# Exercise 8.6

# Statistical Methods in Biology: Design and Analysis of Experiments and Regression
# by S.J. Welham, S.A. Gezan, S.J. Clark & A. Mead (2014)
# Chapman & Hall/CRC Press, Boca Raton, Florida. ISBN: 978-1-4398-0878-8
#  Data from R. Curtis, Rothamsted Research/Bionemax
# Version 1, 30/06/2015

# Set working directory - use setwd() function or from Session menu in RStudio
# e.g. setwd("d:/stats4biol/data)

# load external packages - available from CRAN
library(ggplot2)
library(lsmeans)  # tables of predicted means & contrasts

# Read data & assign factors
nematode <- read.table('nematodes.dat', sep="", header=TRUE)
summary(nematode)

# Plot data
qplot(data=nematode, y=Distance, x=Treatment)

# ANOVA
nematode.aov <- aov(Distance ~ Treatment, data=nematode)
summary(nematode.aov)

# Residual plots
plot(nematode.aov, ask=FALSE)
hist(residuals(nematode.aov))

# Treatment means and contrasts
nematode.lsm <- lsmeans(nematode.aov, ~Treatment)
# Treatment means
nematode.lsm
# Contrasts
contrast(nematode.lsm, method=list(c1=c(0.5,0.5,-1), c2=c(1,-1,0)))

# To partition treatment SS in ANOVA table, need to define new factor to partion 
# lectins vs control treatment
nematode$Control <- rep(1,length(nematode$Treatment))
nematode$Control[nematode$Treatment=='PBS'] <- 2
nematode$Control <- as.factor(nematode$Control)

# ANOVA with partitioned treatment factor 
nematode2.aov <- aov(Distance ~ Control/Treatment, data=nematode)
summary(nematode2.aov)


# End of file