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LOSH.cs <- function(x, listw, zero.policy = NULL, na.action = na.fail,
p.adjust.method = "none", spChk = NULL) {
stopifnot(is.vector(x))
if (!inherits(listw, "listw"))
stop(paste(deparse(substitute(listw)), "is not a listw object"))
if (is.null(zero.policy))
zero.policy <- get("zeroPolicy", envir = .spdepOptions)
stopifnot(is.logical(zero.policy))
if (!is.null(attr(listw$neighbours, "self.included")) &&
attr(listw$neighbours, "self.included"))
stop("Self included among neighbours")
if (is.null(spChk))
spChk <- get.spChkOption()
if (spChk && !chkIDs(x, listw))
stop("Check of data and weights ID integrity failed")
if (!is.numeric(x))
stop(paste(deparse(substitute(x)), "is not a numeric vector"))
# NAOK <- deparse(substitute(na.action)) == "na.pass"
x <- na.action(x)
na.act <- attr(x, "na.action")
rn <- attr(listw, "region.id")
if (!is.null(na.act)) {
subset <- !(1:length(listw$neighbours) %in% na.act)
listw <- subset(listw, subset, zero.policy = zero.policy)
excl <- class(na.act) == "exclude"
}
### calculate LOSH for all locations
res <- LOSH(x, listw, 2, TRUE, zero.policy, na.action, spChk) #### a = 2 is used, because the chi-square-based inference is only valid for a measure of variance.
### add a column for the chi-square-based p-values
res <- cbind(res, "Pr()" = 1)
### p-values using the non-central chi-square distribution
res[,"Pr()"] <- pchisq(res[,"Z.Hi"], df = 2/res[,"Var.Hi"], lower.tail = FALSE)
### correction for multiple hypothesis testing (if p.adjust.method != "none")
res[,"Pr()"] <- p.adjustSP(res[,"Pr()"], listw$neighbours, method = p.adjust.method)
if (!is.null(na.act) && excl) {
res <- naresid(na.act, res)
}
if (!is.null(rn))
rownames(res) <- rn
attr(res, "call") <- match.call()
if (!is.null(na.act))
attr(res, "na.action") <- na.act
class(res) <- c("LOSH", class(res))
res
}
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