factor_levels.R

#
# Start with Basic ANOVA
#
p16.7 <- read.table("CH16PR07.txt",col.names=c("improvement","rd","observation"))
attach(p16.7)
rdf <- factor(rd,levels=c(1:3),labels=c("Low","Moderate","High"))
result <- lm(improvement~rdf)
anova(result)
boxplot(improvement~rdf)
#
# Plot of factor level means
#
install.packages("gplots")
library(gplots)
mu.i <- tapply(improvement,rdf,mean)
barplot2(mu.i)
#
# With standard errors
#
anova(result)
mse <- .6401
(n.i <- tapply(improvement,rdf,length))
(se.i <- sqrt(.6401/n.i))
barplot2(mu.i,plot.ci=TRUE,ci.l=mu.i-se.i,ci.u=mu.i+se.i)
#
# With 95% Confidence Intervals
#
anova(result)
qnorm(.975)
qt(.975,24)
barplot2(mu.i,plot.ci=TRUE,ci.l=mu.i-qt(.975,24)*se.i,ci.u=mu.i+qt(.975,24)*se.i)
barplot2(mu.i,plot.ci=TRUE,ci.l=mu.i-qt(.975,24)*se.i,ci.u=mu.i+qt(.975,24)*se.i,ylim=c(0,10))
barplot2(mu.i,plot.ci=TRUE,ci.l=mu.i-qt(.975,24)*se.i,ci.u=mu.i+qt(.975,24)*se.i,ylim=c(6,10))
barplot2(mu.i,plot.ci=TRUE,ci.l=mu.i-qt(.975,24)*se.i,ci.u=mu.i+qt(.975,24)*se.i,ylim=c(6,10),xpd=FALSE)
#
# Contrasts of interest
# Pre-planned!!!
# 
install.packages("gmodels")
library(gmodels)
# Say we want the difference between Low and Moderate
mu.i[1]-mu.i[2]
levels(rdf)
fit.contrast(result,rdf,c(1,-1,0))
fit.contrast(result,rdf,c(1,-1,0),conf.int=.95)
# Average of low and high to moderate

# Compare Moderate to Grand Mean

# More than one at a time
fit.contrast(result,rdf,
	rbind( "Low-Mod"  = c(1,-1,0),
	       "Mod-High" = c(0,1,-1)),conf.int=.95)
# Weighted mean
fit.contrast(result,rdf,c(.5,.25,.25),conf.int=.95)

# Linear Combinations
c.i  <- c(.5,.25,.25)
mu.i*c.i
sum(mu.i*c.i)
(se <- sqrt(mse*sum(c.i^2/n.i)))
sum(mu.i*c.i)+c(-1,1)*qt(.975,24)*se
#
# Check Comparison
#
c.i  <- c(1,-1,0)
mu.i*c.i
sum(mu.i*c.i)
(se <- sqrt(mse*sum(c.i^2/n.i)))
sum(mu.i*c.i)+c(-1,1)*qt(.975,24)*se
fit.contrast(result,rdf,c(1,-1,0),conf.int=.95)