# Multiple comparison procedures to the means. `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. ```r 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. ## Returns `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. ## Details The `MCP` argument allows you to choose various tests of multiple comparisons at once. For example, `MCP = c("MGM", "MGR")`, and so on. ## Examples ```r # 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) ``` ## 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.

MCPtest function