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Data:
10 4 5 4 4 4 6 4 8 5 4 17 4 4 8 4 7 4 4 5 7 4 4 7 11 7 4 4 4 4 4 4 6 8 23 4 8 6 4 4 7 4 4 4 4 10 6 5 5 4 4 5 5 5 5 4 6 4 4 4 9 18 6 5 4 11 4 10 6 8 8 6 8 4 4 9 9 5 4 4 15 10 9 7 9 6 4 7 4 7 4 15 4 9 4 4 28 4 4 4 5 4 4 12 5 4 6 6 5 4 4 4 10 7 4 4 7 4 4 12 5 8 6 17 4 5 4 5 5 6 4 4 4 6 8 10 4 5 4 4 4 16 4 7 4 4 14 5 5 5 5 7 19 16 4 4 7 9 5 14 4 16 10 5 6 4 4 4 5 4 4 5 4 4 5 8 15 7 5 8 8 5 4 4 11 5 22 4 4 4 5 4 16 5 4 6 5 4 4 4 7 4 8 7 4 6 5 8 8 4 7 4 13 4 4 4 4 7 5 4 5 12 8 4 4 8 5 4 4 7 5 13 4 4 4 6 4 4 4 4 4 5 6 4 4 4 6 9 5 6 13 4 7 5 4 4 4 6 6 8 6 5 9 6 4 9 4 4 4 5 4 4 4 5 5 8 4 4 9 4 4 4 4 4 4 4 4 4 4 4 4 4 5 8 7 4 4 4 5 5 6 12 5 9 12 4 16 4 5 4 4 6 4 4 5 6 5 6 4 4 7 9 5 5 4 4 12 4 6 9 4 5 4 4 4 4 4 11 4 6 4 5 4 4 6 4 7 9 5 14 4 4 4 5 4 4 9 4 4 10 4 4 6 4 9 5 4 5 14 9 4 4 17 4 5 9 7 4 5 7 10 5 4 8 4 4 6
Type of transformation
Full Box-Cox transform
Full Box-Cox transform
Simple Box-Cox transform
Minimum lambda
-2
-2
-8
-7
-6
-5
-4
-3
-2
-1
Maximum lambda
2
2
1
2
3
4
5
6
7
8
Constant term to be added before analysis is performed
(?)
Display table with original and transformed data?
No
No
Yes
Chart options
R Code
par2 <- abs(as.numeric(par2)*100) par3 <- as.numeric(par3)*100 if(par4=='') par4 <- 0 par4 <- as.numeric(par4) numlam <- par2 + par3 + 1 x <- x + par4 n <- length(x) c <- array(NA,dim=c(numlam)) l <- array(NA,dim=c(numlam)) mx <- -1 mxli <- -999 for (i in 1:numlam) { l[i] <- (i-par2-1)/100 if (l[i] != 0) { if (par1 == 'Full Box-Cox transform') x1 <- (x^l[i] - 1) / l[i] if (par1 == 'Simple Box-Cox transform') x1 <- x^l[i] } else { x1 <- log(x) } c[i] <- cor(qnorm(ppoints(x), mean=0, sd=1),x1) if (mx < c[i]) { mx <- c[i] mxli <- l[i] x1.best <- x1 } } c mx mxli x1.best if (mxli != 0) { if (par1 == 'Full Box-Cox transform') x1 <- (x^mxli - 1) / mxli if (par1 == 'Simple Box-Cox transform') x1 <- x^mxli } else { x1 <- log(x) } bitmap(file='test1.png') plot(l,c,main='Box-Cox Normality Plot', xlab='Lambda',ylab='correlation') mtext(paste('Optimal Lambda =',mxli)) grid() dev.off() bitmap(file='test2.png') hist(x,main='Histogram of Original Data',xlab='X',ylab='frequency') grid() dev.off() bitmap(file='test3.png') hist(x1,main='Histogram of Transformed Data', xlab='X',ylab='frequency') grid() dev.off() bitmap(file='test4.png') qqnorm(x) qqline(x) grid() mtext('Original Data') dev.off() bitmap(file='test5.png') qqnorm(x1) qqline(x1) grid() mtext('Transformed Data') dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Box-Cox Normality Plot',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations x',header=TRUE) a<-table.element(a,n) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum correlation',header=TRUE) a<-table.element(a,mx) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'optimal lambda',header=TRUE) a<-table.element(a,mxli) a<-table.row.end(a) if(mx<0) { a<-table.row.start(a) a<-table.element(a,'Warning: maximum correlation is negative! The Box-Cox transformation must not be used.',2) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') if(par5=='Yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Obs.',header=T) a<-table.element(a,'Original',header=T) a<-table.element(a,'Transformed',header=T) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i) a<-table.element(a,x[i]) a<-table.element(a,x1.best[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') }
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Big Analytics Cloud Computing Center
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