Standard Deviation of Time-Persistent Statistics (TPS)
Computes the sample standard deviation of a time-persistent statistic.
sdTPS(times = NULL, numbers = NULL)
times |
A numeric vector of non-decreasing time observations |
numbers |
A numeric vector containing the values of the time-persistent statistic between the time observation |
The lengths of times and numbers either must be
the same, or times may have one more entry than numbers
(interval endpoints vs. interval counts). The sample variance is the
area under the square of the step-function created by the values in
numbers between the first and last element in times divided
by the length of the observation period, less the square of the sample mean.
The sample standard deviation is the square root of the sample variance.
Computes the sample standard deviation of the time-persistent statistic provided.
Barry Lawson (blawson@richmond.edu),
Larry Leemis (leemis@math.wm.edu),
Vadim Kudlay (vadim.kudlay@richmond.edu)
times <- c(1,2,3,4,5)
counts <- c(1,2,1,1,2)
meanTPS(times, counts)
sdTPS(times, counts)
output <- ssq(seed = 54321, maxTime = 1000, saveServerStatus = TRUE)
utilization <- meanTPS(output$serverStatusT, output$serverStatusN)
sdServerStatus <- sdTPS(output$serverStatusT, output$serverStatusN)
# compute and graphically display mean and sd of number in system vs time
output <- ssq(maxArrivals = 60, seed = 54321, saveAllStats = TRUE)
plot(output$numInSystemT, output$numInSystemN, type = "s", bty = "l",
las = 1, xlab = "time", ylab = "number in system")
meanSys <- meanTPS(output$numInSystemT, output$numInSystemN)
sdSys <- sdTPS(output$numInSystemT, output$numInSystemN)
abline(h = meanSys, lty = "solid", col = "red", lwd = 2)
abline(h = c(meanSys - sdSys, meanSys + sdSys),
lty = "dashed", col = "red", lwd = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.