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")