Nothing
sss <- function(rwi, ids=NULL) {
# rwi.stats is robust enough to have a single call regardless of
# whether ids is passed in because if no ids are passed in
# rbar.eff == rbar.bt and n.cores=n.trees
# Thus, if ids is NULL assume one core / tree use and
# we use rbar.bt (which is the same as rbar.eff). If there are ids
# we use rbar.eff which is not the same as rbar.bt. Ditto for getting
# N. Satisfy yourself via:
#ca533.rwi <- detrend(ca533,method="Spline")
#ca533.ids <- autoread.ids(ca533)
#rwi.stats(ca533.rwi)
#rwi.stats(ca533.rwi,ca533.ids)
rwiVars <- rwi.stats(rwi, ids=ids)
rbar <- rwiVars$rbar.eff
N <- rwiVars$n.trees
if(is.null(ids)){
# n is is the number of cores in subsample
n <- rowSums(!is.na(rwi))
}
else {
# n is the number of trees in the subsample.
# calculating n is kind of tedious:
# we need n trees, not n cores in a year
colnames.rwi <- colnames(rwi)
n <- rep(NA_integer_, nrow(rwi))
for(i in seq_len(nrow(rwi))){
cols.with.data <- c(!is.na(rwi[i, ]))
trees.this.year <- ids$tree[rownames(ids) %in% colnames.rwi[cols.with.data]]
n[i] <- length(unique(trees.this.year))
}
}
res <- (n*(1+(N-1)*rbar)) / (N*(1+(n-1)*rbar))
res
}
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