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Data:
0.217499822516866 -9.78478953094158 -4.69092289728636 10.8303737739891 17.4025635868567 -18.695949344517 -1.45165150662704 24.2055636204555 -7.85142459641661 -9.4984274601968 15.4524605741829 -4.57316796503045 -35.3790647494429 -1.71841266886546 16.4887231604473 12.3672525241091 -30.5270631334762 14.0831320895415 12.2671755298376 7.6573009730872 -10.2588701804503 4.98218909929218 18.2374731745486 -18.1217207388952 19.2509327179740 20.7168346335646 27.0084172311441 -13.1935985382014 3.91871968362315 23.2274665865825 39.0004011190054 23.2030259046977 50.985499051697 26.9190549927636 5.61997889463015 -49.70729388275 -6.42398274476005 -41.6977425555609 -7.92694134412627 5.34208987982118 27.7759340629904 -6.52456657092852 3.12509229100933 -4.61574932357839 11.4276121576526 18.3833489895730 -42.4568062507692 7.51193809087255 -16.7513836179227 8.95264961600874 8.81854600123269 -18.5966360042492 11.7598212958319 -7.41354314708516 17.5247266754596 17.9797835233522 -36.4653940696526 4.31529911509816 8.75602862255269 7.65533116235876 -1.38678324054813
Sample Range:
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From:
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bandwidth of density plot
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# lags (autocorrelation function)
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Chart options
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) x <- as.ts(x) library(lattice) bitmap(file='pic1.png') plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic2.png') hist(x) grid() dev.off() bitmap(file='pic3.png') if (par1 > 0) { densityplot(~x,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~x,col='black',main='Density Plot') } dev.off() bitmap(file='pic4.png') qqnorm(x) qqline(x) grid() dev.off() if (par2 > 0) { bitmap(file='lagplot1.png') dum <- cbind(lag(x,k=1),x) dum dum1 <- dum[2:length(x),] dum1 z <- as.data.frame(dum1) z plot(z,main='Lag plot (k=1), lowess, and regression line') lines(lowess(z)) abline(lm(z)) dev.off() if (par2 > 1) { bitmap(file='lagplotpar2.png') dum <- cbind(lag(x,k=par2),x) dum dum1 <- dum[(par2+1):length(x),] dum1 z <- as.data.frame(dum1) z mylagtitle <- 'Lag plot (k=' mylagtitle <- paste(mylagtitle,par2,sep='') mylagtitle <- paste(mylagtitle,'), and lowess',sep='') plot(z,main=mylagtitle) lines(lowess(z)) dev.off() } bitmap(file='pic5.png') acf(x,lag.max=par2,main='Autocorrelation Function') grid() dev.off() } summary(x) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Descriptive Statistics',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations',header=TRUE) a<-table.element(a,length(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'minimum',header=TRUE) a<-table.element(a,min(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,quantile(x,0.25)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,quantile(x,0.75)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum',header=TRUE) a<-table.element(a,max(x)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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