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Paul Bernal

2019-Apr-23 20:44 UTC

[R] Unable to Understand Results of pglm function

Dear Yves,

Hope you are doing great. I have been testing the pglm function from the
pglm package, in order to fit a logit regression to a panel dataset, and I
do not understand the results and/or errors produced by the function, so I
want to be able to understand whether there is a problem with the structure
of my dataset, or I am not using the function properly or if there is
something else going on that I am ignoring. Also, I would like to know what
the start argument is for, or at least an example of how to use it, since I
don?t know how to properly apply it.

Here the details of what I am using and under what environment settings:
1-R version: 3.5.3
2-packages called: plm and pglm
3-Running on a 64-bit Operating System
4-Windows 8

Here is the code with the different things I have tried so
far:> PGLM_Model11 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("random"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL,
data=dataframe3)>
> summary(PGLM_Model11)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 0 iterations
Return code 100: Initial value out of range.
-------------------------------------------->
> PGLM_Model12 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("pooling"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL,
data=dataframe3)>
> summary(PGLM_Model12)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 11 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -14.95426
5  free parameters
Estimates:
                          Estimate Std. error t value Pr(> t)
(Intercept)             93.9680425        Inf       0       1
dataframe3$Draft        -5.3820652        Inf       0       1
dataframe3$TOTALCOST    -0.0001689        Inf       0       1
dataframe3$BUNKER        0.0072934        Inf       0       1
dataframe3$CHARTERVALUE  0.0008862        Inf       0       1
-------------------------------------------->
> PGLM_Model13 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("within"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL, data=dataframe3)
Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
:
  argument "start" is missing, with no default>
> PGLM_Model14 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("between"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL, data=dataframe3)
Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
:
  argument "start" is missing, with no default

Below the dput of the dataset I am using for your reference:
> dput(dataframe3)structure(list(TRANSIT = c(1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), ID = c(1L, 1L, 2L, 2L, 3L, 4L, 5L, 5L,
6L, 7L, 7L, 7L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 21L, 22L, 23L, 24L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 48L, 49L, 50L, 51L, 52L,
53L, 54L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L,
65L, 66L, 67L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 108L, 109L, 110L,
111L, 112L, 113L, 114L, 115L, 115L, 115L, 115L, 116L, 117L, 118L,
119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 128L,
129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L,
139L, 140L, 140L, 141L, 142L, 143L, 144L, 145L, 145L, 146L, 146L,
147L, 148L, 149L, 149L, 150L, 150L, 150L, 151L, 152L, 153L, 154L,
155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L,
166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L,
177L, 178L, 179L, 180L, 181L, 182L, 182L, 183L, 184L, 185L, 186L,
187L, 188L, 189L, 190L, 191L, 192L, 192L, 193L, 193L, 194L, 195L,
196L, 197L, 198L, 199L, 199L, 200L, 201L, 202L, 203L, 204L, 205L,
206L, 207L, 208L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L,
216L, 217L, 218L, 218L, 219L, 220L, 221L, 222L, 222L, 223L, 224L,
225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L,
236L, 237L, 238L, 239L, 240L, 241L, 241L, 241L, 242L, 243L, 244L,
245L, 246L, 247L, 247L, 248L, 248L, 249L, 249L, 250L, 251L, 252L,
253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L,
264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 273L,
274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
285L, 286L, 287L, 288L, 288L, 289L, 290L, 291L, 292L, 293L, 294L,
295L, 296L, 297L, 298L, 299L, 300L, 301L, 301L, 302L, 303L, 304L,
305L, 306L, 307L, 308L, 308L, 309L, 309L, 309L, 310L, 311L, 312L,
313L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L,
323L, 324L, 325L, 326L, 327L, 327L, 328L, 329L, 330L, 331L, 332L,
333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
354L, 354L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L,
363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L,
374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L,
385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L,
396L, 397L, 398L, 399L, 400L, 401L, 402L, 402L, 403L, 404L, 405L,
406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 413L, 414L, 415L,
416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L,
427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 434L, 435L, 436L,
437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L,
448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L,
459L, 460L, 461L, 462L, 463L, 464L, 464L, 465L, 465L, 466L, 467L,
467L, 468L, 468L, 469L, 470L, 471L, 472L, 473L), DATE = structure(c(47L,
75L, 89L, 252L, 3L, 221L, 62L, 99L, 224L, 114L, 154L, 151L, 52L,
9L, 342L, 320L, 370L, 149L, 252L, 112L, 147L, 346L, 231L, 371L,
331L, 171L, 30L, 119L, 366L, 58L, 61L, 103L, 269L, 313L, 373L,
195L, 116L, 376L, 323L, 189L, 245L, 270L, 76L, 258L, 265L, 347L,
178L, 376L, 278L, 311L, 281L, 260L, 203L, 275L, 101L, 150L, 234L,
161L, 231L, 257L, 367L, 254L, 210L, 67L, 21L, 96L, 241L, 331L,
351L, 223L, 309L, 319L, 256L, 12L, 43L, 27L, 28L, 133L, 101L,
266L, 16L, 359L, 370L, 318L, 237L, 78L, 213L, 113L, 337L, 199L,
94L, 330L, 314L, 271L, 328L, 1L, 348L, 244L, 302L, 374L, 208L,
40L, 357L, 232L, 179L, 286L, 193L, 248L, 250L, 284L, 274L, 321L,
289L, 138L, 80L, 253L, 283L, 164L, 133L, 212L, 339L, 59L, 305L,
49L, 162L, 266L, 326L, 11L, 4L, 82L, 65L, 188L, 192L, 334L, 33L,
177L, 221L, 346L, 148L, 86L, 24L, 5L, 89L, 57L, 37L, 338L, 191L,
68L, 218L, 79L, 235L, 254L, 338L, 361L, 4L, 135L, 143L, 123L,
55L, 23L, 18L, 20L, 202L, 128L, 127L, 122L, 156L, 269L, 321L,
276L, 352L, 22L, 7L, 199L, 333L, 145L, 92L, 136L, 311L, 342L,
294L, 325L, 71L, 29L, 25L, 173L, 154L, 85L, 118L, 121L, 44L,
107L, 140L, 151L, 175L, 102L, 108L, 63L, 25L, 51L, 329L, 334L,
345L, 153L, 282L, 304L, 324L, 193L, 367L, 341L, 39L, 231L, 209L,
335L, 321L, 276L, 102L, 91L, 282L, 362L, 68L, 344L, 253L, 98L,
338L, 84L, 251L, 64L, 161L, 227L, 139L, 334L, 365L, 202L, 374L,
159L, 21L, 317L, 42L, 343L, 349L, 292L, 84L, 226L, 194L, 256L,
228L, 336L, 293L, 288L, 155L, 56L, 207L, 89L, 324L, 163L, 157L,
117L, 260L, 341L, 47L, 97L, 320L, 102L, 312L, 348L, 137L, 38L,
27L, 243L, 229L, 123L, 99L, 125L, 54L, 349L, 354L, 290L, 170L,
233L, 308L, 164L, 15L, 142L, 152L, 352L, 306L, 186L, 299L, 289L,
327L, 377L, 255L, 369L, 377L, 272L, 285L, 320L, 324L, 358L, 6L,
70L, 278L, 364L, 278L, 361L, 360L, 316L, 300L, 350L, 368L, 259L,
315L, 374L, 247L, 161L, 318L, 353L, 332L, 190L, 340L, 344L, 291L,
207L, 14L, 372L, 246L, 270L, 344L, 87L, 324L, 295L, 172L, 377L,
257L, 24L, 330L, 167L, 209L, 212L, 236L, 280L, 281L, 268L, 48L,
264L, 53L, 355L, 206L, 115L, 111L, 140L, 50L, 313L, 187L, 375L,
375L, 336L, 217L, 162L, 371L, 239L, 261L, 334L, 371L, 158L, 320L,
350L, 176L, 10L, 309L, 9L, 330L, 204L, 216L, 166L, 363L, 44L,
301L, 279L, 73L, 83L, 328L, 36L, 72L, 35L, 99L, 169L, 321L, 220L,
34L, 215L, 308L, 244L, 88L, 127L, 334L, 14L, 144L, 60L, 69L,
181L, 123L, 45L, 314L, 37L, 258L, 245L, 250L, 242L, 361L, 9L,
132L, 191L, 7L, 165L, 296L, 186L, 356L, 342L, 197L, 136L, 122L,
126L, 193L, 310L, 200L, 311L, 344L, 355L, 297L, 106L, 46L, 238L,
311L, 160L, 262L, 129L, 168L, 120L, 211L, 90L, 41L, 319L, 32L,
131L, 110L, 185L, 222L, 298L, 201L, 143L, 13L, 273L, 229L, 182L,
76L, 95L, 253L, 88L, 307L, 354L, 198L, 64L, 286L, 267L, 124L,
21L, 26L, 257L, 19L, 242L, 341L, 240L, 174L, 249L, 322L, 8L,
109L, 17L, 134L, 93L, 183L, 158L, 245L, 205L, 130L, 31L, 287L,
271L, 277L, 327L, 184L, 263L, 2L, 196L, 60L, 186L, 303L, 50L,
250L, 141L, 166L, 219L, 248L, 156L, 230L, 350L, 329L, 146L, 313L,
66L, 315L, 77L, 225L, 105L, 180L, 104L, 219L, 80L, 190L, 156L,
81L, 74L, 25L, 100L, 214L), .Label = c("1-Aug-17",
"1-Aug-18",
"1-Feb-18", "1-Jan-18", "1-Jul-17",
"1-Mar-18", "1-Nov-17", "1-Oct-17",
"1-Sep-17", "10-Apr-18", "10-Aug-17",
"10-Dec-17", "10-Feb-18",
"10-Jul-17", "10-Jul-18", "10-Mar-18",
"10-May-18", "10-Nov-17",
"10-Oct-17", "10-Sep-17", "11-Apr-18",
"11-Aug-17", "11-Aug-18",
"11-Dec-17", "11-Feb-18", "11-Jun-18",
"11-Mar-18", "11-Sep-17",
"11-Sep-18", "12-Aug-17", "12-Dec-17",
"12-Jul-17", "12-Jul-18",
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"12-Sep-18", "13-Aug-18",
"13-Dec-17", "13-Nov-17", "13-Oct-17",
"14-Jun-18", "14-Sep-17",
"15-Dec-17", "15-Feb-18", "15-Jul-18",
"15-Mar-18", "15-May-18",
"15-Sep-18", "16-Apr-18", "16-Dec-17",
"16-Sep-18", "17-Apr-18",
"17-Aug-18", "17-Feb-18", "17-Jan-18",
"17-Jul-17", "17-Jul-18",
"17-Jun-18", "17-Mar-18", "17-May-18",
"17-Nov-17", "17-Oct-17",
"18-Apr-18", "18-Aug-18", "18-Dec-17",
"18-Feb-18", "18-Jul-17",
"18-Jul-18", "18-Jun-18", "18-Mar-18",
"18-May-18", "18-Sep-17",
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"23-Dec-17", "23-Feb-18",
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"24-Aug-18", "24-Dec-17",
"24-Jan-18", "24-Jul-17", "24-May-18",
"24-Nov-17", "24-Oct-17",
"25-Apr-18", "25-Aug-18", "25-Jul-17",
"25-May-18", "25-Nov-17",
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"26-Jan-18", "26-Jul-17", "26-Jul-18",
"26-Mar-18", "26-May-18",
"27-Apr-18", "27-Aug-17", "27-Aug-18",
"27-Dec-17", "27-Jul-18",
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"31-May-18", "31-Oct-17",
"4-Dec-17", "4-Feb-18", "4-Jan-18",
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"5-Mar-18", "5-May-18", "5-Nov-17",
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2017", "August 26 2017",
"August 30 2017", "August 6 2017", "August 7
2017", "August 8 2017",
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2017", "December 12
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"December 19 2017", "December 21 2017", "December 22
2017", "December 24
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"December 27 2017", "December 28 2017", "December 29
2017", "December 3
2017",
"December 30 2017", "December 4 2017", "December 5
2017", "December 6
2017",
"February 1 2018", "February 10 2018", "February 12
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"February 15 2018", "February 16 2018", "February 19
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2018",
"February 25 2018", "February 28 2018", "February 3
2018", "February 4
2017",
"February 5 2018", "February 8 2018", "January 1
2018", "January 10 2018",
"January 11 2018", "January 13 2018", "January 14
2018", "January 15 2018",
"January 20 2018", "January 23 2018", "January 24
2018", "January 26 2018",
"January 29 2018", "January 3 2018", "January 30
2018", "January 31 2018",
"January 4 2018", "January 6 2018", "January 7
2018", "January 8 2018",
"January 9 2018", "July 13 2018", "July 30 2017",
"June 17 2018",
"June 8 2018", "March 10 2018", "March 13 2018",
"March 18 2018",
"March 22 2018", "March 24 2018", "March 28 2018",
"March 3 2018",
"November 1 2017", "November 10 2017", "November 11
2017", "November 12
2017",
"November 13 2017", "November 15 2017", "November 17
2017", "November 18
2017",
"November 19 2017", "November 21 2017", "November 22
2017", "November 23
2017",
"November 25 2017", "November 27 2017", "November 28
2017", "November 3
2017",
"November 4 2017", "November 5 2017", "November 6
2017", "November 7 2017",
"November 8 2017", "November 9 2017", "October 1
2017", "October 10 2017",
"October 11 2017", "October 12 2017", "October 14
2017", "October 15 2017",
"October 16 2017", "October 17 2017", "October 18
2017", "October 19 2017",
"October 20 2017", "October 21 2017", "October 23
2017", "October 25 2017",
"October 26 2017", "October 27 2017", "October 28
2017", "October 29 2017",
"October 3 2017", "October 30 2017", "October 31
2017", "October 4 2017",
"October 5 2017", "October 6 2017", "October 7
2017", "October 9 2017",
"September 1 2017", "September 10 2017", "September 11
2017",
"September 12 2017", "September 13 2017", "September 15
2017",
"September 16 2017", "September 17 2017", "September 19
2017",
"September 21 2017", "September 22 2017", "September 24
2017",
"September 26 2017", "September 27 2017", "September 29
2017",
"September 3 2017", "September 30 2017", "September 5
2017",
"September 6 2017", "September 7 2017", "September 8
2017", "September 9
2017"
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187L, 228L, 375L, 374L, 139L), .Label = c("Aby Jeannette",
"Adelante",
"ADM Georgina", "ADS Galtesund", "Aeneas",
"Aeolian Fortune",
"Aeolian Light", "AFRICA GRAECA", "AFRICAN ARROW",
"AFRICAN BARI BIRD",
"AFRICAN BLUE CRANE", "AFRICAN FINFOOT", "AFRICAN
JACANA", "AFRICAN KITE",
"AFRICAN LEOPARD", "AFRICAN PUFFIN", "AFRICAN
RAPTOR", "AFTERHOURS",
"AGIA SKEPI", "Agri Kinsale", "Aiantas",
"AKILI", "ALAM MANIS",
"ALBION", "Alexandra", "ALICIA", "Alma",
"Alpha Vision", "AM BREMEN",
"AMAMI K", "AMIS ACE", "AMIS FORTUNE", "AMIS
JUSTICE", "AMSTEL FALCON",
"Andros", "ANDROS ISLAND", "Androusa",
"ANIMA", "Anna S", "Anna Smile",
"ANTIGONI", "Antiparos", "Aom Gaia", "AOM
GAIA", "Aom Milena",
"APEX", "AREQUIPA QUEEN", "Ariana",
"Artemis", "ASHIYA STAR",
"ASTRA CENTAURUS", "ASTREA", "Athina Carras",
"ATLANTIC EAGLE",
"ATLANTIC GRACE", "ATLANTIC HERO", "ATLANTIC
MANZANILLO", "Attalia",
"Axios", "Bahia Blanca", "Bali", "BALTIC
K", "BALTIC WASP", "BBG Ambition",
"BBG Dream", "BBG Endeavor", "Belo Horizonte",
"BELO HORIZONTE",
"BLUE AKIHABARA", "BLUE DIAMOND", "BLUE MARLIN I",
"Bora", "Brasil SW",
"Braveheart", "BRIDGEGATE", "BRIGITTE", "BTG
Denali", "BTG Eiger",
"BTG Everest", "BTG Kailach", "BULK ARGENTINA",
"BULK COLOMBIA",
"BULK HERO", "BULK HONDURAS", "Bulk Pegasus",
"Bulk Portugal",
"BW Hazel", "Captain Adams", "Captain Antonis",
"Cemtex Wisdom",
"CENTENARIO BLU", "Cepheus Ocean", "Cerafina",
"Cetus Ocean",
"CF Diamond", "CHARADE", "CHLOE", "CLARKE
QUAY", "CLIPPER AMSTERDAM",
"Clipper Victory", "CMB Sakura", "Cofco 1",
"COLUMBIA RIVER",
"Coral Diamond", "COREFORTUNE OL", "Cosmar",
"Coventry", "CP GUANGZHOU",
"Crimson Ark", "Crimson Kingdom", "Cymona Star",
"DALIAN STAR",
"De Xu Hai", "Densa Pelican", "DESERT CHALLENGER",
"DEVON BAY",
"DIAMOND QUEEN", "Dias", "Dimitris Apesakis",
"Donousa", "DORIC",
"DORIC SHOGUN", "DORO", "EASTER N", "Efrain
A", "Egret Oasis",
"Eirini P", "Elena", "Emerald Dongji",
"Emerald Star", "ENDLESS HORIZON",
"ENY", "Erikoussa", "ESSEX STRAIT", "Eternal
Bliss", "Eternal Grace",
"EUROPA BAY", "Ever Grace", "EVER SOVEREIGN",
"Everglory", "Evmar",
"FEDERAL TRIDENT", "FH Fang Cheng", "FH Rizhao",
"Fiji", "FILIA JOY",
"Flag Lama", "FLIPPER", "FLORINDA", "Fortune
Harmony", "FORTUNE LADY",
"FORTUNE UNITY", "FRAMURA", "FURNESS VICTORIA",
"Galio", "GANNET BULKER",
"GENCO RAPTOR", "GH CITATION", "GH URBAN SEA",
"Giorgakis", "Giorgis",
"GLOBAL PRIME", "GLOBAL SUCCESS", "GLOBAL VISION",
"Glory", "Golden Jake",
"GOLDEN LIBRA", "Good Wish", "Graecia Aeterna",
"GRAND CONCORD",
"GRAND MARCIA", "Great Rich", "GUARDIANSHIP",
"Hampton Bay",
"Hampton Bridge", "HANTON TRADER I", "Hercules
Ocean", "Hermes",
"Hong Hing", "Hong Jing", "Hong Sheng", "HOPA
I", "Huayang Spirit",
"Huayeng Dream", "Indian Harmony", "INDIGO
EVOLUTION", "INDIGO RIVER",
"INDRA OLDENDORFF", "Innovation", "INNOVATION",
"Inspiration",
"IRIS HALO", "IRIS OLDENDORFF", "ISMENE",
"Istria", "IYO WIND",
"Jag Aalok", "Jag Akshay", "Jag Arnav", "JIA
SHENG SHAN", "JIN RUN",
"Jin Zhu Hai", "John M. Carras", "JOSCO HANGZHOU",
"JPS AFRODITI",
"K SPINEL", "K. GARNET", "K. OPAL", "KANG
CHENG", "Karlovasi",
"Katerina III", "KAVO PALOMA", "Kea",
"Kerkyra", "Key Evolution",
"Key Pacifico", "KING ISLAND", "KING MILO",
"KM Fukuyama", "KM Hong Kong",
"KM Keelung", "KM Yokohama", "KMARIN SINGAPORE",
"KT Birdie",
"KYRA PANAGHIA", "Lady I", "LEO ADVANCE",
"LESEDI QUEEN", "LILA",
"LISSA TOPIC", "LOCH SHUNA", "Long Dar",
"LOUISIANA MAMA", "LOWLANDS
MAINE",
"LUMINOUS HALO", "LUNITA", "LYRIC HARMONY",
"Macheras", "MALMO",
"MANDARIN CROWN", "MANDARIN NOBLE", "Marathassa",
"MARIE GRACE",
"MARINER", "MARITIME PROSPERITY", "MARY LINA",
"Mastro Nikos",
"MBA Future", "Medi Matsuura", "MEDI SALERNO",
"MELBOURNE", "MELIA",
"METSOVO", "MG Explorer", "MG Kronos", "MG
Sakura", "Miao Xiang",
"MISATO K", "Mistral I", "Miyama",
"Mykonos", "Myra", "Myrto",
"N Bonanza", "Nadeshiko", "Naias", "NAUTICAL
MARIE", "NAUTICAL RUNA",
"NAUTICAL SIF", "Navios Amber", "NAVIOS ARC",
"NAVIOS ARMONIA",
"Navios Harmony", "Navios Orbiter", "NAVIOS SOUTHERN
STAR", "NEFELI",
"NEW BLISS", "NEW DIRECTION", "NEWSEAS PEARL",
"NIKKEI SIRIUS",
"NIKKEI VERDE", "Nikolaos", "NIKOLAS XL",
"Nikomarin", "Nord Capella",
"Nord Fortune", "NOSHIMA", "Nuri Bey",
"OCCITAN PAUILLAC", "OCEAN BAO",
"OCEAN BELT", "OCEAN FAVOUR", "Ocean Garlic",
"OCEAN HARVEST",
"OCEAN PRIDE", "OCEAN PRINCE", "OCEAN PRINCESS",
"OCEAN ROYAL",
"OCEAN SPLENDOR", "OCEAN TIANBAO", "OCEAN VENUS",
"Ocean Wind",
"Oceana", "Odysseas L", "OKINAWA", "Olivia
R", "OLYMPOS", "Omicron Light",
"OMICRON NIKOS", "OMICRON SKY", "Omicron Trader",
"ORCHID HALO",
"Orient Genesis", "ORIENT GRACE", "OZGUR AKSOY",
"PACIFIC ADVANCE",
"PACIFIC NEXUS", "PACIFIC TALENT", "PACIFIC
VICTORY", "Palais",
"Pan Ceres", "PAN VIVA", "Panafrican",
"Panamanian", "Panasiatic",
"PANORIA", "Panther Max", "PARADISE ISLAND",
"PAUL OLDENDORFF",
"Peace Ark", "PEAK PEGASUS", "Pedhoulas Farmer",
"Pedhoulas Trader",
"PENTA", "PERIDOT", "PERTH I",
"Phaedra", "PHOENIX K", "Phoenix Ocean",
"Pictor", "PILATUS VENTURE", "Popi S", "PORT
ESTRELA", "Proteas",
"QUEEN JHANSI", "QUEEN KOBE", "Rave", "RB
Eden", "Real Happiness",
"RECCO", "REGAL", "RESURGENCE", "RIGI
VENTURE", "Rosalia D? Amato",
"Rosco Banyan", "Rosco Cypress", "Rosco Ginkgo",
"Rosco Lemon",
"Rosco Litchi", "Rosco Palm", "ROSCO PLUM",
"Rosco Poplar", "Rosco
Sandalwood",
"RR Australia", "SAGAR JYOTI", "SAGAR SHAKTI",
"SAGARJEET", "SAGE
COLORADO",
"SAGE PIONEER", "SAILING SKY", "Sakizaya Power",
"SAN ANTONIO",
"SANTA KATARINA", "SANTA VALENTINA", "SANYU",
"SBI Bolero", "SBI BOLERO",
"SBI Samba", "Scarlet Cardinal", "SCARLET
CARDINAL", "Scarlet Falcon",
"Sea Duty", "Sea Hermes", "Sea Pegasus", "SEA
PIONEER", "Sea Pluto",
"Seatribute", "Shandong Fu Hui", "Shandong Hai
Chang", "Shangdong Fu Ze",
"Shao Shan 5", "Shao Shan 8", "SIFNOS",
"Silver Dragon", "SIMURGH",
"Skiathos", "SKY KNIGHT", "SONGA GLORY",
"SOUTHEND", "SPARNA",
"SPRING AEOLIAN", "SPRING EAGLE", "SPRING ZEPHYR",
"SSI CHALLENGER",
"Stalo", "STAMFORD EAGLE", "STAR AQUARIUS",
"STAR JENNIFER",
"Star Laura", "Star of Sawara", "STAR PISCES",
"Star Renee",
"STAR VANESSA", "STARRY SKY", "STH LONDON",
"STOVE FRIEND", "STOVE OCEAN",
"SUNLEAF GRACE", "SUNLEAF STAR", "SUNNY HOPE",
"SUNNY ROYAL",
"SUZAKU", "Syros I", "Tahiti One", "Tai
Promotion", "TAI PROSPERITY",
"TAI SPRING", "TAI STAR", "TAI SUMMIT",
"Tangerine Island", "TANGERINE
ISLAND",
"TANIKAZE", "TASSOS N", "Taurus Ocean", "TEAL
BULKER", "TENRO MARU",
"Tenten", "THEMISTOCLES", "Theodor Oldendorff",
"THEODOR OLDENDORFF",
"Theodore Jr.", "Theresa Hebei", "Theresa Jilin",
"Theresa Shandong",
"TIGER HENAN", "TIGER NORTH", "TIGER PIONEER",
"Tiger South",
"TN SUNRISE", "TOMORROW", "Topaz",
"TORENIA", "TR Lady", "Trade Unity",
"TRANS OCEANIC", "TRUSTN TRADER II",
"TSCHAIKOWSKY", "TTM DRAGON",
"Tuo Fu 6", "Tycoon", "ULTRA PANTHER",
"Unity", "UNITY DISCOVERY",
"Valadon", "VEGA ROSE", "VELA OCEAN",
"VENUS", "VENUS HALO",
"VICTORIA", "VISHVA ANAND", "Vitahorizon",
"Vitakosmos", "Vivian",
"VSC CASTOR", "VSC TRITON", "XING XI HAI",
"Yarrawonga", "Yue Guan Feng",
"ZEN-NOH GRAIN MAGNOLIA", "ZEN-NOH GRAIN PEGASUS",
"Zheng Zhi",
"Zhi He"), class = "factor"), Draft = c(12L, 12L, 12L, 13L,
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12L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 12L, 12L, 14L, 12L, 14L,
14L, 12L, 12L, 12L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 13L,
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14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 12L, 14L,
14L, 14L, 12L, 14L, 14L, 14L, 12L, 12L, 14L, 14L, 14L, 14L, 12L,
14L, 14L, 12L, 14L, 14L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 13L,
14L, 12L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 14L,
13L, 14L, 12L, 12L, 14L, 13L, 14L, 14L, 14L, 12L, 14L, 14L, 12L,
12L, 14L, 12L, 14L, 12L, 12L, 12L, 14L, 12L, 13L, 14L, 12L, 12L,
14L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 14L, 14L, 12L,
12L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 13L, 13L,
13L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 12L,
12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 13L, 13L, 11L, 12L, 14L,
14L, 12L, 14L, 12L, 11L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L,
12L, 14L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 12L, 14L, 12L,
13L, 14L, 12L, 12L, 14L, 14L, 12L, 12L, 12L, 13L, 12L, 14L, 14L,
14L, 12L, 14L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 6L,
12L, 12L, 14L, 14L, 14L, 14L, 12L, 14L, 12L, 12L, 12L, 12L, 14L,
12L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 13L, 14L, 12L, 13L, 12L,
14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L), TOTALCOST = c(194364L, 219364L, 198260L, 237456L,
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196604L, 196604L, 194496L, 238600L, 236936L, 237476L, 197220L,
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157406L, 157211L, 182211L, 237475L, 182475L, 181599L, 156599L,
238269L, 238402L, 238069L, 161436L, 225031L, 238180L, 237572L,
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200878L, 239019L, 239087L, 239350L, 239352L, 240275L, 164844L,
238400L, 225158L, 202495L, 239681L, 201791L, 226863L, 244092L,
244590L, 239171L, 189811L, 219412L, 228480L, 203650L, 237514L,
247451L, 244739L, 211770L, 244308L, 239197L, 238419L, 224977L,
157362L, 162434L, 162434L, 162434L, 162434L, 239681L, 163316L,
237265L, 243920L, 244088L, 244163L, 202256L, 159592L, 201346L,
239187L, 189800L, 191959L, 239476L, 239171L, 238087L, 238052L,
164169L, 245057L, 244215L, 240812L, 239156L, 156879L, 197853L,
245367L, 164710L, 164710L, 244192L, 211110L, 239156L, 244213L,
237504L, 239018L, 241150L, 244447L, 238506L, 210298L, 243482L,
239166L, 159489L, 184600L, 226439L, 239127L, 235243L, 244296L,
159696L, 189046L, 244355L, 244446L, 187595L, 162595L, 162595L,
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"data.frame",
row.names = c(NA,
-527L))


Any help and/or guidance will be greatly appreciated,

Best regards,

Paul

	[[alternative HTML version deleted]]

Duncan Mackay

2019-Apr-24 02:54 UTC

head link

[R] Unable to Understand Results of pglm function

Hi Paul

I think you may have too many IDs DATE for your model as you posted

I converted your DATE into date format and named it df3
str(df3)
'data.frame':   527 obs. of  11 variables:
 $ TRANSIT     : int  1 1 1 0 1 1 1 1 1 1 ...
 $ ID          : int  1 1 2 2 3 4 5 5 6 7 ...
 $ DATE        : Factor w/ 377 levels
"1-Aug-17","1-Aug-18",..: 47 75 89 252 3 221 62 99 224 114
...
 $ SHIPNAME    : Factor w/ 482 levels "Aby Jeannette",..: 295 295 151
151 19 41 292 292 201 148 ...
 $ Draft       : int  12 12 12 13 12 12 12 12 12 12 ...
 $ TOTALCOST   : int  194364 219364 198260 237456 197159 198992 194337 219337
199198 196604 ...
 $ BUNKER      : int  350 405 276 350 373 355 370 343 345 288 ...
 $ CHARTERVALUE: int  14000 12825 10475 11850 13250 12100 11875 14500 12500
10500 ...
 $ dt          : Date, format: "2018-03-15" "2018-08-19"
"2017-07-20" "2017-12-19" ...
 $ dtym        : chr  "201803" "201808" "201707"
"201712" ...
 $ dty         : chr  "2018" "2018" "2017"
"2017" ...

Here are 2 results 
> model1 = pglm(TRANSIT~ Draft+TOTALCOST+BUNKER+CHARTERVALUE + dty,+      effect=c("time"), 
+      model=c("pooling"), 
+      family=binomial('logit'),
+      index=c("ID"), 
+      start = NULL, data=df3)> 
> summary(model1)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 11 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -14.14988 
6  free parameters
Estimates:
               Estimate Std. error t value Pr(> t)
(Intercept)   1.023e+02        Inf       0       1
Draft        -5.088e+00        Inf       0       1
TOTALCOST    -1.708e-04        Inf       0       1
BUNKER       -6.712e-03        Inf       0       1
CHARTERVALUE  2.524e-04        Inf       0       1
dty2018       2.215e+00        Inf       0       1
--------------------------------------------
> model1=pglm(TRANSIT~ Draft+TOTALCOST+BUNKER+CHARTERVALUE + dty,+      effect=c("time"), 
+      model=c("pooling"), 
+      family=binomial('logit'),
+      index=c("ID","dty"), 
+      start = NULL, data=df3)
Warning messages:
1: In pdata.frame(data, index) :
  duplicate couples (id-time) in resulting pdata.frame
 to find out which, use e.g. table(index(your_pdataframe), useNA =
"ifany")
2: In is.pbalanced.default(index[[1]], index[[2]]) :
  duplicate couples (id-time)
> 
> summary(model1)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 11 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -14.14988 
6  free parameters
Estimates:
               Estimate Std. error t value Pr(> t)
(Intercept)   1.023e+02        Inf       0       1
Draft        -5.088e+00        Inf       0       1
TOTALCOST    -1.708e-04        Inf       0       1
BUNKER       -6.712e-03        Inf       0       1
CHARTERVALUE  2.524e-04        Inf       0       1
dty2018       2.215e+00        Inf       0       1
--------------------------------------------

I have no knowledge of the pglm package and was trying it out on your data
without going through the help properly
NBB your DATE column has several formats which do not help

Regards

Duncan

Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2350

-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Paul Bernal
Sent: Wednesday, 24 April 2019 06:44
To: yves.croissant at univ-reunion.fr
Cc: r-help at r-project.org
Subject: [R] Unable to Understand Results of pglm function

Dear Yves,

Hope you are doing great. I have been testing the pglm function from the
pglm package, in order to fit a logit regression to a panel dataset, and I
do not understand the results and/or errors produced by the function, so I
want to be able to understand whether there is a problem with the structure
of my dataset, or I am not using the function properly or if there is
something else going on that I am ignoring. Also, I would like to know what
the start argument is for, or at least an example of how to use it, since I
don?t know how to properly apply it.

Here the details of what I am using and under what environment settings:
1-R version: 3.5.3
2-packages called: plm and pglm
3-Running on a 64-bit Operating System
4-Windows 8

Here is the code with the different things I have tried so
far:> PGLM_Model11 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("random"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL,
data=dataframe3)>
> summary(PGLM_Model11)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 0 iterations
Return code 100: Initial value out of range.
-------------------------------------------->
> PGLM_Model12 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("pooling"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL,
data=dataframe3)>
> summary(PGLM_Model12)--------------------------------------------
Maximum Likelihood estimation
Newton-Raphson maximisation, 11 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -14.95426
5  free parameters
Estimates:
                          Estimate Std. error t value Pr(> t)
(Intercept)             93.9680425        Inf       0       1
dataframe3$Draft        -5.3820652        Inf       0       1
dataframe3$TOTALCOST    -0.0001689        Inf       0       1
dataframe3$BUNKER        0.0072934        Inf       0       1
dataframe3$CHARTERVALUE  0.0008862        Inf       0       1
-------------------------------------------->
> PGLM_Model13 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("within"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL, data=dataframe3)
Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
:
  argument "start" is missing, with no default>
> PGLM_Model14 <-pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
effect=c("twoways"), model=c("between"),
family=binomial('logit'),
index=c("ID","DATE"), start = NULL, data=dataframe3)
Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
:
  argument "start" is missing, with no default

Below the dput of the dataset I am using for your reference:
> dput(dataframe3)structure(list(TRANSIT = c(1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), ID = c(1L, 1L, 2L, 2L, 3L, 4L, 5L, 5L,
6L, 7L, 7L, 7L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 21L, 22L, 23L, 24L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 48L, 49L, 50L, 51L, 52L,
53L, 54L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L,
65L, 66L, 67L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 108L, 109L, 110L,
111L, 112L, 113L, 114L, 115L, 115L, 115L, 115L, 116L, 117L, 118L,
119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 128L,
129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L,
139L, 140L, 140L, 141L, 142L, 143L, 144L, 145L, 145L, 146L, 146L,
147L, 148L, 149L, 149L, 150L, 150L, 150L, 151L, 152L, 153L, 154L,
155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L,
166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L,
177L, 178L, 179L, 180L, 181L, 182L, 182L, 183L, 184L, 185L, 186L,
187L, 188L, 189L, 190L, 191L, 192L, 192L, 193L, 193L, 194L, 195L,
196L, 197L, 198L, 199L, 199L, 200L, 201L, 202L, 203L, 204L, 205L,
206L, 207L, 208L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L,
216L, 217L, 218L, 218L, 219L, 220L, 221L, 222L, 222L, 223L, 224L,
225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L,
236L, 237L, 238L, 239L, 240L, 241L, 241L, 241L, 242L, 243L, 244L,
245L, 246L, 247L, 247L, 248L, 248L, 249L, 249L, 250L, 251L, 252L,
253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L,
264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 273L,
274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
285L, 286L, 287L, 288L, 288L, 289L, 290L, 291L, 292L, 293L, 294L,
295L, 296L, 297L, 298L, 299L, 300L, 301L, 301L, 302L, 303L, 304L,
305L, 306L, 307L, 308L, 308L, 309L, 309L, 309L, 310L, 311L, 312L,
313L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L,
323L, 324L, 325L, 326L, 327L, 327L, 328L, 329L, 330L, 331L, 332L,
333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
354L, 354L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L,
363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L,
374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L,
385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L,
396L, 397L, 398L, 399L, 400L, 401L, 402L, 402L, 403L, 404L, 405L,
406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 413L, 414L, 415L,
416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L,
427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 434L, 435L, 436L,
437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L,
448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L,
459L, 460L, 461L, 462L, 463L, 464L, 464L, 465L, 465L, 466L, 467L,
467L, 468L, 468L, 469L, 470L, 471L, 472L, 473L), DATE = structure(c(47L,
75L, 89L, 252L, 3L, 221L, 62L, 99L, 224L, 114L, 154L, 151L, 52L,
9L, 342L, 320L, 370L, 149L, 252L, 112L, 147L, 346L, 231L, 371L,
331L, 171L, 30L, 119L, 366L, 58L, 61L, 103L, 269L, 313L, 373L,
195L, 116L, 376L, 323L, 189L, 245L, 270L, 76L, 258L, 265L, 347L,
178L, 376L, 278L, 311L, 281L, 260L, 203L, 275L, 101L, 150L, 234L,
161L, 231L, 257L, 367L, 254L, 210L, 67L, 21L, 96L, 241L, 331L,
351L, 223L, 309L, 319L, 256L, 12L, 43L, 27L, 28L, 133L, 101L,
266L, 16L, 359L, 370L, 318L, 237L, 78L, 213L, 113L, 337L, 199L,
94L, 330L, 314L, 271L, 328L, 1L, 348L, 244L, 302L, 374L, 208L,
40L, 357L, 232L, 179L, 286L, 193L, 248L, 250L, 284L, 274L, 321L,
289L, 138L, 80L, 253L, 283L, 164L, 133L, 212L, 339L, 59L, 305L,
49L, 162L, 266L, 326L, 11L, 4L, 82L, 65L, 188L, 192L, 334L, 33L,
177L, 221L, 346L, 148L, 86L, 24L, 5L, 89L, 57L, 37L, 338L, 191L,
68L, 218L, 79L, 235L, 254L, 338L, 361L, 4L, 135L, 143L, 123L,
55L, 23L, 18L, 20L, 202L, 128L, 127L, 122L, 156L, 269L, 321L,
276L, 352L, 22L, 7L, 199L, 333L, 145L, 92L, 136L, 311L, 342L,
294L, 325L, 71L, 29L, 25L, 173L, 154L, 85L, 118L, 121L, 44L,
107L, 140L, 151L, 175L, 102L, 108L, 63L, 25L, 51L, 329L, 334L,
345L, 153L, 282L, 304L, 324L, 193L, 367L, 341L, 39L, 231L, 209L,
335L, 321L, 276L, 102L, 91L, 282L, 362L, 68L, 344L, 253L, 98L,
338L, 84L, 251L, 64L, 161L, 227L, 139L, 334L, 365L, 202L, 374L,
159L, 21L, 317L, 42L, 343L, 349L, 292L, 84L, 226L, 194L, 256L,
228L, 336L, 293L, 288L, 155L, 56L, 207L, 89L, 324L, 163L, 157L,
117L, 260L, 341L, 47L, 97L, 320L, 102L, 312L, 348L, 137L, 38L,
27L, 243L, 229L, 123L, 99L, 125L, 54L, 349L, 354L, 290L, 170L,
233L, 308L, 164L, 15L, 142L, 152L, 352L, 306L, 186L, 299L, 289L,
327L, 377L, 255L, 369L, 377L, 272L, 285L, 320L, 324L, 358L, 6L,
70L, 278L, 364L, 278L, 361L, 360L, 316L, 300L, 350L, 368L, 259L,
315L, 374L, 247L, 161L, 318L, 353L, 332L, 190L, 340L, 344L, 291L,
207L, 14L, 372L, 246L, 270L, 344L, 87L, 324L, 295L, 172L, 377L,
257L, 24L, 330L, 167L, 209L, 212L, 236L, 280L, 281L, 268L, 48L,
264L, 53L, 355L, 206L, 115L, 111L, 140L, 50L, 313L, 187L, 375L,
375L, 336L, 217L, 162L, 371L, 239L, 261L, 334L, 371L, 158L, 320L,
350L, 176L, 10L, 309L, 9L, 330L, 204L, 216L, 166L, 363L, 44L,
301L, 279L, 73L, 83L, 328L, 36L, 72L, 35L, 99L, 169L, 321L, 220L,
34L, 215L, 308L, 244L, 88L, 127L, 334L, 14L, 144L, 60L, 69L,
181L, 123L, 45L, 314L, 37L, 258L, 245L, 250L, 242L, 361L, 9L,
132L, 191L, 7L, 165L, 296L, 186L, 356L, 342L, 197L, 136L, 122L,
126L, 193L, 310L, 200L, 311L, 344L, 355L, 297L, 106L, 46L, 238L,
311L, 160L, 262L, 129L, 168L, 120L, 211L, 90L, 41L, 319L, 32L,
131L, 110L, 185L, 222L, 298L, 201L, 143L, 13L, 273L, 229L, 182L,
76L, 95L, 253L, 88L, 307L, 354L, 198L, 64L, 286L, 267L, 124L,
21L, 26L, 257L, 19L, 242L, 341L, 240L, 174L, 249L, 322L, 8L,
109L, 17L, 134L, 93L, 183L, 158L, 245L, 205L, 130L, 31L, 287L,
271L, 277L, 327L, 184L, 263L, 2L, 196L, 60L, 186L, 303L, 50L,
250L, 141L, 166L, 219L, 248L, 156L, 230L, 350L, 329L, 146L, 313L,
66L, 315L, 77L, 225L, 105L, 180L, 104L, 219L, 80L, 190L, 156L,
81L, 74L, 25L, 100L, 214L), .Label = c("1-Aug-17",
"1-Aug-18",
"1-Feb-18", "1-Jan-18", "1-Jul-17",
"1-Mar-18", "1-Nov-17", "1-Oct-17",
"1-Sep-17", "10-Apr-18", "10-Aug-17",
"10-Dec-17", "10-Feb-18",
"10-Jul-17", "10-Jul-18", "10-Mar-18",
"10-May-18", "10-Nov-17",
"10-Oct-17", "10-Sep-17", "11-Apr-18",
"11-Aug-17", "11-Aug-18",
"11-Dec-17", "11-Feb-18", "11-Jun-18",
"11-Mar-18", "11-Sep-17",
"11-Sep-18", "12-Aug-17", "12-Dec-17",
"12-Jul-17", "12-Jul-18",
"12-Mar-18", "12-May-18", "12-Oct-17",
"12-Sep-18", "13-Aug-18",
"13-Dec-17", "13-Nov-17", "13-Oct-17",
"14-Jun-18", "14-Sep-17",
"15-Dec-17", "15-Feb-18", "15-Jul-18",
"15-Mar-18", "15-May-18",
"15-Sep-18", "16-Apr-18", "16-Dec-17",
"16-Sep-18", "17-Apr-18",
"17-Aug-18", "17-Feb-18", "17-Jan-18",
"17-Jul-17", "17-Jul-18",
"17-Jun-18", "17-Mar-18", "17-May-18",
"17-Nov-17", "17-Oct-17",
"18-Apr-18", "18-Aug-18", "18-Dec-17",
"18-Feb-18", "18-Jul-17",
"18-Jul-18", "18-Jun-18", "18-Mar-18",
"18-May-18", "18-Sep-17",
"19-Apr-18", "19-Aug-18", "19-Jan-18",
"19-Jul-17", "19-May-18",
"19-Sep-17", "2-Aug-18", "2-Jun-18",
"2-May-18", "2-Oct-17",
"2-Sep-17", "2-Sep-18", "20-Aug-17",
"20-Dec-17", "20-Feb-18",
"20-Jul-17", "20-Jul-18", "20-Jun-18",
"20-Oct-17", "20-Sep-18",
"21-Apr-18", "21-Aug-17", "21-Dec-17",
"21-Feb-18", "21-Jan-18",
"21-Mar-18", "21-Nov-17", "21-Oct-17",
"21-Sep-17", "21-Sep-18",
"22-Apr-18", "22-Aug-17", "22-Feb-18",
"22-Jul-17", "22-May-18",
"22-Nov-17", "23-Aug-17", "23-Aug-18",
"23-Dec-17", "23-Feb-18",
"23-Jul-17", "23-Nov-17", "23-Sep-18",
"24-Aug-18", "24-Dec-17",
"24-Jan-18", "24-Jul-17", "24-May-18",
"24-Nov-17", "24-Oct-17",
"25-Apr-18", "25-Aug-18", "25-Jul-17",
"25-May-18", "25-Nov-17",
"25-Oct-17", "25-Sep-17", "25-Sep-18",
"26-Apr-18", "26-Aug-18",
"26-Jan-18", "26-Jul-17", "26-Jul-18",
"26-Mar-18", "26-May-18",
"27-Apr-18", "27-Aug-17", "27-Aug-18",
"27-Dec-17", "27-Jul-18",
"27-Nov-17", "27-Sep-18", "28-Aug-17",
"28-Dec-17", "28-Feb-18",
"28-Jul-17", "28-Jun-18", "28-Mar-18",
"28-May-18", "28-Nov-17",
"28-Oct-17", "28-Sep-17", "29-Aug-18",
"29-Dec-17", "29-Jan-18",
"29-Jul-17", "29-Jul-18", "29-Jun-18",
"29-Mar-18", "29-Nov-17",
"29-Oct-17", "29-Sep-17", "3-Apr-18",
"3-Aug-17", "3-Dec-17",
"3-Jan-18", "3-Jul-17", "3-May-18",
"3-Nov-17", "3-Sep-17", "3-Sep-18",
"30-Apr-18", "30-Aug-18", "30-Jan-18",
"30-Jul-17", "30-Jun-18",
"30-Mar-18", "30-May-18", "30-Sep-18",
"31-Aug-17", "31-Dec-17",
"31-Jan-18", "31-Jul-17", "31-Jul-18",
"31-May-18", "31-Oct-17",
"4-Dec-17", "4-Feb-18", "4-Jan-18",
"4-Mar-18", "4-Nov-17", "4-Oct-17",
"4-Sep-18", "5-Aug-17", "5-Dec-17",
"5-Feb-18", "5-Jan-18", "5-Jul-17",
"5-Mar-18", "5-May-18", "5-Nov-17",
"5-Sep-17", "6-Aug-17", "6-Jun-18",
"6-Mar-18", "6-Nov-17", "6-Sep-17",
"6-Sep-18", "7-Apr-18", "7-Aug-17",
"7-Feb-18", "7-Jan-18", "7-Jul-17",
"7-Jul-18", "7-Sep-17", "8-Apr-18",
"8-Aug-18", "8-Dec-17", "8-Mar-18",
"8-May-18", "8-Nov-17", "8-Sep-18",
"9-Apr-18", "9-Aug-17", "9-Aug-18",
"9-Feb-18", "9-Mar-18", "9-Nov-17",
"9-Oct-17", "April 23 2018", "April 5 2018",
"August 14 2017",
"August 15 2017", "August 24 2017", "August 25
2017", "August 26 2017",
"August 30 2017", "August 6 2017", "August 7
2017", "August 8 2017",
"December 1 2017", "December 10 2017", "December 11
2017", "December 12
2017",
"December 13 2017", "December 14 2017", "December 15
2017", "December 18
2017",
"December 19 2017", "December 21 2017", "December 22
2017", "December 24
2017",
"December 27 2017", "December 28 2017", "December 29
2017", "December 3
2017",
"December 30 2017", "December 4 2017", "December 5
2017", "December 6
2017",
"February 1 2018", "February 10 2018", "February 12
2018", "February 13
2018",
"February 15 2018", "February 16 2018", "February 19
2018", "February 20
2018",
"February 25 2018", "February 28 2018", "February 3
2018", "February 4
2017",
"February 5 2018", "February 8 2018", "January 1
2018", "January 10 2018",
"January 11 2018", "January 13 2018", "January 14
2018", "January 15 2018",
"January 20 2018", "January 23 2018", "January 24
2018", "January 26 2018",
"January 29 2018", "January 3 2018", "January 30
2018", "January 31 2018",
"January 4 2018", "January 6 2018", "January 7
2018", "January 8 2018",
"January 9 2018", "July 13 2018", "July 30 2017",
"June 17 2018",
"June 8 2018", "March 10 2018", "March 13 2018",
"March 18 2018",
"March 22 2018", "March 24 2018", "March 28 2018",
"March 3 2018",
"November 1 2017", "November 10 2017", "November 11
2017", "November 12
2017",
"November 13 2017", "November 15 2017", "November 17
2017", "November 18
2017",
"November 19 2017", "November 21 2017", "November 22
2017", "November 23
2017",
"November 25 2017", "November 27 2017", "November 28
2017", "November 3
2017",
"November 4 2017", "November 5 2017", "November 6
2017", "November 7 2017",
"November 8 2017", "November 9 2017", "October 1
2017", "October 10 2017",
"October 11 2017", "October 12 2017", "October 14
2017", "October 15 2017",
"October 16 2017", "October 17 2017", "October 18
2017", "October 19 2017",
"October 20 2017", "October 21 2017", "October 23
2017", "October 25 2017",
"October 26 2017", "October 27 2017", "October 28
2017", "October 29 2017",
"October 3 2017", "October 30 2017", "October 31
2017", "October 4 2017",
"October 5 2017", "October 6 2017", "October 7
2017", "October 9 2017",
"September 1 2017", "September 10 2017", "September 11
2017",
"September 12 2017", "September 13 2017", "September 15
2017",
"September 16 2017", "September 17 2017", "September 19
2017",
"September 21 2017", "September 22 2017", "September 24
2017",
"September 26 2017", "September 27 2017", "September 29
2017",
"September 3 2017", "September 30 2017", "September 5
2017",
"September 6 2017", "September 7 2017", "September 8
2017", "September 9
2017"
), class = "factor"), SHIPNAME = structure(c(295L, 295L, 151L,
151L, 19L, 41L, 292L, 292L, 201L, 148L, 148L, 148L, 148L, 413L,
39L, 74L, 460L, 54L, 462L, 8L, 22L, 347L, 307L, 354L, 311L, 296L,
297L, 297L, 118L, 279L, 230L, 230L, 340L, 358L, 473L, 271L, 309L,
451L, 40L, 404L, 120L, 127L, 209L, 90L, 274L, 260L, 252L, 344L,
165L, 363L, 356L, 425L, 192L, 133L, 56L, 440L, 439L, 276L, 361L,
333L, 273L, 308L, 235L, 235L, 426L, 234L, 93L, 111L, 325L, 283L,
107L, 48L, 101L, 212L, 246L, 400L, 338L, 338L, 422L, 20L, 369L,
471L, 7L, 409L, 412L, 310L, 70L, 157L, 357L, 103L, 452L, 49L,
349L, 4L, 226L, 465L, 362L, 128L, 264L, 136L, 50L, 18L, 323L,
11L, 11L, 25L, 408L, 302L, 180L, 394L, 113L, 434L, 477L, 461L,
305L, 174L, 104L, 152L, 132L, 291L, 410L, 250L, 382L, 351L, 23L,
119L, 284L, 480L, 480L, 480L, 480L, 457L, 272L, 262L, 81L, 346L,
239L, 58L, 149L, 402L, 373L, 82L, 251L, 244L, 244L, 135L, 24L,
345L, 156L, 227L, 324L, 215L, 222L, 286L, 55L, 281L, 281L, 280L,
280L, 322L, 393L, 243L, 34L, 418L, 418L, 334L, 334L, 221L, 220L,
6L, 6L, 479L, 479L, 479L, 166L, 196L, 298L, 71L, 160L, 282L,
213L, 147L, 315L, 433L, 458L, 207L, 208L, 186L, 91L, 326L, 466L,
421L, 420L, 98L, 399L, 289L, 134L, 123L, 194L, 173L, 248L, 64L,
202L, 206L, 95L, 396L, 396L, 131L, 211L, 391L, 38L, 84L, 455L,
144L, 168L, 389L, 398L, 398L, 35L, 35L, 367L, 359L, 360L, 105L,
73L, 431L, 430L, 372L, 62L, 312L, 470L, 263L, 86L, 275L, 219L,
414L, 96L, 125L, 365L, 478L, 342L, 45L, 241L, 75L, 121L, 355L,
380L, 379L, 216L, 191L, 417L, 395L, 395L, 31L, 210L, 467L, 146L,
397L, 179L, 181L, 29L, 171L, 482L, 240L, 288L, 330L, 368L, 287L,
401L, 321L, 217L, 233L, 233L, 233L, 366L, 247L, 89L, 472L, 336L,
364L, 364L, 124L, 124L, 163L, 163L, 5L, 37L, 237L, 332L, 183L,
184L, 444L, 442L, 339L, 126L, 293L, 232L, 150L, 203L, 53L, 475L,
468L, 327L, 172L, 481L, 61L, 424L, 2L, 28L, 28L, 224L, 304L,
423L, 66L, 384L, 335L, 387L, 42L, 195L, 200L, 383L, 114L, 443L,
301L, 68L, 67L, 72L, 214L, 386L, 352L, 381L, 65L, 218L, 266L,
102L, 51L, 178L, 30L, 137L, 137L, 175L, 161L, 1L, 448L, 446L,
3L, 190L, 189L, 278L, 278L, 278L, 299L, 116L, 143L, 44L, 43L,
130L, 285L, 328L, 170L, 185L, 87L, 140L, 437L, 145L, 245L, 155L,
261L, 258L, 331L, 85L, 16L, 257L, 204L, 13L, 154L, 459L, 117L,
94L, 320L, 225L, 314L, 259L, 14L, 456L, 162L, 142L, 26L, 303L,
432L, 231L, 435L, 392L, 313L, 370L, 474L, 464L, 450L, 450L, 450L,
450L, 438L, 182L, 236L, 92L, 164L, 79L, 80L, 77L, 169L, 177L,
153L, 176L, 329L, 353L, 341L, 454L, 69L, 238L, 242L, 269L, 268L,
267L, 115L, 108L, 199L, 52L, 27L, 59L, 198L, 197L, 253L, 436L,
306L, 106L, 447L, 378L, 316L, 318L, 99L, 407L, 411L, 36L, 453L,
167L, 63L, 158L, 188L, 377L, 376L, 32L, 193L, 463L, 129L, 429L,
9L, 17L, 449L, 21L, 76L, 78L, 78L, 319L, 33L, 390L, 388L, 343L,
406L, 159L, 270L, 223L, 337L, 88L, 141L, 469L, 100L, 441L, 300L,
290L, 445L, 46L, 415L, 294L, 294L, 110L, 12L, 229L, 97L, 138L,
263L, 249L, 265L, 385L, 405L, 47L, 205L, 350L, 416L, 348L, 476L,
254L, 57L, 15L, 427L, 255L, 428L, 122L, 109L, 60L, 403L, 256L,
10L, 371L, 112L, 112L, 419L, 419L, 83L, 317L, 317L, 277L, 277L,
187L, 228L, 375L, 374L, 139L), .Label = c("Aby Jeannette",
"Adelante",
"ADM Georgina", "ADS Galtesund", "Aeneas",
"Aeolian Fortune",
"Aeolian Light", "AFRICA GRAECA", "AFRICAN ARROW",
"AFRICAN BARI BIRD",
"AFRICAN BLUE CRANE", "AFRICAN FINFOOT", "AFRICAN
JACANA", "AFRICAN KITE",
"AFRICAN LEOPARD", "AFRICAN PUFFIN", "AFRICAN
RAPTOR", "AFTERHOURS",
"AGIA SKEPI", "Agri Kinsale", "Aiantas",
"AKILI", "ALAM MANIS",
"ALBION", "Alexandra", "ALICIA", "Alma",
"Alpha Vision", "AM BREMEN",
"AMAMI K", "AMIS ACE", "AMIS FORTUNE", "AMIS
JUSTICE", "AMSTEL FALCON",
"Andros", "ANDROS ISLAND", "Androusa",
"ANIMA", "Anna S", "Anna Smile",
"ANTIGONI", "Antiparos", "Aom Gaia", "AOM
GAIA", "Aom Milena",
"APEX", "AREQUIPA QUEEN", "Ariana",
"Artemis", "ASHIYA STAR",
"ASTRA CENTAURUS", "ASTREA", "Athina Carras",
"ATLANTIC EAGLE",
"ATLANTIC GRACE", "ATLANTIC HERO", "ATLANTIC
MANZANILLO", "Attalia",
"Axios", "Bahia Blanca", "Bali", "BALTIC
K", "BALTIC WASP", "BBG Ambition",
"BBG Dream", "BBG Endeavor", "Belo Horizonte",
"BELO HORIZONTE",
"BLUE AKIHABARA", "BLUE DIAMOND", "BLUE MARLIN I",
"Bora", "Brasil SW",
"Braveheart", "BRIDGEGATE", "BRIGITTE", "BTG
Denali", "BTG Eiger",
"BTG Everest", "BTG Kailach", "BULK ARGENTINA",
"BULK COLOMBIA",
"BULK HERO", "BULK HONDURAS", "Bulk Pegasus",
"Bulk Portugal",
"BW Hazel", "Captain Adams", "Captain Antonis",
"Cemtex Wisdom",
"CENTENARIO BLU", "Cepheus Ocean", "Cerafina",
"Cetus Ocean",
"CF Diamond", "CHARADE", "CHLOE", "CLARKE
QUAY", "CLIPPER AMSTERDAM",
"Clipper Victory", "CMB Sakura", "Cofco 1",
"COLUMBIA RIVER",
"Coral Diamond", "COREFORTUNE OL", "Cosmar",
"Coventry", "CP GUANGZHOU",
"Crimson Ark", "Crimson Kingdom", "Cymona Star",
"DALIAN STAR",
"De Xu Hai", "Densa Pelican", "DESERT CHALLENGER",
"DEVON BAY",
"DIAMOND QUEEN", "Dias", "Dimitris Apesakis",
"Donousa", "DORIC",
"DORIC SHOGUN", "DORO", "EASTER N", "Efrain
A", "Egret Oasis",
"Eirini P", "Elena", "Emerald Dongji",
"Emerald Star", "ENDLESS HORIZON",
"ENY", "Erikoussa", "ESSEX STRAIT", "Eternal
Bliss", "Eternal Grace",
"EUROPA BAY", "Ever Grace", "EVER SOVEREIGN",
"Everglory", "Evmar",
"FEDERAL TRIDENT", "FH Fang Cheng", "FH Rizhao",
"Fiji", "FILIA JOY",
"Flag Lama", "FLIPPER", "FLORINDA", "Fortune
Harmony", "FORTUNE LADY",
"FORTUNE UNITY", "FRAMURA", "FURNESS VICTORIA",
"Galio", "GANNET BULKER",
"GENCO RAPTOR", "GH CITATION", "GH URBAN SEA",
"Giorgakis", "Giorgis",
"GLOBAL PRIME", "GLOBAL SUCCESS", "GLOBAL VISION",
"Glory", "Golden Jake",
"GOLDEN LIBRA", "Good Wish", "Graecia Aeterna",
"GRAND CONCORD",
"GRAND MARCIA", "Great Rich", "GUARDIANSHIP",
"Hampton Bay",
"Hampton Bridge", "HANTON TRADER I", "Hercules
Ocean", "Hermes",
"Hong Hing", "Hong Jing", "Hong Sheng", "HOPA
I", "Huayang Spirit",
"Huayeng Dream", "Indian Harmony", "INDIGO
EVOLUTION", "INDIGO RIVER",
"INDRA OLDENDORFF", "Innovation", "INNOVATION",
"Inspiration",
"IRIS HALO", "IRIS OLDENDORFF", "ISMENE",
"Istria", "IYO WIND",
"Jag Aalok", "Jag Akshay", "Jag Arnav", "JIA
SHENG SHAN", "JIN RUN",
"Jin Zhu Hai", "John M. Carras", "JOSCO HANGZHOU",
"JPS AFRODITI",
"K SPINEL", "K. GARNET", "K. OPAL", "KANG
CHENG", "Karlovasi",
"Katerina III", "KAVO PALOMA", "Kea",
"Kerkyra", "Key Evolution",
"Key Pacifico", "KING ISLAND", "KING MILO",
"KM Fukuyama", "KM Hong Kong",
"KM Keelung", "KM Yokohama", "KMARIN SINGAPORE",
"KT Birdie",
"KYRA PANAGHIA", "Lady I", "LEO ADVANCE",
"LESEDI QUEEN", "LILA",
"LISSA TOPIC", "LOCH SHUNA", "Long Dar",
"LOUISIANA MAMA", "LOWLANDS
MAINE",
"LUMINOUS HALO", "LUNITA", "LYRIC HARMONY",
"Macheras", "MALMO",
"MANDARIN CROWN", "MANDARIN NOBLE", "Marathassa",
"MARIE GRACE",
"MARINER", "MARITIME PROSPERITY", "MARY LINA",
"Mastro Nikos",
"MBA Future", "Medi Matsuura", "MEDI SALERNO",
"MELBOURNE", "MELIA",
"METSOVO", "MG Explorer", "MG Kronos", "MG
Sakura", "Miao Xiang",
"MISATO K", "Mistral I", "Miyama",
"Mykonos", "Myra", "Myrto",
"N Bonanza", "Nadeshiko", "Naias", "NAUTICAL
MARIE", "NAUTICAL RUNA",
"NAUTICAL SIF", "Navios Amber", "NAVIOS ARC",
"NAVIOS ARMONIA",
"Navios Harmony", "Navios Orbiter", "NAVIOS SOUTHERN
STAR", "NEFELI",
"NEW BLISS", "NEW DIRECTION", "NEWSEAS PEARL",
"NIKKEI SIRIUS",
"NIKKEI VERDE", "Nikolaos", "NIKOLAS XL",
"Nikomarin", "Nord Capella",
"Nord Fortune", "NOSHIMA", "Nuri Bey",
"OCCITAN PAUILLAC", "OCEAN BAO",
"OCEAN BELT", "OCEAN FAVOUR", "Ocean Garlic",
"OCEAN HARVEST",
"OCEAN PRIDE", "OCEAN PRINCE", "OCEAN PRINCESS",
"OCEAN ROYAL",
"OCEAN SPLENDOR", "OCEAN TIANBAO", "OCEAN VENUS",
"Ocean Wind",
"Oceana", "Odysseas L", "OKINAWA", "Olivia
R", "OLYMPOS", "Omicron Light",
"OMICRON NIKOS", "OMICRON SKY", "Omicron Trader",
"ORCHID HALO",
"Orient Genesis", "ORIENT GRACE", "OZGUR AKSOY",
"PACIFIC ADVANCE",
"PACIFIC NEXUS", "PACIFIC TALENT", "PACIFIC
VICTORY", "Palais",
"Pan Ceres", "PAN VIVA", "Panafrican",
"Panamanian", "Panasiatic",
"PANORIA", "Panther Max", "PARADISE ISLAND",
"PAUL OLDENDORFF",
"Peace Ark", "PEAK PEGASUS", "Pedhoulas Farmer",
"Pedhoulas Trader",
"PENTA", "PERIDOT", "PERTH I",
"Phaedra", "PHOENIX K", "Phoenix Ocean",
"Pictor", "PILATUS VENTURE", "Popi S", "PORT
ESTRELA", "Proteas",
"QUEEN JHANSI", "QUEEN KOBE", "Rave", "RB
Eden", "Real Happiness",
"RECCO", "REGAL", "RESURGENCE", "RIGI
VENTURE", "Rosalia D? Amato",
"Rosco Banyan", "Rosco Cypress", "Rosco Ginkgo",
"Rosco Lemon",
"Rosco Litchi", "Rosco Palm", "ROSCO PLUM",
"Rosco Poplar", "Rosco
Sandalwood",
"RR Australia", "SAGAR JYOTI", "SAGAR SHAKTI",
"SAGARJEET", "SAGE
COLORADO",
"SAGE PIONEER", "SAILING SKY", "Sakizaya Power",
"SAN ANTONIO",
"SANTA KATARINA", "SANTA VALENTINA", "SANYU",
"SBI Bolero", "SBI BOLERO",
"SBI Samba", "Scarlet Cardinal", "SCARLET
CARDINAL", "Scarlet Falcon",
"Sea Duty", "Sea Hermes", "Sea Pegasus", "SEA
PIONEER", "Sea Pluto",
"Seatribute", "Shandong Fu Hui", "Shandong Hai
Chang", "Shangdong Fu Ze",
"Shao Shan 5", "Shao Shan 8", "SIFNOS",
"Silver Dragon", "SIMURGH",
"Skiathos", "SKY KNIGHT", "SONGA GLORY",
"SOUTHEND", "SPARNA",
"SPRING AEOLIAN", "SPRING EAGLE", "SPRING ZEPHYR",
"SSI CHALLENGER",
"Stalo", "STAMFORD EAGLE", "STAR AQUARIUS",
"STAR JENNIFER",
"Star Laura", "Star of Sawara", "STAR PISCES",
"Star Renee",
"STAR VANESSA", "STARRY SKY", "STH LONDON",
"STOVE FRIEND", "STOVE OCEAN",
"SUNLEAF GRACE", "SUNLEAF STAR", "SUNNY HOPE",
"SUNNY ROYAL",
"SUZAKU", "Syros I", "Tahiti One", "Tai
Promotion", "TAI PROSPERITY",
"TAI SPRING", "TAI STAR", "TAI SUMMIT",
"Tangerine Island", "TANGERINE
ISLAND",
"TANIKAZE", "TASSOS N", "Taurus Ocean", "TEAL
BULKER", "TENRO MARU",
"Tenten", "THEMISTOCLES", "Theodor Oldendorff",
"THEODOR OLDENDORFF",
"Theodore Jr.", "Theresa Hebei", "Theresa Jilin",
"Theresa Shandong",
"TIGER HENAN", "TIGER NORTH", "TIGER PIONEER",
"Tiger South",
"TN SUNRISE", "TOMORROW", "Topaz",
"TORENIA", "TR Lady", "Trade Unity",
"TRANS OCEANIC", "TRUSTN TRADER II",
"TSCHAIKOWSKY", "TTM DRAGON",
"Tuo Fu 6", "Tycoon", "ULTRA PANTHER",
"Unity", "UNITY DISCOVERY",
"Valadon", "VEGA ROSE", "VELA OCEAN",
"VENUS", "VENUS HALO",
"VICTORIA", "VISHVA ANAND", "Vitahorizon",
"Vitakosmos", "Vivian",
"VSC CASTOR", "VSC TRITON", "XING XI HAI",
"Yarrawonga", "Yue Guan Feng",
"ZEN-NOH GRAIN MAGNOLIA", "ZEN-NOH GRAIN PEGASUS",
"Zheng Zhi",
"Zhi He"), class = "factor"), Draft = c(12L, 12L, 12L, 13L,
12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 14L, 12L,
13L, 12L, 12L, 14L, 12L, 14L, 13L, 12L, 12L, 12L, 14L, 12L, 12L,
11L, 13L, 13L, 14L, 12L, 12L, 13L, 14L, 12L, 13L, 13L, 12L, 14L,
14L, 13L, 12L, 14L, 14L, 13L, 14L, 14L, 12L, 13L, 12L, 12L, 13L,
12L, 12L, 14L, 14L, 14L, 12L, 12L, 12L, 12L, 14L, 13L, 14L, 12L,
13L, 14L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 14L, 13L,
14L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 13L, 14L, 13L, 13L, 12L,
14L, 13L, 13L, 14L, 12L, 12L, 14L, 12L, 12L, 14L, 12L, 13L, 13L,
14L, 14L, 14L, 14L, 12L, 12L, 14L, 13L, 12L, 12L, 12L, 13L, 12L,
14L, 12L, 12L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 12L,
12L, 12L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 12L,
12L, 12L, 14L, 14L, 14L, 14L, 10L, 12L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 13L, 14L, 14L, 14L, 12L, 12L, 12L,
14L, 12L, 12L, 12L, 14L, 14L, 14L, 14L, 12L, 12L, 11L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 10L, 12L, 12L, 12L, 11L,
13L, 14L, 14L, 12L, 13L, 14L, 14L, 12L, 13L, 14L, 12L, 12L, 12L,
14L, 13L, 14L, 12L, 12L, 14L, 14L, 12L, 14L, 13L, 12L, 14L, 12L,
14L, 12L, 12L, 12L, 12L, 14L, 13L, 12L, 13L, 12L, 12L, 14L, 12L,
14L, 14L, 14L, 12L, 12L, 12L, 13L, 12L, 14L, 14L, 14L, 12L, 12L,
12L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 12L, 12L, 14L, 12L, 14L,
14L, 12L, 12L, 12L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 13L,
12L, 13L, 14L, 12L, 12L, 12L, 12L, 13L, 14L, 12L, 14L, 13L, 14L,
14L, 14L, 14L, 14L, 13L, 14L, 14L, 13L, 14L, 12L, 12L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 12L, 14L,
14L, 14L, 12L, 14L, 14L, 14L, 12L, 12L, 14L, 14L, 14L, 14L, 12L,
14L, 14L, 12L, 14L, 14L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 13L,
14L, 12L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 14L,
13L, 14L, 12L, 12L, 14L, 13L, 14L, 14L, 14L, 12L, 14L, 14L, 12L,
12L, 14L, 12L, 14L, 12L, 12L, 12L, 14L, 12L, 13L, 14L, 12L, 12L,
14L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 14L, 14L, 12L,
12L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 13L, 13L,
13L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 12L,
12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 13L, 13L, 11L, 12L, 14L,
14L, 12L, 14L, 12L, 11L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L,
12L, 14L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 12L, 14L, 12L,
13L, 14L, 12L, 12L, 14L, 14L, 12L, 12L, 12L, 13L, 12L, 14L, 14L,
14L, 12L, 14L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 6L,
12L, 12L, 14L, 14L, 14L, 14L, 12L, 14L, 12L, 12L, 12L, 12L, 14L,
12L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 13L, 14L, 12L, 13L, 12L,
14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L), TOTALCOST = c(194364L, 219364L, 198260L, 237456L,
197159L, 198992L, 194337L, 219337L, 199198L, 196604L, 230607L,
196604L, 196604L, 194496L, 238600L, 236936L, 237476L, 197220L,
236950L, 197300L, 182042L, 237938L, 199221L, 237475L, 239190L,
157406L, 157211L, 182211L, 237475L, 182475L, 181599L, 156599L,
238269L, 238402L, 238069L, 161436L, 225031L, 238180L, 237572L,
189861L, 239005L, 239049L, 163814L, 240064L, 239171L, 238410L,
200878L, 239019L, 239087L, 239350L, 239352L, 240275L, 164844L,
238400L, 225158L, 202495L, 239681L, 201791L, 226863L, 244092L,
244590L, 239171L, 189811L, 219412L, 228480L, 203650L, 237514L,
247451L, 244739L, 211770L, 244308L, 239197L, 238419L, 224977L,
157362L, 162434L, 162434L, 162434L, 162434L, 239681L, 163316L,
237265L, 243920L, 244088L, 244163L, 202256L, 159592L, 201346L,
239187L, 189800L, 191959L, 239476L, 239171L, 238087L, 238052L,
164169L, 245057L, 244215L, 240812L, 239156L, 156879L, 197853L,
245367L, 164710L, 164710L, 244192L, 211110L, 239156L, 244213L,
237504L, 239018L, 241150L, 244447L, 238506L, 210298L, 243482L,
239166L, 159489L, 184600L, 226439L, 239127L, 235243L, 244296L,
159696L, 189046L, 244355L, 244446L, 187595L, 162595L, 162595L,
162595L, 170604L, 188774L, 244103L, 188680L, 163611L, 200551L,
244055L, 170606L, 169154L, 194154L, 170905L, 200551L, 191412L,
166412L, 243969L, 170483L, 210719L, 168554L, 164016L, 245158L,
245131L, 245186L, 239166L, 116360L, 155698L, 155698L, 155698L,
155698L, 223827L, 191968L, 159650L, 189999L, 201193L, 201011L,
226218L, 201218L, 243970L, 244291L, 243993L, 243993L, 236035L,
236035L, 236035L, 244070L, 159692L, 194183L, 169110L, 241994L,
238216L, 238301L, 242948L, 169810L, 189280L, 164662L, 164156L,
189156L, 163989L, 163924L, 159577L, 159650L, 170566L, 170598L,
188975L, 189006L, 99983L, 191595L, 166907L, 228744L, 166621L,
243593L, 244001L, 239035L, 172934L, 238288L, 241665L, 241665L,
193991L, 238361L, 238361L, 164215L, 168867L, 194304L, 241732L,
237745L, 237911L, 195374L, 195374L, 244044L, 244044L, 169118L,
244040L, 244040L, 198518L, 244106L, 236206L, 244136L, 191390L,
164516L, 165137L, 232682L, 244021L, 244101L, 236136L, 244101L,
194181L, 169181L, 244058L, 212313L, 238240L, 242502L, 239175L,
166221L, 184500L, 170027L, 237701L, 211035L, 244050L, 243745L,
242782L, 164482L, 166341L, 189482L, 174552L, 244213L, 190960L,
184494L, 169116L, 239123L, 239121L, 165097L, 206396L, 241738L,
165622L, 242651L, 250331L, 178778L, 169133L, 238280L, 244044L,
193182L, 194156L, 194156L, 169156L, 196240L, 244060L, 244060L,
244060L, 196050L, 243546L, 243546L, 195500L, 195500L, 170389L,
195389L, 243549L, 243503L, 211398L, 243510L, 238436L, 238546L,
243907L, 243654L, 238709L, 238656L, 244171L, 244136L, 243215L,
243957L, 243957L, 164455L, 164455L, 243287L, 238203L, 243738L,
243266L, 243294L, 243548L, 243262L, 243262L, 237628L, 243266L,
243382L, 243927L, 243574L, 168364L, 243598L, 243596L, 243647L,
191094L, 243655L, 244550L, 243907L, 200636L, 210208L, 243632L,
243632L, 243367L, 243048L, 212125L, 244651L, 243357L, 202542L,
243778L, 243502L, 170036L, 237911L, 195234L, 195220L, 170220L,
239391L, 244397L, 244397L, 238631L, 225921L, 244034L, 244051L,
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"data.frame",
row.names = c(NA,
-527L))


Any help and/or guidance will be greatly appreciated,

Best regards,

Paul

	[[alternative HTML version deleted]]

______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Paul Bernal

2019-Apr-24 03:32 UTC

head link

[R] Unable to Understand Results of pglm function

Hi Dr. Mackay,

Thank you so much for your valuable feedback.

I wonder why, when  applying the summary function over the pglm model, it
shows infinite as value for the standard errors.

Is this something we should expect? Or is there something else that needs
to be adjusted, either in the dataset(in the dataset's structure) or in the
pglm parameters.

Best regards,

Paul

El mar., 23 de abril de 2019 9:55 p. m., Duncan Mackay <dulcalma at
bigpond.com>
escribi?:
> Hi Paul
>
> I think you may have too many IDs DATE for your model as you posted
>
> I converted your DATE into date format and named it df3
> str(df3)
> 'data.frame':   527 obs. of  11 variables:
>  $ TRANSIT     : int  1 1 1 0 1 1 1 1 1 1 ...
>  $ ID          : int  1 1 2 2 3 4 5 5 6 7 ...
>  $ DATE        : Factor w/ 377 levels
"1-Aug-17","1-Aug-18",..: 47 75 89
> 252 3 221 62 99 224 114 ...
>  $ SHIPNAME    : Factor w/ 482 levels "Aby Jeannette",..: 295 295
151 151
> 19 41 292 292 201 148 ...
>  $ Draft       : int  12 12 12 13 12 12 12 12 12 12 ...
>  $ TOTALCOST   : int  194364 219364 198260 237456 197159 198992 194337
> 219337 199198 196604 ...
>  $ BUNKER      : int  350 405 276 350 373 355 370 343 345 288 ...
>  $ CHARTERVALUE: int  14000 12825 10475 11850 13250 12100 11875 14500
> 12500 10500 ...
>  $ dt          : Date, format: "2018-03-15"
"2018-08-19" "2017-07-20"
> "2017-12-19" ...
>  $ dtym        : chr  "201803" "201808"
"201707" "201712" ...
>  $ dty         : chr  "2018" "2018" "2017"
"2017" ...
>
> Here are 2 results
>
> > model1 = pglm(TRANSIT~ Draft+TOTALCOST+BUNKER+CHARTERVALUE + dty,
> +      effect=c("time"),
> +      model=c("pooling"),
> +      family=binomial('logit'),
> +      index=c("ID"),
> +      start = NULL, data=df3)
> >
> > summary(model1)
> --------------------------------------------
> Maximum Likelihood estimation
> Newton-Raphson maximisation, 11 iterations
> Return code 2: successive function values within tolerance limit
> Log-Likelihood: -14.14988
> 6  free parameters
> Estimates:
>                Estimate Std. error t value Pr(> t)
> (Intercept)   1.023e+02        Inf       0       1
> Draft        -5.088e+00        Inf       0       1
> TOTALCOST    -1.708e-04        Inf       0       1
> BUNKER       -6.712e-03        Inf       0       1
> CHARTERVALUE  2.524e-04        Inf       0       1
> dty2018       2.215e+00        Inf       0       1
> --------------------------------------------
>
> > model1=pglm(TRANSIT~ Draft+TOTALCOST+BUNKER+CHARTERVALUE + dty,
> +      effect=c("time"),
> +      model=c("pooling"),
> +      family=binomial('logit'),
> +      index=c("ID","dty"),
> +      start = NULL, data=df3)
> Warning messages:
> 1: In pdata.frame(data, index) :
>   duplicate couples (id-time) in resulting pdata.frame
>  to find out which, use e.g. table(index(your_pdataframe), useNA =
"ifany")
> 2: In is.pbalanced.default(index[[1]], index[[2]]) :
>   duplicate couples (id-time)
>
> >
> > summary(model1)
> --------------------------------------------
> Maximum Likelihood estimation
> Newton-Raphson maximisation, 11 iterations
> Return code 2: successive function values within tolerance limit
> Log-Likelihood: -14.14988
> 6  free parameters
> Estimates:
>                Estimate Std. error t value Pr(> t)
> (Intercept)   1.023e+02        Inf       0       1
> Draft        -5.088e+00        Inf       0       1
> TOTALCOST    -1.708e-04        Inf       0       1
> BUNKER       -6.712e-03        Inf       0       1
> CHARTERVALUE  2.524e-04        Inf       0       1
> dty2018       2.215e+00        Inf       0       1
> --------------------------------------------
>
> I have no knowledge of the pglm package and was trying it out on your data
> without going through the help properly
> NBB your DATE column has several formats which do not help
>
> Regards
>
> Duncan
>
> Duncan Mackay
> Department of Agronomy and Soil Science
> University of New England
> Armidale NSW 2350
>
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Paul
> Bernal
> Sent: Wednesday, 24 April 2019 06:44
> To: yves.croissant at univ-reunion.fr
> Cc: r-help at r-project.org
> Subject: [R] Unable to Understand Results of pglm function
>
> Dear Yves,
>
> Hope you are doing great. I have been testing the pglm function from the
> pglm package, in order to fit a logit regression to a panel dataset, and I
> do not understand the results and/or errors produced by the function, so I
> want to be able to understand whether there is a problem with the structure
> of my dataset, or I am not using the function properly or if there is
> something else going on that I am ignoring. Also, I would like to know what
> the start argument is for, or at least an example of how to use it, since I
> don?t know how to properly apply it.
>
> Here the details of what I am using and under what environment settings:
> 1-R version: 3.5.3
> 2-packages called: plm and pglm
> 3-Running on a 64-bit Operating System
> 4-Windows 8
>
> Here is the code with the different things I have tried so far:
> > PGLM_Model11 <-
>
>
pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
> effect=c("twoways"), model=c("random"),
family=binomial('logit'),
> index=c("ID","DATE"), start = NULL, data=dataframe3)
> >
> > summary(PGLM_Model11)
> --------------------------------------------
> Maximum Likelihood estimation
> Newton-Raphson maximisation, 0 iterations
> Return code 100: Initial value out of range.
> --------------------------------------------
> >
> > PGLM_Model12 <-
>
>
pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
> effect=c("twoways"), model=c("pooling"),
family=binomial('logit'),
> index=c("ID","DATE"), start = NULL, data=dataframe3)
> >
> > summary(PGLM_Model12)
> --------------------------------------------
> Maximum Likelihood estimation
> Newton-Raphson maximisation, 11 iterations
> Return code 2: successive function values within tolerance limit
> Log-Likelihood: -14.95426
> 5  free parameters
> Estimates:
>                           Estimate Std. error t value Pr(> t)
> (Intercept)             93.9680425        Inf       0       1
> dataframe3$Draft        -5.3820652        Inf       0       1
> dataframe3$TOTALCOST    -0.0001689        Inf       0       1
> dataframe3$BUNKER        0.0072934        Inf       0       1
> dataframe3$CHARTERVALUE  0.0008862        Inf       0       1
> --------------------------------------------
> >
> > PGLM_Model13 <-
>
>
pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
> effect=c("twoways"), model=c("within"),
family=binomial('logit'),
> index=c("ID","DATE"), start = NULL, data=dataframe3)
> Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
> :
>   argument "start" is missing, with no default
> >
> > PGLM_Model14 <-
>
>
pglm(dataframe3$TRANSIT~dataframe3$Draft+dataframe3$TOTALCOST+dataframe3$BUNKER+dataframe3$CHARTERVALUE,
> effect=c("twoways"), model=c("between"),
family=binomial('logit'),
> index=c("ID","DATE"), start = NULL, data=dataframe3)
> Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start,
> :
>   argument "start" is missing, with no default
>
> Below the dput of the dataset I am using for your reference:
>
> > dput(dataframe3)
> structure(list(TRANSIT = c(1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
> 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,
> 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
> 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
> 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
> 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
> 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
> 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
> 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
> 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
> 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
> 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
> 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
> 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
> 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
> 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
> 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
> 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
> 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
> 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
> 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
> 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
> 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
> 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
> 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
> 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L), ID = c(1L, 1L, 2L, 2L, 3L, 4L, 5L, 5L,
> 6L, 7L, 7L, 7L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
> 17L, 18L, 19L, 20L, 21L, 21L, 22L, 23L, 24L, 24L, 25L, 26L, 27L,
> 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
> 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 48L, 49L, 50L, 51L, 52L,
> 53L, 54L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L,
> 65L, 66L, 67L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
> 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
> 90L, 91L, 92L, 93L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
> 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 108L, 109L, 110L,
> 111L, 112L, 113L, 114L, 115L, 115L, 115L, 115L, 116L, 117L, 118L,
> 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 128L,
> 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L,
> 139L, 140L, 140L, 141L, 142L, 143L, 144L, 145L, 145L, 146L, 146L,
> 147L, 148L, 149L, 149L, 150L, 150L, 150L, 151L, 152L, 153L, 154L,
> 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L,
> 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L,
> 177L, 178L, 179L, 180L, 181L, 182L, 182L, 183L, 184L, 185L, 186L,
> 187L, 188L, 189L, 190L, 191L, 192L, 192L, 193L, 193L, 194L, 195L,
> 196L, 197L, 198L, 199L, 199L, 200L, 201L, 202L, 203L, 204L, 205L,
> 206L, 207L, 208L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L,
> 216L, 217L, 218L, 218L, 219L, 220L, 221L, 222L, 222L, 223L, 224L,
> 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L,
> 236L, 237L, 238L, 239L, 240L, 241L, 241L, 241L, 242L, 243L, 244L,
> 245L, 246L, 247L, 247L, 248L, 248L, 249L, 249L, 250L, 251L, 252L,
> 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L,
> 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 273L,
> 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
> 285L, 286L, 287L, 288L, 288L, 289L, 290L, 291L, 292L, 293L, 294L,
> 295L, 296L, 297L, 298L, 299L, 300L, 301L, 301L, 302L, 303L, 304L,
> 305L, 306L, 307L, 308L, 308L, 309L, 309L, 309L, 310L, 311L, 312L,
> 313L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L,
> 323L, 324L, 325L, 326L, 327L, 327L, 328L, 329L, 330L, 331L, 332L,
> 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
> 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
> 354L, 354L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L,
> 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L,
> 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L,
> 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L,
> 396L, 397L, 398L, 399L, 400L, 401L, 402L, 402L, 403L, 404L, 405L,
> 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 413L, 414L, 415L,
> 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L,
> 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 434L, 435L, 436L,
> 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L,
> 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L,
> 459L, 460L, 461L, 462L, 463L, 464L, 464L, 465L, 465L, 466L, 467L,
> 467L, 468L, 468L, 469L, 470L, 471L, 472L, 473L), DATE = structure(c(47L,
> 75L, 89L, 252L, 3L, 221L, 62L, 99L, 224L, 114L, 154L, 151L, 52L,
> 9L, 342L, 320L, 370L, 149L, 252L, 112L, 147L, 346L, 231L, 371L,
> 331L, 171L, 30L, 119L, 366L, 58L, 61L, 103L, 269L, 313L, 373L,
> 195L, 116L, 376L, 323L, 189L, 245L, 270L, 76L, 258L, 265L, 347L,
> 178L, 376L, 278L, 311L, 281L, 260L, 203L, 275L, 101L, 150L, 234L,
> 161L, 231L, 257L, 367L, 254L, 210L, 67L, 21L, 96L, 241L, 331L,
> 351L, 223L, 309L, 319L, 256L, 12L, 43L, 27L, 28L, 133L, 101L,
> 266L, 16L, 359L, 370L, 318L, 237L, 78L, 213L, 113L, 337L, 199L,
> 94L, 330L, 314L, 271L, 328L, 1L, 348L, 244L, 302L, 374L, 208L,
> 40L, 357L, 232L, 179L, 286L, 193L, 248L, 250L, 284L, 274L, 321L,
> 289L, 138L, 80L, 253L, 283L, 164L, 133L, 212L, 339L, 59L, 305L,
> 49L, 162L, 266L, 326L, 11L, 4L, 82L, 65L, 188L, 192L, 334L, 33L,
> 177L, 221L, 346L, 148L, 86L, 24L, 5L, 89L, 57L, 37L, 338L, 191L,
> 68L, 218L, 79L, 235L, 254L, 338L, 361L, 4L, 135L, 143L, 123L,
> 55L, 23L, 18L, 20L, 202L, 128L, 127L, 122L, 156L, 269L, 321L,
> 276L, 352L, 22L, 7L, 199L, 333L, 145L, 92L, 136L, 311L, 342L,
> 294L, 325L, 71L, 29L, 25L, 173L, 154L, 85L, 118L, 121L, 44L,
> 107L, 140L, 151L, 175L, 102L, 108L, 63L, 25L, 51L, 329L, 334L,
> 345L, 153L, 282L, 304L, 324L, 193L, 367L, 341L, 39L, 231L, 209L,
> 335L, 321L, 276L, 102L, 91L, 282L, 362L, 68L, 344L, 253L, 98L,
> 338L, 84L, 251L, 64L, 161L, 227L, 139L, 334L, 365L, 202L, 374L,
> 159L, 21L, 317L, 42L, 343L, 349L, 292L, 84L, 226L, 194L, 256L,
> 228L, 336L, 293L, 288L, 155L, 56L, 207L, 89L, 324L, 163L, 157L,
> 117L, 260L, 341L, 47L, 97L, 320L, 102L, 312L, 348L, 137L, 38L,
> 27L, 243L, 229L, 123L, 99L, 125L, 54L, 349L, 354L, 290L, 170L,
> 233L, 308L, 164L, 15L, 142L, 152L, 352L, 306L, 186L, 299L, 289L,
> 327L, 377L, 255L, 369L, 377L, 272L, 285L, 320L, 324L, 358L, 6L,
> 70L, 278L, 364L, 278L, 361L, 360L, 316L, 300L, 350L, 368L, 259L,
> 315L, 374L, 247L, 161L, 318L, 353L, 332L, 190L, 340L, 344L, 291L,
> 207L, 14L, 372L, 246L, 270L, 344L, 87L, 324L, 295L, 172L, 377L,
> 257L, 24L, 330L, 167L, 209L, 212L, 236L, 280L, 281L, 268L, 48L,
> 264L, 53L, 355L, 206L, 115L, 111L, 140L, 50L, 313L, 187L, 375L,
> 375L, 336L, 217L, 162L, 371L, 239L, 261L, 334L, 371L, 158L, 320L,
> 350L, 176L, 10L, 309L, 9L, 330L, 204L, 216L, 166L, 363L, 44L,
> 301L, 279L, 73L, 83L, 328L, 36L, 72L, 35L, 99L, 169L, 321L, 220L,
> 34L, 215L, 308L, 244L, 88L, 127L, 334L, 14L, 144L, 60L, 69L,
> 181L, 123L, 45L, 314L, 37L, 258L, 245L, 250L, 242L, 361L, 9L,
> 132L, 191L, 7L, 165L, 296L, 186L, 356L, 342L, 197L, 136L, 122L,
> 126L, 193L, 310L, 200L, 311L, 344L, 355L, 297L, 106L, 46L, 238L,
> 311L, 160L, 262L, 129L, 168L, 120L, 211L, 90L, 41L, 319L, 32L,
> 131L, 110L, 185L, 222L, 298L, 201L, 143L, 13L, 273L, 229L, 182L,
> 76L, 95L, 253L, 88L, 307L, 354L, 198L, 64L, 286L, 267L, 124L,
> 21L, 26L, 257L, 19L, 242L, 341L, 240L, 174L, 249L, 322L, 8L,
> 109L, 17L, 134L, 93L, 183L, 158L, 245L, 205L, 130L, 31L, 287L,
> 271L, 277L, 327L, 184L, 263L, 2L, 196L, 60L, 186L, 303L, 50L,
> 250L, 141L, 166L, 219L, 248L, 156L, 230L, 350L, 329L, 146L, 313L,
> 66L, 315L, 77L, 225L, 105L, 180L, 104L, 219L, 80L, 190L, 156L,
> 81L, 74L, 25L, 100L, 214L), .Label = c("1-Aug-17",
"1-Aug-18",
> "1-Feb-18", "1-Jan-18", "1-Jul-17",
"1-Mar-18", "1-Nov-17", "1-Oct-17",
> "1-Sep-17", "10-Apr-18", "10-Aug-17",
"10-Dec-17", "10-Feb-18",
> "10-Jul-17", "10-Jul-18", "10-Mar-18",
"10-May-18", "10-Nov-17",
> "10-Oct-17", "10-Sep-17", "11-Apr-18",
"11-Aug-17", "11-Aug-18",
> "11-Dec-17", "11-Feb-18", "11-Jun-18",
"11-Mar-18", "11-Sep-17",
> "11-Sep-18", "12-Aug-17", "12-Dec-17",
"12-Jul-17", "12-Jul-18",
> "12-Mar-18", "12-May-18", "12-Oct-17",
"12-Sep-18", "13-Aug-18",
> "13-Dec-17", "13-Nov-17", "13-Oct-17",
"14-Jun-18", "14-Sep-17",
> "15-Dec-17", "15-Feb-18", "15-Jul-18",
"15-Mar-18", "15-May-18",
> "15-Sep-18", "16-Apr-18", "16-Dec-17",
"16-Sep-18", "17-Apr-18",
> "17-Aug-18", "17-Feb-18", "17-Jan-18",
"17-Jul-17", "17-Jul-18",
> "17-Jun-18", "17-Mar-18", "17-May-18",
"17-Nov-17", "17-Oct-17",
> "18-Apr-18", "18-Aug-18", "18-Dec-17",
"18-Feb-18", "18-Jul-17",
> "18-Jul-18", "18-Jun-18", "18-Mar-18",
"18-May-18", "18-Sep-17",
> "19-Apr-18", "19-Aug-18", "19-Jan-18",
"19-Jul-17", "19-May-18",
> "19-Sep-17", "2-Aug-18", "2-Jun-18",
"2-May-18", "2-Oct-17",
> "2-Sep-17", "2-Sep-18", "20-Aug-17",
"20-Dec-17", "20-Feb-18",
> "20-Jul-17", "20-Jul-18", "20-Jun-18",
"20-Oct-17", "20-Sep-18",
> "21-Apr-18", "21-Aug-17", "21-Dec-17",
"21-Feb-18", "21-Jan-18",
> "21-Mar-18", "21-Nov-17", "21-Oct-17",
"21-Sep-17", "21-Sep-18",
> "22-Apr-18", "22-Aug-17", "22-Feb-18",
"22-Jul-17", "22-May-18",
> "22-Nov-17", "23-Aug-17", "23-Aug-18",
"23-Dec-17", "23-Feb-18",
> "23-Jul-17", "23-Nov-17", "23-Sep-18",
"24-Aug-18", "24-Dec-17",
> "24-Jan-18", "24-Jul-17", "24-May-18",
"24-Nov-17", "24-Oct-17",
> "25-Apr-18", "25-Aug-18", "25-Jul-17",
"25-May-18", "25-Nov-17",
> "25-Oct-17", "25-Sep-17", "25-Sep-18",
"26-Apr-18", "26-Aug-18",
> "26-Jan-18", "26-Jul-17", "26-Jul-18",
"26-Mar-18", "26-May-18",
> "27-Apr-18", "27-Aug-17", "27-Aug-18",
"27-Dec-17", "27-Jul-18",
> "27-Nov-17", "27-Sep-18", "28-Aug-17",
"28-Dec-17", "28-Feb-18",
> "28-Jul-17", "28-Jun-18", "28-Mar-18",
"28-May-18", "28-Nov-17",
> "28-Oct-17", "28-Sep-17", "29-Aug-18",
"29-Dec-17", "29-Jan-18",
> "29-Jul-17", "29-Jul-18", "29-Jun-18",
"29-Mar-18", "29-Nov-17",
> "29-Oct-17", "29-Sep-17", "3-Apr-18",
"3-Aug-17", "3-Dec-17",
> "3-Jan-18", "3-Jul-17", "3-May-18",
"3-Nov-17", "3-Sep-17", "3-Sep-18",
> "30-Apr-18", "30-Aug-18", "30-Jan-18",
"30-Jul-17", "30-Jun-18",
> "30-Mar-18", "30-May-18", "30-Sep-18",
"31-Aug-17", "31-Dec-17",
> "31-Jan-18", "31-Jul-17", "31-Jul-18",
"31-May-18", "31-Oct-17",
> "4-Dec-17", "4-Feb-18", "4-Jan-18",
"4-Mar-18", "4-Nov-17", "4-Oct-17",
> "4-Sep-18", "5-Aug-17", "5-Dec-17",
"5-Feb-18", "5-Jan-18", "5-Jul-17",
> "5-Mar-18", "5-May-18", "5-Nov-17",
"5-Sep-17", "6-Aug-17", "6-Jun-18",
> "6-Mar-18", "6-Nov-17", "6-Sep-17",
"6-Sep-18", "7-Apr-18", "7-Aug-17",
> "7-Feb-18", "7-Jan-18", "7-Jul-17",
"7-Jul-18", "7-Sep-17", "8-Apr-18",
> "8-Aug-18", "8-Dec-17", "8-Mar-18",
"8-May-18", "8-Nov-17", "8-Sep-18",
> "9-Apr-18", "9-Aug-17", "9-Aug-18",
"9-Feb-18", "9-Mar-18", "9-Nov-17",
> "9-Oct-17", "April 23 2018", "April 5 2018",
"August 14 2017",
> "August 15 2017", "August 24 2017", "August 25
2017", "August 26 2017",
> "August 30 2017", "August 6 2017", "August 7
2017", "August 8 2017",
> "December 1 2017", "December 10 2017", "December
11 2017", "December 12
> 2017",
> "December 13 2017", "December 14 2017", "December
15 2017", "December 18
> 2017",
> "December 19 2017", "December 21 2017", "December
22 2017", "December 24
> 2017",
> "December 27 2017", "December 28 2017", "December
29 2017", "December 3
> 2017",
> "December 30 2017", "December 4 2017", "December 5
2017", "December 6
> 2017",
> "February 1 2018", "February 10 2018", "February
12 2018", "February 13
> 2018",
> "February 15 2018", "February 16 2018", "February
19 2018", "February 20
> 2018",
> "February 25 2018", "February 28 2018", "February
3 2018", "February 4
> 2017",
> "February 5 2018", "February 8 2018", "January 1
2018", "January 10 2018",
> "January 11 2018", "January 13 2018", "January 14
2018", "January 15 2018",
> "January 20 2018", "January 23 2018", "January 24
2018", "January 26 2018",
> "January 29 2018", "January 3 2018", "January 30
2018", "January 31 2018",
> "January 4 2018", "January 6 2018", "January 7
2018", "January 8 2018",
> "January 9 2018", "July 13 2018", "July 30
2017", "June 17 2018",
> "June 8 2018", "March 10 2018", "March 13
2018", "March 18 2018",
> "March 22 2018", "March 24 2018", "March 28
2018", "March 3 2018",
> "November 1 2017", "November 10 2017", "November
11 2017", "November 12
> 2017",
> "November 13 2017", "November 15 2017", "November
17 2017", "November 18
> 2017",
> "November 19 2017", "November 21 2017", "November
22 2017", "November 23
> 2017",
> "November 25 2017", "November 27 2017", "November
28 2017", "November 3
> 2017",
> "November 4 2017", "November 5 2017", "November 6
2017", "November 7 2017",
> "November 8 2017", "November 9 2017", "October 1
2017", "October 10 2017",
> "October 11 2017", "October 12 2017", "October 14
2017", "October 15 2017",
> "October 16 2017", "October 17 2017", "October 18
2017", "October 19 2017",
> "October 20 2017", "October 21 2017", "October 23
2017", "October 25 2017",
> "October 26 2017", "October 27 2017", "October 28
2017", "October 29 2017",
> "October 3 2017", "October 30 2017", "October 31
2017", "October 4 2017",
> "October 5 2017", "October 6 2017", "October 7
2017", "October 9 2017",
> "September 1 2017", "September 10 2017",
"September 11 2017",
> "September 12 2017", "September 13 2017",
"September 15 2017",
> "September 16 2017", "September 17 2017",
"September 19 2017",
> "September 21 2017", "September 22 2017",
"September 24 2017",
> "September 26 2017", "September 27 2017",
"September 29 2017",
> "September 3 2017", "September 30 2017",
"September 5 2017",
> "September 6 2017", "September 7 2017", "September
8 2017", "September 9
> 2017"
> ), class = "factor"), SHIPNAME = structure(c(295L, 295L, 151L,
> 151L, 19L, 41L, 292L, 292L, 201L, 148L, 148L, 148L, 148L, 413L,
> 39L, 74L, 460L, 54L, 462L, 8L, 22L, 347L, 307L, 354L, 311L, 296L,
> 297L, 297L, 118L, 279L, 230L, 230L, 340L, 358L, 473L, 271L, 309L,
> 451L, 40L, 404L, 120L, 127L, 209L, 90L, 274L, 260L, 252L, 344L,
> 165L, 363L, 356L, 425L, 192L, 133L, 56L, 440L, 439L, 276L, 361L,
> 333L, 273L, 308L, 235L, 235L, 426L, 234L, 93L, 111L, 325L, 283L,
> 107L, 48L, 101L, 212L, 246L, 400L, 338L, 338L, 422L, 20L, 369L,
> 471L, 7L, 409L, 412L, 310L, 70L, 157L, 357L, 103L, 452L, 49L,
> 349L, 4L, 226L, 465L, 362L, 128L, 264L, 136L, 50L, 18L, 323L,
> 11L, 11L, 25L, 408L, 302L, 180L, 394L, 113L, 434L, 477L, 461L,
> 305L, 174L, 104L, 152L, 132L, 291L, 410L, 250L, 382L, 351L, 23L,
> 119L, 284L, 480L, 480L, 480L, 480L, 457L, 272L, 262L, 81L, 346L,
> 239L, 58L, 149L, 402L, 373L, 82L, 251L, 244L, 244L, 135L, 24L,
> 345L, 156L, 227L, 324L, 215L, 222L, 286L, 55L, 281L, 281L, 280L,
> 280L, 322L, 393L, 243L, 34L, 418L, 418L, 334L, 334L, 221L, 220L,
> 6L, 6L, 479L, 479L, 479L, 166L, 196L, 298L, 71L, 160L, 282L,
> 213L, 147L, 315L, 433L, 458L, 207L, 208L, 186L, 91L, 326L, 466L,
> 421L, 420L, 98L, 399L, 289L, 134L, 123L, 194L, 173L, 248L, 64L,
> 202L, 206L, 95L, 396L, 396L, 131L, 211L, 391L, 38L, 84L, 455L,
> 144L, 168L, 389L, 398L, 398L, 35L, 35L, 367L, 359L, 360L, 105L,
> 73L, 431L, 430L, 372L, 62L, 312L, 470L, 263L, 86L, 275L, 219L,
> 414L, 96L, 125L, 365L, 478L, 342L, 45L, 241L, 75L, 121L, 355L,
> 380L, 379L, 216L, 191L, 417L, 395L, 395L, 31L, 210L, 467L, 146L,
> 397L, 179L, 181L, 29L, 171L, 482L, 240L, 288L, 330L, 368L, 287L,
> 401L, 321L, 217L, 233L, 233L, 233L, 366L, 247L, 89L, 472L, 336L,
> 364L, 364L, 124L, 124L, 163L, 163L, 5L, 37L, 237L, 332L, 183L,
> 184L, 444L, 442L, 339L, 126L, 293L, 232L, 150L, 203L, 53L, 475L,
> 468L, 327L, 172L, 481L, 61L, 424L, 2L, 28L, 28L, 224L, 304L,
> 423L, 66L, 384L, 335L, 387L, 42L, 195L, 200L, 383L, 114L, 443L,
> 301L, 68L, 67L, 72L, 214L, 386L, 352L, 381L, 65L, 218L, 266L,
> 102L, 51L, 178L, 30L, 137L, 137L, 175L, 161L, 1L, 448L, 446L,
> 3L, 190L, 189L, 278L, 278L, 278L, 299L, 116L, 143L, 44L, 43L,
> 130L, 285L, 328L, 170L, 185L, 87L, 140L, 437L, 145L, 245L, 155L,
> 261L, 258L, 331L, 85L, 16L, 257L, 204L, 13L, 154L, 459L, 117L,
> 94L, 320L, 225L, 314L, 259L, 14L, 456L, 162L, 142L, 26L, 303L,
> 432L, 231L, 435L, 392L, 313L, 370L, 474L, 464L, 450L, 450L, 450L,
> 450L, 438L, 182L, 236L, 92L, 164L, 79L, 80L, 77L, 169L, 177L,
> 153L, 176L, 329L, 353L, 341L, 454L, 69L, 238L, 242L, 269L, 268L,
> 267L, 115L, 108L, 199L, 52L, 27L, 59L, 198L, 197L, 253L, 436L,
> 306L, 106L, 447L, 378L, 316L, 318L, 99L, 407L, 411L, 36L, 453L,
> 167L, 63L, 158L, 188L, 377L, 376L, 32L, 193L, 463L, 129L, 429L,
> 9L, 17L, 449L, 21L, 76L, 78L, 78L, 319L, 33L, 390L, 388L, 343L,
> 406L, 159L, 270L, 223L, 337L, 88L, 141L, 469L, 100L, 441L, 300L,
> 290L, 445L, 46L, 415L, 294L, 294L, 110L, 12L, 229L, 97L, 138L,
> 263L, 249L, 265L, 385L, 405L, 47L, 205L, 350L, 416L, 348L, 476L,
> 254L, 57L, 15L, 427L, 255L, 428L, 122L, 109L, 60L, 403L, 256L,
> 10L, 371L, 112L, 112L, 419L, 419L, 83L, 317L, 317L, 277L, 277L,
> 187L, 228L, 375L, 374L, 139L), .Label = c("Aby Jeannette",
"Adelante",
> "ADM Georgina", "ADS Galtesund", "Aeneas",
"Aeolian Fortune",
> "Aeolian Light", "AFRICA GRAECA", "AFRICAN
ARROW", "AFRICAN BARI BIRD",
> "AFRICAN BLUE CRANE", "AFRICAN FINFOOT", "AFRICAN
JACANA", "AFRICAN KITE",
> "AFRICAN LEOPARD", "AFRICAN PUFFIN", "AFRICAN
RAPTOR", "AFTERHOURS",
> "AGIA SKEPI", "Agri Kinsale", "Aiantas",
"AKILI", "ALAM MANIS",
> "ALBION", "Alexandra", "ALICIA",
"Alma", "Alpha Vision", "AM BREMEN",
> "AMAMI K", "AMIS ACE", "AMIS FORTUNE",
"AMIS JUSTICE", "AMSTEL FALCON",
> "Andros", "ANDROS ISLAND", "Androusa",
"ANIMA", "Anna S", "Anna Smile",
> "ANTIGONI", "Antiparos", "Aom Gaia",
"AOM GAIA", "Aom Milena",
> "APEX", "AREQUIPA QUEEN", "Ariana",
"Artemis", "ASHIYA STAR",
> "ASTRA CENTAURUS", "ASTREA", "Athina Carras",
"ATLANTIC EAGLE",
> "ATLANTIC GRACE", "ATLANTIC HERO", "ATLANTIC
MANZANILLO", "Attalia",
> "Axios", "Bahia Blanca", "Bali", "BALTIC
K", "BALTIC WASP", "BBG Ambition",
> "BBG Dream", "BBG Endeavor", "Belo
Horizonte", "BELO HORIZONTE",
> "BLUE AKIHABARA", "BLUE DIAMOND", "BLUE MARLIN
I", "Bora", "Brasil SW",
> "Braveheart", "BRIDGEGATE", "BRIGITTE",
"BTG Denali", "BTG Eiger",
> "BTG Everest", "BTG Kailach", "BULK
ARGENTINA", "BULK COLOMBIA",
> "BULK HERO", "BULK HONDURAS", "Bulk Pegasus",
"Bulk Portugal",
> "BW Hazel", "Captain Adams", "Captain
Antonis", "Cemtex Wisdom",
> "CENTENARIO BLU", "Cepheus Ocean",
"Cerafina", "Cetus Ocean",
> "CF Diamond", "CHARADE", "CHLOE",
"CLARKE QUAY", "CLIPPER AMSTERDAM",
> "Clipper Victory", "CMB Sakura", "Cofco 1",
"COLUMBIA RIVER",
> "Coral Diamond", "COREFORTUNE OL", "Cosmar",
"Coventry", "CP GUANGZHOU",
> "Crimson Ark", "Crimson Kingdom", "Cymona
Star", "DALIAN STAR",
> "De Xu Hai", "Densa Pelican", "DESERT
CHALLENGER", "DEVON BAY",
> "DIAMOND QUEEN", "Dias", "Dimitris Apesakis",
"Donousa", "DORIC",
> "DORIC SHOGUN", "DORO", "EASTER N",
"Efrain A", "Egret Oasis",
> "Eirini P", "Elena", "Emerald Dongji",
"Emerald Star", "ENDLESS HORIZON",
> "ENY", "Erikoussa", "ESSEX STRAIT",
"Eternal Bliss", "Eternal Grace",
> "EUROPA BAY", "Ever Grace", "EVER SOVEREIGN",
"Everglory", "Evmar",
> "FEDERAL TRIDENT", "FH Fang Cheng", "FH
Rizhao", "Fiji", "FILIA JOY",
> "Flag Lama", "FLIPPER", "FLORINDA",
"Fortune Harmony", "FORTUNE LADY",
> "FORTUNE UNITY", "FRAMURA", "FURNESS
VICTORIA", "Galio", "GANNET BULKER",
> "GENCO RAPTOR", "GH CITATION", "GH URBAN
SEA", "Giorgakis", "Giorgis",
> "GLOBAL PRIME", "GLOBAL SUCCESS", "GLOBAL
VISION", "Glory", "Golden Jake",
> "GOLDEN LIBRA", "Good Wish", "Graecia
Aeterna", "GRAND CONCORD",
> "GRAND MARCIA", "Great Rich", "GUARDIANSHIP",
"Hampton Bay",
> "Hampton Bridge", "HANTON TRADER I", "Hercules
Ocean", "Hermes",
> "Hong Hing", "Hong Jing", "Hong Sheng",
"HOPA I", "Huayang Spirit",
> "Huayeng Dream", "Indian Harmony", "INDIGO
EVOLUTION", "INDIGO RIVER",
> "INDRA OLDENDORFF", "Innovation",
"INNOVATION", "Inspiration",
> "IRIS HALO", "IRIS OLDENDORFF", "ISMENE",
"Istria", "IYO WIND",
> "Jag Aalok", "Jag Akshay", "Jag Arnav",
"JIA SHENG SHAN", "JIN RUN",
> "Jin Zhu Hai", "John M. Carras", "JOSCO
HANGZHOU", "JPS AFRODITI",
> "K SPINEL", "K. GARNET", "K. OPAL",
"KANG CHENG", "Karlovasi",
> "Katerina III", "KAVO PALOMA", "Kea",
"Kerkyra", "Key Evolution",
> "Key Pacifico", "KING ISLAND", "KING MILO",
"KM Fukuyama", "KM Hong Kong",
> "KM Keelung", "KM Yokohama", "KMARIN
SINGAPORE", "KT Birdie",
> "KYRA PANAGHIA", "Lady I", "LEO ADVANCE",
"LESEDI QUEEN", "LILA",
> "LISSA TOPIC", "LOCH SHUNA", "Long Dar",
"LOUISIANA MAMA", "LOWLANDS
> MAINE",
> "LUMINOUS HALO", "LUNITA", "LYRIC HARMONY",
"Macheras", "MALMO",
> "MANDARIN CROWN", "MANDARIN NOBLE",
"Marathassa", "MARIE GRACE",
> "MARINER", "MARITIME PROSPERITY", "MARY
LINA", "Mastro Nikos",
> "MBA Future", "Medi Matsuura", "MEDI
SALERNO", "MELBOURNE", "MELIA",
> "METSOVO", "MG Explorer", "MG Kronos",
"MG Sakura", "Miao Xiang",
> "MISATO K", "Mistral I", "Miyama",
"Mykonos", "Myra", "Myrto",
> "N Bonanza", "Nadeshiko", "Naias",
"NAUTICAL MARIE", "NAUTICAL RUNA",
> "NAUTICAL SIF", "Navios Amber", "NAVIOS ARC",
"NAVIOS ARMONIA",
> "Navios Harmony", "Navios Orbiter", "NAVIOS
SOUTHERN STAR", "NEFELI",
> "NEW BLISS", "NEW DIRECTION", "NEWSEAS
PEARL", "NIKKEI SIRIUS",
> "NIKKEI VERDE", "Nikolaos", "NIKOLAS XL",
"Nikomarin", "Nord Capella",
> "Nord Fortune", "NOSHIMA", "Nuri Bey",
"OCCITAN PAUILLAC", "OCEAN BAO",
> "OCEAN BELT", "OCEAN FAVOUR", "Ocean Garlic",
"OCEAN HARVEST",
> "OCEAN PRIDE", "OCEAN PRINCE", "OCEAN
PRINCESS", "OCEAN ROYAL",
> "OCEAN SPLENDOR", "OCEAN TIANBAO", "OCEAN
VENUS", "Ocean Wind",
> "Oceana", "Odysseas L", "OKINAWA",
"Olivia R", "OLYMPOS", "Omicron Light",
> "OMICRON NIKOS", "OMICRON SKY", "Omicron
Trader", "ORCHID HALO",
> "Orient Genesis", "ORIENT GRACE", "OZGUR
AKSOY", "PACIFIC ADVANCE",
> "PACIFIC NEXUS", "PACIFIC TALENT", "PACIFIC
VICTORY", "Palais",
> "Pan Ceres", "PAN VIVA", "Panafrican",
"Panamanian", "Panasiatic",
> "PANORIA", "Panther Max", "PARADISE ISLAND",
"PAUL OLDENDORFF",
> "Peace Ark", "PEAK PEGASUS", "Pedhoulas
Farmer", "Pedhoulas Trader",
> "PENTA", "PERIDOT", "PERTH I",
"Phaedra", "PHOENIX K", "Phoenix Ocean",
> "Pictor", "PILATUS VENTURE", "Popi S",
"PORT ESTRELA", "Proteas",
> "QUEEN JHANSI", "QUEEN KOBE", "Rave",
"RB Eden", "Real Happiness",
> "RECCO", "REGAL", "RESURGENCE", "RIGI
VENTURE", "Rosalia D? Amato",
> "Rosco Banyan", "Rosco Cypress", "Rosco
Ginkgo", "Rosco Lemon",
> "Rosco Litchi", "Rosco Palm", "ROSCO PLUM",
"Rosco Poplar", "Rosco
> Sandalwood",
> "RR Australia", "SAGAR JYOTI", "SAGAR
SHAKTI", "SAGARJEET", "SAGE
> COLORADO",
> "SAGE PIONEER", "SAILING SKY", "Sakizaya
Power", "SAN ANTONIO",
> "SANTA KATARINA", "SANTA VALENTINA", "SANYU",
"SBI Bolero", "SBI BOLERO",
> "SBI Samba", "Scarlet Cardinal", "SCARLET
CARDINAL", "Scarlet Falcon",
> "Sea Duty", "Sea Hermes", "Sea Pegasus",
"SEA PIONEER", "Sea Pluto",
> "Seatribute", "Shandong Fu Hui", "Shandong Hai
Chang", "Shangdong Fu Ze",
> "Shao Shan 5", "Shao Shan 8", "SIFNOS",
"Silver Dragon", "SIMURGH",
> "Skiathos", "SKY KNIGHT", "SONGA GLORY",
"SOUTHEND", "SPARNA",
> "SPRING AEOLIAN", "SPRING EAGLE", "SPRING
ZEPHYR", "SSI CHALLENGER",
> "Stalo", "STAMFORD EAGLE", "STAR AQUARIUS",
"STAR JENNIFER",
> "Star Laura", "Star of Sawara", "STAR
PISCES", "Star Renee",
> "STAR VANESSA", "STARRY SKY", "STH LONDON",
"STOVE FRIEND", "STOVE OCEAN",
> "SUNLEAF GRACE", "SUNLEAF STAR", "SUNNY
HOPE", "SUNNY ROYAL",
> "SUZAKU", "Syros I", "Tahiti One", "Tai
Promotion", "TAI PROSPERITY",
> "TAI SPRING", "TAI STAR", "TAI SUMMIT",
"Tangerine Island", "TANGERINE
> ISLAND",
> "TANIKAZE", "TASSOS N", "Taurus Ocean",
"TEAL BULKER", "TENRO MARU",
> "Tenten", "THEMISTOCLES", "Theodor
Oldendorff", "THEODOR OLDENDORFF",
> "Theodore Jr.", "Theresa Hebei", "Theresa
Jilin", "Theresa Shandong",
> "TIGER HENAN", "TIGER NORTH", "TIGER
PIONEER", "Tiger South",
> "TN SUNRISE", "TOMORROW", "Topaz",
"TORENIA", "TR Lady", "Trade Unity",
> "TRANS OCEANIC", "TRUSTN TRADER II",
"TSCHAIKOWSKY", "TTM DRAGON",
> "Tuo Fu 6", "Tycoon", "ULTRA PANTHER",
"Unity", "UNITY DISCOVERY",
> "Valadon", "VEGA ROSE", "VELA OCEAN",
"VENUS", "VENUS HALO",
> "VICTORIA", "VISHVA ANAND", "Vitahorizon",
"Vitakosmos", "Vivian",
> "VSC CASTOR", "VSC TRITON", "XING XI HAI",
"Yarrawonga", "Yue Guan Feng",
> "ZEN-NOH GRAIN MAGNOLIA", "ZEN-NOH GRAIN PEGASUS",
"Zheng Zhi",
> "Zhi He"), class = "factor"), Draft = c(12L, 12L, 12L,
13L, 12L,
> 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 14L, 12L,
> 13L, 12L, 12L, 14L, 12L, 14L, 13L, 12L, 12L, 12L, 14L, 12L, 12L,
> 11L, 13L, 13L, 14L, 12L, 12L, 13L, 14L, 12L, 13L, 13L, 12L, 14L,
> 14L, 13L, 12L, 14L, 14L, 13L, 14L, 14L, 12L, 13L, 12L, 12L, 13L,
> 12L, 12L, 14L, 14L, 14L, 12L, 12L, 12L, 12L, 14L, 13L, 14L, 12L,
> 13L, 14L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 14L, 13L,
> 14L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 13L, 14L, 13L, 13L, 12L,
> 14L, 13L, 13L, 14L, 12L, 12L, 14L, 12L, 12L, 14L, 12L, 13L, 13L,
> 14L, 14L, 14L, 14L, 12L, 12L, 14L, 13L, 12L, 12L, 12L, 13L, 12L,
> 14L, 12L, 12L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 12L,
> 12L, 12L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 12L,
> 12L, 12L, 14L, 14L, 14L, 14L, 10L, 12L, 11L, 12L, 12L, 12L, 12L,
> 12L, 12L, 12L, 12L, 12L, 12L, 13L, 14L, 14L, 14L, 12L, 12L, 12L,
> 14L, 12L, 12L, 12L, 14L, 14L, 14L, 14L, 12L, 12L, 11L, 12L, 12L,
> 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 10L, 12L, 12L, 12L, 11L,
> 13L, 14L, 14L, 12L, 13L, 14L, 14L, 12L, 13L, 14L, 12L, 12L, 12L,
> 14L, 13L, 14L, 12L, 12L, 14L, 14L, 12L, 14L, 13L, 12L, 14L, 12L,
> 14L, 12L, 12L, 12L, 12L, 14L, 13L, 12L, 13L, 12L, 12L, 14L, 12L,
> 14L, 14L, 14L, 12L, 12L, 12L, 13L, 12L, 14L, 14L, 14L, 12L, 12L,
> 12L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 12L, 12L, 14L, 12L, 14L,
> 14L, 12L, 12L, 12L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 13L,
> 12L, 13L, 14L, 12L, 12L, 12L, 12L, 13L, 14L, 12L, 14L, 13L, 14L,
> 14L, 14L, 14L, 14L, 13L, 14L, 14L, 13L, 14L, 12L, 12L, 14L, 14L,
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 12L, 14L,
> 14L, 14L, 12L, 14L, 14L, 14L, 12L, 12L, 14L, 14L, 14L, 14L, 12L,
> 14L, 14L, 12L, 14L, 14L, 12L, 13L, 12L, 12L, 12L, 14L, 14L, 13L,
> 14L, 12L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 14L,
> 13L, 14L, 12L, 12L, 14L, 13L, 14L, 14L, 14L, 12L, 14L, 14L, 12L,
> 12L, 14L, 12L, 14L, 12L, 12L, 12L, 14L, 12L, 13L, 14L, 12L, 12L,
> 14L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 14L, 14L, 12L,
> 12L, 14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 12L, 13L, 13L,
> 13L, 14L, 14L, 12L, 12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 12L,
> 12L, 12L, 12L, 12L, 13L, 12L, 14L, 13L, 13L, 13L, 11L, 12L, 14L,
> 14L, 12L, 14L, 12L, 11L, 12L, 12L, 12L, 12L, 14L, 12L, 12L, 12L,
> 12L, 14L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 12L, 12L, 14L, 12L,
> 13L, 14L, 12L, 12L, 14L, 14L, 12L, 12L, 12L, 13L, 12L, 14L, 14L,
> 14L, 12L, 14L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 6L,
> 12L, 12L, 14L, 14L, 14L, 14L, 12L, 14L, 12L, 12L, 12L, 12L, 14L,
> 12L, 14L, 12L, 12L, 12L, 14L, 12L, 12L, 13L, 14L, 12L, 13L, 12L,
> 14L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
> 12L, 12L), TOTALCOST = c(194364L, 219364L, 198260L, 237456L,
> 197159L, 198992L, 194337L, 219337L, 199198L, 196604L, 230607L,
> 196604L, 196604L, 194496L, 238600L, 236936L, 237476L, 197220L,
> 236950L, 197300L, 182042L, 237938L, 199221L, 237475L, 239190L,
> 157406L, 157211L, 182211L, 237475L, 182475L, 181599L, 156599L,
> 238269L, 238402L, 238069L, 161436L, 225031L, 238180L, 237572L,
> 189861L, 239005L, 239049L, 163814L, 240064L, 239171L, 238410L,
> 200878L, 239019L, 239087L, 239350L, 239352L, 240275L, 164844L,
> 238400L, 225158L, 202495L, 239681L, 201791L, 226863L, 244092L,
> 244590L, 239171L, 189811L, 219412L, 228480L, 203650L, 237514L,
> 247451L, 244739L, 211770L, 244308L, 239197L, 238419L, 224977L,
> 157362L, 162434L, 162434L, 162434L, 162434L, 239681L, 163316L,
> 237265L, 243920L, 244088L, 244163L, 202256L, 159592L, 201346L,
> 239187L, 189800L, 191959L, 239476L, 239171L, 238087L, 238052L,
> 164169L, 245057L, 244215L, 240812L, 239156L, 156879L, 197853L,
> 245367L, 164710L, 164710L, 244192L, 211110L, 239156L, 244213L,
> 237504L, 239018L, 241150L, 244447L, 238506L, 210298L, 243482L,
> 239166L, 159489L, 184600L, 226439L, 239127L, 235243L, 244296L,
> 159696L, 189046L, 244355L, 244446L, 187595L, 162595L, 162595L,
> 162595L, 170604L, 188774L, 244103L, 188680L, 163611L, 200551L,
> 244055L, 170606L, 169154L, 194154L, 170905L, 200551L, 191412L,
> 166412L, 243969L, 170483L, 210719L, 168554L, 164016L, 245158L,
> 245131L, 245186L, 239166L, 116360L, 155698L, 155698L, 155698L,
> 155698L, 223827L, 191968L, 159650L, 189999L, 201193L, 201011L,
> 226218L, 201218L, 243970L, 244291L, 243993L, 243993L, 236035L,
> 236035L, 236035L, 244070L, 159692L, 194183L, 169110L, 241994L,
> 238216L, 238301L, 242948L, 169810L, 189280L, 164662L, 164156L,
> 189156L, 163989L, 163924L, 159577L, 159650L, 170566L, 170598L,
> 188975L, 189006L, 99983L, 191595L, 166907L, 228744L, 166621L,
> 243593L, 244001L, 239035L, 172934L, 238288L, 241665L, 241665L,
> 193991L, 238361L, 238361L, 164215L, 168867L, 194304L, 241732L,
> 237745L, 237911L, 195374L, 195374L, 244044L, 244044L, 169118L,
> 244040L, 244040L, 198518L, 244106L, 236206L, 244136L, 191390L,
> 164516L, 165137L, 232682L, 244021L, 244101L, 236136L, 244101L,
> 194181L, 169181L, 244058L, 212313L, 238240L, 242502L, 239175L,
> 166221L, 184500L, 170027L, 237701L, 211035L, 244050L, 243745L,
> 242782L, 164482L, 166341L, 189482L, 174552L, 244213L, 190960L,
> 184494L, 169116L, 239123L, 239121L, 165097L, 206396L, 241738L,
> 165622L, 242651L, 250331L, 178778L, 169133L, 238280L, 244044L,
> 193182L, 194156L, 194156L, 169156L, 196240L, 244060L, 244060L,
> 244060L, 196050L, 243546L, 243546L, 195500L, 195500L, 170389L,
> 195389L, 243549L, 243503L, 211398L, 243510L, 238436L, 238546L,
> 243907L, 243654L, 238709L, 238656L, 244171L, 244136L, 243215L,
> 243957L, 243957L, 164455L, 164455L, 243287L, 238203L, 243738L,
> 243266L, 243294L, 243548L, 243262L, 243262L, 237628L, 243266L,
> 243382L, 243927L, 243574L, 168364L, 243598L, 243596L, 243647L,
> 191094L, 243655L, 244550L, 243907L, 200636L, 210208L, 243632L,
> 243632L, 243367L, 243048L, 212125L, 244651L, 243357L, 202542L,
> 243778L, 243502L, 170036L, 237911L, 195234L, 195220L, 170220L,
> 239391L, 244397L, 244397L, 238631L, 225921L, 244034L, 244051L,
> 243310L, 189976L, 164976L, 164976L, 164999L, 165154L, 243439L,
> 211003L, 244034L, 243859L, 243859L, 170008L, 175602L, 238078L,
> 243484L, 243619L, 243333L, 243289L, 200618L, 243392L, 243376L,
> 164873L, 235797L, 243930L, 191502L, 243906L, 195351L, 170527L,
> 195307L, 243551L, 175551L, 244759L, 238122L, 178863L, 170249L,
> 243701L, 200549L, 236254L, 189982L, 163055L, 203863L, 243561L,
> 165089L, 164574L, 193750L, 238061L, 240569L, 175435L, 164313L,
> 243153L, 189825L, 189825L, 164825L, 189825L, 164340L, 203691L,
> 168483L, 243970L, 193608L, 243054L, 243115L, 243115L, 243043L,
> 243115L, 201917L, 204065L, 177917L, 178745L, 178735L, 243911L,
> 200920L, 242726L, 243042L, 204204L, 181109L, 179157L, 200093L,
> 179164L, 243676L, 235476L, 243862L, 243873L, 243945L, 243927L,
> 168102L, 168102L, 243734L, 243929L, 179053L, 246381L, 204130L,
> 200546L, 200301L, 174699L, 199699L, 178309L, 243549L, 204424L,
> 216428L, 203785L, 204101L, 245074L, 243224L, 163661L, 179036L,
> 199248L, 243458L, 199190L, 200330L, 200406L, 174754L, 243138L,
> 195257L, 244796L, 243069L, 179132L, 204171L, 243718L, 243719L,
> 200616L, 175749L, 179010L, 243037L, 178405L, 243953L, 243923L,
> 243485L, 200891L, 239635L, 243661L, 204041L, 179002L, 204070L,
> 206036L, 198896L, 164487L, 166891L, 246375L, 200217L, 179153L,
> 210112L, 243941L, 243052L, 243724L, 246328L, 164311L, 243736L,
> 154373L, 192956L, 237690L, 193282L, 244901L, 198985L, 246315L,
> 179272L, 204007L, 202386L, 246315L, 202386L, 178856L, 243704L,
> 243750L, 164533L, 246330L, 204082L, 243790L, 189359L, 164359L,
> 168286L, 168286L, 175262L, 164395L, 189395L, 164299L, 189299L,
> 189110L, 154953L, 166251L, 175373L, 235883L), BUNKER = c(350L,
> 405L, 276L, 350L, 373L, 355L, 370L, 343L, 345L, 288L, 313L, 358L,
> 440L, 292L, 318L, 360L, 318L, 288L, 350L, 349L, 350L, 318L, 345L,
> 313L, 313L, 378L, 298L, 363L, 315L, 435L, 423L, 440L, 343L, 355L,
> 313L, 318L, 435L, 313L, 345L, 318L, 349L, 353L, 368L, 362L, 348L,
> 345L, 296L, 313L, 365L, 355L, 368L, 362L, 378L, 348L, 313L, 418L,
> 348L, 418L, 345L, 362L, 318L, 350L, 300L, 343L, 348L, 349L, 298L,
> 313L, 303L, 388L, 370L, 360L, 362L, 338L, 313L, 350L, 313L, 423L,
> 313L, 343L, 353L, 313L, 318L, 360L, 292L, 423L, 298L, 343L, 313L,
> 367L, 368L, 303L, 355L, 353L, 370L, 296L, 303L, 355L, 343L, 313L,
> 353L, 370L, 313L, 303L, 418L, 373L, 353L, 349L, 349L, 363L, 367L,
> 355L, 365L, 443L, 440L, 350L, 363L, 318L, 423L, 364L, 313L, 422L,
> 358L, 430L, 358L, 343L, 370L, 298L, 362L, 378L, 419L, 445L, 362L,
> 313L, 432L, 373L, 355L, 318L, 353L, 283L, 338L, 255L, 276L, 276L,
> 430L, 313L, 367L, 276L, 300L, 313L, 283L, 350L, 313L, 313L, 362L,
> 288L, 425L, 313L, 348L, 426L, 345L, 313L, 353L, 355L, 443L, 355L,
> 423L, 343L, 355L, 348L, 303L, 298L, 318L, 367L, 313L, 435L, 313L,
> 425L, 355L, 318L, 368L, 370L, 343L, 430L, 348L, 300L, 313L, 423L,
> 350L, 443L, 338L, 276L, 292L, 358L, 378L, 313L, 443L, 313L, 348L,
> 338L, 370L, 313L, 318L, 360L, 363L, 358L, 345L, 353L, 318L, 313L,
> 338L, 345L, 345L, 313L, 355L, 348L, 313L, 422L, 363L, 313L, 276L,
> 318L, 350L, 363L, 313L, 292L, 350L, 368L, 418L, 298L, 375L, 313L,
> 315L, 353L, 313L, 288L, 348L, 360L, 413L, 318L, 345L, 365L, 292L,
> 348L, 318L, 362L, 426L, 313L, 365L, 367L, 315L, 368L, 425L, 276L,
> 345L, 360L, 350L, 405L, 362L, 313L, 350L, 343L, 360L, 313L, 355L,
> 303L, 358L, 419L, 350L, 298L, 367L, 313L, 343L, 405L, 419L, 345L,
> 303L, 367L, 265L, 378L, 345L, 318L, 432L, 350L, 445L, 303L, 364L,
> 296L, 418L, 365L, 370L, 313L, 362L, 318L, 313L, 353L, 373L, 360L,
> 345L, 313L, 353L, 422L, 365L, 315L, 365L, 313L, 313L, 360L, 413L,
> 345L, 318L, 338L, 355L, 313L, 349L, 418L, 360L, 303L, 313L, 355L,
> 313L, 318L, 367L, 425L, 270L, 318L, 349L, 353L, 318L, 349L, 345L,
> 368L, 318L, 313L, 362L, 338L, 303L, 296L, 345L, 364L, 283L, 368L,
> 368L, 343L, 423L, 367L, 368L, 313L, 298L, 355L, 405L, 292L, 368L,
> 355L, 440L, 313L, 313L, 313L, 438L, 358L, 313L, 292L, 338L, 313L,
> 313L, 373L, 360L, 345L, 423L, 348L, 370L, 292L, 303L, 345L, 265L,
> 364L, 315L, 338L, 350L, 368L, 313L, 318L, 370L, 303L, 423L, 388L,
> 343L, 362L, 355L, 426L, 350L, 365L, 345L, 355L, 343L, 443L, 313L,
> 270L, 360L, 350L, 435L, 445L, 313L, 348L, 355L, 430L, 362L, 349L,
> 349L, 298L, 313L, 292L, 375L, 367L, 318L, 315L, 368L, 296L, 300L,
> 318L, 296L, 425L, 355L, 288L, 353L, 370L, 362L, 355L, 318L, 313L,
> 435L, 343L, 435L, 292L, 355L, 440L, 338L, 313L, 355L, 288L, 440L,
> 435L, 303L, 360L, 270L, 435L, 283L, 373L, 353L, 265L, 265L, 425L,
> 367L, 353L, 367L, 448L, 368L, 283L, 350L, 343L, 353L, 303L, 355L,
> 368L, 373L, 343L, 375L, 348L, 413L, 362L, 303L, 298L, 313L, 300L,
> 440L, 349L, 355L, 318L, 355L, 388L, 363L, 440L, 292L, 373L, 349L,
> 300L, 315L, 338L, 373L, 353L, 348L, 370L, 362L, 338L, 440L, 440L,
> 350L, 296L, 343L, 368L, 349L, 423L, 364L, 348L, 349L, 423L, 353L,
> 345L, 370L, 292L, 355L, 349L, 355L, 276L, 440L, 283L, 358L, 375L,
> 348L, 440L, 355L, 423L, 445L, 368L, 348L, 355L, 367L), CHARTERVALUE >
c(14000L,
> 12825L, 10475L, 11850L, 13250L, 12100L, 11875L, 14500L, 12500L,
> 10500L, 13375L, 14500L, 13400L, 11000L, 12750L, 11625L, 11875L,
> 10500L, 11850L, 11900L, 11850L, 12750L, 12500L, 12000L, 12250L,
> 12750L, 10450L, 12900L, 12425L, 13375L, 12075L, 13400L, 12625L,
> 11125L, 12000L, 11875L, 13400L, 12000L, 12500L, 12750L, 11900L,
> 13625L, 12750L, 11800L, 12500L, 12500L, 9850L, 12000L, 12350L,
> 11125L, 12750L, 11800L, 12750L, 12500L, 12250L, 13125L, 13125L,
> 13125L, 12500L, 11800L, 11875L, 11850L, 11500L, 12625L, 13125L,
> 11900L, 10425L, 12250L, 12375L, 12400L, 11875L, 11625L, 11800L,
> 12400L, 12000L, 14000L, 12000L, 13125L, 12250L, 12625L, 13875L,
> 12400L, 11875L, 11625L, 11000L, 12075L, 10450L, 12625L, 13375L,
> 12875L, 13125L, 12375L, 11125L, 13625L, 11875L, 9850L, 12375L,
> 12100L, 14500L, 12000L, 13875L, 11875L, 12400L, 12375L, 13125L,
> 13250L, 13875L, 11900L, 11900L, 12900L, 12875L, 12100L, 12350L,
> 12375L, 13125L, 11850L, 12900L, 12750L, 13125L, 13875L, 13375L,
> 13025L, 14500L, 13400L, 14500L, 12625L, 11875L, 10450L, 11800L,
> 12750L, 12625L, 12250L, 11800L, 12250L, 13250L, 13250L, 12100L,
> 12750L, 13625L, 11125L, 12400L, 10250L, 10475L, 10475L, 13400L,
> 13375L, 12875L, 10475L, 11500L, 12400L, 11125L, 11850L, 13375L,
> 12400L, 11800L, 10500L, 13375L, 13375L, 12500L, 12625L, 12500L,
> 12000L, 13875L, 11125L, 12375L, 11125L, 13125L, 12625L, 12100L,
> 12500L, 12375L, 10450L, 12750L, 12875L, 12250L, 13400L, 12250L,
> 13375L, 11125L, 12750L, 12750L, 11875L, 14500L, 13400L, 12500L,
> 11500L, 13375L, 13125L, 11850L, 12375L, 12400L, 10475L, 11000L,
> 14500L, 12750L, 12400L, 12375L, 12250L, 12500L, 12400L, 11875L,
> 12250L, 12750L, 11625L, 12900L, 14500L, 12500L, 13875L, 11875L,
> 13375L, 12400L, 12500L, 12500L, 13375L, 12100L, 12500L, 12400L,
> 13025L, 12900L, 12400L, 10475L, 12750L, 11850L, 12900L, 13375L,
> 11000L, 11850L, 13125L, 13125L, 10450L, 12500L, 12250L, 12425L,
> 13875L, 12000L, 10500L, 13125L, 11625L, 12975L, 12750L, 12500L,
> 12350L, 11000L, 13125L, 12750L, 11800L, 12625L, 13375L, 12350L,
> 12875L, 12425L, 12750L, 12675L, 10475L, 12500L, 11625L, 11850L,
> 12825L, 11800L, 13375L, 14000L, 12625L, 11625L, 12400L, 11125L,
> 12375L, 14500L, 12625L, 14000L, 10425L, 12875L, 13375L, 14500L,
> 12825L, 12625L, 12500L, 12375L, 12875L, 9875L, 12750L, 12500L,
> 12750L, 13250L, 11850L, 12250L, 12375L, 13875L, 9850L, 13125L,
> 12350L, 11875L, 12000L, 11800L, 11875L, 12000L, 13875L, 13250L,
> 11625L, 12500L, 12400L, 13625L, 13025L, 12350L, 12425L, 12350L,
> 12400L, 12400L, 11625L, 12975L, 12500L, 11875L, 12400L, 11125L,
> 12000L, 11900L, 13125L, 11625L, 12375L, 12250L, 12100L, 13375L,
> 12750L, 12875L, 12675L, 10000L, 11875L, 11900L, 13625L, 12750L,
> 11900L, 12500L, 12750L, 12750L, 12000L, 11800L, 12400L, 12375L,
> 9850L, 12500L, 13875L, 11125L, 12750L, 12750L, 12625L, 12075L,
> 12875L, 13125L, 12250L, 10450L, 11125L, 12825L, 11000L, 13125L,
> 11125L, 13125L, 12000L, 12000L, 13375L, 13375L, 14500L, 12000L,
> 11000L, 12400L, 12250L, 12000L, 13250L, 11625L, 12500L, 13125L,
> 13125L, 11875L, 11000L, 12375L, 12500L, 9875L, 13875L, 12425L,
> 12400L, 14000L, 12750L, 12400L, 11875L, 11875L, 12375L, 12075L,
> 12400L, 14500L, 11800L, 12100L, 12625L, 14000L, 12350L, 12500L,
> 12100L, 12625L, 12375L, 12250L, 10000L, 11625L, 14000L, 13375L,
> 12250L, 13375L, 12500L, 11125L, 13400L, 11800L, 11900L, 11900L,
> 10425L, 12400L, 11000L, 12500L, 12875L, 12750L, 12425L, 12750L,
> 9850L, 11500L, 12750L, 9850L, 13375L, 11125L, 10500L, 13875L,
> 11875L, 11800L, 11125L, 12750L, 12250L, 13375L, 12625L, 13375L,
> 11000L, 11125L, 13125L, 12400L, 13375L, 12100L, 10500L, 13075L,
> 13375L, 12375L, 11625L, 10000L, 13400L, 11125L, 13250L, 13875L,
> 9875L, 9875L, 13375L, 12875L, 13875L, 12875L, 13500L, 12750L,
> 11125L, 11850L, 12625L, 13875L, 12375L, 12100L, 13125L, 13250L,
> 12625L, 12500L, 13125L, 12975L, 11800L, 12375L, 10425L, 13375L,
> 11500L, 13075L, 11900L, 12100L, 11875L, 11125L, 12400L, 12900L,
> 13400L, 11000L, 13250L, 11900L, 11500L, 12425L, 12400L, 13250L,
> 13625L, 12500L, 11875L, 11800L, 12400L, 13125L, 13075L, 14000L,
> 9850L, 14500L, 13125L, 11900L, 13125L, 13875L, 13125L, 11900L,
> 13125L, 13875L, 12500L, 11875L, 11000L, 11125L, 11900L, 11125L,
> 10475L, 13075L, 11125L, 14500L, 12500L, 13125L, 13125L, 12100L,
> 13125L, 12250L, 13125L, 12500L, 11125L, 12875L)), class =
"data.frame",
> row.names = c(NA,
> -527L))
>
>
> Any help and/or guidance will be greatly appreciated,
>
> Best regards,
>
> Paul
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
>
	[[alternative HTML version deleted]]

R help - Apr 2019 - Unable to Understand Results of pglm function

[R] Unable to Understand Results of pglm function

[R] Unable to Understand Results of pglm function

[R] Unable to Understand Results of pglm function