Package 'MCPtests' reference manual

Package 'MCPtests'

Title:	Multiples Comparisons Procedures
Description:	Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.
Authors:	Ben Deivide [aut, cre], Daniel Furtado [aut], Diego Arthur [aut], Matheus Fernando [ctb]
Maintainer:	Ben Deivide <[email protected]>
License:	GPL (>= 2)
Version:	1.0.1
Built:	2024-12-02 06:30:47 UTC
Source:	CRAN

Title:

Multiples Comparisons Procedures

Description:

Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.

Authors:

Ben Deivide [aut, cre], Daniel Furtado [aut], Diego Arthur [aut], Matheus Fernando [ctb]

Maintainer:

Ben Deivide <[email protected]>

License:

GPL (>= 2)

Version:

1.0.1

Built:

2024-12-02 06:30:47 UTC

Source:

CRAN

Help Index

Barley grain yields in bushels per acre

Description

Set of results presented by Duncan (1955), in which Scott-Knott (1974) presented an application of the development of the Scott-Knott test in a randomized block design. References: 1) SCOTT, A. J.; KNOTT, M. A cluster analysis method for grouping means in the analysis of variance. Biometrics, International Biometric Society, v. 30, n. 3, p. 507-512, 1974. 2) DUNCAN, D. B. Multiple range and multiple F Tests. Biometrics, International Biometric Society, v. 11, n. 1, p. 1-42, 1955.

Usage

ex1ScottKnott
ex1ScottKnott

Format

A list with 4 variables:

treatment: Seven varieties of barley conduted by E. Shulkcum of this Institute at Accomc, Virginia, in 1951
averages: Averages of the seven varieties of barley
mserror: Result of mean squared error of the analysis of variance of the experiment
dferror: Degrees of freedom of error of the experiment
replication: Number of replications of treatments.

Graphical User Interface for MCPtests package

Description

guiMCP A Graphical User Interface (GUI) for the MCP package

Usage

guiMCP(gui = TRUE)
guiMCP(gui = TRUE)

Arguments

gui

Logical argument, TRUE or FALSE. The default is TRUE

Value

guiMCP presents a GUI for the results of the multiple comparison procedures in the literature. 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(MCPtests)
if (interactive()) {
  guiMCP(gui = FALSE)
}

# Loading package
library(MCPtests)
if (interactive()) {
  guiMCP(gui = FALSE)
}

Plotting the result of the multiple comparison procedures

Description

MCPbarplot creates a bar plot with vertical or horizontal bars to compare the mean treatments by the tests: means grouping based on midrange test, means grouping based on range test, Student-Newman-Keuls and Tukey based on midrange tests, Scott-Knott's test

Usage

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

Arguments

`x`	An object of the `MCPtest` function
`MCP`	Allows choosing the multiple comparison test. The defaut is "all". This option will perform all tests available in the `MCPtest` object.
`col`	A specification for the plotting color. The defaut is `heat.colors(10)`.
`horiz`	a logical value. If `FALSE`, the bars are drawn vertically with the first bar to the left. If `TRUE`, the bars are drawn horizontally with the first at the bottom.
`...`	Parameters of the `barplot` function

Details

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

Value

MCPbarplot return the bar plot of the tests chosen ("MGM", "MGR", "SNKM", "TM", "SK") 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 MCPtests package
library(MCPtests)

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

MCPbarplot(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'}.

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

# Plot for papers
MCPbarplot(results, MCP = "all", col = gray.colors(10))
# 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 MCPtests package
library(MCPtests)

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

MCPbarplot(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'}.

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

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

Multiple comparison procedures to the means.

Description

MCPtest applies several tests of multiple comparisons present in the literature. The tests chosen are based on the evaluation of the researcher to decide the choice of test for analysis in the experiment.

Usage

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

Arguments

`y`	Model (aov or lm), numeric vector containing the response variable or the mean of the treatments.
`trt`	Constant (y = model) or a vector containing the treatments.
`dferror`	Degrees of freedom of the Mean Square Error.
`mserror`	Mean Square Error.
`replication`	Number de repetitions of the treatments in the experiment. For unbalanced data should be informed the harmonic mean of repetitions. This argument should be informed only if `ismean = TRUE`.
`alpha`	Significant level. The default is `alpha = 0.05`.
`main`	Title of the analysis.
`MCP`	Allows choosing the multiple comparison test; the defaut is "all". This option will go perform all tests. However, the options are: the Skott-Knott midrange test ("MGM"), the Skott-Knott Range test ("MGR"), the Tukey midrange test ("TM"), the Scott-Knott's test ("SK").
`ismean`	Logic. If `FALSE` (default), the `y` argument represents a model (aov or lm) or a numeric vector containing the response variable. If `TRUE` the `y` argument represents the mean of treatments.
`parallel`	Logic. If `FALSE` (default), the `MCPtest` function runs without parallelization. Otherwise, the code is executed with parallelization. Note that the parallelization is not always more efficient.

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

MCPtest 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 MCPtest function is stored in an object, the results will be printed and also stored in the object.

References

BATISTA, Ben Deivide de Oliveira. Testes de comparacoes multiplas baseados na distribuicao da midrange estudentizada externamente. 2016. 194f. Tese (Doutorado em Estatistica e Experimentacao Agropecuaria) - Universidade Federal de Lavras, 2016. (Portuguese, Brazil)

SCOTT, A. J.; KNOTT, M. A cluster analysis method for grouping means in the analysis of variance. Biometrics, International Biometric Society, v. 30, n. 3, p. 507-512, 1974.

DUNCAN, D. B. Multiple range and multiple F Tests. Biometrics, International Biometric Society, v. 11, n. 1,p. 1-42, 1955.

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 MCPtests package
library(MCPtests)

# applying the tests
results <- MCPtest(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)

MCPtest(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
MCPtest(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

MCPtest(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)

# 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 MCPtests package
library(MCPtests)

# applying the tests
results <- MCPtest(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)

MCPtest(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
MCPtest(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

MCPtest(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 `MCPtest` function.

Description

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

Usage

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

Arguments

`x`	object from the `MCPtest` functions.
`MCP`	Allows choosing the multiple comparison test. The defaut is "all". This option will go perform all tests from the `MCPtest` object.
`extension`	Type of format of the file. Four options `"csv"`, `"txt"` `"xlsx"` and `"latex"`. The default is `"csv"`.
`dataMR`	Allows to choose the results to bee written. Three options are available: `"groups"`, `"summary"` or `"all"`. The option `"groups"` writes the treatment mean groups avaluated by the chosen test in the `MCP` argument. The `"summary"` writes the descriptive statistics of the response variable. The options `"all"` should be chosen for both results.

Details

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

Value

MCPwrite 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 MCPtests package
library(MCPtests)

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

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

#Observation: The MCPwrite function export
#             only one extension at a time
# 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 MCPtests package
library(MCPtests)

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

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

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

Package 'MCPtests'

Help Index

Barley grain yields in bushels per acre

Description

Usage

Format

Graphical User Interface for MCPtests package

Description

Usage

Arguments

Value

Examples

Plotting the result of the multiple comparison procedures

Description

Usage

Arguments

Details

Value

Examples

Multiple comparison procedures to the means.

Description

Usage

Arguments

Details

Value

References

Examples

Export the results of the MCPtest function.

Description

Usage

Arguments

Details

Value

Examples

Export the results of the `MCPtest` function.