JosiahParry
commented
1 year ago A rough implementation of this can be seen below.
This is somewhat inefficeint, however, because run_perm() runs on all observations. For the local join count not all observations will have a p-value. So it may be useful to modify run_perm() to take a vector of indexes as an alternative to the number of observations in the vector. In the below example the new implementation is significantly faster. The number of observations a p-value is required for is 23 ~46% of the observations. So we could theoretically cut the computation further in half in this use case by modifying run_perm().
local_jc_uni <- function(x, listw, nsim, alternative = "two.sided") {
xj <- find_xj(x, listw$neighbours)
# identify which observations should have a p-value
x_index <- which(x == 1L)
xj_index <- which(unlist(lapply(xj, function(x) any(x == 1L))) == TRUE)
index <- intersect(xj_index, x_index)
obs <- x * lag.listw(listw, x)
crd <- card(listw$neighbours)
lww <- listw$weights
env <- new.env()
assign("crd", crd, envir = env) # cardinality
assign("lww", lww, envir = env) # weights
assign("nsim", 999, envir=env) # weights
assign("xi", x, envir = env) # x col
assign("obs", obs, envir = env) # observed values
varlist = ls(envir = env)
permBB_int <- function(i, env) {
crdi <- get("crd", envir = env)[i]
x <- get("xi", envir = env)
x_i <- x[-i]
w_i <- get("lww", envir = env)[[i]]
nsim <- get("nsim", envir = env)
obs <- get("obs", envir = env)
sx_i <- matrix(sample(x_i,
size = crdi * nsim,
replace = TRUE),
ncol = crdi,
nrow = nsim)
res_i <- x[i] * (sx_i %*% w_i)
# sum(res_i)
rank(c(res_i, obs[i]))[(nsim + 1)]
}
probs <- probs_lut("BB", nsim, alternative)
if (alternative == "two.sided") probs <- probs / 2
p_ranks <- run_perm(permBB_int, length(x), env, NULL, varlist)
# two-sided p-value
ps <- probs
p_res <- rep(NA_real_, length(x))
p_res[index] <- ps[index]
res <- data.frame(obs, p_res)
colnames(res) <- c("BB", attr(probs, "Prname"))
res
}Example Bench Mark
data(oldcol)
x <- ifelse(COL.OLD$CRIME < 35, 0L, 1L)
listw <- nb2listw(COL.nb, style = "B")
bm <- bench::mark(
og = local_joincount_uni(x, listw, nsim),
new = local_jc_uni(x, listw, nsim),
check = FALSE
)
#> # A tibble: 2 × 13
#> expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc total_time result
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm> <list>
#> 1 og 219.14ms 229.91ms 4.41 179.52MB 7.35 3 5 680ms <NULL>
#> 2 new 8.28ms 8.48ms 110. 7.41MB 7.98 55 4 501ms <NULL>
ggplot2::autoplot(bm)
rsbivand
In relation to #98 and #99.
The current
permute_listw()approach to calculating simulated p-values is inefficient. #99 introduces a better approach to accomplishing this. A similar approach should be taken for local join counts that were introduced in #94 and #97