| total.plot {Reliability} | R Documentation |
total.plot plots the mean value function for all models and the raw
data into one window.
total.plot(duane.par1, duane.par2, lit.par1, lit.par2, lit.par3, mor.par1,
mor.par2, musa.par1, musa.par2, t, linear = T, xlab = "time",
ylab = "Cumulated failures and estimated mean value functions",
main = NULL)
duane.par1 |
parameter value for rho for Duane model |
duane.par2 |
parameter value for theta for Duane model |
lit.par1 |
parameter value for theta0 for Littlewood-Verall model |
lit.par2 |
parameter value for theta1 for Littlewood-Verall model |
lit.par3 |
parameter value for rho for Littlewood-Verall model |
mor.par1 |
parameter value for D for Moranda-Geometric model |
mor.par2 |
parameter value for theta for Moranda-Geometric model |
musa.par1 |
parameter value for theta0 for Musa-Okumoto model |
musa.par2 |
parameter value for theta1 for Musa-Okumoto model |
t |
time between failure data |
linear |
logical. Should the linear or the quadratic form of the mean value
function for the Littlewood-Verrall model be used of computation?
If TRUE, which is the default, the linear form of the mean
value function is used. |
xlab |
a title for the x axis |
ylab |
a title for the y axis |
main |
an overall title for the plot |
This function gives a plot of the mean value functions for all models. Here
the estimated parameter values, which are obtained by using duane,
littlewood.verall, moranda.geometric und
musa.okumoto can be put in. Internally the functions
mvf.duane, mvf.ver.lin, mvf.ver.quad,
mvf.mor and mvf.musa are used to get the mean value
functions for all models.
A graph of the mean value functions for all models and of the raw data.
Andreas Wittmann andreas_wittmann@gmx.de
J.D. Musa, A. Iannino, and K. Okumoto. Software Reliability: Measurement, Prediction, Application. McGraw-Hill, 1987.
Michael R. Lyu. Handbook of Software Realibility Engineering. IEEE Computer Society Press, 1996. http://www.cse.cuhk.edu.hk/~lyu/book/reliability/
duane.plot, littlewood.verall.plot,
moranda.geometric.plot, musa.okumoto.plot
# time between-failure-data from DACS Software Reliability Dataset
# homepage, see system code 1. Number of failures is 136.
t <- c(3, 30, 113, 81, 115, 9, 2, 20, 20, 15, 138, 50, 77, 24,
108, 88, 670, 120, 26, 114, 325, 55, 242, 68, 422, 180,
10, 1146, 600, 15, 36, 4, 0, 8, 227, 65, 176, 58, 457,
300, 97, 263, 452, 255, 197, 193, 6, 79, 816, 1351, 148,
21, 233, 134, 357, 193, 236, 31, 369, 748, 0, 232, 330,
365, 1222, 543, 10, 16, 529, 379, 44, 129, 810, 290, 300,
529, 281, 160, 828, 1011, 445, 296, 1755, 1064, 1783,
860, 983, 707, 33, 868, 724, 2323, 2930, 1461, 843, 12,
261, 1800, 865, 1435, 30, 143, 108, 0, 3110, 1247, 943,
700, 875, 245, 729, 1897, 447, 386, 446, 122, 990, 948,
1082, 22, 75, 482, 5509, 100, 10, 1071, 371, 790, 6150,
3321, 1045, 648, 5485, 1160, 1864, 4116)
duane.par1 <- duane(t)$rho
duane.par2 <- duane(t)$theta
lit.par1 <- littlewood.verall(t, linear = TRUE)$theta0
lit.par2 <- littlewood.verall(t, linear = TRUE)$theta1
lit.par3 <- littlewood.verall(t, linear = TRUE)$rho
mor.par1 <- moranda.geometric(t)$D
mor.par2 <- moranda.geometric(t)$theta
musa.par1 <- musa.okumoto(t)$theta0
musa.par2 <- musa.okumoto(t)$theta1
total.plot(duane.par1, duane.par2, lit.par1, lit.par2, lit.par3, mor.par1,
mor.par2, musa.par1, musa.par2, t, linear = TRUE,
xlab = "time (in seconds)", main = "all models")