Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
425.25 417.75 410.25 395.25 546.75 539.25 425.25 349.50 357.00 357.00 364.50 380.25 334.50 288.75 251.25 251.25 395.25 410.25 296.25 167.25 235.50 235.50 288.75 319.50 312.00 235.50 273.75 258.75 387.75 357.00 235.50 144.75 228.00 251.25 273.75 303.75 243.00 190.50 213.00 220.50 417.75 417.75 303.75 288.75 334.50 312.00 372.75 448.50 463.50 357.00 327.00 296.25 501.75 516.75 478.50 516.75 509.25 448.50 516.75 592.50 623.25 531.75 471.00 516.75 714.00 774.75 759.75 789.75 782.25 706.50 835.50 866.25 911.25 774.75 721.50 782.25 927.00 1056.00 1025.25 1025.25 1040.25 987.75 1124.25 1124.25 1101.00 972.00 995.25 1010.25 1109.25 1238.25 1146.75 1192.50 1154.25 1131.75 1306.50 1268.25 1215.00 1139.25 1215.00 1253.25 1299.00 1359.75 1299.00 1336.50 1290.75 1283.25 1473.00 1488.75 1428.00 1321.50 1412.25 1450.50 1496.25 1564.50 1496.25 1549.50 1526.25 1443.00 1617.75 1617.75
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