# the matrix function can be used for data sets with only numeric variables

data <- matrix(scan(),ncol=2,byrow=TRUE)        
 1  1
 2  1
 3  1
 5  2
 3  2
 4  2
 6  3
 7  3
 5  3 

data

tastetest <- data.frame(data)
colnames(tastetest) <- c("taste","treat")

rm(data)   # remove unneeded objects

 # we must make sure that treat is a factor, not numeric

tastetest$treat <- as.factor(tastetest$treat)

 # set a reference level for use with Dunnetts method

tastetest$treat <- relevel(tastetest$treat, ref="1")


tastetest.lm <- lm(taste ~ treat, data = tastetest)

anova(tastetest.lm)
summary(tastetest.lm)

 # check model assumptions
par(mfrow=c(2,2))  
plot(tastetest.lm)
par(mfrow=c(1,1))  

 # ------------------------------------------------------------------------
 #    using emmeans for contrasts 
 # ------------------------------------------------------------------------

library(emmeans)

treatmeans <- emmeans(tastetest.lm, "treat")

contrast1vs2  <- list(First2 = c(1, -1, 0))

contrast12vs3  <- list(First2vs3 = c(1, 1, -2))

contrast(treatmeans, contrast1vs2, adjust="none")

contrast(treatmeans, contrast12vs3, adjust="none")

 # ------------------------------------------------------------------------
 #   using multcomp for Tukey and Dunnett methods
 # ------------------------------------------------------------------------

library(multcomp)

methods.Dunnett <- glht(tastetest.lm,linfct=mcp(treat="Dunnett"))
summary(methods.Dunnett)

methods.Tukey <- glht(tastetest.lm,linfct=mcp(treat="Tukey"))
summary(methods.Tukey)

 # ------------------------------------------------------------------------
 #   using DescTools for Tukey, Fisher's LSD and Scheffe methods
 # ------------------------------------------------------------------------

library(DescTools)

tastetest.aov <- aov(taste ~ treat, data = tastetest)  # these functions use aov objects

# Fisher's and Tukey's methods

PostHocTest(aov(taste ~ treat, data = tastetest), method = "lsd", conf.level=.95)
PostHocTest(aov(taste ~ treat, data = tastetest), method = "hsd", conf.level=.95)

# find levels of Species for defining a contrast

levels(tastetest$treat)

# compare Milk Tea to the two others:

Sch.c1 <- ScheffeTest(tastetest.aov, contrasts=matrix( c(1.,1.,-2.), ncol=1) )

Sch.c1


 # ------------------------------------------------------------------------
 #     methods for adjusting p values
 # ------------------------------------------------------------------------

 # p values from Table 5.1 in the text

pvalues1 <- c(.004,.002,.13,.29,.73,.08,.02)

pvalues2 <- c(.40,.24,.01,.0001,.0001,.001)

pvalues3 <- c(.001,.0001,.41,.07,.15,.42,.002,.006)

pvalsall <- c(pvalues1,pvalues2,pvalues3)


 # adjusted p values

p.adjust(pvalsall, "bonferroni")

p.adjust(pvalsall, "holm")

p.adjust(pvalsall, "fdr")

 
 # rounded values

round(pvalsall, 3)

round(p.adjust(pvalsall, "bonferroni"), 3)

round(p.adjust(pvalsall, "holm"), 3)

round(p.adjust(pvalsall, "fdr"), 3)