#
# Exercise 11.6
#
# 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 Pearce (1965, Section 6.2)
# Version 1, 1/08/2015
# Set working directory - use setwd() function or from Session menu in RStudio
# e.g. setwd("d:/stats4biol/data)
# Set up packages to be used later - available from CRAN
library(lsmeans) # for calculating predicted means
library(ggplot2) # for plotting
# Read data & assign factors
shoot <- read.table("shoot.dat",sep="",header=T)
shoot$Tree <- as.factor(shoot$Tree)
shoot$DBranch <- as.factor(shoot$DBranch)
shoot$Treatment <- as.factor(shoot$Treatment)
summary(shoot)
# Plot data
qplot(y=Length, x=Treatment, colour=Tree, data=shoot)
# Look at number of observations per tree
table(shoot$Tree, shoot$Treatment)
# Structural component: Tree
# Explanatory component: Treatment
# Fit intra-block model, accounting for Treatments after removing Tree effects
shoot.lm <- lm(Length~Tree+Treatment, data=shoot)
# Check residuals
plot(shoot.lm, ask=FALSE)
# Check ANOVA table - no evidence of real treatment differences
anova(shoot.lm)
# Get table of predicted means from intra-block analysis
shoot.lsm <- lsmeans(shoot.lm, ~Treatment)
shoot.lsm
# pairwise contrasts
contrast(shoot.lsm, method="pairwise")
# A mixed model, which combines information across trees as well as within trees
# would use all of the treatment information - see Exercise 16.3
# End of file