How to do Dunnett test on R?

[Jolied]

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?

[Avante]

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.