Description Usage Arguments Details Value Author(s) References See Also Examples
total.plot
plots the relative error for the the mean value function for all
models into one window.
1 2 3 |
duane.par1 |
parameter value for |
duane.par2 |
parameter value for |
lit.par1 |
parameter value for |
lit.par2 |
parameter value for |
lit.par3 |
parameter value for |
mor.par1 |
parameter value for |
mor.par2 |
parameter value for |
musa.par1 |
parameter value for |
musa.par2 |
parameter value for |
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 |
ymin |
the minimal y limit of the plot |
ymax |
the maximal y limit of the plot |
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 relative error for the mean value functions for all models, this is
\mbox{relative error} = \frac{μ(t_i) - i}{i}, i = 1, 2, ...,
where μ(t) is a mean value function and i is the number of failures.
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 relative error for the mean value functions for all models.
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
,
total.plot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # 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
rel.plot(duane.par1, duane.par2, lit.par1, lit.par2, lit.par3, mor.par1,
mor.par2, musa.par1, musa.par2, t, linear = TRUE, ymin = -1,
ymax = 2.5, xlab = "time (in seconds)", main = "relative error")
## Not run:
## rel.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 = "relative error")
## End(Not run)
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