pdf("g08.pdf");options(width=64)
#setwd("C:\\Users\\kolassa\\Class555")
setwd("~/Taught1/960-555/Data")
<pre>
#<a name=block1> Block 1</a>
expensesd<-as.data.frame(scan("friedman.dat",
   what=list(cat="",g1=0,g2=0,g3=0,g4=0,g5=0,g6=0,g7=0)))
#<a name=block2> Block 2</a>
expenserank<-t(apply(as.matrix(expensesd[,-1]),1,rank))
 rownames(expenserank)<-expensesd[[1]]
#<a name=block3> Block 3</a>
apply(expenserank,2,mean)
#<a name=block4> Block 4</a>
friedman.test(as.matrix(expensesd[,-1]))
#<a name=block5> Block 5</a>
temp1<-temp<-as.data.frame(scan("chicken.dat",what=
   list(source="", lev=0,fish=0,weight=0,w2=0)))
temp1$weight<-temp1$w2;temp$h<-0;temp1$h<-1
temp$w2<-NULL;temp1$w2<-NULL
chicken<-rbind(temp,temp1)
boxplot(split(chicken$weight,chicken$source),
  horizontal=TRUE, main="Weight gain for Chickens", 
  ylab="Protein Source", xlab="Weight Gain (g.)")
#<a name=block6> Block 6</a>
#If necessary, install a version of muStat from archives using
#library(devtools)
#install_version("muStat")
#or download the package from CRAN archives.
library(muStat)#For Prentice test.
prentice.test(chicken$weight,chicken$source,
   blocks=chicken$lev)
#<a name=block7> Block 7</a>
library(MultNonParam)#For aov.P
#aov.P requires data sorted by block.  Put block ends as the
#third argument.
chicken<-chicken[order(chicken$lev),]
aov.P(chicken$weight,as.numeric(as.factor(chicken$source)),
 c(8,16,24))
#<a name=block8> Block 8</a>
library(crank)#For page.trend.test
#Perform the test of Page.
page.trend.test(expensesd[,-1],FALSE)
#<a name=block9> Block 9</a>
library(MultNonParam)#For page.test.unbalanced
cat('\n Page test with replicates  \n')
page.test.unbalanced(chicken$weight,chicken$lev,
   chicken$source)
twinbrain<-as.data.frame(scan("IQ_Brain_Size",
  what=list(CCMIDSA=0,FIQ=0,HC=0,ORDER=0,PAIR=0,SEX=0,
  TOTSA=0, TOTVOL=0,WEIGHT=0),skip=27,nmax=20))
fir<-twinbrain[twinbrain$ORDER==1,]
fir$v1<-fir$TOTVOL
sec<-twinbrain[twinbrain$ORDER==2,]
sec$v2<-sec$TOTVOL
brainpairs<-merge(fir,sec,by="PAIR")[,c("v1","v2")]
brainpairs$diff<-brainpairs$v2-brainpairs$v1
#<a name=block10> Block 10</a>
plot(brainpairs$v1,brainpairs$v2, xlab="Volume for Twin 1",
   ylab="Volume for Twin 2",main="Twin Brain Volumes")
#<a name=block11> Block 11</a>
cat('\n Permutation test for twin brain data  \n')
obsd<-c(cor(brainpairs$v1,brainpairs$v2),
   cor(brainpairs$v1,brainpairs$v2,method="spearman"))
#<a name=block12> Block 12</a>
out<-array(NA,c(2,20001))
dimnames(out)[[1]]<-c("Pearson","Spearman")
for(j in seq(dim(out)[2])){
   newv1<-sample(brainpairs$v1)
   out[,j]<-c(cor(newv1,brainpairs$v2),
      cor(newv1,brainpairs$v2,method="spearman"))
}
cat("\n Monte Carlo One-Sided p value\n")
apply(apply(out,2,">=",obsd),1,"mean")
#<a name=block13> Block 13</a>
cat("\n Asymptotic Critical Value\n")
-qnorm(0.025)/sqrt(length(brainpairs$v1)-1)
#<a name=block14> Block 14</a>
c(cor.test(brainpairs$v1,brainpairs$v2)$p.value,
   cor.test(brainpairs$v1,brainpairs$v2,method="spearman")$p.value)
</pre>