two small fixes

- ranks should break ties by averaging, according to manuscript
- reserve "rank-normalized" for when ranks are normalized (converted to a z or similar scale)

Author: tjmahr

NEWS.md

@@ -7,14 +7,14 @@
 <!-- Items for next release go here* -->
 
 * Two new plots have been added for inspecting the distribution of ranks.
-  Rank-normalized histograms were introduced by the Stan team's [new paper on 
+  Rank histograms were introduced by the Stan team's [new paper on 
   MCMC diagnostics](https://arxiv.org/abs/1903.08008). (#178, #179)
 
   `mcmc_rank_hist()`: A traditional traceplot (`mcmc_trace()`) visualizes how
-  sampled values the MCMC chains mix over the course of sampling. A
-  rank-normalized histogram (`mcmc_rank_hist()`) visualizes how the *ranks* of
-  values from the chains mix together. An ideal plot would show the ranks mixing
-  or overlapping in a uniform distribution. 
+  sampled values the MCMC chains mix over the course of sampling. A rank
+  histogram (`mcmc_rank_hist()`) visualizes how the *ranks* of values from the
+  chains mix together. An ideal plot would show the ranks mixing or overlapping
+  in a uniform distribution.
 
   `mcmc_rank_overlay()`: Instead of drawing each chain's histogram in a separate
   panel, this plot draws the top edge of the chains' histograms in a single

R/mcmc-traces.R

@@ -54,7 +54,7 @@
 #'   }
 #'   \item{`mcmc_rank_hist()`}{
 #'    Whereas traditional trace plots visualize how the chains mix over the
-#'    course of sampling, rank-normalized histograms visualize how the values
+#'    course of sampling, rank histograms visualize how the values
 #'    from the chains mix together in terms of ranking. An ideal plot would
 #'    show the rankings mixing or overlapping in a uniform distribution.
 #'    See Vehtari et al. (2019) for details.
@@ -469,7 +469,9 @@ mcmc_trace_data <- function(x,
   first_cols <- syms(c("parameter", "value", "value_rank"))
   data <- data %>%
     group_by(.data$parameter) %>%
-    mutate(value_rank = dplyr::row_number(.data$value)) %>%
+    mutate(
+      value_rank = rank(.data$value, ties.method = "average")
+    ) %>%
     ungroup() %>%
     select(!!! first_cols, dplyr::everything())