Type:	Package
Title:	Multiple Comparisons Procedures Based on Studentized Midrange and Range Distributions
Version:	3.1.3
Description:	Apply tests of multiple comparisons based on studentized 'midrange' and 'range' distributions. The tests are: Tukey Midrange ('TM' test), Student-Newman-Keuls Midrange ('SNKM' test), Means Grouping Midrange ('MGM' test) and Means Grouping Range ('MGR' test). The first two tests were published by Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. The last two were published by Batista and Ferreira (2023) <doi:10.28951/bjb.v41i4.640>.
Imports:	stats, utils, graphics, grDevices, SMR, writexl, xtable, tcltk
Suggests:	testthat, knitr, rmarkdown, covr
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://bendeivide.github.io/midrangeMCP/, https://github.com/bendeivide/midrangeMCP
BugReports:	https://github.com/bendeivide/midrangeMCP/issues
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-05-28 21:06:49 UTC; ben10
Author:	Ben Deivide [aut, cre], Daniel Furtado [aut], Henrique Jose de Paula Alves [ctb], Diego Arthur [ctb]
Maintainer:	Ben Deivide <ben.deivide@ufsj.edu.br>
Repository:	CRAN
Date/Publication:	2025-05-28 21:30:02 UTC

Plotting the result of the multiple comparison procedures

Description

MRbarplot creates a bar plot with vertical or horizontal bars to compare the mean treatments by the tests: means grouping based on midrange, means grouping based on range, Student-Newman-Keuls and Tukey based on midrange.

Usage

MRbarplot(x, MCP = "all", col = grDevices::heat.colors(10), horiz = FALSE, ...)

Arguments

Details

The MCP argument allows choosing several tests of multiple comparisons from the MRtest object. For plots in papers, use col = gray.colors(10). For details, see colors function.

Value

MRbarplot return the bar plot of the tests chosen ("MGM", "MGR", "SNKM" and "TM") to evaluate the treatment means.

Examples

# Simulated data (completely randomized design)

rv <- c(100.08, 105.66, 97.64, 100.11, 102.60, 121.29, 100.80,
        99.11, 104.43, 122.18, 119.49, 124.37, 123.19, 134.16,
        125.67, 128.88, 148.07, 134.27, 151.53, 127.31)

# Treatments
treat <- factor(rep(LETTERS[1:5], each = 4))

# Anova
res     <- aov(rv~treat)

# Loading the midrangeMCP package
library(midrangeMCP)

# Choosing tests
results <- MRtest(y = res, trt = "treat", alpha = 0.05,
                   main = "Multiple Comparison Procedures",
                   MCP = c("MGM", "TM"))

MRbarplot(results, MCP = "all") # It will be shown two
                             # graphs. First, for the
                             # results of \code{'MGM'}
                             # and the second for the
                             # results of \code{'TM'}.

MRbarplot(results, MCP = "MGM") # It will be shown
                                 # only the graph
                                 # for the result of
                                 # \code{'MGM'}

# Plot for papers
MRbarplot(results, MCP = "all", col = gray.colors(10))

Multiple comparison procedures to the means of a factor using the studentized range and midrange distributions.

Description

MRtest applies the Means grouping based on midrange, Means Grouping based on Range, Student-Newman-Keuls based on midrange and Tukey based on midrange tests. These are new tests for multiple comparisons proposed by the authors (BATISTA, 2016), that are being published.

Usage

MRtest(
  y,
  trt = NULL,
  dferror = NULL,
  mserror = NULL,
  replication = NULL,
  alpha = 0.05,
  main = NULL,
  MCP = "all",
  ismean = FALSE
)

Arguments

Details

The MCP argument allows you to choose various tests of multiple comparisons at once. For example, MCP = c("MGM", "MGR"), and so on.

Value

MRtest returns the print of a list of results. First, the summary of y. Second, the statistics of the test chosen. And finally, the mean group results for each test. If MRtest function is stored in an object, the results will be printed and also stored in the object.

Examples

# Simulated data (completely randomized design)

# Response variable
rv <- c(100.08, 105.66, 97.64, 100.11, 102.60, 121.29, 100.80,
        99.11, 104.43, 122.18, 119.49, 124.37, 123.19, 134.16,
        125.67, 128.88, 148.07, 134.27, 151.53, 127.31)

# Treatments
treat <- factor(rep(LETTERS[1:5], each = 4))

# Anova
res     <- anova(aov(rv~treat))
DFerror <- res$Df[2]
MSerror <- res$`Mean Sq`[2]

# Loading the midrangeMCP package
library(midrangeMCP)

# applying the tests
results <- MRtest(y = rv,
                  trt = treat,
                  dferror = DFerror,
                  mserror = MSerror,
                  alpha = 0.05,
                  main = "Multiple Comparison Procedure: MGM test",
                  MCP = c("MGM"))

# Other option for the MCP argument is "all". All tests are used.

results$Groups     # Results of the tests
results$Statistics # Main arguments of the tests
results$Summary    # Summary of the response variable

# Using the y argument as aov or lm model
res  <- aov(rv~treat)

MRtest(y = res, trt = "treat", alpha = 0.05,
       main = "Multiple Comparison Procedure: MGM test",
       MCP = c("MGM"))

# For unbalanced data: It will be used the harmonic mean of
#                       the number of experiment replicates

# Using the previous example
rv <- rv[-1]
treat <- treat[-1]

res  <- lm(rv~treat) # Linear model

# Multiple comparison procedure: MGR test
MRtest(y = res, trt = "treat", alpha = 0.05,
       main = "Multiple Comparison Procedure: MGR test",
       MCP = c("MGR"))

# Assuming that the available data are the averages
#  of the treatments and the analysis of variance

# Analysis of Variance Table

# Response: rv
#            Df Sum Sq Mean Sq F value    Pr(>F)
# treat      4 4135.2 1033.80  14.669 4.562e-05 ***
# Residuals 15 1057.1   70.47

mean.treat <- c(100.87, 105.95, 117.62, 127.97, 140.30)
treat <- factor(LETTERS[1:5])
DFerror <- 15
MSerror <- 70.47488
replic <- 4

MRtest(y = mean.treat,
       trt = treat,
       dferror = DFerror,
       mserror = MSerror,
       replication = replic,
       alpha = 0.05,
       main = "Multiple Comparison Procedure: MGM test",
       MCP = c("MGM"),
       ismean = TRUE)

Export the results of the MRtest function.

Description

The x object from a MRtest is written to file arguments.

Usage

MRwrite(x, MCP = "all", extension = "csv", dataMR = "all")

Arguments

Details

Note that the choice of the tests in the MRwrite function must be in accordance with the tests chosen in the x argument.

Value

MRwrite writes the most important results for the chosen tests in the MCP argument.

Examples

# Simulated data (completely randomized design)

rv <- c(100.08, 105.66, 97.64, 100.11, 102.60, 121.29, 100.80,
        99.11, 104.43, 122.18, 119.49, 124.37, 123.19, 134.16,
        125.67, 128.88, 148.07, 134.27, 151.53, 127.31)

# Treatments
treat <- factor(rep(LETTERS[1:5], each = 4))

# Anova
res <- aov(rv~treat)

# Loading the midrangeMCP package
library(midrangeMCP)

# Choosing any tests
results <- MRtest(y = res, trt = "treat", alpha = 0.05,
                   main = "Multiple Comparison Procedures",
                   MCP = c("MGM", "TM"))

#Export file in latex (Output in Console)
MRwrite(results, MCP = "all", extension = "latex", dataMR = "all")

#Observation: The MRwrite function export
#             only one extension at a time

Graphical User Interface for midrangeMCP function

Description

guimidrangeMCP A Graphical User Interface (GUI) for function that returns the MGM, MGR, SNKM and TM tests

Usage

guimidrangeMCP(gui = TRUE)

Arguments

Value

guimidrangeMCP presents a GUI for the results of the four multiple comparison procedures MGM, MGR, SNKM and TM tests. In addition, the GUI returns a graph of the results, as well as the export of these results to three types of file extension and latex code.

Examples

# Loading package
library(midrangeMCP)
if (interactive()) {
  guimidrangeMCP(gui = FALSE)
}