interseries.cor <- function(rwl, n=NULL, prewhiten=TRUE, biweight=TRUE,
method = c("spearman", "pearson", "kendall")) {
method2 <- match.arg(method)
nseries <- length(rwl)
res.cor <- numeric(nseries)
p.val <- numeric(nseries)
rwl.mat <- as.matrix(rwl)
tmp <- normalize.xdate(rwl=rwl.mat, n=n,
prewhiten=prewhiten, biweight=biweight,
leave.one.out = TRUE)
series <- tmp[["series"]]
master <- tmp[["master"]]
for (i in seq_len(nseries)) {
tmp2 <- cor.test(series[, i], master[, i],
method = method2, alternative = "greater")
res.cor[i] <- tmp2[["estimate"]]
p.val[i] <- tmp2[["p.value"]]
}
res <- data.frame(res.cor = res.cor, p.val = p.val, row.names = names(rwl))
# change res.cor to r, rho, or tau based on method
res
}
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