Para otra vez ten en cuenta que para que sea reproducible significa que sea
reproducible el error o resultado que obtienes.

Volviendo a tu problema, el error se produce en mice porque en otro grupo
de datos hay alguna anormalidad que produce el error, o porque estas usando
demasiados datos, en particular  too many (1068) weights. Me inclino por lo
segundo. Busca si puedes reformular el problema de manera de cumplir con
esa restricción. Tú intentas hacerlo con mice.impute.polyreg, pero me
parece que no le pasas los datos que te piden en especial:
y Incomplete data vector of length n
 ry Vector of missing data pattern (FALSE=missing, TRUE=observed)
 x Matrix (n x p) of complete covariates

Espero eso te sirva. Lamentablemente parece que el problema esta en el
paquete y por las veces que has insistido con tu pregunta pareciera que
nadie tiene experiencia con éste.

Daniel Merino





2013/10/30 Amalia Carolina Guaymas Canavire <acarolinagc@gmail.com>

> Muchas gracias, pero claro en una muestra de 50 datos se ejecuta, en la
> muestra original de 1000 registros
> me tira error :(
>
>
> 2013/10/30 daniel <daniel319@gmail.com>
>
>> Amalia,
>>
>> No obtengo tus resultados. Corrí tus formulas y datos y el resultado es
>> x <- structure(list(ï..psraid = c(202517L, 202518L, 202520L, 202523L,
>> + 202527L, 202537L, 202543L, 202544L, 202551L, 202566L, 202570L,
>> + 202571L, 202606L, 202619L, 202624L, 202629L, 202631L, 202632L,
>> + 202633L, 202648L, 202657L, 202663L, 202676L, 202683L, 202685L,
>> + 202706L, 202708L, 202709L, 202710L, 202734L, 202735L, 202736L,
>> + 202737L, 202746L, 202753L, 202764L, 202769L, 202772L, 202773L,
>> + 202776L, 202811L, 202816L, 202824L, 202832L, 202842L, 202845L,
>> + 202848L, 202856L, 202858L, 202861L), sample = structure(c(1L,
>> + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>> + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>> + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>> + 1L), .Label = c("Cell", "Landline"), class = "factor"), state =
>> + structure(c(31L,
>> + 38L, 24L, 12L, 32L, 9L, 47L, 12L, 9L, 19L, 32L, 39L, 12L, 22L,
>> + 24L, 7L, 14L, 10L, 14L, 26L, 4L, 21L, 24L, 13L, 17L, 16L, 44L,
>> + 8L, 16L, 4L, 9L, 28L, 44L, 10L, 37L, 25L, 26L, 4L, 33L, 4L, 4L,
>> + 12L, 4L, 26L, 36L, 27L, 6L, 45L, 4L, 4L), .Label = c(" 1", " 4",
>> + " 5", " 6", " 8", " 9", "Delaware", "District of Columbia", "Florida",
>> + "Georgia", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas",
>> + "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts",
>> + "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana",
>> + "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico",
>> + "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma",
>> + "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "Tennessee",
>> + "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia",
>> + "Wisconsin"), class = "factor"), cregion = structure(c(2L, 2L,
>> + 1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 3L,
>> + 1L, 1L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 3L, 3L,
>> + 2L, 4L, 1L, 4L, 1L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 2L, 4L, 4L, 4L
>> + ), .Label = c("Midwest", "Northeast", "South", "West"), class =
>> "factor"),
>> +     usr = structure(c(2L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 2L, 2L,
>> +     3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
>> +     3L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L,
>> +     2L, 1L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L), .Label = c("Rural",
>> +     "Suburban", "Urban"), class = "factor"), pial1a =
>> + structure(c(NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>> +     NA_integer_, NA_integer_, NA_integer_, NA_integer_), .Label =
>> c("No",
>> +     "Yes"), class = "factor"), pial1b = structure(c(2L, 2L, 2L,
>> +     3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L,
>> +     2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
>> +     3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L,
>> +     3L, 1L), .Label = c("(DO NOT READ) Don't know", "No", "Yes"
>> +     ), class = "factor"), pial1c = structure(c(3L, 3L, 3L, 2L,
>> +     3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 3L, 3L,
>> +     3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L,
>> +     3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 3L, 4L, 3L,
>> +     3L), .Label = c("(DO NOT READ) Don't know", "(DO NOT READ) Refused",
>> +     "No", "Yes"), class = "factor"), pial1d = structure(c(1L,
>> +     2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
>> +     2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
>> +     1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
>> +     2L, 2L, 2L, 1L), .Label = c("No", "Yes"), class = "factor"),
>> +     pial1e = structure(c(2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
>> +     2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
>> +     1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
>> +     1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("No",
>> +     "Yes"), class = "factor"), pial2 = structure(c(1L, 1L, 1L,
>> +     3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
>> +     3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 1L,
>> +     3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
>> +     1L, 1L), .Label = c("No, not a smartphone", "Not sure/Don't know",
>> +     "Yes, smartphone"), class = "factor"), pial3a = structure(c(5L,
>> +     5L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 4L, 5L,
>> +     4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
>> +     4L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 4L, 1L, 4L, 5L, 5L, 5L, 5L,
>> +     5L, 5L, 5L, 4L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>> READ)
>> + Refused",
>> +     "Have done this but not in last 30 days (VOL.)", "No, have not done
>> + this",
>> +     "Yes, have done this"), class = "factor"), pial3b = structure(c(4L,
>> +     4L, 4L, 4L, 4L, 5L, 5L, 3L, 5L, 5L, 4L, 5L, 5L, 5L, 3L, 4L,
>> +     4L, 4L, 4L, 4L, 5L, 4L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
>> +     4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 5L, 5L, 4L, 4L, 4L, 5L, 5L,
>> +     5L, 5L, 3L, 4L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>> READ)
>> + Refused",
>> +     "Cell phone is not able to do this (VOL.)", "No, have not done
>> this",
>> +     "Yes, have done this"), class = "factor"), pial3c = structure(c(5L,
>> +     5L, 5L, 5L, 5L, 6L, 6L, 3L, 5L, 6L, 5L, 5L, 6L, 5L, 3L, 5L,
>> +     5L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 3L,
>> +     5L, 5L, 5L, 5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
>> +     6L, 6L, 3L, 5L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>> READ)
>> + Refused",
>> +     "Cell phone is not able to do this (VOL.)", "Have done this but not
>> in
>>  + last 30 days (VOL.)",
>> +     "No, have not done this", "Yes, have done this"), class = "factor"),
>> +     pial4 = structure(c(NA, NA, NA, NA, NA, 2L, 3L, NA, NA, 5L,
>> +     NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA, 3L, 5L,
>> +     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 5L, NA, NA, NA,
>> +     NA, NA, NA, NA, 3L, 5L, 6L, 2L, NA, NA), .Label = c("(DO NOT READ)
>> + Don't know",
>> +     "(DO NOT READ) Refused", "No, did not purchase", "Yes, purchased at
>> + another store",
>> +     "Yes, purchased at store", "Yes, purchased online"), class =
>> "factor"),
>> +     employ = structure(c(2L, 2L, 4L, 2L, 4L, 2L, 3L, 2L, 2L,
>> +     2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 4L, 3L, 2L,
>> +     3L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 3L, 3L,
>> +     2L, 4L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 3L, 4L), .Label = c("(DO NOT
>> READ)
>> + DonÃ¢â‚¬â„¢t know/Refused",
>> +     "Employed full-time", "Employed part-time", "Not employed"
>> +     ), class = "factor"), par = structure(c(2L, 2L, 2L, 3L, 1L,
>> +     2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
>> +     2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>> +     2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L
>> +     ), .Label = c("(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused",
>> +     "No", "Yes"), class = "factor"), sex = structure(c(1L, 1L,
>> +     1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
>> +     1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
>> +     2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
>> +     1L, 1L, 2L), .Label = c("Female", "Male"), class = "factor"),
>> +     age = structure(c(42L, 46L, 49L, 16L, 37L, 12L, 22L, 40L,
>> +     6L, 17L, 36L, 8L, 10L, 4L, 31L, 12L, 16L, 41L, 8L, 11L, 9L,
>> +     8L, 61L, 21L, 45L, 12L, 32L, 18L, 25L, 48L, 34L, 38L, 46L,
>> +     26L, 51L, 50L, 11L, 1L, 5L, 1L, 46L, 44L, 46L, 2L, 8L, 9L,
>> +     12L, 17L, 31L, 44L), .Label = c("(DO NOT READ) Refused",
>> +     "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
>> +     "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
>> +     "38", "39", "40", "41", "42", "43", "44", "45", "46", "47",
>> +     "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
>> +     "58", "59", "60", "61", "62", "63", "64", "65", "66", "67",
>> +     "68", "69", "70", "71", "72", "73", "74", "75", "76", "77",
>> +     "78", "79", "80", "81", "82", "83", "84", "85", "86", "87",
>> +     "88", "89", "90", "92"), class = "factor"), educ2 = structure(c(3L,
>> +     3L, 9L, 7L, 7L, 2L, 3L, 2L, 6L, 6L, 1L, 3L, 2L, 9L, 4L, 7L,
>> +     2L, 3L, 9L, 3L, 9L, 3L, 7L, 8L, 3L, 3L, 3L, 7L, 3L, 4L, 4L,
>> +     7L, 9L, 3L, 3L, 7L, 3L, 2L, 9L, 2L, 6L, 3L, 2L, 3L, 7L, 3L,
>> +     2L, 2L, 2L, 7L), .Label = c("Don't know/Refused (VOL.)",
>> +     "Four year college or university degree/BachelorÃ¢â‚¬â„¢s degree
>> (e.g.,
>> + BS, BA, AB)",
>> +     "High school graduate (Grade 12 with diploma or GED certificate)",
>> +     "High school incomplete (Grades 9-11 or Grade 12 with NO diploma)",
>> +     "Less than high school (Grades 1-8 or no formal schooling)",
>> +     "Postgraduate or professional degree, including masterÃ¢â‚¬â„¢s,
>>  + doctorate, medical or law degree (e.g., MA, MS, PhD, MD, JD)",
>> +     "Some college, no degree (includes some community college)",
>> +     "Some postgraduate or professional schooling, no postgraduate
>> degree",
>> +     "Two year associate degree from a college or university"), class =
>> + "factor"),
>> +     hisp = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
>> +     2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
>>  +     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>> +     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("(DO NOT READ)
>> + DonÃ¢â‚¬â„¢t know/Refused",
>> +     "No", "Yes"), class = "factor"), race = structure(c(7L, 7L,
>> +     7L, 7L, 5L, 7L, 7L, 7L, 3L, 2L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
>> +     7L, 7L, 7L, 7L, 4L, 7L, 7L, 7L, 7L, 3L, 4L, 7L, 3L, 7L, 6L,
>> +     7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 3L, 7L,
>> +     7L, 7L, 7L), .Label = c("(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused",
>> +     "Asian or Pacific Islander", "Black or African-American",
>> +     "Mixed race", "Native American/American Indian", "Other (SPECIFY)",
>> +     "White"), class = "factor"), inc = structure(c(8L, 6L, 10L,
>> +     7L, 1L, 10L, 6L, 8L, 9L, 7L, 10L, 2L, 7L, 4L, 2L, 6L, 9L,
>> +     3L, 4L, 1L, 3L, 10L, 5L, 8L, 5L, 5L, 3L, 4L, 3L, 1L, 4L,
>> +     3L, 6L, 1L, 10L, 4L, 7L, 1L, 2L, 6L, 9L, 5L, 4L, 5L, 1L,
>> +     5L, 5L, 7L, 8L, 2L), .Label = c(" 1", " 2", " 3", " 4", " 5",
>> +     " 6", " 7", " 8", " 9", "(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused"
>> +     ), class = "factor"), weight = c(2.9024390244, 2.3414634146,
>> +     1, 3.6097560976, 3.0243902439, 4.3170731707, 6.6585365854,
>> +     3.6341463415, 1.3170731707, 2.8292682927, 2.4390243902,
>> 2.4146341463,
>> +     2.9268292683, 4.8292682927, 7.1463414634, 7.1463414634,
>> 2.3658536585,
>> +     4.487804878, 1.756097561, 2.756097561, 5.1707317073, 1.9268292683,
>> +     2.8780487805, 2.2195121951, 2.3414634146, 7.1463414634,
>> 7.1463414634,
>> +     7.1463414634, 2.6097560976, 2.4634146341, 5.6097560976,
>> 4.5365853659,
>> +     2.487804878, 5.8780487805, 4.0243902439, 2.0731707317, 6.512195122,
>> +     3.7804878049, 1.756097561, 1.1951219512, 1.2682926829, 1.9024390244,
>> +     1.5365853659, 1.6097560976, 1.1463414634, 3.6829268293,
>> 7.1463414634,
>> +     4.7317073171, 2.2195121951, 1.5609756098), standwt = c(0.9676919303,
>> +     0.7806590362, 0.3334064634, 1.2035160141, 1.0083512551,
>> 1.4393400979,
>> +     2.2199991341, 1.2116478791, 0.4391207078, 0.9432963354, 0.813186496,
>> +     0.8050546311, 0.9758237952, 1.6101092621, 2.3826364333,
>> 2.3826364333,
>> +     0.7887909011, 1.4962631527, 0.5854942771, 0.9189007405,
>> 1.7239553715,
>> +     0.6424173318, 0.9595600653, 0.7399997114, 0.7806590362,
>> 2.3826364333,
>> +     2.3826364333, 2.3826364333, 0.8701095507, 0.821318361, 1.8703289408,
>> +     1.5125268826, 0.8294502259, 1.9597794554, 1.3417577184,
>> 0.6912085216,
>> +     2.1712079444, 1.2604390688, 0.5854942771, 0.398461383, 0.4228569779,
>> +     0.6342854669, 0.5123074925, 0.5367030874, 0.3821976531, 1.227911609,
>> +     2.3826364333, 1.5775818023, 0.7399997114, 0.5204393574)), .Names =
>> + c("ï..psraid",
>> + "sample", "state", "cregion", "usr", "pial1a", "pial1b", "pial1c",
>> + "pial1d", "pial1e", "pial2", "pial3a", "pial3b", "pial3c", "pial4",
>> + "employ", "par", "sex", "age", "educ2", "hisp", "race", "inc",
>> + "weight", "standwt"), row.names = 954:1003, class = "data.frame")
>> >
>> > dataset <- data.frame(x)
>> > library(mice)
>> Loading required package: lattice
>> Loading required package: MASS
>> Loading required package: nnet
>> mice 2.18 2013-07-31
>> Warning messages:
>> 1: package ‘mice’ was built under R version 3.0.2
>> 2: package ‘lattice’ was built under R version 3.0.2
>> > md.pattern(x)
>>    ï..psraid sample state cregion usr pial1b pial1c pial1d pial1e pial2
>> pial3a pial3b pial3c employ par sex age educ2 hisp race inc weight standwt
>> pial4 pial1a
>> 13         1      1     1       1   1      1      1      1      1     1
>>    1      1      1      1   1   1   1     1    1    1   1      1       1
>>   1      0  1
>> 37         1      1     1       1   1      1      1      1      1     1
>>    1      1      1      1   1   1   1     1    1    1   1      1       1
>>   0      0  2
>>            0      0     0       0   0      0      0      0      0     0
>>    0      0      0      0   0   0   0     0    0    0   0      0       0
>>  37     50 87
>> > md.pairs(x)
>> $rr
>>           ï..psraid sample state cregion usr pial1a pial1b pial1c pial1d
>> pial1e pial2 pial3a pial3b pial3c pial4 employ par sex age educ2 hisp race
>> inc weight standwt
>> ï..psraid        50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> sample           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> state            50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> cregion          50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> usr              50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial1a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1b           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial1c           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial1d           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial1e           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial2            50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial3a           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial3b           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial3c           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial4            13     13    13      13  13      0     13     13     13
>>     13    13     13     13     13    13     13  13  13  13    13   13   13
>>  13     13      13
>> employ           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> par              50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> sex              50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> age              50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> educ2            50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> hisp             50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> race             50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> inc              50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> weight           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> standwt          50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>>
>> $rm
>>           ï..psraid sample state cregion usr pial1a pial1b pial1c pial1d
>> pial1e pial2 pial3a pial3b pial3c pial4 employ par sex age educ2 hisp race
>> inc weight standwt
>> ï..psraid         0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> sample            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> state             0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> cregion           0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> usr               0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial1a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1b            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial1c            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial1d            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial1e            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial2             0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial3a            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial3b            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial3c            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial4             0      0     0       0   0     13      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> employ            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> par               0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> sex               0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> age               0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> educ2             0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> hisp              0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> race              0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> inc               0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> weight            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> standwt           0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>>
>> $mr
>>           ï..psraid sample state cregion usr pial1a pial1b pial1c pial1d
>> pial1e pial2 pial3a pial3b pial3c pial4 employ par sex age educ2 hisp race
>> inc weight standwt
>> ï..psraid         0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sample            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> state             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> cregion           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> usr               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1a           50     50    50      50  50      0     50     50     50
>>     50    50     50     50     50    13     50  50  50  50    50   50   50
>>  50     50      50
>> pial1b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1d            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1e            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial4            37     37    37      37  37      0     37     37     37
>>     37    37     37     37     37     0     37  37  37  37    37   37   37
>>  37     37      37
>> employ            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> par               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sex               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> age               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> educ2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> hisp              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> race              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> inc               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> weight            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> standwt           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>>
>> $mm
>>           ï..psraid sample state cregion usr pial1a pial1b pial1c pial1d
>> pial1e pial2 pial3a pial3b pial3c pial4 employ par sex age educ2 hisp race
>> inc weight standwt
>> ï..psraid         0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sample            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> state             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> cregion           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> usr               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1a            0      0     0       0   0     50      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> pial1b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1d            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1e            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial4             0      0     0       0   0     37      0      0      0
>>      0     0      0      0      0    37      0   0   0   0     0    0    0
>>   0      0       0
>> employ            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> par               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sex               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> age               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> educ2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> hisp              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> race              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> inc               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> weight            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> standwt           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>>
>> > # en caso de que mice haga uso de números aleatorios fijo el seed para
>> que sea reproducible
>> > set.seed(123)
>> > imp <- mice(dataset,method="polyreg",maxit=5)
>>
>>  iter imp variable
>>   1   1  pial4
>>   1   2  pial4
>>   1   3  pial4
>>   1   4  pial4
>>   1   5  pial4
>>   2   1  pial4
>>   2   2  pial4
>>   2   3  pial4
>>   2   4  pial4
>>   2   5  pial4
>>   3   1  pial4
>>   3   2  pial4
>>   3   3  pial4
>>   3   4  pial4
>>   3   5  pial4
>>   4   1  pial4
>>   4   2  pial4
>>   4   3  pial4
>>   4   4  pial4
>>   4   5  pial4
>>   5   1  pial4
>>   5   2  pial4
>>   5   3  pial4
>>   5   4  pial4
>>   5   5  pial4
>> > imp
>> Multiply imputed data set
>> Call:
>> mice(data = dataset, method = "polyreg", maxit = 5)
>> Number of multiple imputations:  5
>> Missing cells per column:
>> ï..psraid    sample     state   cregion       usr    pial1a    pial1b
>>  pial1c    pial1d    pial1e     pial2    pial3a    pial3b    pial3c
>> pial4    employ       par
>>         0         0         0         0         0        50         0
>>     0         0         0         0         0         0         0        37
>>         0         0
>>       sex       age     educ2      hisp      race       inc    weight
>> standwt
>>         0         0         0         0         0         0         0
>>     0
>> Imputation methods:
>> ï..psraid    sample     state   cregion       usr    pial1a    pial1b
>>  pial1c    pial1d    pial1e     pial2    pial3a    pial3b    pial3c
>> pial4    employ       par
>> "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg"
>> "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg"
>> "polyreg" "polyreg" "polyreg"
>>       sex       age     educ2      hisp      race       inc    weight
>> standwt
>> "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg" "polyreg"
>> "polyreg"
>> VisitSequence:
>> pial4
>>    15
>> PredictorMatrix:
>>           ï..psraid sample state cregion usr pial1a pial1b pial1c pial1d
>> pial1e pial2 pial3a pial3b pial3c pial4 employ par sex age educ2 hisp race
>> inc weight standwt
>> ï..psraid         0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sample            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> state             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> cregion           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> usr               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1d            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial1e            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3a            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3b            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial3c            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> pial4             1      0     1       1   1      0      1      1      1
>>      1     1      1      1      1     0      1   1   1   1     1    1    1
>>   1      1       0
>> employ            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> par               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> sex               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> age               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> educ2             0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> hisp              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> race              0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> inc               0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> weight            0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> standwt           0      0     0       0   0      0      0      0      0
>>      0     0      0      0      0     0      0   0   0   0     0    0    0
>>   0      0       0
>> Random generator seed value:  NA
>> > sessionInfo()
>> R version 3.0.1 (2013-05-16)
>> Platform: i386-w64-mingw32/i386 (32-bit)
>>
>> locale:
>> [1] LC_COLLATE=Spanish_Argentina.1252  LC_CTYPE=Spanish_Argentina.1252
>>  LC_MONETARY=Spanish_Argentina.1252 LC_NUMERIC=C
>> [5] LC_TIME=Spanish_Argentina.1252
>>
>> attached base packages:
>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>
>> other attached packages:
>> [1] mice_2.18       nnet_7.3-7      MASS_7.3-29     lattice_0.20-24
>> devtools_1.3
>>
>> loaded via a namespace (and not attached):
>>  [1] digest_0.6.3   evaluate_0.5.1 grid_3.0.1     httr_0.2
>> memoise_0.1    parallel_3.0.1 RCurl_1.95-4.1 rpart_4.1-3    stringr_0.6.2
>>  tools_3.0.1    whisker_0.3-2
>>
>> Espero te sea de utilidad,
>>
>> Daniel Merino
>>
>>
>>
>> El 30 de octubre de 2013 11:44, Amalia Carolina Guaymas Canavire <
>> acarolinagc@gmail.com> escribió:
>>
>>> Saludo gente, antes que nada gracias por la ayuda que puedan aportarme,
>>> soy
>>> iniciante en R, estoy usando el paquete Mice para realizar imputaciones
>>> múltiples sobre variables en su mayoría categóricas. El problema está que
>>> cuando expresó este comando  imp <- mice(dataset,method="polr",maxit=1)
>>> donde el dataset es un data.frame me tirá este error :
>>>
>>>  iter imp variable
>>>   1   1  pial1a  pial2  pial3a  pial3b  pial3cError en nnet.default(X, Y,
>>> w, mask = mask, size = 0, skip = TRUE, softmax = TRUE,  :
>>>   too many (1068) weights
>>>
>>> buscando en foros encontre que debo modificar el nnet, concretamente
>>> maxNWts indicando un valor mayor al valor con problema, para modificar
>>> eso
>>> se me ocurre usar  mice.impute.polyreg(dataset x=NULL, nnet.maxit =
>>> 100,nnet.trace = FALSE, nnet.maxNWts = 1500) pero el problema que me
>>> piden
>>> X que representa Matrix (n x p) of complete covariates, pero al ser las
>>> vbles categóricas, no me queda claro como estimarla. En si alguna ayuda
>>> para poder solucionar este problema en donde lo que se busca en poder
>>> aplicar regresión logística y por ello me veo con el problema del nnet.
>>> GRACIAS:
>>>
>>> codigo que uso
>>> x <- read.table("C:/Omnibus_Jan_2013nom.txt", header=TRUE, sep=",",
>>> na.strings = c ("NA", ""),quote="\"", dec=",")
>>> dataset <- data.frame(x)
>>> library(mice)
>>> md.pattern(x)
>>> md.pairs(x)
>>> imp <- mice(dataset,method="polyreg",maxit=5)
>>> mice.impute.polyreg(dataset, nnet.maxit = 100,nnet.trace = FALSE,
>>> nnet.maxNWts = 1500)
>>> traceback()
>>>
>>> MI TABLA
>>> structure(list(ï..psraid = c(202517L, 202518L, 202520L, 202523L,
>>> 202527L, 202537L, 202543L, 202544L, 202551L, 202566L, 202570L,
>>> 202571L, 202606L, 202619L, 202624L, 202629L, 202631L, 202632L,
>>> 202633L, 202648L, 202657L, 202663L, 202676L, 202683L, 202685L,
>>> 202706L, 202708L, 202709L, 202710L, 202734L, 202735L, 202736L,
>>> 202737L, 202746L, 202753L, 202764L, 202769L, 202772L, 202773L,
>>> 202776L, 202811L, 202816L, 202824L, 202832L, 202842L, 202845L,
>>> 202848L, 202856L, 202858L, 202861L), sample = structure(c(1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L), .Label = c("Cell", "Landline"), class = "factor"), state =
>>> structure(c(31L,
>>> 38L, 24L, 12L, 32L, 9L, 47L, 12L, 9L, 19L, 32L, 39L, 12L, 22L,
>>> 24L, 7L, 14L, 10L, 14L, 26L, 4L, 21L, 24L, 13L, 17L, 16L, 44L,
>>> 8L, 16L, 4L, 9L, 28L, 44L, 10L, 37L, 25L, 26L, 4L, 33L, 4L, 4L,
>>> 12L, 4L, 26L, 36L, 27L, 6L, 45L, 4L, 4L), .Label = c(" 1", " 4",
>>> " 5", " 6", " 8", " 9", "Delaware", "District of Columbia", "Florida",
>>> "Georgia", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas",
>>> "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts",
>>> "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana",
>>> "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico",
>>> "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma",
>>> "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "Tennessee",
>>> "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia",
>>> "Wisconsin"), class = "factor"), cregion = structure(c(2L, 2L,
>>> 1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 3L,
>>> 1L, 1L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 3L, 3L,
>>> 2L, 4L, 1L, 4L, 1L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 2L, 4L, 4L, 4L
>>> ), .Label = c("Midwest", "Northeast", "South", "West"), class =
>>> "factor"),
>>>     usr = structure(c(2L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 2L, 2L,
>>>     3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
>>>     3L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L,
>>>     2L, 1L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L), .Label = c("Rural",
>>>     "Suburban", "Urban"), class = "factor"), pial1a =
>>> structure(c(NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
>>>     NA_integer_, NA_integer_, NA_integer_, NA_integer_), .Label = c("No",
>>>     "Yes"), class = "factor"), pial1b = structure(c(2L, 2L, 2L,
>>>     3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L,
>>>     2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
>>>     3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L,
>>>     3L, 1L), .Label = c("(DO NOT READ) Don't know", "No", "Yes"
>>>     ), class = "factor"), pial1c = structure(c(3L, 3L, 3L, 2L,
>>>     3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 3L, 3L,
>>>     3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L,
>>>     3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 3L, 4L, 3L,
>>>     3L), .Label = c("(DO NOT READ) Don't know", "(DO NOT READ) Refused",
>>>     "No", "Yes"), class = "factor"), pial1d = structure(c(1L,
>>>     2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
>>>     2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
>>>     1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
>>>     2L, 2L, 2L, 1L), .Label = c("No", "Yes"), class = "factor"),
>>>     pial1e = structure(c(2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
>>>     2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
>>>     1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
>>>     1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("No",
>>>     "Yes"), class = "factor"), pial2 = structure(c(1L, 1L, 1L,
>>>     3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
>>>     3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 1L,
>>>     3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
>>>     1L, 1L), .Label = c("No, not a smartphone", "Not sure/Don't know",
>>>     "Yes, smartphone"), class = "factor"), pial3a = structure(c(5L,
>>>     5L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 4L, 5L,
>>>     4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
>>>     4L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 4L, 1L, 4L, 5L, 5L, 5L, 5L,
>>>     5L, 5L, 5L, 4L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>>> READ)
>>> Refused",
>>>     "Have done this but not in last 30 days (VOL.)", "No, have not done
>>> this",
>>>     "Yes, have done this"), class = "factor"), pial3b = structure(c(4L,
>>>     4L, 4L, 4L, 4L, 5L, 5L, 3L, 5L, 5L, 4L, 5L, 5L, 5L, 3L, 4L,
>>>     4L, 4L, 4L, 4L, 5L, 4L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
>>>     4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 5L, 5L, 4L, 4L, 4L, 5L, 5L,
>>>     5L, 5L, 3L, 4L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>>> READ)
>>> Refused",
>>>     "Cell phone is not able to do this (VOL.)", "No, have not done this",
>>>     "Yes, have done this"), class = "factor"), pial3c = structure(c(5L,
>>>     5L, 5L, 5L, 5L, 6L, 6L, 3L, 5L, 6L, 5L, 5L, 6L, 5L, 3L, 5L,
>>>     5L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 3L,
>>>     5L, 5L, 5L, 5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
>>>     6L, 6L, 3L, 5L), .Label = c("(DO NOT READ) Don't know", "(DO NOT
>>> READ)
>>> Refused",
>>>     "Cell phone is not able to do this (VOL.)", "Have done this but not
>>> in
>>> last 30 days (VOL.)",
>>>     "No, have not done this", "Yes, have done this"), class = "factor"),
>>>     pial4 = structure(c(NA, NA, NA, NA, NA, 2L, 3L, NA, NA, 5L,
>>>     NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA, 3L, 5L,
>>>     NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 5L, NA, NA, NA,
>>>     NA, NA, NA, NA, 3L, 5L, 6L, 2L, NA, NA), .Label = c("(DO NOT READ)
>>> Don't know",
>>>     "(DO NOT READ) Refused", "No, did not purchase", "Yes, purchased at
>>> another store",
>>>     "Yes, purchased at store", "Yes, purchased online"), class =
>>> "factor"),
>>>     employ = structure(c(2L, 2L, 4L, 2L, 4L, 2L, 3L, 2L, 2L,
>>>     2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 4L, 3L, 2L,
>>>     3L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 3L, 3L,
>>>     2L, 4L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 3L, 4L), .Label = c("(DO NOT
>>> READ)
>>> DonÃ¢â‚¬â„¢t know/Refused",
>>>     "Employed full-time", "Employed part-time", "Not employed"
>>>     ), class = "factor"), par = structure(c(2L, 2L, 2L, 3L, 1L,
>>>     2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
>>>     2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>>>     2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L
>>>     ), .Label = c("(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused",
>>>     "No", "Yes"), class = "factor"), sex = structure(c(1L, 1L,
>>>     1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
>>>     1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
>>>     2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
>>>     1L, 1L, 2L), .Label = c("Female", "Male"), class = "factor"),
>>>     age = structure(c(42L, 46L, 49L, 16L, 37L, 12L, 22L, 40L,
>>>     6L, 17L, 36L, 8L, 10L, 4L, 31L, 12L, 16L, 41L, 8L, 11L, 9L,
>>>     8L, 61L, 21L, 45L, 12L, 32L, 18L, 25L, 48L, 34L, 38L, 46L,
>>>     26L, 51L, 50L, 11L, 1L, 5L, 1L, 46L, 44L, 46L, 2L, 8L, 9L,
>>>     12L, 17L, 31L, 44L), .Label = c("(DO NOT READ) Refused",
>>>     "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
>>>     "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
>>>     "38", "39", "40", "41", "42", "43", "44", "45", "46", "47",
>>>     "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
>>>     "58", "59", "60", "61", "62", "63", "64", "65", "66", "67",
>>>     "68", "69", "70", "71", "72", "73", "74", "75", "76", "77",
>>>     "78", "79", "80", "81", "82", "83", "84", "85", "86", "87",
>>>     "88", "89", "90", "92"), class = "factor"), educ2 = structure(c(3L,
>>>     3L, 9L, 7L, 7L, 2L, 3L, 2L, 6L, 6L, 1L, 3L, 2L, 9L, 4L, 7L,
>>>     2L, 3L, 9L, 3L, 9L, 3L, 7L, 8L, 3L, 3L, 3L, 7L, 3L, 4L, 4L,
>>>     7L, 9L, 3L, 3L, 7L, 3L, 2L, 9L, 2L, 6L, 3L, 2L, 3L, 7L, 3L,
>>>     2L, 2L, 2L, 7L), .Label = c("Don't know/Refused (VOL.)",
>>>     "Four year college or university degree/BachelorÃ¢â‚¬â„¢s degree
>>> (e.g.,
>>> BS, BA, AB)",
>>>     "High school graduate (Grade 12 with diploma or GED certificate)",
>>>     "High school incomplete (Grades 9-11 or Grade 12 with NO diploma)",
>>>     "Less than high school (Grades 1-8 or no formal schooling)",
>>>     "Postgraduate or professional degree, including masterÃ¢â‚¬â„¢s,
>>> doctorate, medical or law degree (e.g., MA, MS, PhD, MD, JD)",
>>>     "Some college, no degree (includes some community college)",
>>>     "Some postgraduate or professional schooling, no postgraduate
>>> degree",
>>>     "Two year associate degree from a college or university"), class =
>>> "factor"),
>>>     hisp = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
>>>     2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
>>>     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>>>     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("(DO NOT READ)
>>> DonÃ¢â‚¬â„¢t know/Refused",
>>>     "No", "Yes"), class = "factor"), race = structure(c(7L, 7L,
>>>     7L, 7L, 5L, 7L, 7L, 7L, 3L, 2L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
>>>     7L, 7L, 7L, 7L, 4L, 7L, 7L, 7L, 7L, 3L, 4L, 7L, 3L, 7L, 6L,
>>>     7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 3L, 7L,
>>>     7L, 7L, 7L), .Label = c("(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused",
>>>     "Asian or Pacific Islander", "Black or African-American",
>>>     "Mixed race", "Native American/American Indian", "Other (SPECIFY)",
>>>     "White"), class = "factor"), inc = structure(c(8L, 6L, 10L,
>>>     7L, 1L, 10L, 6L, 8L, 9L, 7L, 10L, 2L, 7L, 4L, 2L, 6L, 9L,
>>>     3L, 4L, 1L, 3L, 10L, 5L, 8L, 5L, 5L, 3L, 4L, 3L, 1L, 4L,
>>>     3L, 6L, 1L, 10L, 4L, 7L, 1L, 2L, 6L, 9L, 5L, 4L, 5L, 1L,
>>>     5L, 5L, 7L, 8L, 2L), .Label = c(" 1", " 2", " 3", " 4", " 5",
>>>     " 6", " 7", " 8", " 9", "(DO NOT READ) DonÃ¢â‚¬â„¢t know/Refused"
>>>     ), class = "factor"), weight = c(2.9024390244, 2.3414634146,
>>>     1, 3.6097560976, 3.0243902439, 4.3170731707, 6.6585365854,
>>>     3.6341463415, 1.3170731707, 2.8292682927, 2.4390243902, 2.4146341463,
>>>     2.9268292683, 4.8292682927, 7.1463414634, 7.1463414634, 2.3658536585,
>>>     4.487804878, 1.756097561, 2.756097561, 5.1707317073, 1.9268292683,
>>>     2.8780487805, 2.2195121951, 2.3414634146, 7.1463414634, 7.1463414634,
>>>     7.1463414634, 2.6097560976, 2.4634146341, 5.6097560976, 4.5365853659,
>>>     2.487804878, 5.8780487805, 4.0243902439, 2.0731707317, 6.512195122,
>>>     3.7804878049, 1.756097561, 1.1951219512, 1.2682926829, 1.9024390244,
>>>     1.5365853659, 1.6097560976, 1.1463414634, 3.6829268293, 7.1463414634,
>>>     4.7317073171, 2.2195121951, 1.5609756098), standwt = c(0.9676919303,
>>>     0.7806590362, 0.3334064634, 1.2035160141, 1.0083512551, 1.4393400979,
>>>     2.2199991341, 1.2116478791, 0.4391207078, 0.9432963354, 0.813186496,
>>>     0.8050546311, 0.9758237952, 1.6101092621, 2.3826364333, 2.3826364333,
>>>     0.7887909011, 1.4962631527, 0.5854942771, 0.9189007405, 1.7239553715,
>>>     0.6424173318, 0.9595600653, 0.7399997114, 0.7806590362, 2.3826364333,
>>>     2.3826364333, 2.3826364333, 0.8701095507, 0.821318361, 1.8703289408,
>>>     1.5125268826, 0.8294502259, 1.9597794554, 1.3417577184, 0.6912085216,
>>>     2.1712079444, 1.2604390688, 0.5854942771, 0.398461383, 0.4228569779,
>>>     0.6342854669, 0.5123074925, 0.5367030874, 0.3821976531, 1.227911609,
>>>     2.3826364333, 1.5775818023, 0.7399997114, 0.5204393574)), .Names =
>>> c("ï..psraid",
>>> "sample", "state", "cregion", "usr", "pial1a", "pial1b", "pial1c",
>>> "pial1d", "pial1e", "pial2", "pial3a", "pial3b", "pial3c", "pial4",
>>> "employ", "par", "sex", "age", "educ2", "hisp", "race", "inc",
>>> "weight", "standwt"), row.names = 954:1003, class = "data.frame")
>>> > dput(tail(dataset, 50))
>>>
>>> --
>>>
>>> *************** *  :) *sonrei que te queda lindo :):):):): **amy **cgc
>>> **************************
>>> *
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>>
>>> _______________________________________________
>>> R-help-es mailing list
>>> R-help-es@r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-help-es
>>>
>>>
>>
>>
>> --
>> Daniel
>>
>
>
>
> --
>
> *************** *  :) *sonrei que te queda lindo :):):):): **amy **cgc
> **************************
> *
>



-- 
Daniel

	[[alternative HTML version deleted]]

