Error in pglm function when performing logit regression on Panel Data

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Error in pglm function when performing logit regression on Panel Data

PaulJr
Dear friends,

The following error is generated when trying to fit a logistic regression
with the pglm function:

> PGLM_Model2 <-
pglm(dataframe2$TRANSIT~dataframe2$Draft+dataframe2$TOTALCOST+dataframe2$BUNKER+dataframe2$CHARTERVALUE,
effect=c("twoways"), family=binomial('logit'), index=dataframe2$ID,
data=dataframe2)
Error in pdata.frame(data, index) :
  'index' can be of length 3 at the most (one index variable for
individual, time, group)

This doesn´t make sense to me because the maximum length for ID is 3 and I
checked it twice.

A few things to keep in mind:
1. I am using R version 3.5.3
2. My worstation has Windows 8 and it is a 64-bit OS

I would like to know what I´m doing wrong and how to solve this issue (if
possible). I provide a dput() of my dataset below:

dput(dataframe2)
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(210L,
30L, 172L, 75L, 100L, 98L, 255L, 214L, 282L, 175L, 312L, 219L,
340L, 327L, 305L, 265L, 360L, 181L, 75L, 80L, 86L, 311L, 284L,
362L, 287L, 240L, 24L, 141L, 351L, 168L, 231L, 350L, 108L, 253L,
368L, 320L, 352L, 375L, 272L, 318L, 61L, 111L, 136L, 87L, 101L,
313L, 186L, 375L, 128L, 250L, 132L, 91L, 243L, 121L, 304L, 199L,
15L, 200L, 284L, 85L, 353L, 79L, 371L, 110L, 2L, 78L, 52L, 287L,
319L, 244L, 247L, 262L, 83L, 62L, 336L, 207L, 331L, 42L, 304L,
104L, 206L, 333L, 360L, 260L, 37L, 233L, 55L, 116L, 296L, 124L,
7L, 285L, 254L, 112L, 281L, 19L, 315L, 60L, 209L, 370L, 225L,
252L, 328L, 325L, 201L, 140L, 223L, 67L, 70L, 137L, 120L, 267L,
146L, 238L, 31L, 77L, 134L, 314L, 42L, 16L, 299L, 195L, 216L,
338L, 220L, 104L, 278L, 21L, 129L, 234L, 29L, 242L, 153L, 293L,
164L, 149L, 98L, 311L, 119L, 32L, 64L, 160L, 172L, 167L, 334L,
298L, 122L, 169L, 374L, 344L, 26L, 79L, 298L, 337L, 129L, 178L,
180L, 306L, 109L, 23L, 248L, 329L, 224L, 266L, 237L, 264L, 46L,
108L, 267L, 123L, 321L, 22L, 246L, 124L, 290L, 358L, 302L, 179L,
250L, 305L, 156L, 276L, 213L, 332L, 103L, 363L, 312L, 346L, 82L,
236L, 71L, 174L, 43L, 219L, 14L, 349L, 235L, 297L, 103L, 72L,
283L, 293L, 310L, 270L, 133L, 215L, 274L, 223L, 353L, 303L, 68L,
284L, 279L, 294L, 267L, 123L, 349L, 198L, 133L, 339L, 169L, 309L,
77L, 138L, 298L, 345L, 73L, 5L, 200L, 58L, 12L, 293L, 348L, 224L,
370L, 182L, 2L, 258L, 193L, 307L, 316L, 152L, 345L, 18L, 275L,
83L, 59L, 295L, 155L, 144L, 359L, 135L, 202L, 172L, 274L, 271L,
88L, 38L, 91L, 303L, 210L, 114L, 265L, 349L, 251L, 315L, 217L,
25L, 207L, 56L, 127L, 306L, 214L, 40L, 28L, 316L, 323L, 148L,
184L, 9L, 245L, 314L, 162L, 84L, 239L, 321L, 218L, 187L, 194L,
146L, 280L, 377L, 81L, 357L, 377L, 117L, 139L, 265L, 274L, 330L,
204L, 196L, 128L, 343L, 128L, 337L, 335L, 257L, 203L, 317L, 356L,
89L, 256L, 370L, 65L, 200L, 260L, 322L, 289L, 94L, 301L, 309L,
150L, 202L, 161L, 365L, 63L, 111L, 309L, 76L, 274L, 158L, 273L,
377L, 85L, 64L, 285L, 47L, 279L, 16L, 27L, 131L, 132L, 106L,
230L, 99L, 4L, 324L, 53L, 263L, 36L, 43L, 3L, 253L, 188L, 373L,
373L, 295L, 191L, 220L, 362L, 41L, 93L, 293L, 362L, 145L, 265L,
317L, 49L, 1L, 247L, 327L, 285L, 277L, 190L, 13L, 341L, 71L,
205L, 130L, 342L, 300L, 281L, 291L, 232L, 229L, 214L, 147L, 267L,
57L, 208L, 157L, 245L, 60L, 113L, 237L, 293L, 161L, 268L, 211L,
170L, 241L, 306L, 107L, 254L, 334L, 87L, 61L, 70L, 54L, 337L,
327L, 11L, 122L, 246L, 361L, 159L, 187L, 326L, 305L, 51L, 179L,
264L, 177L, 223L, 249L, 154L, 250L, 309L, 324L, 165L, 115L, 166L,
39L, 250L, 183L, 95L, 308L, 90L, 176L, 372L, 173L, 292L, 262L,
163L, 355L, 35L, 151L, 226L, 185L, 189L, 180L, 102L, 118L, 127L,
366L, 136L, 33L, 77L, 113L, 221L, 323L, 96L, 5L, 140L, 105L,
10L, 2L, 192L, 85L, 288L, 54L, 303L, 48L, 364L, 69L, 269L, 286L,
261L, 228L, 143L, 347L, 50L, 145L, 61L, 369L, 354L, 66L, 142L,
112L, 126L, 280L, 92L, 97L, 20L, 367L, 211L, 187L, 212L, 3L,
70L, 44L, 13L, 17L, 67L, 46L, 227L, 317L, 283L, 45L, 253L, 74L,
256L, 171L, 376L, 34L, 222L, 8L, 17L, 31L, 94L, 46L, 197L, 6L,
103L, 259L, 125L), .Label = c("April 10, 2018", "April 11, 2018",
"April 16, 2018", "April 17, 2018", "April 18, 2018", "April 19, 2018",
"April 21, 2018", "April 22, 2018", "April 23 2018", "April 25, 2018",
"April 26, 2018", "April 27, 2018", "April 3, 2018", "April 30, 2018",
"April 5 2018", "April 7, 2018", "April 8, 2018", "April 9, 2018",
"August 1, 2017", "August 1, 2018", "August 10, 2017", "August 11, 2017",
"August 11, 2018", "August 12, 2017", "August 13, 2018", "August 14 2017",
"August 15 2017", "August 17, 2018", "August 18, 2018", "August 19, 2018",
"August 2, 2018", "August 20, 2017", "August 21, 2017", "August 22, 2017",
"August 23, 2017", "August 23, 2018", "August 24 2017", "August 24, 2018",
"August 25 2017", "August 25, 2018", "August 26 2017", "August 26, 2018",
"August 27, 2017", "August 27, 2018", "August 28, 2017", "August 29, 2018",
"August 3, 2017", "August 30 2017", "August 30, 2018", "August 31, 2017",
"August 5, 2017", "August 6 2017", "August 6, 2017", "August 7 2017",
"August 7, 2017", "August 8 2017", "August 8, 2018", "August 9, 2017",
"August 9, 2018", "December 1 2017", "December 10 2017", "December 10,
2017",
"December 11 2017", "December 11, 2017", "December 12 2017",
"December 12, 2017", "December 13 2017", "December 13, 2017",
"December 14 2017", "December 15 2017", "December 15, 2017",
"December 16, 2017", "December 18 2017", "December 18, 2017",
"December 19 2017", "December 20, 2017", "December 21 2017",
"December 21, 2017", "December 22 2017", "December 23, 2017",
"December 24 2017", "December 24, 2017", "December 27 2017",
"December 27, 2017", "December 28 2017", "December 28, 2017",
"December 29 2017", "December 29, 2017", "December 3 2017", "December 3,
2017",
"December 30 2017", "December 31, 2017", "December 4 2017", "December 4,
2017",
"December 5 2017", "December 5, 2017", "December 6 2017", "December 8,
2017",
"February 1 2018", "February 1, 2018", "February 10 2018", "February 10,
2018",
"February 11, 2018", "February 12 2018", "February 13 2018",
"February 15 2018", "February 15, 2018", "February 16 2018",
"February 17, 2018", "February 18, 2018", "February 19 2018",
"February 20 2018", "February 20, 2018", "February 21, 2018",
"February 22, 2018", "February 23, 2018", "February 25 2018",
"February 28 2018", "February 28, 2018", "February 3 2018", "February 4
2017",
"February 4, 2018", "February 5 2018", "February 5, 2018", "February 7,
2018",
"February 8 2018", "February 9, 2018", "January 1 2018", "January 1, 2018",
"January 10 2018", "January 11 2018", "January 13 2018", "January 14 2018",
"January 15 2018", "January 17, 2018", "January 19, 2018", "January 20
2018",
"January 21, 2018", "January 23 2018", "January 24 2018", "January 24,
2018",
"January 26 2018", "January 26, 2018", "January 29 2018", "January 29,
2018",
"January 3 2018", "January 3, 2018", "January 30 2018", "January 30, 2018",
"January 31 2018", "January 31, 2018", "January 4 2018", "January 4, 2018",
"January 5, 2018", "January 6 2018", "January 7 2018", "January 7, 2018",
"January 8 2018", "January 9 2018", "July 1, 2017", "July 10, 2017",
"July 10, 2018", "July 12, 2017", "July 12, 2018", "July 13 2018",
"July 15, 2018", "July 17, 2017", "July 17, 2018", "July 18, 2017",
"July 18, 2018", "July 19, 2017", "July 20, 2017", "July 20, 2018",
"July 22, 2017", "July 23, 2017", "July 24, 2017", "July 25, 2017",
"July 26, 2017", "July 26, 2018", "July 27, 2018", "July 28, 2017",
"July 29, 2017", "July 29, 2018", "July 3, 2017", "July 30 2017",
"July 30, 2017", "July 31, 2017", "July 31, 2018", "July 5, 2017",
"July 7, 2017", "July 7, 2018", "June 11, 2018", "June 14, 2018",
"June 17 2018", "June 17, 2018", "June 18, 2018", "June 2, 2018",
"June 20, 2018", "June 28, 2018", "June 29, 2018", "June 30, 2018",
"June 6, 2018", "June 8 2018", "March 1, 2018", "March 10 2018",
"March 10, 2018", "March 11, 2018", "March 12, 2018", "March 13 2018",
"March 15, 2018", "March 17, 2018", "March 18 2018", "March 18, 2018",
"March 21, 2018", "March 22 2018", "March 24 2018", "March 26, 2018",
"March 28 2018", "March 28, 2018", "March 29, 2018", "March 3 2018",
"March 30, 2018", "March 4, 2018", "March 5, 2018", "March 6, 2018",
"March 8, 2018", "March 9, 2018", "May 10, 2018", "May 12, 2018",
"May 15, 2018", "May 17, 2018", "May 18, 2018", "May 19, 2018",
"May 2, 2018", "May 22, 2018", "May 24, 2018", "May 25, 2018",
"May 26, 2018", "May 28, 2018", "May 3, 2018", "May 30, 2018",
"May 31, 2018", "May 5, 2018", "May 8, 2018", "November 1 2017",
"November 1, 2017", "November 10 2017", "November 10, 2017",
"November 11 2017", "November 12 2017", "November 13 2017", "November 13,
2017",
"November 15 2017", "November 17 2017", "November 17, 2017",
"November 18 2017", "November 19 2017", "November 21 2017", "November 21,
2017",
"November 22 2017", "November 22, 2017", "November 23 2017",
"November 23, 2017", "November 24, 2017", "November 25 2017",
"November 25, 2017", "November 27 2017", "November 27, 2017",
"November 28 2017", "November 28, 2017", "November 29, 2017",
"November 3 2017", "November 3, 2017", "November 4 2017", "November 4,
2017",
"November 5 2017", "November 5, 2017", "November 6 2017", "November 6,
2017",
"November 7 2017", "November 8 2017", "November 8, 2017", "November 9
2017",
"November 9, 2017", "October 1 2017", "October 1, 2017", "October 10 2017",
"October 10, 2017", "October 11 2017", "October 12 2017", "October 12,
2017",
"October 13, 2017", "October 14 2017", "October 15 2017", "October 16
2017",
"October 17 2017", "October 17, 2017", "October 18 2017", "October 19
2017",
"October 2, 2017", "October 20 2017", "October 20, 2017", "October 21
2017",
"October 21, 2017", "October 23 2017", "October 24, 2017", "October 25
2017",
"October 25, 2017", "October 26 2017", "October 27 2017", "October 28
2017",
"October 28, 2017", "October 29 2017", "October 29, 2017", "October 3
2017",
"October 30 2017", "October 31 2017", "October 31, 2017", "October 4 2017",
"October 4, 2017", "October 5 2017", "October 6 2017", "October 7 2017",
"October 9 2017", "October 9, 2017", "September 1 2017", "September 1,
2017",
"September 10 2017", "September 10, 2017", "September 11 2017",
"September 11, 2017", "September 11, 2018", "September 12 2017",
"September 12, 2018", "September 13 2017", "September 14, 2017",
"September 15 2017", "September 15, 2018", "September 16 2017",
"September 16, 2018", "September 17 2017", "September 18, 2017",
"September 19 2017", "September 19, 2017", "September 2, 2017",
"September 2, 2018", "September 20, 2018", "September 21 2017",
"September 21, 2017", "September 21, 2018", "September 22 2017",
"September 23, 2018", "September 24 2017", "September 25, 2017",
"September 25, 2018", "September 26 2017", "September 27 2017",
"September 27, 2018", "September 28, 2017", "September 29 2017",
"September 29, 2017", "September 3 2017", "September 3, 2017",
"September 3, 2018", "September 30 2017", "September 30, 2018",
"September 4, 2018", "September 5 2017", "September 5, 2017",
"September 6 2017", "September 6, 2017", "September 6, 2018",
"September 7 2017", "September 7, 2017", "September 8 2017",
"September 8, 2018", "September 9 2017"), class = "factor"),
    IMO = c(9146950L, 9146950L, 9149366L, 9149366L, 9174634L,
    9180918L, 9185798L, 9185798L, 9187447L, 9187708L, 9187708L,
    9187708L, 9187708L, 9205835L, 9207778L, 9209532L, 9215543L,
    9216212L, 9221607L, 9221621L, 9222479L, 9224697L, 9224714L,
    9225201L, 9231274L, 9244908L, 9245196L, 9245196L, 9248904L,
    9249908L, 9251327L, 9251327L, 9254563L, 9254707L, 9254719L,
    9267431L, 9268980L, 9279537L, 9280770L, 9284300L, 9284867L,
    9284879L, 9285017L, 9285562L, 9286865L, 9286932L, 9286968L,
    9288459L, 9288473L, 9288514L, 9288538L, 9290696L, 9290854L,
    9291092L, 9291119L, 9291406L, 9291406L, 9291432L, 9294496L,
    9296626L, 9301055L, 9302774L, 9303039L, 9303039L, 9303510L,
    9304239L, 9305087L, 9309485L, 9310276L, 9311165L, 9313307L,
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    9799745L, 9799769L, 9811490L, 9811878L, 9812482L, 9817561L
    ), SHIPNAME = structure(c(295L, 295L, 151L, 151L, 19L, 41L,
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    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 guidance will be greatly appreciated,

Best regards,

Paul

        [[alternative HTML version deleted]]

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Re: Error in pglm function when performing logit regression on Panel Data

R help mailing list-2

> On Apr 23, 2019, at 2:43 PM, Paul Bernal <[hidden email]> wrote:
>
> Dear friends,
>
> The following error is generated when trying to fit a logistic regression
> with the pglm function:
>
>> PGLM_Model2 <-
> pglm(dataframe2$TRANSIT~dataframe2$Draft+dataframe2$TOTALCOST+dataframe2$BUNKER+dataframe2$CHARTERVALUE,
> effect=c("twoways"), family=binomial('logit'), index=dataframe2$ID,
> data=dataframe2)
> Error in pdata.frame(data, index) :
>  'index' can be of length 3 at the most (one index variable for
> individual, time, group)
>
> This doesn´t make sense to me because the maximum length for ID is 3 and I
> checked it twice.
>
> A few things to keep in mind:
> 1. I am using R version 3.5.3
> 2. My worstation has Windows 8 and it is a 64-bit OS
>
> I would like to know what I´m doing wrong and how to solve this issue (if
> possible). I provide a dput() of my dataset below:
>
> dput(dataframe2)'

<snip of dput output>

> Any guidance will be greatly appreciated,
>
> Best regards,
>
> Paul


Hi Paul,

I do not use pglm, but installed it.

Thanks for providing the dput() output of your data frame.

As is typical with R modeling functions, there is a reason for the 'data' argument, rather than specifying the 'dataframe$' prefix syntax for each DV and IV.

It shortens the code required to specify the formula, but perhaps more importantly, it defines the environment within which the function can find the variables to be used by the function, as specified by the various arguments.

In this case, that is critical, since, as per the examples in ?pglm, the 'index' argument is to be specified as a character vector and not as the vector objects themselves. That is, use "ID" and not ID. Therefore, the function will search for the vector/column named "ID" within the dataframe2 object, which is the argument to 'data'.

Unfortunately, the help for the function does not make it clear that the value for 'index' should be a character vector, it simply says 'the index'. Thus one needs to look at the examples for some guidance.

You might want to point that out to the maintainer of the package (copied here now) as an improvement in the documentation.

Thus:

> pglm(TRANSIT ~ Draft + TOTALCOST + BUNKER + CHARTERVALUE, data = dataframe2, effect = "twoways", family = binomial("logit"), index = "ID")
Maximum Likelihood estimation
Newton-Raphson maximisation, 0 iterations
Return code 100: Initial value out of range.


You now have other issues to deal with, which appear to be coming from the maxLik() function used from the package of the same name, which is a dependency for pglm, and I will leave those to you to resolve, probably using the 'start' argument to pglm().

Regards,

Marc Schwartz

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