Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
96.86 96.89 96.90 96.94 96.88 96.89 96.89 96.95 97.03 97.29 97.12 97.15 97.18 97.21 97.24 97.27 97.30 97.33 97.36 97.39 97.42 97.90 97.98 98.03 98.03 97.94 98.12 98.19 98.34 98.42 98.43 98.45 98.77 99.24 99.46 99.54 99.55 99.24 99.43 99.47 99.57 99.62 99.64 99.75 99.85 100.28 100.52 100.57 100.57 100.27 100.27 100.18 100.16 100.18 100.18 100.59 100.69 101.06 101.15 101.16 101.16 100.81 100.94 101.13 101.29 101.34 101.35 101.70 102.05 102.48 102.66 102.72 102.73 102.18 102.22 102.37 102.53 102.61 102.62 103.00 103.17 103.52 103.69 103.73 99.57 99.09 99.14 99.36 99.60 99.65 99.80 100.15 100.45 100.89 101.13 101.17 101.21 101.10 101.17 101.11 101.20 101.15 100.92 101.10 101.22 101.25 101.39 101.43
Sample Range:
(leave blank to include all observations)
From:
To:
# blockwidth
Chart options
R Code
par1 <- as.numeric(par1) x <- na.omit(x) (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