Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
-706.026542296181 -59.4405579520267 -969.833720383066 1994.56903063601 102.064814516947 1282.18411025837 -307.754606959943 225.069461418321 -862.471322666482 -2850.16172979440 -3875.76341994283 2996.98129088558 1931.51753614196 488.179241950267 -601.581627830989 -3495.10356420977 265.163825175723 640.313274555833 -3418.45097379814 2072.03398243007 2662.9984145817 -1064.81719671349 -9.57987333730985 -2208.8867746199 -4275.21498681124 381.837227886395 1914.99171248840 1019.08609621188 884.745394297871 -1702.08983953302 -2064.71644375347 -2452.89205796152 1313.39939683182 -569.737683944086 -187.9171774845 -128.334928612711 -2601.14827891639 581.657297679053 -299.792761503996 2868.57371158154 2723.0124492194 -1846.18418996859 -6526.92686969168 -3652.78139505582 2790.5729463211 -7703.28365973859 -1373.42544129638 -2976.48819823291 5585.1945937738 -2568.07437379205 -4750.48266905558 1324.51875012340 -1689.96832952026 -7068.09981170815 1855.55513995599 2269.56153308754 -4661.24493442205 3260.97170059017 2160.39821896397 4793.18303923311 -2209.06420471032 -2213.50510931699 -3114.14394163762 3411.46075067066 -4693.81993673823 5920.52037235391 -2077.91680939984 -3642.83868232441 332.648590721953 2734.67402718683 7903.1305482291 5491.06198558059 4354.83029956606 1619.51905250655 5543.22938817069 -366.008933224093 -3459.03179080274 1134.72876550400 -2525.56367971036 -926.76612281528 -2956.34221718809 -885.143314774538
Sample Range:
(leave blank to include all observations)
From:
To:
# blockwidth
Chart options
R Code
par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) arr <- array(NA,dim=c(par1,np+1)) darr <- array(NA,dim=c(par1,np+1)) ari <- array(0,dim=par1) dx <- diff(x) j <- 0 for (i in 1:n) { j = j + 1 ari[j] = ari[j] + 1 arr[j,ari[j]] <- x[i] darr[j,ari[j]] <- dx[i] if (j == par1) j = 0 } ari arr darr arr.mean <- array(NA,dim=par1) arr.median <- array(NA,dim=par1) arr.midrange <- array(NA,dim=par1) for (j in 1:par1) { arr.mean[j] <- mean(arr[j,],na.rm=TRUE) arr.median[j] <- median(arr[j,],na.rm=TRUE) arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2 } overall.mean <- mean(x) overall.median <- median(x) overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2 bitmap(file='plot1.png') plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.mean,0) dev.off() bitmap(file='plot2.png') plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.median,0) dev.off() bitmap(file='plot3.png') plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.midrange,0) dev.off() bitmap(file='plot4.png') z <- data.frame(t(arr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries')) dev.off() bitmap(file='plot4b.png') z <- data.frame(t(darr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries')) dev.off() bitmap(file='plot5.png') z <- data.frame(arr) names(z) <- c(1:np) (boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks')) dev.off() bitmap(file='plot6.png') z <- data.frame(cbind(arr.mean,arr.median,arr.midrange)) names(z) <- list('mean','median','midrange') (boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots')) dev.off()
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation