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Data:
Усп 44 44 28 58 52 49 49 33 67 46 47 72 45 49 63 51 41 46 44 44 28 58 52 49 49 33 67 46 47 72 45 49 63 51 41 46
Type of transformation
Полное преобразование Бокса-Кокса
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?
Нет
No
Yes
Chart options
R Code
library(car) 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),sort(x1)) if (mx < c[i]) { mx <- c[i] mxli <- l[i] x1.best <- x1 } } print(c) print(mx) print(mxli) print(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) } mypT <- powerTransform(x) summary(mypT) 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') qqPlot(x) grid() mtext('Original Data') dev.off() bitmap(file='test5.png') qqPlot(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) a<-table.row.start(a) a<-table.element(a,'transformation formula',header=TRUE) if (par1 == 'Full Box-Cox transform') { a<-table.element(a,'for all lambda <> 0 : T(Y) = (Y^lambda - 1) / lambda') } else { a<-table.element(a,'for all lambda <> 0 : T(Y) = Y^lambda') } 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') } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Maximum Likelihood Estimation of Lambda',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('summary(mypT)'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab')
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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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