I thought I'd update this post as I've found a way of producing a compact letters display after non-parametric analysis with post-hoc pairwise comparisons.
NOTE: This requires the open source and readily available statistical program R (I used RStudio, a more user-friendly version).
After performing your non-parametric analysis in JMP, export the results table with pairwise p-values into an excel sheet. You then need to format the field of the comparison so that it is in one cell, with a single '-' separating the variables being compared.
Then, in R, create a function using this script from https://rdrr.io/github/GegznaV/spMisc/src/R/eval_glue.R ::
#' [!] Format, parse and evaluate expression in a string
#'
#' A wrapper function to format and parse a string and evaluate it
#' as an expression. \code{eval_(X)} is a wrapper for
#' \code{eval(parse(text = glue::glue(X)))}.
#'
#' @details
#' It's a good practice to write backticks (`) arround variable names
#' to prevent from errors in situations, where variables have uncommon
#' names. See examples.
#'
#' @param ... Strings to be formatted and evaluated as an expression.
#' @param .open [\code{character(1)}: ‘\{’] \cr
#' The opening delimiter. Doubling the full delimiter escapes it.
#' @param .close [\code{character(1)}: ‘\}’] \cr
#' The closing delimiter. Doubling the full delimiter escapes it.
#' @inheritParams glue::glue
#' @inheritParams base::eval
#'
#' @export
#' @seealso Format and interpolate a string (\code{\link[glue]{glue}}).\cr
#' Evaluate an (unevaluated) expression (\code{\link[base]{eval}}).\cr
#' Parse expressions (\code{\link[base]{parse}}).
#' @examples
#' library("spMisc")
#'
#' AA <- "nn"
#'
#' glue::glue("{AA} <- 3")
#'
#' eval_glue("{AA} <- 3")
#'
#' AA
#'
#' nn
#'
#' # It's a good practice to write backticks (`) arround variable names
#' # to prevent from errors in situations, where variables have uncommon
#' # names:
#'
#' BB <- "A B"
#'
#' eval_glue("`{BB}` <- 8")
#'
#' `A B`
#'
eval_glue <- function(..., envir = parent.frame(),
.sep = "", .open = "{", .close = "}") {
x2 <- glue::glue(..., .envir = envir, .open = .open, .close = .close)
eval(parse(text = x2), envir = envir)
}
Following this, make another function from this script from https://github.com/GegznaV/biostat/blob/master/R/make_cld_df.R ::
#' **[!!]** Make a data frame with cld
#'
#' Compute a compact letter display (cld) of statistically significant
#' differences in pairwise comparisons and output he results as a dataframe.
#'
#' @note
#' This function is based on code in function [rcompanion::cldList()]
#' from package \pkg{rcompanion} by Salvatore Mangiafico.
#'
#' @param swap_compared_names (logical) \cr If `TRUE`, group names are swapped
#' from, e.g., "2-1" or "second-first" to "1-2" or "first-second". This leads to
#' different order of cld letters and different order of compared groups (if
#' without swapping the result is incorrect order of groups.)
#'
#' @inheritParams rcompanion::cldList
#' @inheritParams multcompView::multcompLetters
#'
#' @export
#' @keywords internal
# @param comparison_order (character) Character vector with desired order of groups.
make_cld_df <- function(
formula = NULL,
data = NULL,
comparison = NULL,
p.value = NULL,
threshold = 0.05,
print.comp = FALSE,
remove.space = TRUE,
remove.equal = TRUE,
# remove.zero = TRUE,
swap.colon = TRUE,
swap.vs = FALSE,
reversed = FALSE,
swap_compared_names = FALSE,
# comparison_order = NULL,
...) {
if (!is.null(formula)) {
# # **[!!!]** Formula interface needs review.
# p.value = eval(parse(text = paste0("data", "$", all.vars(formula[[2]])[1])))
# comparison = eval(parse(text = paste0("data", "$", all.vars(formula[[3]])[1])))
p.value <- eval_glue("data${all.vars(formula[[2]])[1]}")
comparison <- eval_glue("data${all.vars(formula[[3]])[1]}")
}
significant_difference <- p.value < threshold
# if (sum(significant_difference) == 0) {
# stop("No significant differences.", call. = FALSE)
# }
if (remove.space == TRUE) {
comparison <- gsub(" ", "", comparison)
}
if (remove.equal == TRUE) {
comparison <- gsub("=", "", comparison)
}
# if (remove.zero == TRUE) {
# comparison = gsub("0", "", comparison)
# }
if (swap.colon == TRUE) {
comparison <- gsub(":", "-", comparison)
}
if (swap.vs == TRUE) {
comparison <- gsub("vs", "-", comparison)
}
if (swap_compared_names == TRUE) {
part_1 <- stringr::str_extract(comparison, ".*(?=-)")
part_2 <- stringr::str_extract(comparison, "(?<=-).*")
comparison <- paste0(part_2, "-", part_1)
}
comparison
names(significant_difference) <- comparison
if (print.comp == TRUE) {
Y <- data.frame(Comparisons = names(significant_difference))
cat("\n\n")
print(Y)
cat("\n\n")
}
MCL <- multcompView::multcompLetters(significant_difference, reversed = reversed)
regular_cld <- as.character(MCL$Letters)
spaced_cld <- as.character(MCL$monospacedLetters)
if (is.null(MCL$monospacedLetters)) {
spaced_cld <- regular_cld
} else {
spaced_cld <- spaced_cld
}
res <- data.frame(
group = names(MCL$Letters),
cld = as.character(MCL$Letters),
spaced_cld = gsub(" ", "_", spaced_cld)
)
structure(res, class = c("cld_object", class(res)))
}
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Helper function
# cld_update <- function(res) {
# res$MonoLetter <- gsub(" ", "_", res$MonoLetter)
# names(res) <- c("Group", "cld", "spaced_cld")
# structure(res, class = c("cld_object", class(res)))
# }
You can then apply the newly made make_cld_df function to your data in the form of::
x<-make_cld_df(p.value~pairwisecomparison, data=data.
then call x
