mvf.mor: Mean value function for the Moranda-Geometric model

In Reliability: Functions for estimating parameters in software reliability models

Description

mvf.mor returns the mean value function for the Moranda-Geometric model.

Usage

mvf.mor(D, theta, t)

Arguments

D	parameter value for D
theta	parameter value for theta
t	time between failure data

Details

This function gives the values of the mean value function for the Moranda-Geometric model, this is written as

μ(t) = \frac{1}{θ} \log\{[D θ \exp(θ)] t + 1\}.

Further there is a verifying if the parameter theta satisfy the assumptions of the Moranda-Geometric model. So the paramter theta have to be larger than zero, in equation θ > 0.

Value

The mean value function for the Moranda-Geometric model.

Author(s)

Andreas Wittmann andreas\[e-mail]

References

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/

See Also

moranda.geometric, moranda.geometric.plot

Examples

# 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)

mor.par1 <- moranda.geometric(t)$D
mor.par2 <- moranda.geometric(t)$theta

mvf.mor(mor.par1, mor.par2, t)

Example output

  [1]  0.03403132  0.33908905  1.26333325  0.90938287  1.28535726  0.10201204
  [7]  0.02269058  0.22636071  0.22636071  0.16988384  1.53781363  0.56364969
 [13]  0.86493038  0.27148805  1.20822310  0.98706264  6.99301355  1.34036722
 [19]  0.29403371  1.27434669  3.53638963  0.61960433  2.66093818  0.76474140
 [25]  4.53709291  1.99497394  0.11333156 11.34168234  6.31411158  0.16988384
 [31]  0.40658234  0.04536902  0.00000000  0.09068950  2.50077536  0.73129234
 [37]  1.95164682  0.65314167  4.89244487  3.27459839  1.08672777  2.88415358
 [43]  4.84186246  2.79925779  2.17862137  2.13548179  0.06803532  0.88716246
 [49]  8.37493252 13.08522221  1.64710970  0.23764705  2.56491318  1.49401612
 [55]  3.86913865  2.13548179  2.59694571  0.35034540  3.99324870  7.73690374
 [61]  0.00000000  2.55423028  3.58855428  3.95191908 11.99646835  5.75313915
 [67]  0.11333156  0.18118524  5.61421247  4.09639668  0.49640641  1.43920547
 [73]  8.31902085  3.16942717  3.27459839  5.61421247  3.07454929  1.77789380
 [79]  8.48653495 10.15301587  4.77094545  3.23256123 16.32444159 10.62366257
 [85] 16.54002310  8.78271183  9.90224500  7.34751373  0.37284914  8.85643360
 [91]  7.50940275 20.49644225 24.54279869 13.99201132  8.62562576  0.13596153
 [97]  2.86294558 16.67037858  8.82880297 13.77942467  0.33908905  1.59249692
[103]  1.20822310  0.00000000 25.67199126 12.20966482  9.54140575  7.28067328
[109]  8.92083506  2.69289821  7.55689966 17.40659307  4.79121957  4.16845111
[115]  4.78108372  1.36235121  9.96507704  9.58667912 10.78232260  0.24893039
[121]  0.84268664  5.14445482 38.40271512  1.11989743  0.11333156 10.68543433
[127]  4.01389840  8.13211379 41.24329706 26.95843411 10.45554085  6.78081453
[133] 38.29256917 11.46306383 17.15756179 31.48219622

Reliability documentation built on May 2, 2019, 4:55 p.m.