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Data:
0.355911391 0.192295904 0.272267288 0.258689189 0.18567135 0.373877063 0.34691589 0.359880227 0.286165509 0.414310922 0.482794147 0.317571037 0.346366101 -0.13487635 -0.187377539 0.285965011 -0.081923433 0.165051707 -0.012919634 0.139960994 0.073744737 0.121091076 0.028232563 0.206453634 0.257216569 0.065350838 0.05924699 -0.052759239 0.047943153 0.029757832 0.10780113 0.084875467 -0.067866223 -0.050792543 -0.160144441 -0.169203507 0.148743398 -0.082307667 -0.127673304 -0.01080757 -0.315287757 0.042567136 0.005232124 -0.170879984 -0.044496029 -0.112199933 -0.150843154 -0.252805389 0.004218806 -0.105782565 -0.155260875 -0.063622103 -0.171783206 -0.045750814 -0.108936836 -0.101035287 -0.264863038 -0.106527791 -0.128082303 -0.208906985 -0.15472301 -0.107099467 -0.290272108 0.076359113 -0.233991104 -0.127904111 -0.137345117 -0.146830587 -0.153801771 -0.039895855 -0.332238711 -0.158714763 0.002967834 -0.001027051 -0.010295419 0.034237962 -0.159297351 -0.158707613 0.110832302 -0.003154811 -0.021356073 0.112124864 0.044872239 0.017477842 0.10245901 0.098946073 -0.206535883 -0.006024052 -0.219470394 0.057492629 0.132658623 0.014386318 0.277202467 -0.051526973 0.058962934 -0.099306341 0.197363426 -0.1163449 0.136960891 -0.084933941 -0.005866992 -0.031379518 0.098016068 0.009935834 0.049865297 0.194801823 0.067147354 -0.078575054 0.0080191 0.001027051 -0.015792757 0.025921638 -0.041486393 0.07401796 0.059698617 -0.019134923 -0.008463373 0.14716635 0.122507323 0.052540291
Sample Range:
(leave blank to include all observations)
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) 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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