inst/test_objs/wqmmlesss/prob3_5.R
In Auburngrads/SMRD: Statistical Methods For Reliability Data
data.ld <-
structure(list(kilocycles = c(5L, 21L, 28L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L), event = structure(c(2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Censored", "Failed"), class = "factor")), class = c("life.data",
"data.frame"), row.names = c(NA, -25L), right.censor.names = "a,alive,c,censor,censored,end,mend,noreport,r,r-censored,right-censored,removed,right,rightcensored,s,survived,survive,suspend,suspended,2", left.censor.names = "l,l-censored,left-censored,left,leftcensored,start,mstart,3", interval.censor.names = "b,bin,i,interval,i-censored,intervalcensored,interval-censored,4", failure.censor.names = "event,exact,d,dead,died,f,fail,failed,failure,report,repair,repaired,replaced,replacement,1", sinterval.censor.names = "s,sinterval,smallinterval,small-interval,5", response.column = c(kilocycles = "kilocycles"), censor.column = c(event = "event"), data.title = "prob3_5", time.units = c(kilocycles = "kilocycles"), data.note = "")
debug1 <-
FALSE
distribution <-
"lognormal"
distribution.number <-
4
dummy <-
c(1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2)
e <-
c(1e-04, 1e-04)
escale <-
10000
explan.vars <-
NULL
gamthr <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
int <-
1
intercept <-
TRUE
intercept.increment <-
1
ivec <-
c(number.case = 25, nter = 1, nyresp = 1, ntyresp = 0, distribution.number = 4,
lcheck = 0, nparm = 2, int = 1, maxit = 500, kprint = 0, ierfit = 0,
iervcv = 0)
kprint <-
0
likelihood.method <-
"smoothed"
mathsoft.gamthr <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
maxit <-
500
ndscrat <-
95
niscrat <-
6
non.pos.resp <-
c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)
nparm <-
2
nter <-
1
ntyresp <-
0
number.cases <-
25L
nyresp <-
1L
oldClasses <-
"life.data"
param.names <-
c("mu", "sigma")
parameter.fixed <-
c(FALSE, FALSE)
regression <-
FALSE
relationship <-
NULL
rvec <-
c(0, escale = 10000, log.likelihood = 0)
startna <-
c(TRUE, TRUE)
test <-
3
the.case.weights <-
c(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)
the.censor.codes <-
c(1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2)
the.truncation.codes <-
c(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)
the.xmat <-
structure(c(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), .Dim = c(25L, 1L))
theta.start <-
c(3.31250029027601, 0.362037042131571)
theta.start.comp <-
c(3.31250029027601, 0.362037042131571)
tyresp <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
yresp <-
structure(c(5, 21, 28, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30), .Dim = c(25L,
1L), .Dimnames = list(NULL, c(kilocycles = "kilocycles")))
zout <-
list(ivec = c(25L, 1L, 1L, 0L, 4L, 0L, 2L, 1L, 500L, 0L, 0L,
0L), rvec = structure(c(0, 10000, -19.3166732788086), Csingle = TRUE),
number.cases = 25L, nparm = 2L, xmat = structure(c(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), Csingle = TRUE), yresp = structure(c(1.60943794250488,
3.04452252388, 3.33220458030701, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168
), Csingle = TRUE), structure(c(1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), Csingle = TRUE), tyresp = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Csingle = TRUE), truncation.codes = structure(c(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), Csingle = TRUE), parameter.fixed = c(FALSE,
FALSE), e = structure(c(1.0000000116861e-07, 1.0000000116861e-07
), Csingle = TRUE), ndscrat = c(-0.2, -0.2, -0.2, -0.2, -0.2,
-0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2,
-0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2,
5, 0, 5, 1, -1, 0, 0, 1, 5.26820252973438, 1.57927481509688,
-26.3410126486719, 1.57927481509688, 3.06526013618277e-08,
2.41326260969316e-07, 1.3042392421276, 0.796628242848494,
0.796628242848494, 0.602106350891527, 32.60598105319, -3.98314121424247,
-3.98314121424247, 0.602106350891527, -14.0639387430496,
0.456965763889547, -14.0639387430496, 0.456965763889547,
3.62037049605502e-08, 1.0000000116861e-07, 0.0366859848486467,
0.023581602114047, 0.00986853838323104, 0, 0, 0.042405940311003,
0, -5.55111512312578e-17, 0, 0, 0, -26.3410126486719, 1.57927481509688,
0.121676910129144, 0.992569760541507, 0.992569760541507,
-0.121676910129144, 21.9123693063225, -4.44089209850063e-16,
-4.49293380277993e-16, 0.0753637800549612, 0.602106350891527,
0, -3.98314121424247, 32.60598105319, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), niscrat = c(1L, 2L, 0L, 1L,
2L, 0L), theta.hat = structure(c(5.26820230484009, 1.57927477359772
), Csingle = TRUE), first.derivative = structure(c(3.065260045787e-08,
2.4132626208484e-07), Csingle = TRUE), vcv.matrix = structure(c(1.30423927307129,
0.79662823677063, 0.79662823677063, 0.602106332778931), Csingle = TRUE),
correlation.matrix = structure(c(1, 0.89896023273468, 0.89896023273468,
1), Csingle = TRUE), residuals = structure(c(0.0985947623848915,
0.244622603058815, 0.293499946594238, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675), Csingle = TRUE),
fitted.values.and.deviance = structure(c(194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
-5.66897773742676, -5.41171312332153, -5.45949697494507,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -2.31673717498779, -1.40803861618042,
-1.22587776184082, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 0.225029692053795,
0.225029692053795, 0.225029692053795, 1, 1, 1, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734,
0.316666543483734, 0.316666543483734, 0.316666543483734), Csingle = TRUE))
Auburngrads/SMRD documentation built on Sept. 14, 2020, 2:21 a.m.
R Package Documentation
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data.ld <-
structure(list(kilocycles = c(5L, 21L, 28L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L), event = structure(c(2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Censored", "Failed"), class = "factor")), class = c("life.data",
"data.frame"), row.names = c(NA, -25L), right.censor.names = "a,alive,c,censor,censored,end,mend,noreport,r,r-censored,right-censored,removed,right,rightcensored,s,survived,survive,suspend,suspended,2", left.censor.names = "l,l-censored,left-censored,left,leftcensored,start,mstart,3", interval.censor.names = "b,bin,i,interval,i-censored,intervalcensored,interval-censored,4", failure.censor.names = "event,exact,d,dead,died,f,fail,failed,failure,report,repair,repaired,replaced,replacement,1", sinterval.censor.names = "s,sinterval,smallinterval,small-interval,5", response.column = c(kilocycles = "kilocycles"), censor.column = c(event = "event"), data.title = "prob3_5", time.units = c(kilocycles = "kilocycles"), data.note = "")
debug1 <-
FALSE
distribution <-
"lognormal"
distribution.number <-
4
dummy <-
c(1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2)
e <-
c(1e-04, 1e-04)
escale <-
10000
explan.vars <-
NULL
gamthr <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
int <-
1
intercept <-
TRUE
intercept.increment <-
1
ivec <-
c(number.case = 25, nter = 1, nyresp = 1, ntyresp = 0, distribution.number = 4,
lcheck = 0, nparm = 2, int = 1, maxit = 500, kprint = 0, ierfit = 0,
iervcv = 0)
kprint <-
0
likelihood.method <-
"smoothed"
mathsoft.gamthr <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
maxit <-
500
ndscrat <-
95
niscrat <-
6
non.pos.resp <-
c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)
nparm <-
2
nter <-
1
ntyresp <-
0
number.cases <-
25L
nyresp <-
1L
oldClasses <-
"life.data"
param.names <-
c("mu", "sigma")
parameter.fixed <-
c(FALSE, FALSE)
regression <-
FALSE
relationship <-
NULL
rvec <-
c(0, escale = 10000, log.likelihood = 0)
startna <-
c(TRUE, TRUE)
test <-
3
the.case.weights <-
c(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)
the.censor.codes <-
c(1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2)
the.truncation.codes <-
c(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)
the.xmat <-
structure(c(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), .Dim = c(25L, 1L))
theta.start <-
c(3.31250029027601, 0.362037042131571)
theta.start.comp <-
c(3.31250029027601, 0.362037042131571)
tyresp <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)
yresp <-
structure(c(5, 21, 28, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30), .Dim = c(25L,
1L), .Dimnames = list(NULL, c(kilocycles = "kilocycles")))
zout <-
list(ivec = c(25L, 1L, 1L, 0L, 4L, 0L, 2L, 1L, 500L, 0L, 0L,
0L), rvec = structure(c(0, 10000, -19.3166732788086), Csingle = TRUE),
number.cases = 25L, nparm = 2L, xmat = structure(c(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), Csingle = TRUE), yresp = structure(c(1.60943794250488,
3.04452252388, 3.33220458030701, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168,
3.40119743347168, 3.40119743347168, 3.40119743347168, 3.40119743347168
), Csingle = TRUE), structure(c(1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), Csingle = TRUE), tyresp = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Csingle = TRUE), truncation.codes = structure(c(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), Csingle = TRUE), parameter.fixed = c(FALSE,
FALSE), e = structure(c(1.0000000116861e-07, 1.0000000116861e-07
), Csingle = TRUE), ndscrat = c(-0.2, -0.2, -0.2, -0.2, -0.2,
-0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2,
-0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2,
5, 0, 5, 1, -1, 0, 0, 1, 5.26820252973438, 1.57927481509688,
-26.3410126486719, 1.57927481509688, 3.06526013618277e-08,
2.41326260969316e-07, 1.3042392421276, 0.796628242848494,
0.796628242848494, 0.602106350891527, 32.60598105319, -3.98314121424247,
-3.98314121424247, 0.602106350891527, -14.0639387430496,
0.456965763889547, -14.0639387430496, 0.456965763889547,
3.62037049605502e-08, 1.0000000116861e-07, 0.0366859848486467,
0.023581602114047, 0.00986853838323104, 0, 0, 0.042405940311003,
0, -5.55111512312578e-17, 0, 0, 0, -26.3410126486719, 1.57927481509688,
0.121676910129144, 0.992569760541507, 0.992569760541507,
-0.121676910129144, 21.9123693063225, -4.44089209850063e-16,
-4.49293380277993e-16, 0.0753637800549612, 0.602106350891527,
0, -3.98314121424247, 32.60598105319, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), niscrat = c(1L, 2L, 0L, 1L,
2L, 0L), theta.hat = structure(c(5.26820230484009, 1.57927477359772
), Csingle = TRUE), first.derivative = structure(c(3.065260045787e-08,
2.4132626208484e-07), Csingle = TRUE), vcv.matrix = structure(c(1.30423927307129,
0.79662823677063, 0.79662823677063, 0.602106332778931), Csingle = TRUE),
correlation.matrix = structure(c(1, 0.89896023273468, 0.89896023273468,
1), Csingle = TRUE), residuals = structure(c(0.0985947623848915,
0.244622603058815, 0.293499946594238, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675,
0.306606113910675, 0.306606113910675, 0.306606113910675), Csingle = TRUE),
fitted.values.and.deviance = structure(c(194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
194.066818237305, 194.066818237305, 194.066818237305, 194.066818237305,
-5.66897773742676, -5.41171312332153, -5.45949697494507,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
-0.126203879714012, -0.126203879714012, -0.126203879714012,
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