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Data:
1213.8 1245.6 1306.3 1255.8 1257.6 1287.8 1300.4 1320.9 1370.8 1327.3 1320 1345.3 1346.7 1395.4 1462 1491.6 1461.8 1477.9 1490.3 1521.1 1561.9 1552.6 1523.6 1548.3 1552.4 1587 1621.3 1648.7 1641.8 1650.6 1688.6 1670.7 1682.2 1678.9 1650.6 1662.4 1664.5 1683.2 1736.2 1747.6 1749 1759.7 1793.6 1817.4 1858.4 1839.9 1809.1 1877.7 1880.3 1930.9 2039.3 1992.7 1987.8 1984.4 2016.5 2016.7 2064.1 2031.5 2000.3 2057.8 2041.2 2093.2 2158.3 2128.8 2131.9 2170.3 2190.8 2217.7 2254.4 2223.3 2210.5 2250.8 2249.1 2288.6 2329.2 2313.8 2309.8 2345.9 2361.3 2372 2410.4 2398.5 2362.3 2419.1 2421.6 2465 2480.5 2506.1 2506.6 2525.8 2550 2578.3 2807.8 2815.3 2767.7 2815.4 2838.8 2864 2948.6 2922.8 2917.2 2936.8 2993.4 3007.8 3046.3 3011.5 2958.6 3019.8 2998.5 3040.4 3166 3110 3099.2 3150.3 3163.6 3182.6 3244.4 3223.2 3143.6 3217 3182.3 3217.2 3262.5 3227.9 3171.6 3219 3195.4 3221.6 3262.1 3179.5 3133.6 3219.2 3245 3265.3 3312.5 3383.6 3386.3 3411.1 3467.2 3487.7 3575.5 3571.5 3582.3 3637.1 3685
Sample Range:
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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 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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