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,
  ...
)

Arguments

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))

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
)

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

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)

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")

Arguments

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

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

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)

Arguments

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)
}