inst/test_objs/superalloy_gmlest.R

In Auburngrads/SMRD: Statistical Methods For Reliability Data

gmlest.out <-
structure(list(data.ld = structure(list(kCycles = c(5.733, 13.949, 
15.616, 56.723, 12.076, 152.68, 43.331, 18.067, 9.75, 156.725, 
112.968, 138.114, 122.372, 21.3, 6.705, 112.002, 11.865, 13.181, 
8.489, 12.434, 13.03, 57.923, 121.075, 200.027, 211.629, 155), 
    Status = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L), .Label = c("Failure", "Right"), class = "factor"), 
    CaseWeghts = 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, 1), PseudoSress = c(145.9, 
    85.2, 116.4, 87.2, 100.1, 85.8, 99.8, 113, 120.4, 86.4, 85.6, 
    86.7, 89.7, 114.8, 144.5, 91.3, 142.5, 100.5, 118.4, 118.6, 
    118, 80.8, 87.3, 80.6, 80.3, 84.3), LogPseudoSress = c(4.98292, 
    4.445, 4.75703, 4.4682, 4.60617, 4.45202, 4.60317, 4.72739, 
    4.79082, 4.45899, 4.44969, 4.46245, 4.49647, 4.74319, 4.97328, 
    4.51415, 4.95934, 4.61016, 4.77407, 4.77576, 4.77068, 4.39198, 
    4.46935, 4.3895, 4.38577, 4.43438), LogPseudoSress2 = c(24.82951, 
    19.75804, 22.62936, 19.96485, 21.2168, 19.82047, 21.18916, 
    22.3482, 22.95195, 19.88257, 19.7997, 19.91349, 20.21825, 
    22.49787, 24.73351, 20.37756, 24.59507, 21.25355, 22.79173, 
    22.80785, 22.75943, 19.28946, 19.97509, 19.2677, 19.23498, 
    19.66374)), class = c("regrs3.life.data", "life.data", "data.frame"
), row.names = c("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"), 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(kCycles = "kCycles"), censor.column = c(Status = "Status"), data.title = "Nelson's Super Alloy Fatigue Data", time.units = "Kilocycles", x.columns = c(PseudoSress = "PseudoSress", 
LogPseudoSress = "LogPseudoSress", LogPseudoSress2 = "LogPseudoSress2"
), data.note = "", date.made = "Sat Feb 23 17:34:25 CST 2019"), 
    model = 0, distribution = "weibull", parameter.fixed = c(`bmu-0` = FALSE, 
    `bmu-2` = FALSE, `bmu-3` = FALSE, `bsigma-0` = FALSE, `bsigma-2` = FALSE
    ), explan.vars = list(mu.relat = c(2, 3), sigma.relat = 2), 
    log.likelihood = NULL, theta.hat = c(`bmu-0` = 243.214474255294, 
    `bmu-2` = -96.5435832628794, `bmu-3` = 9.66734019745402, 
    `bsigma-0` = 4.46958754237951, `bsigma-2` = -1.17640497907495
    ), thetas.hat = c(3.93314730054851, -18.5287895422341, 17.3157287455713, 
    -0.95508402376135, -0.225777410958156), correlation.matrix = structure(c(1, 
    -0.999864885054494, 0.999465973516344, 0.28056235404254, 
    -0.280301635034949, -0.999864885054494, 1, -0.999867626354862, 
    -0.283662580146236, 0.283480594234346, 0.999465973516343, 
    -0.999867626354862, 1, 0.28687572037781, -0.28678345603092, 
    0.280562354042541, -0.283662580146238, 0.286875720377812, 
    1, -0.999104264665556, -0.28030163503495, 0.283480594234347, 
    -0.286783456030922, -0.999104264665556, 1), .Dim = c(5L, 
    5L), .Dimnames = list(c("bmu-0", "bmu-2", "bmu-3", "bsigma-0", 
    "bsigma-2"), c("bmu-0", "bmu-2", "bmu-3", "bsigma-0", "bsigma-2"
    ))), vcv.matrix = structure(c(3365.61139844144, -1431.9325639533, 
    152.05886136563, 67.9291707122193, -14.550363416491, -1431.9325639533, 
    609.394497739133, -64.7296870396054, -29.22444310763, 6.26165049981306, 
    152.05886136563, -64.7296870396054, 6.87738713318873, 3.1397889760902, 
    -0.672948848962695, 67.9291707122197, -29.2244431076302, 
    3.13978897609022, 17.4176621351786, -3.73097181565901, -14.5503634164911, 
    6.26165049981309, -0.672948848962698, -3.73097181565901, 
    0.80063108750747), .Dim = c(5L, 5L), .Dimnames = list(c("bmu-0", 
    "bmu-2", "bmu-3", "bsigma-0", "bsigma-2"), c("bmu-0", "bmu-2", 
    "bmu-3", "bsigma-0", "bsigma-2"))), vcvs.vector = c(0.0083325516926483, 
    -0.104202357996489, 0.100116888269475, -0.00132092008396769, 
    -0.00182253053530805, -0.104202357996489, 22.4463534822615, 
    -22.251583068438, -0.0672771353870599, 0.230640777070734, 
    0.100116888269475, -22.251583068438, 22.064343795641, 0.0656788942337425, 
    -0.23133399678483, -0.00132092008396769, -0.0672771353870651, 
    0.0656788942337477, 0.0330967442977312, -0.00750016486134998, 
    -0.00182253053530805, 0.230640777070735, -0.231333996784832, 
    -0.00750016486134992, 0.0294903358427977), kodet = c(1, 1, 
    1, 1, 1), residuals = structure(c(-1.74889255, -5.23742199, 
    0.0889175385, -1.77300429, -2.93769217, 0.0358057693, 0.30403316, 
    0.0927041322, -0.958507895, 0.250582308, -0.663640261, 0.052435983, 
    0.54771328, 0.832336605, -1.11647809, 0.740665853, 1.10643899, 
    -2.64659929, -1.59055114, -0.366746634, -0.287912697, -3.24423194, 
    -0.081387423, -0.811613083, -0.783007801, -0.329645813), .Dim = c(26L, 
    1L)), fitted.values = c(2.18082476, 5.0859189, 2.71947503, 
    4.84540701, 3.62833047, 5.01172876, 3.65076828, 2.8629725, 
    2.57584214, 4.93916321, 5.03590345, 4.90404415, 4.56585503, 
    2.78449202, 2.18345356, 4.39904499, 2.19091797, 3.59841466, 
    2.64408731, 2.63671803, 2.65908933, 5.67473459, 4.83341455, 
    5.70381498, 5.74763012, 5.19960022), get.rmodel.info.out = list(
        explan.vars = list(mu.relat = c(2, 3), sigma.relat = 2), 
        kparv = c(1, 2), mrelat = structure(c(2, 3, 2, 0), .Dim = c(2L, 
        2L)), nrelat = 2L, nrvar = 2:1, dist.pnames = c("mu", 
        "sigma"), model.pnames = c("bmu-0", "bmu-2", "bmu-3", 
        "bsigma-0", "bsigma-2")), time.units = "Kilocycles"), class = "gmlest")