inst/test_objs/wqmmlesss/bulb.R
In Auburngrads/SMRD: Statistical Methods For Reliability Data
data.ld <-
structure(list(hours = c(1067L, 919L, 1196L, 785L, 1126L, 936L,
918L, 1156L, 920L, 948L, 855L, 1092L, 1162L, 1170L, 929L, 950L,
905L, 972L, 1035L, 1045L, 1157L, 1195L, 1195L, 1340L, 1122L,
938L, 970L, 1237L, 956L, 1102L, 1022L, 978L, 832L, 1009L, 1157L,
1151L, 1009L, 765L, 958L, 902L, 923L, 1333L, 811L, 1217L, 1085L,
896L, 958L, 1311L, 1037L, 702L, 521L, 933L, 928L, 1153L, 946L,
858L, 1071L, 1069L, 830L, 1063L, 930L, 807L, 954L, 1063L, 1002L,
909L, 1077L, 1021L, 1062L, 1157L, 999L, 932L, 1035L, 944L, 1049L,
940L, 1122L, 1115L, 833L, 1320L, 901L, 1324L, 818L, 1250L, 1203L,
1078L, 890L, 1303L, 1011L, 1102L, 996L, 780L, 900L, 1106L, 704L,
621L, 854L, 1178L, 1138L, 951L, 1187L, 1067L, 1188L, 1037L, 958L,
760L, 1101L, 949L, 992L, 966L, 824L, 653L, 980L, 935L, 878L,
934L, 910L, 1058L, 730L, 980L, 844L, 814L, 1103L, 1000L, 788L,
1143L, 935L, 1069L, 1170L, 1067L, 1037L, 1151L, 863L, 990L, 1035L,
1112L, 931L, 970L, 932L, 904L, 1026L, 1147L, 883L, 867L, 990L,
1258L, 1192L, 992L, 1150L, 1091L, 1039L, 1083L, 1040L, 1289L,
699L, 1083L, 880L, 1029L, 658L, 912L, 1023L, 984L, 856L, 924L,
801L, 1122L, 1292L, 1116L, 880L, 1173L, 1134L, 932L, 938L, 1078L,
1180L, 1106L, 1184L, 954L, 824L, 529L, 998L, 996L, 1133L, 765L,
775L, 1105L, 1081L, 1171L, 705L, 1425L, 610L, 916L, 1001L, 895L,
709L, 860L, 1110L, 1149L, 972L, 1002L, 990L, 1141L, 1127L, 1181L,
856L, 716L, 1308L, 943L, 1272L, 917L, 1069L, 976L, 1187L, 1107L,
1230L, 836L, 1034L, 1248L, 1061L, 1550L, 1240L, 932L, 1165L,
1303L, 1085L, 813L, 1340L, 1137L, 773L, 787L, 1438L, 1009L, 1002L,
1061L, 1277L, 892L, 900L, 1384L, 1148L, 1117L, 1225L, 1176L,
709L, 1485L, 1225L, 1011L, 1028L, 1227L, 1277L, 1222L, 912L,
885L, 1562L, 1118L, 1197L, 976L, 1080L, 924L, 1233L, 1135L, 623L,
983L, 883L, 1088L, 1029L, 1201L, 898L, 970L, 1058L, 1160L, 831L,
1023L, 1354L, 1218L, 1121L, 1172L, 1169L, 1113L, 1308L, 1166L,
1470L, 1635L, 1141L, 1555L, 1054L, 1461L, 1057L, 1228L, 1187L,
1016L, 744L, 1197L, 1122L, 666L, 1022L, 964L, 1085L, 612L, 1003L,
1235L, 942L, 1055L, 893L, 1235L, 1056L, 968L, 1056L, 1014L, 1096L,
1013L, 889L, 1430L, 926L, 1297L, 1033L, 1024L, 1103L, 1385L,
1077L, 813L, 1121L, 960L, 1156L, 1033L, 1255L, 225L, 525L, 675L,
1211L, 995L, 924L, 732L, 935L, 1173L, 1024L, 1254L, 1014L, 798L,
1080L, 862L, 1220L, 1024L, 1170L, 1120L, 898L, 918L, 1086L, 1028L,
1122L, 872L, 826L, 1337L, 965L, 1297L, 1096L, 1068L, 943L, 1490L,
918L, 609L, 985L, 1233L, 985L, 985L, 1075L, 1240L, 985L, 1105L,
1243L, 1204L, 1203L, 1310L, 1262L, 1234L, 1104L, 1303L, 1185L,
759L, 1404L, 944L, 1343L, 932L, 1055L, 1381L, 816L, 1067L, 1252L,
1248L, 1324L, 1000L, 984L, 1220L, 972L, 1022L, 956L, 1093L, 1358L,
1024L, 1240L, 1157L, 1415L, 1385L, 824L, 1690L, 1302L, 1233L,
1331L, 1109L, 827L, 1209L, 1202L, 1229L, 1079L, 1176L, 1772L,
769L, 905L)), class = c("life.data", "data.frame"), row.names = c(NA,
-417L), 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(hours = "hours"), data.title = "Bulb Data", time.units = "Hours", data.note = "")
debug1 <-
FALSE
distribution <-
"lognormal"
distribution.number <-
4
dummy <-
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, 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, 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,
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, 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, 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, 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, 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, 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, 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,
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, 1)
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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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 = 417, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
maxit <-
500
ndscrat <-
879
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, 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, 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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 <-
417L
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 <-
5
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1)
the.censor.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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1)
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1,
1), .Dim = c(417L, 1L))
theta.start <-
c(6.93162315693081, 0.196924136592854)
theta.start.comp <-
c(`(Intercept)` = 6.93162315693081, the.quantiles = 0.196924136592854
)
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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
yresp <-
structure(c(1067, 919, 1196, 785, 1126, 936, 918, 1156, 920,
948, 855, 1092, 1162, 1170, 929, 950, 905, 972, 1035, 1045, 1157,
1195, 1195, 1340, 1122, 938, 970, 1237, 956, 1102, 1022, 978,
832, 1009, 1157, 1151, 1009, 765, 958, 902, 923, 1333, 811, 1217,
1085, 896, 958, 1311, 1037, 702, 521, 933, 928, 1153, 946, 858,
1071, 1069, 830, 1063, 930, 807, 954, 1063, 1002, 909, 1077,
1021, 1062, 1157, 999, 932, 1035, 944, 1049, 940, 1122, 1115,
833, 1320, 901, 1324, 818, 1250, 1203, 1078, 890, 1303, 1011,
1102, 996, 780, 900, 1106, 704, 621, 854, 1178, 1138, 951, 1187,
1067, 1188, 1037, 958, 760, 1101, 949, 992, 966, 824, 653, 980,
935, 878, 934, 910, 1058, 730, 980, 844, 814, 1103, 1000, 788,
1143, 935, 1069, 1170, 1067, 1037, 1151, 863, 990, 1035, 1112,
931, 970, 932, 904, 1026, 1147, 883, 867, 990, 1258, 1192, 992,
1150, 1091, 1039, 1083, 1040, 1289, 699, 1083, 880, 1029, 658,
912, 1023, 984, 856, 924, 801, 1122, 1292, 1116, 880, 1173, 1134,
932, 938, 1078, 1180, 1106, 1184, 954, 824, 529, 998, 996, 1133,
765, 775, 1105, 1081, 1171, 705, 1425, 610, 916, 1001, 895, 709,
860, 1110, 1149, 972, 1002, 990, 1141, 1127, 1181, 856, 716,
1308, 943, 1272, 917, 1069, 976, 1187, 1107, 1230, 836, 1034,
1248, 1061, 1550, 1240, 932, 1165, 1303, 1085, 813, 1340, 1137,
773, 787, 1438, 1009, 1002, 1061, 1277, 892, 900, 1384, 1148,
1117, 1225, 1176, 709, 1485, 1225, 1011, 1028, 1227, 1277, 1222,
912, 885, 1562, 1118, 1197, 976, 1080, 924, 1233, 1135, 623,
983, 883, 1088, 1029, 1201, 898, 970, 1058, 1160, 831, 1023,
1354, 1218, 1121, 1172, 1169, 1113, 1308, 1166, 1470, 1635, 1141,
1555, 1054, 1461, 1057, 1228, 1187, 1016, 744, 1197, 1122, 666,
1022, 964, 1085, 612, 1003, 1235, 942, 1055, 893, 1235, 1056,
968, 1056, 1014, 1096, 1013, 889, 1430, 926, 1297, 1033, 1024,
1103, 1385, 1077, 813, 1121, 960, 1156, 1033, 1255, 225, 525,
675, 1211, 995, 924, 732, 935, 1173, 1024, 1254, 1014, 798, 1080,
862, 1220, 1024, 1170, 1120, 898, 918, 1086, 1028, 1122, 872,
826, 1337, 965, 1297, 1096, 1068, 943, 1490, 918, 609, 985, 1233,
985, 985, 1075, 1240, 985, 1105, 1243, 1204, 1203, 1310, 1262,
1234, 1104, 1303, 1185, 759, 1404, 944, 1343, 932, 1055, 1381,
816, 1067, 1252, 1248, 1324, 1000, 984, 1220, 972, 1022, 956,
1093, 1358, 1024, 1240, 1157, 1415, 1385, 824, 1690, 1302, 1233,
1331, 1109, 827, 1209, 1202, 1229, 1079, 1176, 1772, 769, 905
), .Dim = c(417L, 1L), .Dimnames = list(NULL, c(hours = "hours")))
zout <-
list(ivec = c(417L, 1L, 1L, 0L, 4L, 0L, 2L, 1L, 500L, 0L, 0L,
1L), rvec = structure(c(0, 10000, -2815.22509765625), Csingle = TRUE),
number.cases = 417L, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1), Csingle = TRUE),
yresp = structure(c(6.97260618209839, 6.82328605651855, 7.08673810958862,
6.66568374633789, 7.02642679214478, 6.84161567687988, 6.82219743728638,
7.05272102355957, 6.82437372207642, 6.85435438156128, 6.75110149383545,
6.99576616287231, 7.05789804458618, 7.06475925445557, 6.83410882949829,
6.85646200180054, 6.80793476104736, 6.87935590744019, 6.94215679168701,
6.95177221298218, 7.05358552932739, 7.08590126037598, 7.08590126037598,
7.2004246711731, 7.02286815643311, 6.84375, 6.87729597091675,
7.12044429779053, 6.86275768280029, 7.00488185882568, 6.92951679229736,
6.8855094909668, 6.72383260726929, 6.91671514511108, 7.05358552932739,
7.0483865737915, 6.91671514511108, 6.63987588882446, 6.8648476600647,
6.80461454391479, 6.82762908935547, 7.19518709182739, 6.69826793670654,
7.10414409637451, 6.98933506011963, 6.79794025421143, 6.8648476600647,
7.1785454750061, 6.94408702850342, 6.55393362045288, 6.25575017929077,
6.8384051322937, 6.83303165435791, 7.05012273788452, 6.8522424697876,
6.75460386276245, 6.97634792327881, 6.97447872161865, 6.72142553329468,
6.96885061264038, 6.8351845741272, 6.69332361221313, 6.86066389083862,
6.96885061264038, 6.90975332260132, 6.81234502792358, 6.98193454742432,
6.92853784561157, 6.96790933609009, 7.05358552932739, 6.90675497055054,
6.8373327255249, 6.94215679168701, 6.85012626647949, 6.9555926322937,
6.84588003158569, 7.02286815643311, 7.01660966873169, 6.7250337600708,
7.185387134552, 6.80350542068481, 7.1884126663208, 6.706862449646,
7.13089895248413, 7.09257364273071, 6.98286294937134, 6.79122161865234,
7.17242479324341, 6.9186954498291, 7.00488185882568, 6.90374708175659,
6.65929412841797, 6.80239486694336, 7.00850534439087, 6.55677843093872,
6.43133115768433, 6.74993133544922, 7.07157325744629, 7.03702783584595,
6.85751390457153, 7.07918453216553, 6.97260618209839, 7.08002662658691,
6.94408702850342, 6.8648476600647, 6.63331842422485, 7.00397396087646,
6.85540866851807, 6.89972305297852, 6.87316370010376, 6.71417045593262,
6.48157691955566, 6.8875527381897, 6.84054660797119, 6.77764654159546,
6.83947658538818, 6.8134446144104, 6.96413564682007, 6.59304475784302,
6.8875527381897, 6.73815250396729, 6.70196056365967, 7.00578880310059,
6.90775537490845, 6.66949796676636, 7.04141187667847, 6.84054660797119,
6.97447872161865, 7.06475925445557, 6.97260618209839, 6.94408702850342,
7.0483865737915, 6.76041460037231, 6.897705078125, 6.94215679168701,
7.01391553878784, 6.83625936508179, 6.87729597091675, 6.8373327255249,
6.80682945251465, 6.93342304229736, 7.04490518569946, 6.7833251953125,
6.76503896713257, 6.897705078125, 7.13727855682373, 7.08338785171509,
6.89972305297852, 7.0475172996521, 6.99485015869141, 6.94601392745972,
6.98749017715454, 6.94697618484497, 7.16162204742432, 6.5496506690979,
6.98749017715454, 6.7799220085144, 6.93634271621704, 6.48920488357544,
6.81563997268677, 6.93049478530884, 6.89162588119507, 6.75227022171021,
6.82871198654175, 6.68586111068726, 7.02286815643311, 7.16394662857056,
7.01750612258911, 6.7799220085144, 7.06731986999512, 7.03350639343262,
6.8373327255249, 6.84375, 6.98286294937134, 7.07326984405518,
7.00850534439087, 7.07665395736694, 6.86066389083862, 6.71417045593262,
6.27098846435547, 6.90575313568115, 6.90374708175659, 7.03262424468994,
6.63987588882446, 6.65286302566528, 7.00760078430176, 6.98564195632935,
7.06561326980591, 6.55819797515869, 7.26192712783813, 6.41345882415771,
6.82001638412476, 6.90875482559204, 6.79682350158691, 6.56385564804077,
6.75693225860596, 7.01211547851562, 7.04664707183838, 6.87935590744019,
6.90975332260132, 6.897705078125, 7.03966045379639, 7.02731466293335,
7.07411670684814, 6.75227022171021, 6.57368040084839, 7.1762547492981,
6.84906625747681, 7.14834594726562, 6.82110738754272, 6.97447872161865,
6.88346242904663, 7.07918453216553, 7.00940895080566, 7.11476945877075,
6.72862863540649, 6.94119024276733, 7.12929773330688, 6.96696710586548,
7.34601020812988, 7.1228666305542, 6.8373327255249, 7.06047630310059,
7.17242479324341, 6.98933506011963, 6.70073127746582, 7.2004246711731,
7.03614854812622, 6.65027904510498, 6.66822814941406, 7.27100849151611,
6.91671514511108, 6.90975332260132, 6.96696710586548, 7.15226888656616,
6.79346609115601, 6.80239486694336, 7.23273324966431, 7.0457763671875,
7.01840162277222, 7.11069631576538, 7.06987428665161, 6.56385564804077,
7.3031702041626, 7.11069631576538, 6.9186954498291, 6.93537044525146,
7.11232757568359, 7.15226888656616, 7.10824394226074, 6.81563997268677,
6.78558778762817, 7.3537220954895, 7.01929664611816, 7.08757352828979,
6.88346242904663, 6.98471641540527, 6.82871198654175, 7.11720561981201,
7.03438806533813, 6.43454647064209, 6.89060926437378, 6.7833251953125,
6.99209642410278, 6.93634271621704, 7.09090995788574, 6.80016994476318,
6.87729597091675, 6.96413564682007, 7.05617523193359, 6.7226300239563,
6.93049478530884, 7.21081829071045, 7.10496520996094, 7.02197647094727,
7.06646680831909, 7.06390380859375, 7.01481437683105, 7.1762547492981,
7.06133413314819, 7.29301786422729, 7.39939785003662, 7.03966045379639,
7.34923076629639, 6.9603476524353, 7.28687620162964, 6.96319007873535,
7.1131420135498, 7.07918453216553, 6.92362880706787, 6.61204099655151,
7.08757352828979, 7.02286815643311, 6.50128984451294, 6.92951679229736,
6.87109136581421, 6.98933506011963, 6.41673231124878, 6.91075086593628,
7.11882638931274, 6.8480052947998, 6.96129608154297, 6.79458665847778,
7.11882638931274, 6.96224355697632, 6.87523221969604, 6.96224355697632,
6.92165803909302, 6.99942255020142, 6.9206714630127, 6.7900972366333,
7.26542949676514, 6.8308744430542, 7.167809009552, 6.94022226333618,
6.931471824646, 7.00578880310059, 7.23345518112183, 6.98193454742432,
6.70073127746582, 7.02197647094727, 6.86693334579468, 7.05272102355957,
6.94022226333618, 7.13489103317261, 5.41610050201416, 6.26339817047119,
6.51471281051636, 7.09920167922974, 6.90274286270142, 6.82871198654175,
6.59578037261963, 6.84054660797119, 7.06731986999512, 6.931471824646,
7.13409376144409, 6.92165803909302, 6.6821084022522, 6.98471641540527,
6.75925540924072, 7.10660600662231, 6.931471824646, 7.06475925445557,
7.02108383178711, 6.80016994476318, 6.82219743728638, 6.99025630950928,
6.93537044525146, 7.02286815643311, 6.7707896232605, 6.71659469604492,
7.19818353652954, 6.87212800979614, 7.167809009552, 6.99942255020142,
6.97354316711426, 6.84906625747681, 7.30653142929077, 6.82219743728638,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 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(hours = c(1067L, 919L, 1196L, 785L, 1126L, 936L,
918L, 1156L, 920L, 948L, 855L, 1092L, 1162L, 1170L, 929L, 950L,
905L, 972L, 1035L, 1045L, 1157L, 1195L, 1195L, 1340L, 1122L,
938L, 970L, 1237L, 956L, 1102L, 1022L, 978L, 832L, 1009L, 1157L,
1151L, 1009L, 765L, 958L, 902L, 923L, 1333L, 811L, 1217L, 1085L,
896L, 958L, 1311L, 1037L, 702L, 521L, 933L, 928L, 1153L, 946L,
858L, 1071L, 1069L, 830L, 1063L, 930L, 807L, 954L, 1063L, 1002L,
909L, 1077L, 1021L, 1062L, 1157L, 999L, 932L, 1035L, 944L, 1049L,
940L, 1122L, 1115L, 833L, 1320L, 901L, 1324L, 818L, 1250L, 1203L,
1078L, 890L, 1303L, 1011L, 1102L, 996L, 780L, 900L, 1106L, 704L,
621L, 854L, 1178L, 1138L, 951L, 1187L, 1067L, 1188L, 1037L, 958L,
760L, 1101L, 949L, 992L, 966L, 824L, 653L, 980L, 935L, 878L,
934L, 910L, 1058L, 730L, 980L, 844L, 814L, 1103L, 1000L, 788L,
1143L, 935L, 1069L, 1170L, 1067L, 1037L, 1151L, 863L, 990L, 1035L,
1112L, 931L, 970L, 932L, 904L, 1026L, 1147L, 883L, 867L, 990L,
1258L, 1192L, 992L, 1150L, 1091L, 1039L, 1083L, 1040L, 1289L,
699L, 1083L, 880L, 1029L, 658L, 912L, 1023L, 984L, 856L, 924L,
801L, 1122L, 1292L, 1116L, 880L, 1173L, 1134L, 932L, 938L, 1078L,
1180L, 1106L, 1184L, 954L, 824L, 529L, 998L, 996L, 1133L, 765L,
775L, 1105L, 1081L, 1171L, 705L, 1425L, 610L, 916L, 1001L, 895L,
709L, 860L, 1110L, 1149L, 972L, 1002L, 990L, 1141L, 1127L, 1181L,
856L, 716L, 1308L, 943L, 1272L, 917L, 1069L, 976L, 1187L, 1107L,
1230L, 836L, 1034L, 1248L, 1061L, 1550L, 1240L, 932L, 1165L,
1303L, 1085L, 813L, 1340L, 1137L, 773L, 787L, 1438L, 1009L, 1002L,
1061L, 1277L, 892L, 900L, 1384L, 1148L, 1117L, 1225L, 1176L,
709L, 1485L, 1225L, 1011L, 1028L, 1227L, 1277L, 1222L, 912L,
885L, 1562L, 1118L, 1197L, 976L, 1080L, 924L, 1233L, 1135L, 623L,
983L, 883L, 1088L, 1029L, 1201L, 898L, 970L, 1058L, 1160L, 831L,
1023L, 1354L, 1218L, 1121L, 1172L, 1169L, 1113L, 1308L, 1166L,
1470L, 1635L, 1141L, 1555L, 1054L, 1461L, 1057L, 1228L, 1187L,
1016L, 744L, 1197L, 1122L, 666L, 1022L, 964L, 1085L, 612L, 1003L,
1235L, 942L, 1055L, 893L, 1235L, 1056L, 968L, 1056L, 1014L, 1096L,
1013L, 889L, 1430L, 926L, 1297L, 1033L, 1024L, 1103L, 1385L,
1077L, 813L, 1121L, 960L, 1156L, 1033L, 1255L, 225L, 525L, 675L,
1211L, 995L, 924L, 732L, 935L, 1173L, 1024L, 1254L, 1014L, 798L,
1080L, 862L, 1220L, 1024L, 1170L, 1120L, 898L, 918L, 1086L, 1028L,
1122L, 872L, 826L, 1337L, 965L, 1297L, 1096L, 1068L, 943L, 1490L,
918L, 609L, 985L, 1233L, 985L, 985L, 1075L, 1240L, 985L, 1105L,
1243L, 1204L, 1203L, 1310L, 1262L, 1234L, 1104L, 1303L, 1185L,
759L, 1404L, 944L, 1343L, 932L, 1055L, 1381L, 816L, 1067L, 1252L,
1248L, 1324L, 1000L, 984L, 1220L, 972L, 1022L, 956L, 1093L, 1358L,
1024L, 1240L, 1157L, 1415L, 1385L, 824L, 1690L, 1302L, 1233L,
1331L, 1109L, 827L, 1209L, 1202L, 1229L, 1079L, 1176L, 1772L,
769L, 905L)), class = c("life.data", "data.frame"), row.names = c(NA,
-417L), 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(hours = "hours"), data.title = "Bulb Data", time.units = "Hours", data.note = "")
debug1 <-
FALSE
distribution <-
"lognormal"
distribution.number <-
4
dummy <-
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, 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, 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,
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, 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, 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, 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, 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, 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, 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,
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, 1)
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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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 = 417, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
maxit <-
500
ndscrat <-
879
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, 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, 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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 <-
417L
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 <-
5
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1)
the.censor.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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1)
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1,
1), .Dim = c(417L, 1L))
theta.start <-
c(6.93162315693081, 0.196924136592854)
theta.start.comp <-
c(`(Intercept)` = 6.93162315693081, the.quantiles = 0.196924136592854
)
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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
yresp <-
structure(c(1067, 919, 1196, 785, 1126, 936, 918, 1156, 920,
948, 855, 1092, 1162, 1170, 929, 950, 905, 972, 1035, 1045, 1157,
1195, 1195, 1340, 1122, 938, 970, 1237, 956, 1102, 1022, 978,
832, 1009, 1157, 1151, 1009, 765, 958, 902, 923, 1333, 811, 1217,
1085, 896, 958, 1311, 1037, 702, 521, 933, 928, 1153, 946, 858,
1071, 1069, 830, 1063, 930, 807, 954, 1063, 1002, 909, 1077,
1021, 1062, 1157, 999, 932, 1035, 944, 1049, 940, 1122, 1115,
833, 1320, 901, 1324, 818, 1250, 1203, 1078, 890, 1303, 1011,
1102, 996, 780, 900, 1106, 704, 621, 854, 1178, 1138, 951, 1187,
1067, 1188, 1037, 958, 760, 1101, 949, 992, 966, 824, 653, 980,
935, 878, 934, 910, 1058, 730, 980, 844, 814, 1103, 1000, 788,
1143, 935, 1069, 1170, 1067, 1037, 1151, 863, 990, 1035, 1112,
931, 970, 932, 904, 1026, 1147, 883, 867, 990, 1258, 1192, 992,
1150, 1091, 1039, 1083, 1040, 1289, 699, 1083, 880, 1029, 658,
912, 1023, 984, 856, 924, 801, 1122, 1292, 1116, 880, 1173, 1134,
932, 938, 1078, 1180, 1106, 1184, 954, 824, 529, 998, 996, 1133,
765, 775, 1105, 1081, 1171, 705, 1425, 610, 916, 1001, 895, 709,
860, 1110, 1149, 972, 1002, 990, 1141, 1127, 1181, 856, 716,
1308, 943, 1272, 917, 1069, 976, 1187, 1107, 1230, 836, 1034,
1248, 1061, 1550, 1240, 932, 1165, 1303, 1085, 813, 1340, 1137,
773, 787, 1438, 1009, 1002, 1061, 1277, 892, 900, 1384, 1148,
1117, 1225, 1176, 709, 1485, 1225, 1011, 1028, 1227, 1277, 1222,
912, 885, 1562, 1118, 1197, 976, 1080, 924, 1233, 1135, 623,
983, 883, 1088, 1029, 1201, 898, 970, 1058, 1160, 831, 1023,
1354, 1218, 1121, 1172, 1169, 1113, 1308, 1166, 1470, 1635, 1141,
1555, 1054, 1461, 1057, 1228, 1187, 1016, 744, 1197, 1122, 666,
1022, 964, 1085, 612, 1003, 1235, 942, 1055, 893, 1235, 1056,
968, 1056, 1014, 1096, 1013, 889, 1430, 926, 1297, 1033, 1024,
1103, 1385, 1077, 813, 1121, 960, 1156, 1033, 1255, 225, 525,
675, 1211, 995, 924, 732, 935, 1173, 1024, 1254, 1014, 798, 1080,
862, 1220, 1024, 1170, 1120, 898, 918, 1086, 1028, 1122, 872,
826, 1337, 965, 1297, 1096, 1068, 943, 1490, 918, 609, 985, 1233,
985, 985, 1075, 1240, 985, 1105, 1243, 1204, 1203, 1310, 1262,
1234, 1104, 1303, 1185, 759, 1404, 944, 1343, 932, 1055, 1381,
816, 1067, 1252, 1248, 1324, 1000, 984, 1220, 972, 1022, 956,
1093, 1358, 1024, 1240, 1157, 1415, 1385, 824, 1690, 1302, 1233,
1331, 1109, 827, 1209, 1202, 1229, 1079, 1176, 1772, 769, 905
), .Dim = c(417L, 1L), .Dimnames = list(NULL, c(hours = "hours")))
zout <-
list(ivec = c(417L, 1L, 1L, 0L, 4L, 0L, 2L, 1L, 500L, 0L, 0L,
1L), rvec = structure(c(0, 10000, -2815.22509765625), Csingle = TRUE),
number.cases = 417L, nparm = 2L, xmat = structure(c(1, 1,
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