#5710 Lab Week of 4/13

#Install packages through RGui, then load libraries
install.packages(c("psych","Rcmdr","HH"),contriburl = "http://cran.stat.ucla.edu/bin/windows/contrib/2.6/")
library(psych)
library(HH)
library(granova)

#Bring in Adler data set
data(Adler, package="car")
attach(Adler)

#Get descriptives of rating by expectation and instruction
describe.by(rating, expectation)
describe.by(rating, instruction)

#Get descriptives of rating by each level of expectation and instruction
tapply(Adler$rating, list(expectation=Adler$expectation, instruction=Adler$instruction), mean, na.rm=TRUE)

#Plot means
plotMeans(Adler$rating, Adler$instruction, Adler$expectation, error.bars="se")

#Run ANOVA
Anova = (lm(rating ~ expectation*instruction, data=Adler))
tapply(Adler$rating, list(expectation=Adler$expectation, instruction=Adler$instruction), mean, na.rm=TRUE) # means
tapply(Adler$rating, list(expectation=Adler$expectation, instruction=Adler$instruction), sd, na.rm=TRUE) # std. deviations
tapply(Adler$rating, list(expectation=Adler$expectation, instruction=Adler$instruction), function(x) sum(!is.na(x))) # counts

#Plot interaction with main effects
interaction2wt(rating~expectation + instruction, data=Adler)

#Plot interaction with simple effects
interaction2wt(rating~expectation + instruction, data=Adler, simple=T)

#Detach HH
detach(package:HH)

#Conduct post hoc with Tukey's HSD
aovmod = aov(rating~expectation*instruction, data=Adler)
model.tables(aovmod)
TukeyHSD(aovmod)
plot(TukeyHSD(aovmod))

#Select 'Anova' from active model list

#Use Rcmdr menu to get basic diagnostic plots or cycle through them with:
plot(aovmod)
                    
#Get influence plot
influencePlot(Anova, labels=FALSE)

#Granova graph of one-way with instruction 
granova.1w(rating, instruction, tr=T)

#Run robust ANOVA
source("http://www.unt.edu/rss/class/mike/Rallfun-v9_2.txt")
newdat = as.matrix (cbind(as.numeric (Adler$instruction), as.numeric(Adler$expectation), Adler$rating))
t2way(3,2, newdat, MAT=T)