# analysing_data_shipped_with_r_a_quick_tutorial
# copy of the anorexia dataset available from my site:
# mydataframe <- 
# read.table("http://www.robin-beaumont.co.uk/virtualclassroom/book2data/mwu_example1.dat",
#  header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
#### or use the method described in the tutorial:   
install.packages("MASS", dependencies = TRUE) 
install.packages("Rcmdr", dependencies = TRUE) 
library(Rcmdr)
library(MASS)  
data(anorexia, package="MASS")
library(multcomp, pos=4)
library(abind, pos=4)
# create a new variable
anorexia$weightgain <- with(anorexia, Postwt - Prewt)
anorexia
# quick summary stats:
summary(anorexia)
# boxplot:
Boxplot(weightgain~Treat, data=anorexia, id.method="y")
# ANOVA:
comparing_means <- aov(weightgain ~ Treat, data=anorexia)
summary(comparing_means)
numSummary(anorexia$weightgain , groups=anorexia$Treat, statistics=c("mean",
   "sd"))
.Pairs <- glht(comparing_means, linfct = mcp(Treat = "Tukey"))
summary(.Pairs) # pairwise tests
confint(.Pairs) # confidence intervals
cld(.Pairs) # compact letter display
old.oma <- par(oma=c(0,5,0,0))
plot(confint(.Pairs))
par(old.oma)
remove(.Pairs)