Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
96.1 96.5 96.9 97.8 98.9 100.2 101.2 101 101.6 102.4 103.7 103.7 104.6 104.5 104.5 105.6 106.1 107.6 107.7 108.3 108.1 108.1 108 108.2 108.9 109.8 109.9 109.8 110.9 111.1 112.2 112.7 114.6 114.2 114.7 114.7 116 116.3 116.4 116.6 118.1 117.2 108.3 109.5 110.5 110.6 111.2 111.1 111 112.4 112.5 112.4 111.8 111.6 112.9 112.8 113.7 113.8 114 113.8 113.9 114.4 114.4 114.5 113.8 114.3 115 115.4 115.3 114.9 114.3 114.5 115.5 115.8 115.8 116 114.9 114.1 114.1 113.5 115 114.7 115.4 116.1 116.6 117.2 118.2 118 117.7 118.5 117.5 118 117.7 116.3 115 115.7 113.6 114.8 114.9 117.3 117.3 117.7 120 119.6 119.2 117.3 117.5 119 112.5 118.9 118.4 119.4 120.6 118.6 122 122.6 120.6 117.4 116.4 122.2 121 122.4 124.9 126.1 124.5 123.2 126.4 123.9 116 126.6 125.9 126.6 116.7 126.4 129 128.7 128.4 129.2 133.3 128.9 132.7 127.7 131.8 133.9
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