Olá Estou modelando um conjunto de dados e a variável resposta parece ter distribuição geométrica, qual é a melhor forma de modelar esses dados? Abraços dput(dados) structure(list(AFtotal = structure(c(103L, 76L, 58L, 105L, 125L, 163L, 106L, 146L, 227L, 187L, 5L, 117L, 147L, 39L, 232L, 2L, 67L, 150L, 46L, 118L, 18L, 171L, 157L, 226L, 80L, 96L, 142L, 162L, 128L, 152L, 109L, 10L, 108L, 102L, 47L, 208L, 122L, 78L, 176L, 3L, 196L, 40L, 209L, 119L, 179L, 29L, 1L, 81L, 190L, 24L, 204L, 28L, 92L, 136L, 104L, 85L, 91L, 15L, 141L, 13L, 11L, 121L, 112L, 210L, 31L, 6L, 70L, 156L, 114L, 68L, 107L, 112L, 211L, 167L, 1L, 215L, 88L, 213L, 194L, 136L, 220L, 199L, 136L, 9L, 136L, 118L, 38L, 192L, 42L, 62L, 155L, 19L, 79L, 48L, 138L, 172L, 134L, 231L, 231L, 216L, 136L, 195L, 130L, 101L, 217L, 205L, 174L, 36L, 111L, 140L, 106L, 99L, 22L, 218L, 170L, 7L, 8L, 136L, 19L, 62L, 25L, 123L, 1L, 137L, 220L, 97L, 52L, 169L, 190L, 216L, 1L, 53L, 172L, 100L, 79L, 86L, 201L, 61L, 198L, 17L, 218L, 67L, 106L, 193L, 1L, 138L, 164L, 200L, 190L, 133L, 129L, 74L, 186L, 72L, 41L, 177L, 48L, 113L, 50L, 229L, 178L, 106L, 124L, 75L, 14L, 148L, 218L, 191L, 19L, 219L, 30L, 1L, 71L, 112L, 172L, 21L, 221L, 16L, 203L, 27L, 69L, 143L, 82L, 207L, 223L, 32L, 54L, 60L, 59L, 1L, 97L, 149L, 63L, 132L, 212L, 214L, 1L, 1L, 165L, 77L, 20L, 73L, 26L, 168L, 160L, 233L, 139L, 1L, 188L, 95L, 175L, 206L, 93L, 144L, 135L, 56L, 6L, 189L, 197L, 45L, 30L, 35L, 1L, 4L, 136L, 154L, 89L, 65L, 184L, 225L, 230L, 151L, 1L, 173L, 43L, 44L, 33L, 182L, 34L, 153L, 1L, 1L, 90L, 106L, 110L, 98L, 1L, 219L, 1L, 1L, 116L, 49L, 64L, 57L, 131L, 185L, 145L, 17L, 94L, 1L, 180L, 1L, 120L, 159L, 51L, 172L, 181L, 183L, 7L, 87L, 106L, 228L, 55L, 126L, 157L, 218L, 158L, 115L, 133L, 222L, 84L, 166L, 1L, 37L, 1L, 1L, 202L, 66L, 12L, 1L, 83L, 23L, 110L, 233L, 231L, 161L, 106L, 224L, 127L), .Label = c("0,00", "1.031,00", "1.035,00", "1.042,50", "1.064,00", "1.080,00", "1.120,00", "1.155,00", "1.171,50", "1.177,50", "1.179,00", "1.200,00", "1.204,50", "1.210,00", "1.218,00", "1.220,00", "1.230,00", "1.233,00", "1.260,00", "1.275,00", "1.287,00", "1.290,00", "1.314,00", "1.320,00", "1.356,00", "1.359,50", "1.400,00", "1.410,00", "1.418,00", "1.440,00", "1.466,00", "1.480,00", "1.500,00", "1.566,00", "1.572,00", "1.590,00", "1.611,00", "1.652,00", "1.680,00", "1.722,00", "1.734,00", "1.740,00", "1.752,00", "1.800,00", "1.818,00", "1.821,00", "1.833,00", "1.836,00", "1.854,00", "1.872,00", "1.900,00", "1.920,00", "1.938,00", "10.080,00", "10.230,00", "10.557,00", "10.680,00", "11.238,00", "12.000,00", "12.270,00", "12.420,00", "120,00", "13.644,00", "13.860,00", "14.590,00", "150,00", "165,00", "180,00", "2.002,50", "2.060,00", "2.078,00", "2.100,00", "2.120,00", "2.157,00", "2.320,00", "2.332,50", "2.376,00", "2.382,00", "2.430,00", "2.457,00", "2.475,00", "2.504,00", "2.520,00", "2.526,00", "2.532,00", "2.556,00", "2.572,00", "2.580,00", "2.616,00", "2.640,00", "2.645,00", "2.655,00", "2.706,00", "2.739,00", "2.745,00", "2.767,50", "2.784,00", "2.798,00", "2.820,00", "2.841,00", "2.895,00", "2.937,00", "2.940,00", "2.965,00", "21.515,00", "240,00", "247,50", "259,00", "26.862,00", "264,00", "273,00", "297,00", "3.019,00", "3.035,00", "3.036,00", "3.282,00", "3.306,00", "3.360,00", "3.497,00", "3.504,00", "3.510,00", "3.540,00", "3.600,00", "3.615,00", "3.660,00", "3.710,00", "3.840,00", "3.892,50", "3.900,00", "3.960,00", "3.977,00", "3.996,00", "300,00", "318,00", "330,00", "360,00", "372,00", "396,00", "4.020,00", "4.086,00", "4.092,00", "4.100,00", "4.140,00", "4.158,00", "4.200,00", "4.219,50", "4.331,00", "4.359,00", "4.554,00", "4.618,00", "4.680,00", "4.770,00", "407,50", "410,00", "420,00", "424,00", "480,00", "495,00", "5.052,00", "5.160,00", "5.340,00", "5.622,00", "5.688,00", "5.715,00", "5.745,00", "5.832,00", "5.940,00", "5.958,00", "510,00", "516,00", "537,00", "540,00", "565,50", "570,00", "575,00", "576,00", "585,00", "6.097,50", "6.285,00", "6.495,00", "6.556,00", "6.576,00", "6.720,00", "6.746,00", "6.828,00", "639,00", "645,00", "655,00", "66,00", "660,00", "678,00", "690,00", "693,00", "696,00", "7.056,00", "7.074,00", "7.200,00", "7.236,00", "7.245,00", "7.500,00", "7.600,00", "7.680,00", "7.860,00", "7.920,00", "720,00", "738,00", "754,00", "754,50", "780,00", "795,00", "8.268,00", "8.467,50", "8.592,00", "8.755,00", "8.820,00", "80,00", "801,00", "810,00", "812,00", "840,00", "876,00", "882,00", "895,50", "9.120,00", "9.150,00", "9.310,00", "9.508,00", "924,00", "930,00", "933,00", "960,00", "99,00", "990,00"), class = "factor")), .Names = "AFtotal", class = "data.frame", row.names = c(NA, -299L)) -- Sérgio Henrique Almeida da Silva Junior Doutorando em Epidemiologia em Saúde Pública Escola Nacional de Saúde Pública Sérgio Arouca - ENSP/FIOCRUZ http://lattes.cnpq.br/1611345552843383 Tel: (21) 94429486/78101651 id: 123*20942
modelagem envolve muito mais que os dados com os quais vc esta' trabalhando... na verdade, o processo gerador dos dados e' q e' relevante... vc precisa entender a "historia" dos dados... sobre os seus dados, pergunte-se: 1) eles sao sempre inteiros? 2) se sim, eles representam o numero de tentativas (de alguma coisa) ate' o primeiro sucesso (ou falha)? 3) se sim, cada uma dessas tentativas tem a mesma chance de sucesso (ou falha)? se sua resposta for "nao" para qq uma das perguntas acima, e' pouco provavel que uma geometrica seja a resposta q vc procura... b 2012/8/17 Sérgio Henrique almeida da silva ju <sergio.edfisica@gmail.com>:
Olá
Estou modelando um conjunto de dados e a variável resposta parece ter distribuição geométrica, qual é a melhor forma de modelar esses dados?
Abraços
dput(dados) structure(list(AFtotal = structure(c(103L, 76L, 58L, 105L, 125L, 163L, 106L, 146L, 227L, 187L, 5L, 117L, 147L, 39L, 232L, 2L, 67L, 150L, 46L, 118L, 18L, 171L, 157L, 226L, 80L, 96L, 142L, 162L, 128L, 152L, 109L, 10L, 108L, 102L, 47L, 208L, 122L, 78L, 176L, 3L, 196L, 40L, 209L, 119L, 179L, 29L, 1L, 81L, 190L, 24L, 204L, 28L, 92L, 136L, 104L, 85L, 91L, 15L, 141L, 13L, 11L, 121L, 112L, 210L, 31L, 6L, 70L, 156L, 114L, 68L, 107L, 112L, 211L, 167L, 1L, 215L, 88L, 213L, 194L, 136L, 220L, 199L, 136L, 9L, 136L, 118L, 38L, 192L, 42L, 62L, 155L, 19L, 79L, 48L, 138L, 172L, 134L, 231L, 231L, 216L, 136L, 195L, 130L, 101L, 217L, 205L, 174L, 36L, 111L, 140L, 106L, 99L, 22L, 218L, 170L, 7L, 8L, 136L, 19L, 62L, 25L, 123L, 1L, 137L, 220L, 97L, 52L, 169L, 190L, 216L, 1L, 53L, 172L, 100L, 79L, 86L, 201L, 61L, 198L, 17L, 218L, 67L, 106L, 193L, 1L, 138L, 164L, 200L, 190L, 133L, 129L, 74L, 186L, 72L, 41L, 177L, 48L, 113L, 50L, 229L, 178L, 106L, 124L, 75L, 14L, 148L, 218L, 191L, 19L, 219L, 30L, 1L, 71L, 112L, 172L, 21L, 221L, 16L, 203L, 27L, 69L, 143L, 82L, 207L, 223L, 32L, 54L, 60L, 59L, 1L, 97L, 149L, 63L, 132L, 212L, 214L, 1L, 1L, 165L, 77L, 20L, 73L, 26L, 168L, 160L, 233L, 139L, 1L, 188L, 95L, 175L, 206L, 93L, 144L, 135L, 56L, 6L, 189L, 197L, 45L, 30L, 35L, 1L, 4L, 136L, 154L, 89L, 65L, 184L, 225L, 230L, 151L, 1L, 173L, 43L, 44L, 33L, 182L, 34L, 153L, 1L, 1L, 90L, 106L, 110L, 98L, 1L, 219L, 1L, 1L, 116L, 49L, 64L, 57L, 131L, 185L, 145L, 17L, 94L, 1L, 180L, 1L, 120L, 159L, 51L, 172L, 181L, 183L, 7L, 87L, 106L, 228L, 55L, 126L, 157L, 218L, 158L, 115L, 133L, 222L, 84L, 166L, 1L, 37L, 1L, 1L, 202L, 66L, 12L, 1L, 83L, 23L, 110L, 233L, 231L, 161L, 106L, 224L, 127L), .Label = c("0,00", "1.031,00", "1.035,00", "1.042,50", "1.064,00", "1.080,00", "1.120,00", "1.155,00", "1.171,50", "1.177,50", "1.179,00", "1.200,00", "1.204,50", "1.210,00", "1.218,00", "1.220,00", "1.230,00", "1.233,00", "1.260,00", "1.275,00", "1.287,00", "1.290,00", "1.314,00", "1.320,00", "1.356,00", "1.359,50", "1.400,00", "1.410,00", "1.418,00", "1.440,00", "1.466,00", "1.480,00", "1.500,00", "1.566,00", "1.572,00", "1.590,00", "1.611,00", "1.652,00", "1.680,00", "1.722,00", "1.734,00", "1.740,00", "1.752,00", "1.800,00", "1.818,00", "1.821,00", "1.833,00", "1.836,00", "1.854,00", "1.872,00", "1.900,00", "1.920,00", "1.938,00", "10.080,00", "10.230,00", "10.557,00", "10.680,00", "11.238,00", "12.000,00", "12.270,00", "12.420,00", "120,00", "13.644,00", "13.860,00", "14.590,00", "150,00", "165,00", "180,00", "2.002,50", "2.060,00", "2.078,00", "2.100,00", "2.120,00", "2.157,00", "2.320,00", "2.332,50", "2.376,00", "2.382,00", "2.430,00", "2.457,00", "2.475,00", "2.504,00", "2.520,00", "2.526,00", "2.532,00", "2.556,00", "2.572,00", "2.580,00", "2.616,00", "2.640,00", "2.645,00", "2.655,00", "2.706,00", "2.739,00", "2.745,00", "2.767,50", "2.784,00", "2.798,00", "2.820,00", "2.841,00", "2.895,00", "2.937,00", "2.940,00", "2.965,00", "21.515,00", "240,00", "247,50", "259,00", "26.862,00", "264,00", "273,00", "297,00", "3.019,00", "3.035,00", "3.036,00", "3.282,00", "3.306,00", "3.360,00", "3.497,00", "3.504,00", "3.510,00", "3.540,00", "3.600,00", "3.615,00", "3.660,00", "3.710,00", "3.840,00", "3.892,50", "3.900,00", "3.960,00", "3.977,00", "3.996,00", "300,00", "318,00", "330,00", "360,00", "372,00", "396,00", "4.020,00", "4.086,00", "4.092,00", "4.100,00", "4.140,00", "4.158,00", "4.200,00", "4.219,50", "4.331,00", "4.359,00", "4.554,00", "4.618,00", "4.680,00", "4.770,00", "407,50", "410,00", "420,00", "424,00", "480,00", "495,00", "5.052,00", "5.160,00", "5.340,00", "5.622,00", "5.688,00", "5.715,00", "5.745,00", "5.832,00", "5.940,00", "5.958,00", "510,00", "516,00", "537,00", "540,00", "565,50", "570,00", "575,00", "576,00", "585,00", "6.097,50", "6.285,00", "6.495,00", "6.556,00", "6.576,00", "6.720,00", "6.746,00", "6.828,00", "639,00", "645,00", "655,00", "66,00", "660,00", "678,00", "690,00", "693,00", "696,00", "7.056,00", "7.074,00", "7.200,00", "7.236,00", "7.245,00", "7.500,00", "7.600,00", "7.680,00", "7.860,00", "7.920,00", "720,00", "738,00", "754,00", "754,50", "780,00", "795,00", "8.268,00", "8.467,50", "8.592,00", "8.755,00", "8.820,00", "80,00", "801,00", "810,00", "812,00", "840,00", "876,00", "882,00", "895,50", "9.120,00", "9.150,00", "9.310,00", "9.508,00", "924,00", "930,00", "933,00", "960,00", "99,00", "990,00"), class = "factor")), .Names = "AFtotal", class = "data.frame", row.names = c(NA, -299L))
-- Sérgio Henrique Almeida da Silva Junior Doutorando em Epidemiologia em Saúde Pública Escola Nacional de Saúde Pública Sérgio Arouca - ENSP/FIOCRUZ http://lattes.cnpq.br/1611345552843383 Tel: (21) 94429486/78101651 id: 123*20942
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Oi Obrigado por sempre ajudar Benilton. Deixa eu tentar explicar o objetivo do trabalho. Na verdade que vai trabalhar com esses dados é um amigo no doutorado. A variável resposta é Atividade Física e ele quer modelar, na verdade, seria gasto energético por AF, os 0 são indivíduos que não praticam AF, ou seja, tem gasto 0, acima desse valor, são gastos energéticos de atividades físicas quaisquer... Os dados não são normais e quando tento normalizar com boxcox não roda por conta dos 0. Quando fiz um histograma dessa variável, me pareceu que os mesmos apresentam distribuição geométrica, parei por aí... Obrigado mais uma vez. Enviado via iPod Em 16/08/2012, às 22:11, Benilton Carvalho <beniltoncarvalho@gmail.com> escreveu:
modelagem envolve muito mais que os dados com os quais vc esta' trabalhando... na verdade, o processo gerador dos dados e' q e' relevante... vc precisa entender a "historia" dos dados...
sobre os seus dados, pergunte-se: 1) eles sao sempre inteiros? 2) se sim, eles representam o numero de tentativas (de alguma coisa) ate' o primeiro sucesso (ou falha)? 3) se sim, cada uma dessas tentativas tem a mesma chance de sucesso (ou falha)?
se sua resposta for "nao" para qq uma das perguntas acima, e' pouco provavel que uma geometrica seja a resposta q vc procura...
b
2012/8/17 Sérgio Henrique almeida da silva ju <sergio.edfisica@gmail.com>:
Olá
Estou modelando um conjunto de dados e a variável resposta parece ter distribuição geométrica, qual é a melhor forma de modelar esses dados?
Abraços
dput(dados) structure(list(AFtotal = structure(c(103L, 76L, 58L, 105L, 125L, 163L, 106L, 146L, 227L, 187L, 5L, 117L, 147L, 39L, 232L, 2L, 67L, 150L, 46L, 118L, 18L, 171L, 157L, 226L, 80L, 96L, 142L, 162L, 128L, 152L, 109L, 10L, 108L, 102L, 47L, 208L, 122L, 78L, 176L, 3L, 196L, 40L, 209L, 119L, 179L, 29L, 1L, 81L, 190L, 24L, 204L, 28L, 92L, 136L, 104L, 85L, 91L, 15L, 141L, 13L, 11L, 121L, 112L, 210L, 31L, 6L, 70L, 156L, 114L, 68L, 107L, 112L, 211L, 167L, 1L, 215L, 88L, 213L, 194L, 136L, 220L, 199L, 136L, 9L, 136L, 118L, 38L, 192L, 42L, 62L, 155L, 19L, 79L, 48L, 138L, 172L, 134L, 231L, 231L, 216L, 136L, 195L, 130L, 101L, 217L, 205L, 174L, 36L, 111L, 140L, 106L, 99L, 22L, 218L, 170L, 7L, 8L, 136L, 19L, 62L, 25L, 123L, 1L, 137L, 220L, 97L, 52L, 169L, 190L, 216L, 1L, 53L, 172L, 100L, 79L, 86L, 201L, 61L, 198L, 17L, 218L, 67L, 106L, 193L, 1L, 138L, 164L, 200L, 190L, 133L, 129L, 74L, 186L, 72L, 41L, 177L, 48L, 113L, 50L, 229L, 178L, 106L, 124L, 75L, 14L, 148L, 218L, 191L, 19L, 219L, 30L, 1L, 71L, 112L, 172L, 21L, 221L, 16L, 203L, 27L, 69L, 143L, 82L, 207L, 223L, 32L, 54L, 60L, 59L, 1L, 97L, 149L, 63L, 132L, 212L, 214L, 1L, 1L, 165L, 77L, 20L, 73L, 26L, 168L, 160L, 233L, 139L, 1L, 188L, 95L, 175L, 206L, 93L, 144L, 135L, 56L, 6L, 189L, 197L, 45L, 30L, 35L, 1L, 4L, 136L, 154L, 89L, 65L, 184L, 225L, 230L, 151L, 1L, 173L, 43L, 44L, 33L, 182L, 34L, 153L, 1L, 1L, 90L, 106L, 110L, 98L, 1L, 219L, 1L, 1L, 116L, 49L, 64L, 57L, 131L, 185L, 145L, 17L, 94L, 1L, 180L, 1L, 120L, 159L, 51L, 172L, 181L, 183L, 7L, 87L, 106L, 228L, 55L, 126L, 157L, 218L, 158L, 115L, 133L, 222L, 84L, 166L, 1L, 37L, 1L, 1L, 202L, 66L, 12L, 1L, 83L, 23L, 110L, 233L, 231L, 161L, 106L, 224L, 127L), .Label = c("0,00", "1.031,00", "1.035,00", "1.042,50", "1.064,00", "1.080,00", "1.120,00", "1.155,00", "1.171,50", "1.177,50", "1.179,00", "1.200,00", "1.204,50", "1.210,00", "1.218,00", "1.220,00", "1.230,00", "1.233,00", "1.260,00", "1.275,00", "1.287,00", "1.290,00", "1.314,00", "1.320,00", "1.356,00", "1.359,50", "1.400,00", "1.410,00", "1.418,00", "1.440,00", "1.466,00", "1.480,00", "1.500,00", "1.566,00", "1.572,00", "1.590,00", "1.611,00", "1.652,00", "1.680,00", "1.722,00", "1.734,00", "1.740,00", "1.752,00", "1.800,00", "1.818,00", "1.821,00", "1.833,00", "1.836,00", "1.854,00", "1.872,00", "1.900,00", "1.920,00", "1.938,00", "10.080,00", "10.230,00", "10.557,00", "10.680,00", "11.238,00", "12.000,00", "12.270,00", "12.420,00", "120,00", "13.644,00", "13.860,00", "14.590,00", "150,00", "165,00", "180,00", "2.002,50", "2.060,00", "2.078,00", "2.100,00", "2.120,00", "2.157,00", "2.320,00", "2.332,50", "2.376,00", "2.382,00", "2.430,00", "2.457,00", "2.475,00", "2.504,00", "2.520,00", "2.526,00", "2.532,00", "2.556,00", "2.572,00", "2.580,00", "2.616,00", "2.640,00", "2.645,00", "2.655,00", "2.706,00", "2.739,00", "2.745,00", "2.767,50", "2.784,00", "2.798,00", "2.820,00", "2.841,00", "2.895,00", "2.937,00", "2.940,00", "2.965,00", "21.515,00", "240,00", "247,50", "259,00", "26.862,00", "264,00", "273,00", "297,00", "3.019,00", "3.035,00", "3.036,00", "3.282,00", "3.306,00", "3.360,00", "3.497,00", "3.504,00", "3.510,00", "3.540,00", "3.600,00", "3.615,00", "3.660,00", "3.710,00", "3.840,00", "3.892,50", "3.900,00", "3.960,00", "3.977,00", "3.996,00", "300,00", "318,00", "330,00", "360,00", "372,00", "396,00", "4.020,00", "4.086,00", "4.092,00", "4.100,00", "4.140,00", "4.158,00", "4.200,00", "4.219,50", "4.331,00", "4.359,00", "4.554,00", "4.618,00", "4.680,00", "4.770,00", "407,50", "410,00", "420,00", "424,00", "480,00", "495,00", "5.052,00", "5.160,00", "5.340,00", "5.622,00", "5.688,00", "5.715,00", "5.745,00", "5.832,00", "5.940,00", "5.958,00", "510,00", "516,00", "537,00", "540,00", "565,50", "570,00", "575,00", "576,00", "585,00", "6.097,50", "6.285,00", "6.495,00", "6.556,00", "6.576,00", "6.720,00", "6.746,00", "6.828,00", "639,00", "645,00", "655,00", "66,00", "660,00", "678,00", "690,00", "693,00", "696,00", "7.056,00", "7.074,00", "7.200,00", "7.236,00", "7.245,00", "7.500,00", "7.600,00", "7.680,00", "7.860,00", "7.920,00", "720,00", "738,00", "754,00", "754,50", "780,00", "795,00", "8.268,00", "8.467,50", "8.592,00", "8.755,00", "8.820,00", "80,00", "801,00", "810,00", "812,00", "840,00", "876,00", "882,00", "895,50", "9.120,00", "9.150,00", "9.310,00", "9.508,00", "924,00", "930,00", "933,00", "960,00", "99,00", "990,00"), class = "factor")), .Names = "AFtotal", class = "data.frame", row.names = c(NA, -299L))
-- Sérgio Henrique Almeida da Silva Junior Doutorando em Epidemiologia em Saúde Pública Escola Nacional de Saúde Pública Sérgio Arouca - ENSP/FIOCRUZ http://lattes.cnpq.br/1611345552843383 Tel: (21) 94429486/78101651 id: 123*20942
_______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
A geométrica realmente não tem muito a ver com este tipo de problema. Uma sugestão para retirar os zeros da sua amostra seria adicionar o gasto energético basal a todas as pessoas, já que não estar praticando atividade física não implica que não exista gasto energético. Abs, Daniel 2012/8/16 Sérgio Henrique <sergio.edfisica@gmail.com>
Oi
Obrigado por sempre ajudar Benilton.
Deixa eu tentar explicar o objetivo do trabalho.
Na verdade que vai trabalhar com esses dados é um amigo no doutorado.
A variável resposta é Atividade Física e ele quer modelar, na verdade, seria gasto energético por AF, os 0 são indivíduos que não praticam AF, ou seja, tem gasto 0, acima desse valor, são gastos energéticos de atividades físicas quaisquer... Os dados não são normais e quando tento normalizar com boxcox não roda por conta dos 0. Quando fiz um histograma dessa variável, me pareceu que os mesmos apresentam distribuição geométrica, parei por aí...
Obrigado mais uma vez.
Enviado via iPod
Em 16/08/2012, às 22:11, Benilton Carvalho <beniltoncarvalho@gmail.com> escreveu:
modelagem envolve muito mais que os dados com os quais vc esta' trabalhando... na verdade, o processo gerador dos dados e' q e' relevante... vc precisa entender a "historia" dos dados...
sobre os seus dados, pergunte-se: 1) eles sao sempre inteiros? 2) se sim, eles representam o numero de tentativas (de alguma coisa) ate' o primeiro sucesso (ou falha)? 3) se sim, cada uma dessas tentativas tem a mesma chance de sucesso (ou falha)?
se sua resposta for "nao" para qq uma das perguntas acima, e' pouco provavel que uma geometrica seja a resposta q vc procura...
b
2012/8/17 Sérgio Henrique almeida da silva ju <sergio.edfisica@gmail.com :
Olá
Estou modelando um conjunto de dados e a variável resposta parece ter distribuição geométrica, qual é a melhor forma de modelar esses dados?
Abraços
dput(dados) structure(list(AFtotal = structure(c(103L, 76L, 58L, 105L, 125L, 163L, 106L, 146L, 227L, 187L, 5L, 117L, 147L, 39L, 232L, 2L, 67L, 150L, 46L, 118L, 18L, 171L, 157L, 226L, 80L, 96L, 142L, 162L, 128L, 152L, 109L, 10L, 108L, 102L, 47L, 208L, 122L, 78L, 176L, 3L, 196L, 40L, 209L, 119L, 179L, 29L, 1L, 81L, 190L, 24L, 204L, 28L, 92L, 136L, 104L, 85L, 91L, 15L, 141L, 13L, 11L, 121L, 112L, 210L, 31L, 6L, 70L, 156L, 114L, 68L, 107L, 112L, 211L, 167L, 1L, 215L, 88L, 213L, 194L, 136L, 220L, 199L, 136L, 9L, 136L, 118L, 38L, 192L, 42L, 62L, 155L, 19L, 79L, 48L, 138L, 172L, 134L, 231L, 231L, 216L, 136L, 195L, 130L, 101L, 217L, 205L, 174L, 36L, 111L, 140L, 106L, 99L, 22L, 218L, 170L, 7L, 8L, 136L, 19L, 62L, 25L, 123L, 1L, 137L, 220L, 97L, 52L, 169L, 190L, 216L, 1L, 53L, 172L, 100L, 79L, 86L, 201L, 61L, 198L, 17L, 218L, 67L, 106L, 193L, 1L, 138L, 164L, 200L, 190L, 133L, 129L, 74L, 186L, 72L, 41L, 177L, 48L, 113L, 50L, 229L, 178L, 106L, 124L, 75L, 14L, 148L, 218L, 191L, 19L, 219L, 30L, 1L, 71L, 112L, 172L, 21L, 221L, 16L, 203L, 27L, 69L, 143L, 82L, 207L, 223L, 32L, 54L, 60L, 59L, 1L, 97L, 149L, 63L, 132L, 212L, 214L, 1L, 1L, 165L, 77L, 20L, 73L, 26L, 168L, 160L, 233L, 139L, 1L, 188L, 95L, 175L, 206L, 93L, 144L, 135L, 56L, 6L, 189L, 197L, 45L, 30L, 35L, 1L, 4L, 136L, 154L, 89L, 65L, 184L, 225L, 230L, 151L, 1L, 173L, 43L, 44L, 33L, 182L, 34L, 153L, 1L, 1L, 90L, 106L, 110L, 98L, 1L, 219L, 1L, 1L, 116L, 49L, 64L, 57L, 131L, 185L, 145L, 17L, 94L, 1L, 180L, 1L, 120L, 159L, 51L, 172L, 181L, 183L, 7L, 87L, 106L, 228L, 55L, 126L, 157L, 218L, 158L, 115L, 133L, 222L, 84L, 166L, 1L, 37L, 1L, 1L, 202L, 66L, 12L, 1L, 83L, 23L, 110L, 233L, 231L, 161L, 106L, 224L, 127L), .Label = c("0,00", "1.031,00", "1.035,00", "1.042,50", "1.064,00", "1.080,00", "1.120,00", "1.155,00", "1.171,50", "1.177,50", "1.179,00", "1.200,00", "1.204,50", "1.210,00", "1.218,00", "1.220,00", "1.230,00", "1.233,00", "1.260,00", "1.275,00", "1.287,00", "1.290,00", "1.314,00", "1.320,00", "1.356,00", "1.359,50", "1.400,00", "1.410,00", "1.418,00", "1.440,00", "1.466,00", "1.480,00", "1.500,00", "1.566,00", "1.572,00", "1.590,00", "1.611,00", "1.652,00", "1.680,00", "1.722,00", "1.734,00", "1.740,00", "1.752,00", "1.800,00", "1.818,00", "1.821,00", "1.833,00", "1.836,00", "1.854,00", "1.872,00", "1.900,00", "1.920,00", "1.938,00", "10.080,00", "10.230,00", "10.557,00", "10.680,00", "11.238,00", "12.000,00", "12.270,00", "12.420,00", "120,00", "13.644,00", "13.860,00", "14.590,00", "150,00", "165,00", "180,00", "2.002,50", "2.060,00", "2.078,00", "2.100,00", "2.120,00", "2.157,00", "2.320,00", "2.332,50", "2.376,00", "2.382,00", "2.430,00", "2.457,00", "2.475,00", "2.504,00", "2.520,00", "2.526,00", "2.532,00", "2.556,00", "2.572,00", "2.580,00", "2.616,00", "2.640,00", "2.645,00", "2.655,00", "2.706,00", "2.739,00", "2.745,00", "2.767,50", "2.784,00", "2.798,00", "2.820,00", "2.841,00", "2.895,00", "2.937,00", "2.940,00", "2.965,00", "21.515,00", "240,00", "247,50", "259,00", "26.862,00", "264,00", "273,00", "297,00", "3.019,00", "3.035,00", "3.036,00", "3.282,00", "3.306,00", "3.360,00", "3.497,00", "3.504,00", "3.510,00", "3.540,00", "3.600,00", "3.615,00", "3.660,00", "3.710,00", "3.840,00", "3.892,50", "3.900,00", "3.960,00", "3.977,00", "3.996,00", "300,00", "318,00", "330,00", "360,00", "372,00", "396,00", "4.020,00", "4.086,00", "4.092,00", "4.100,00", "4.140,00", "4.158,00", "4.200,00", "4.219,50", "4.331,00", "4.359,00", "4.554,00", "4.618,00", "4.680,00", "4.770,00", "407,50", "410,00", "420,00", "424,00", "480,00", "495,00", "5.052,00", "5.160,00", "5.340,00", "5.622,00", "5.688,00", "5.715,00", "5.745,00", "5.832,00", "5.940,00", "5.958,00", "510,00", "516,00", "537,00", "540,00", "565,50", "570,00", "575,00", "576,00", "585,00", "6.097,50", "6.285,00", "6.495,00", "6.556,00", "6.576,00", "6.720,00", "6.746,00", "6.828,00", "639,00", "645,00", "655,00", "66,00", "660,00", "678,00", "690,00", "693,00", "696,00", "7.056,00", "7.074,00", "7.200,00", "7.236,00", "7.245,00", "7.500,00", "7.600,00", "7.680,00", "7.860,00", "7.920,00", "720,00", "738,00", "754,00", "754,50", "780,00", "795,00", "8.268,00", "8.467,50", "8.592,00", "8.755,00", "8.820,00", "80,00", "801,00", "810,00", "812,00", "840,00", "876,00", "882,00", "895,50", "9.120,00", "9.150,00", "9.310,00", "9.508,00", "924,00", "930,00", "933,00", "960,00", "99,00", "990,00"), class = "factor")), .Names = "AFtotal", class = "data.frame", row.names = c(NA, -299L))
-- Sérgio Henrique Almeida da Silva Junior Doutorando em Epidemiologia em Saúde Pública Escola Nacional de Saúde Pública Sérgio Arouca - ENSP/FIOCRUZ http://lattes.cnpq.br/1611345552843383 Tel: (21) 94429486/78101651 id: 123*20942
_______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
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participantes (4)
-
Benilton Carvalho -
Daniel C Bezerra -
Sérgio Henrique -
Sérgio Henrique almeida da silva ju