R-help to extra exercise on multivariate methods

# Read the data into a data frame, give names to the variables, and take a look at the data:

salsinol<-read.table("http://www.math.uio.no/avdc/kurs/STK4900/data/salsinol.dat")

names(salsinol)<-c("patient","group","day1","day2","day3","day4")

salsinol

# Check that the data correspond to those given op page 105 i BS

# (where you also find a description of the data)

# Attach the data frame:

attach(salsinol)

# Put the data together as multivariate responses:

resp<-cbind(salsinol$day1,salsinol$day2,salsinol$day3,salsinol$day4)

# QUESTION a)

# Perform multivariate analysis of variance by means of "aov" and display the results: 

mod1<-aov(resp~factor(group))

mod1

summary(mod1)

# Study the results. What do they tell you?

# QUESTION b) 

# # Perform multivariate analysis of variance by means of "manova" and display the results: 

mod2<-manova(resp~factor(group))

mod2

summary(mod2, test="Wilks")

# Comment of differences between the two analyses.

# QUESTION c) 

# Fit a linear model to the response at day 1 and look at the results:

mod3<-lm(day1~factor(group))

summary(mod3)

# Use the output to answer the questions in the exercise.

# Can you explain the result?

# QUESTION d) 

# We now consider the measurement on day 1 as a covariate, and the measurements for the last three days as a multivariate response:

# Perform the commands:

resp2<-cbind(salsinol$day2,salsinol$day3,salsinol$day4)

mod4<-aov(resp2~factor(group)+day1)

mod5<-manova(resp2~factor(group)+day1)

# Inspect the results (using similar commands as above).

# What are the conclusions of the model fits? (cf. question e in the exercise)