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Data:
6151.2 5847.6 5662.8 5807.7 5907 6036.3 5668.2 5578.5 5760.6 5918.1 6030 6242.4 6425.1 6610.8 6943.5 5316.3 4356.6 4073.1 4239.9 4401.3 4590.6 4671 4772.1 4875.3 4601.7 4482.3 4455.6 4487.7 4606.8 4727.7 4617.9 4507.8 4398.6 4334.7 4272.9 4209.6 3963.3 3717 3469.5 3587.1 3703.5 3819.6 3777 3732.9 3687.6 3756.3 3824.7 3893.7 4039.2 4184.7 4329.9 4867.8 5405.7 5943.6 6440.7 6938.4 7435.8 6696.3 5957.1 5217.9 4781.7 4345.2 3909 3944.7 3980.1 4015.5 3983.7 3951.6 3919.8 3992.1 4064.4 4136.7 3950.1 3763.2 3577.2 3690.3 3804 3917.7 3900.9 3884.1 3867 3915 3962.4 4009.5 3820.2 3631.2 3441.9 3557.7 3674.1 3789.9 3886.2 3981.9 4078.2 4181.4 4284.9 4388.4 4190.1 3991.8 3793.5 3734.7 3675.9 3617.4 3557.7 3498 3438.6 3478.5 3518.7 3558.9 3401.1 3230.7 3060.3 3043.5 3026.4 3009.6 3159 3308.1 3457.5 3327.6 3198 3068.1 3108 3147.6 3187.5
Sample Range:
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From:
To:
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) print(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) print(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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R Server
Big Analytics Cloud Computing Center
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