return original pysal ranks

Author: rsbivand

R/local-joincount-univariate.R

@@ -123,21 +123,23 @@ local_joincount_uni <- function(fx, chosen, listw,
   ranks[index] <- p_ranks[, 1]
   larger <- rep(NA_integer_, length(x))
   larger[index] <- p_ranks[, 3]
+  olarger <- larger
   low_extreme <- (nsim - larger[index]) < larger[index]
   larger[index][low_extreme] <- nsim - larger[index][low_extreme]
   p_pysal_gt <- rep(NA_real_, length(x))
   p_pysal_gt[index] <- (larger[index] + 1.0) / (nsim + 1.0)
   largereq <- rep(NA_integer_, length(x))
   largereq[index] <- p_ranks[, 2]
+  olargereq <- largereq
   low_extreme <- (nsim - largereq[index]) < largereq[index]
   largereq[index][low_extreme] <- nsim - largereq[index][low_extreme]
   p_pysal_ge <- rep(NA_real_, length(x))
   p_pysal_ge[index] <- (largereq[index] + 1.0) / (nsim + 1.0)
 
   res <- data.frame(obs, p_res, ranks, p_pysal_ge, p_pysal_gt, 
-    largereq, larger)
+    largereq, larger, olargereq, olarger)
   names(res) <- c("BB", attr(probs, "Prname"), "sim_rank", "p_sim_pysal_ge",
-    "p_sim_pysal_gt", "largereq", "larger")
+    "p_sim_pysal_gt", "largereq", "larger", "olargereq", "olarger")
   attr(res, "ncpus") <- ncpus
   attr(res, "nsim") <- nsim
   attr(res, "probs") <- probs

man/local_joincount_uni.Rd

@@ -32,7 +32,7 @@ local_joincount_uni(
 
 }
 \value{
-a \code{data.frame} with six columns \code{BB} (observed BB - neighbour same as focus), \code{Pr()} (pseudo-p from punif rank), \code{sim_rank} (simulation rank with current ties.method), \code{p_sim_pysal_ge} (pseudo-p fromPySAL esda, greater than or equal to observed BB), \code{p_sim_pysal_gt} (pseudo-p fromPySAL esda, greater than observed BB), \code{largereq} (count of simulated values greater than or equal to observed BB) and \code{larger} (count of simulated values greater than observed BB), and number of rows equal to the length of \code{x}.
+a \code{data.frame} with six columns \code{BB} (observed BB - neighbour same as focus), \code{Pr()} (pseudo-p from punif rank), \code{sim_rank} (simulation rank with current ties.method), \code{p_sim_pysal_ge} (pseudo-p fromPySAL esda, greater than or equal to observed BB), \code{p_sim_pysal_gt} (pseudo-p fromPySAL esda, greater than observed BB), \code{largereq} (count of simulated values greater than or equal to observed BB) after folding, \code{olarger} (count of simulated values greater than observed BB) before folding, \code{olargereq} (count of simulated values greater than or equal to observed BB) before folding,and number of rows equal to the length of \code{x}.
 }
 \description{
 The univariate local join count statistic is used to identify clusters of rarely occurring binary variables. The binary variable of interest should occur less