How to do Dunnett test on R?



  • Supposing I performed the ANOVA test in my data and gave significantly different, I would like to perform the Dunnett test just to compare my treatments with the control group. Should I use some specific package or in the R standard itself already have?



  • To do the Dunnet test, you can use the function glht package multcomp.

    How there is no data in the question I will use the base iris.

    Immediately before running the test one must initialize the random number generator because apparently the distribution calculations t multivariate call the random number generator. With set.seed() or results are reproducible.

    library(multcomp)
    

    data(iris)

    fit <- aov(Sepal.Length ~ Species, data = iris)

    set.seed(204) # Torna os resultados reprodutíveis
    summary(glht(fit, linfct = mcp(Species = "Dunnett")))

    #Simultaneous Tests for General Linear Hypotheses

    #Multiple Comparisons of Means: Dunnett Contrasts

    #Fit: aov(formula = Sepal.Length ~ Species, data = iris)

    #Linear Hypotheses:

    Estimate Std. Error t value Pr(>|t|)

    #versicolor - setosa == 0 0.930 0.103 9.033 <1e-10 ***
    #virginica - setosa == 0 1.582 0.103 15.366 <1e-10 ***
    #---
    #Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
    #(Adjusted p values reported -- single-step method)

    Note.

    The idea of calling set.seed comes from the answers to a question on Cross Validated, https://stats.stackexchange.com/questions/83116/dunnetts-test-in-r-returning-different-values-each-time#83125 in English.


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