ExpoLogisticsurvival: Survival related functions for the Exponentiated Logistic(EL)...

In reliaR: Package for some probability distributions.

Description

Conditional reliability function (crf), hazard function, hazard rate average (HRA) and survival function for the Exponentiated Logistic(EL) distribution with shape parameter alpha and scale parameter beta.

Usage

crf.expo.logistic(x, t = 0, alpha, beta)
hexpo.logistic(x, alpha, beta)
hra.expo.logistic(x, alpha, beta)
sexpo.logistic(x, alpha, beta)

Arguments

x	vector of quantiles.
alpha	shape parameter.
beta	scale parameter.
t	age component.

Value

crf.expo.logistic gives the conditional reliability function (crf), hexpo.logistic gives the hazard function, hra.expo.logistic gives the hazard rate average (HRA) function, and sexpo.logistic gives the survival function for the Exponentiated Logistic(EL) distribution.

References

Ali, M.M., Pal, M. and Woo, J. (2007). Some Exponentiated Distributions, The Korean Communications in Statistics, 14(1), 93-109.

Shirke, D.T., Kumbhar, R.R. and Kundu, D.(2005). Tolerance intervals for exponentiated scale family of distributions, Journal of Applied Statistics, 32, 1067-1074

See Also

dexpo.logistic for other Exponentiated Logistic(EL) distribution related functions;

Examples

## load data set
data(dataset2)
## Maximum Likelihood(ML) Estimates of alpha & beta for the data(dataset2)
## Estimates of alpha & beta using 'maxLik' package
## alpha.est = 5.31302, beta.est = 139.04515

## Reliability indicators for data(dataset2):

## Reliability function
sexpo.logistic(dataset2, 5.31302, 139.04515)

## Hazard function
hexpo.logistic(dataset2, 5.31302, 139.04515)

## hazard rate average(hra)
hra.expo.logistic(dataset2, 5.31302, 139.04515)

## Conditional reliability function (age component=0)
crf.expo.logistic(dataset2, 0.00, 5.31302, 139.04515)

## Conditional reliability function (age component=3.0)
crf.expo.logistic(dataset2, 3.0, 5.31302, 139.04515)

Example output

  [1] 0.968519448 0.966754951 0.958190852 0.949696100 0.937088944 0.937088944
  [7] 0.929988672 0.929988672 0.929988672 0.926777668 0.921198265 0.921198265
 [13] 0.904034713 0.891785523 0.883089108 0.883089108 0.875519641 0.873970773
 [19] 0.873970773 0.872410299 0.870838244 0.864434762 0.864434762 0.857848387
 [25] 0.837017298 0.835210545 0.826018320 0.772152086 0.757149483 0.754975716
 [31] 0.754975716 0.739559837 0.716985469 0.703169398 0.693860463 0.691521989
 [37] 0.672670086 0.667921266 0.644007716 0.607798051 0.605377275 0.593271972
 [43] 0.593271972 0.588431149 0.586011431 0.586011431 0.583592312 0.573923859
 [49] 0.554646107 0.549843510 0.540263771 0.533104095 0.525968606 0.518859623
 [55] 0.514136142 0.507076308 0.507076308 0.504730119 0.500048842 0.488413935
 [61] 0.476883870 0.467741144 0.467741144 0.460934307 0.456421267 0.443006682
 [67] 0.429788315 0.423256250 0.391416935 0.391416935 0.383168808 0.381123317
 [73] 0.370996659 0.365002327 0.343559248 0.339752491 0.303278438 0.269742720
 [79] 0.264966463 0.263389045 0.245028922 0.237686411 0.236239416 0.236239416
 [85] 0.227706905 0.203619382 0.203619382 0.203619382 0.203619382 0.196081299
 [91] 0.196081299 0.181716965 0.166117146 0.165049124 0.144927072 0.126194198
 [97] 0.123705693 0.119657774 0.090798482 0.090183563 0.081976347 0.074479416
[103] 0.050191205 0.050191205 0.046480362 0.027812361 0.026656484 0.020798254
[109] 0.018828525 0.004164848 0.002519918
  [1] 0.0005942168 0.0006215974 0.0007499797 0.0008709822 0.0010410975
  [6] 0.0010410975 0.0011326953 0.0011326953 0.0011326953 0.0011732371
 [11] 0.0012424705 0.0012424705 0.0014468119 0.0015856846 0.0016812033
 [16] 0.0016812033 0.0017624460 0.0017788636 0.0017788636 0.0017953355
 [21] 0.0018118607 0.0018784781 0.0018784781 0.0019458778 0.0021521803
 [26] 0.0021696155 0.0022572683 0.0027398551 0.0028661006 0.0028841323
 [31] 0.0028841323 0.0030102139 0.0031894942 0.0032963080 0.0033671065
 [36] 0.0033847485 0.0035249524 0.0035597216 0.0037316400 0.0039826094
 [41] 0.0039990130 0.0040803730 0.0040803730 0.0041126027 0.0041286488
 [46] 0.0041286488 0.0041446486 0.0042081778 0.0043329167 0.0043636049
 [51] 0.0044243711 0.0044694047 0.0045139681 0.0045580563 0.0045871819
 [56] 0.0046304677 0.0046304677 0.0046447882 0.0046732666 0.0047435068
 [61] 0.0048123715 0.0048664656 0.0048664656 0.0049064514 0.0049328295
 [66] 0.0050106209 0.0050863953 0.0051235262 0.0053016496 0.0053016496
 [71] 0.0053470436 0.0053582547 0.0054134906 0.0054459797 0.0055609715
 [76] 0.0055811892 0.0057720162 0.0059430669 0.0059671009 0.0059750208
 [81] 0.0060665739 0.0061028675 0.0061099987 0.0061099987 0.0061519087
 [86] 0.0062689488 0.0062689488 0.0062689488 0.0062689488 0.0063051984
 [91] 0.0063051984 0.0063737886 0.0064475711 0.0064525960 0.0065466462
 [96] 0.0066331633 0.0066445826 0.0066631211 0.0067940019 0.0067967664
[101] 0.0068335699 0.0068670354 0.0069744721 0.0069744721 0.0069907566
[106] 0.0070721668 0.0070771798 0.0071025374 0.0071110451 0.0071740930
[111] 0.0071811341
  [1] 0.002665560 0.002254015 0.001525296 0.001323416 0.001225983 0.001225983
  [7] 0.001209715 0.001209715 0.001209715 0.001207009 0.001207059 0.001207059
 [13] 0.001230336 0.001258567 0.001281744 0.001281744 0.001303311 0.001307848
 [19] 0.001307848 0.001312456 0.001317134 0.001336509 0.001336509 0.001356884
 [25] 0.001423284 0.001429138 0.001459071 0.001636543 0.001686028 0.001693191
 [31] 0.001693191 0.001743931 0.001818031 0.001863267 0.001893702 0.001901342
 [37] 0.001962873 0.001978358 0.002056283 0.002174291 0.002182189 0.002221712
 [43] 0.002221712 0.002237533 0.002245445 0.002245445 0.002253358 0.002285015
 [49] 0.002348307 0.002364117 0.002395711 0.002419379 0.002443018 0.002466624
 [55] 0.002482340 0.002505881 0.002505881 0.002513718 0.002529378 0.002568430
 [61] 0.002607332 0.002638334 0.002638334 0.002661511 0.002676925 0.002722978
 [67] 0.002768729 0.002791485 0.002903969 0.002903969 0.002933577 0.002940952
 [73] 0.002977664 0.002999558 0.003078950 0.003093232 0.003233344 0.003368346
 [79] 0.003388143 0.003394715 0.003472541 0.003504398 0.003510729 0.003510729
 [85] 0.003548432 0.003658627 0.003658627 0.003658627 0.003658627 0.003694390
 [91] 0.003694390 0.003764470 0.003843816 0.003849385 0.003958042 0.004066667
 [97] 0.004081738 0.004106614 0.004299485 0.004303950 0.004365313 0.004424587
[103] 0.004645831 0.004645831 0.004685077 0.004920707 0.004938315 0.005036263
[109] 0.005073285 0.005514160 0.005623617
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [1] 1.046078 1.045400 1.042507 1.040129 1.037206 1.037206 1.035792 1.035792
  [9] 1.035792 1.035196 1.034217 1.034217 1.031569 1.029943 1.028892 1.028892
 [17] 1.028038 1.027869 1.027869 1.027701 1.027534 1.026873 1.026873 1.026224
 [25] 1.024349 1.024198 1.023453 1.019746 1.018870 1.018748 1.018748 1.017911
 [33] 1.016771 1.016119 1.015697 1.015593 1.014783 1.014586 1.013639 1.012324
 [41] 1.012240 1.011831 1.011831 1.011671 1.011592 1.011592 1.011513 1.011203
 [49] 1.010606 1.010462 1.010179 1.009971 1.009767 1.009567 1.009436 1.009243
 [57] 1.009243 1.009179 1.009053 1.008746 1.008448 1.008217 1.008217 1.008047
 [65] 1.007936 1.007612 1.007300 1.007149 1.006437 1.006437 1.006259 1.006216
 [73] 1.006001 1.005876 1.005439 1.005363 1.004658 1.004044 1.003959 1.003931
 [81] 1.003611 1.003485 1.003460 1.003460 1.003316 1.002918 1.002918 1.002918
 [89] 1.002918 1.002796 1.002796 1.002567 1.002323 1.002307 1.002000 1.001722
 [97] 1.001685 1.001626 1.001213 1.001204 1.001089 1.000985 1.000655 1.000655
[105] 1.000605 1.000358 1.000343 1.000267 1.000241 1.000053 1.000032

reliaR documentation built on May 1, 2019, 9:51 p.m.