, "stsObserved") <- rowSumsBy.sts(ax$stsObserved, units)
class(res) <- "hhh4sims"
}
} else {
return(x)
}
}
## done
res
}
rowSumsBy.matrix <- function (x, by, na.rm = FALSE)
{
dn <- dim(x)
res <- vapply(X = split.default(x = seq_len(dn[2L]), f = by),
FUN = function (idxg)
.rowSums(x[, idxg, drop = FALSE],
dn[1L], length(idxg), na.rm = na.rm),
FUN.VALUE = numeric(dn[1L]), USE.NAMES = TRUE)
if (dn[1L] == 1L) t(res) else res
}
rowSumsBy.sts <- function (x, by, na.rm = FALSE)
{
## map, neighbourhood, upperbound, control get lost by aggregation of units
.sts(epoch = x@epoch, freq = x@freq, start = x@start,
observed = rowSumsBy.matrix(x@observed, by, na.rm),
state = rowSumsBy.matrix(x@state, by, na.rm) > 0,
alarm = rowSumsBy.matrix(x@alarm, by, na.rm) > 0,
populationFrac = rowSumsBy.matrix(x@populationFrac, by, na.rm),
epochAsDate = x@epochAsDate, multinomialTS = x@multinomialTS)
}
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