- R-br - DINF UFPR

Dúvida gráfico lattice da função plot.nlme
by Fernando Souza 13 Dez '17

13 Dez '17

Caros colegas, Devido ao elevado número de individuos avaliados há uma sobreposição dos identificados no eixo y do gráfico, o que o torna incompreensível. Como posso organizar esses indicadores? Ou como posso removê-los. library(nlme) library(lattice) GALINHA<-structure(list(ID = structure(c(128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 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"238", "241", "6", "297", "340", "327", "313", "355", "322", "121", "237", "51", "58", "81", "43", "59", "115", "339", "369", "184", "24", "119", "191", "8", "308", "204", "63", "21", "5", "22", "85", "68", "80", "194", "2", "62", "183", "86", "4", "94", "30", "28", "35", "261", "49", "83", "56", "61", "228", "27", "108", "179", "17", "31", "48", "198", "278", "79", "60", "130", "98", "66", "120", "15", "229", "36", "38", "132", "92", "306", "91", "25", "209", "235", "45", "88", "215", "111", "84", "34", "53", "196", "10", "46", "65", "99", "203", "197", "149", "33", "134", "102", "181", "82", "103", "3", "193", "95", "137", "44", "41", "185", "104", "172", "214", "267", "216", "173", "110", "125", "72", "76", "90", "210", "1", "328", "131", "29", "174", "37", "89", "113", "67", "78", "365", "255", "240", "52", "169", "106", "69", "93", "200", "116", "145", "234", "54", "256", "77", "97", "133", "118", "117", "87", "221", "155", "47", "40", "252", "268", "223", "139", "167", "154", "318", "190", "147", "109", "364", "202", "175", "363", "263", "224", "32", "182", "244", "274", "163", "219", "16", "122", "226", "366", "220", "13", "225", "156", "195", "26", "320", "246", "232", "211", "150", "170", "201", "142", "199", "114", "239", "20", "269", "279", "282", "324", "7", "148", "140", "354", "96", "177", "136", "171", "319", "285", "356", "164", "290", "129", "358", "346", "257", "126", "233", "152", "107", "165", "321", "73", "168", "265", "330", "227", "128", "276", "230", "166", "213", "236", "242", "292", "50", "124", "294", "153", "231", "264", "222", "286", "253", "11", "316", "100", "127", "310", "301", "305", "123", "362", "39", "247", "176", "326", "19", "249", "258", "270", "250", "74", "341", "317", "360", "105", "57", "333", "259", "9", "112", "157", "55", "218", "151", "64", "42", "248", "207", "135", "192", "18", "357", "275", "298", "303", "287", "289", "344", "180", "205", "245", "187", "159", "101", "291", "141", "302", "212", "348", "349", "143", "272", "295", "277", "160", "309", "359", "158", "146", "342", "14", "144", "178", "329", "260", "304", "273", "162", "314", "208", "138", "293", "343", "206", "350", "351", "243", "338", "300", "284", "283", "254", "188", "312", "361", "315", "288", "251", "266", "334", "331", "353", "161", "352", "325", "311", "332", "335", "262", "280", "189", "347", "271", "296", "307", "323", "299", "345"), class = c("ordered", "factor")), TRAT = structure(c(3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 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6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), PESO = c(0.0395, 0.0431, 0.046, 0.043, 0.043, 0.0436, 0.042, 0.0405, 0.0417, 0.045, 0.043, 0.0416, 0.041, 0.042, 0.0445, 0.044, 0.047, 0.043, 0.049, 0.0435, 0.044, 0.0421, 0.0444, 0.041, 0.042, 0.0415, 0.0411, 0.0422, 0.042, 0.042, 0.045, 0.045, 0.0416, 0.044, 0.044, 0.041, 0.04, 0.039, 0.0408, 0.037, 0.044, 0.042, 0.041, 0.04, 0.0435, 0.039, 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0.1895, 0.1737, 0.1577, 0.1716, 0.1605, 0.1879, 0.1814, 0.1578, 0.1684, 0.157, 0.1619, 0.1793, 0.1828, 0.1705, 0.184, 0.1516, 0.1754, 0.1818, 0.1984, 0.1711, 0.1765, 0.2006, 0.1728, 0.1843, 0.178, 0.1707, 0.1761, 0.175, 0.1882, 0.1915, 0.1719, 0.1618, 0.165, 0.153, 0.167, 0.184, 0.165, 0.17, 0.1745, 0.1825, 0.1461, 0.2103, 0.1687, 0.17, 0.183, 0.18, 0.1683, 0.1898, 0.1731, 0.1827, 0.1693, 0.1859, 0.1723, 0.2012, 0.1988, 0.1447, 0.1898, 0.1522, 0.1659, 0.178, 0.18, 0.1824, 0.1778, 0.1824, 0.1854, 0.181, 0.1684, 0.1825, 0.1898, 0.186, 0.1813, 0.1852, 0.189, 0.1812, 0.1847, 0.167, 0.1523, 0.1881, 0.1753, 0.2212, 0.204, 0.205, 0.203, 0.203, 0.1786, 0.1906, 0.1907, 0.1894, 0.186, 0.18, 0.197, 0.1349, 0.1805, 0.1968, 0.1885, 0.1794, 0.1953, 0.1835, 0.2037, 0.21, 0.1988, 0.1806, 0.1847, 0.1941, 0.1857, 0.182, 0.1814, 0.178, 0.19, 0.179, 0.1911, 0.1925, 0.1763, 0.1927, 0.207, 0.183, 0.1831, 0.188, 0.1753, 0.2071, 0.1976, 0.1941, 0.1688, 0.1778, 0.1941, 0.1653, 0.19, 0.194, 0.1889, 0.1835, 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2.98, NA, 2.813, 2.7309, 2.9303, 2.8687, NA, 2.8316, 2.8766, 2.7308, 2.7438, 2.6303, 2.6875, 2.6631, 2.7013, NA, 3.0026, 2.681, 2.9199, 2.7356, NA, 2.5868, 2.8237, 2.7014, 2.9015, 2.9353, 2.7509, 2.8947, 2.9439, NA, NA, 2.852, NA, NA, 2.7421, 2.8189, 2.8385, 2.7892, 3.0434, 2.6936, 2.966, 2.8017, 2.8051, 2.8421, 2.8422, 2.9182, 2.9074, 2.6816, NA, 2.6885, 2.7776, 2.6932, 2.8139, 2.7443, 2.8721, 2.7336, 2.6389, 2.6348, 2.5962, 2.6568, NA, NA, NA, NA)), .Names = c("ID", "TRAT", "SEMANA", "PESO"), row.names = c(NA, -2590L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = PESO ~ SEMANA | ID, FUN = function (x) max(x, na.rm = TRUE), order.groups = TRUE) #-------------------------------ESTIMATIVA DOS PARAMETROS----------- modelo1.list <- nlsList(PESO ~ a * exp(-b * exp( -c * SEMANA)),start = c(a = 4.10,b = 4.53,c = 0.37 ),na.action=na.omit,data =GALINHA) #--------------------------GRÁFICO------------------------------------ plot(intervals(modelo1.list),layout = c(3,1)) -- ========================================= Fernando Souza Zootecnista, DSc. Produção e Alimentação Animal Celular: (31)99796-8781 (Vivo) E-mail:nandodesouza@gmail.com <e-mail%3Anandodesouza(a)gmail.com> Lattes: http://lattes.cnpq.br/6519538815038307 Blog: https://producaoanimalcomr.wordpress.com/ ==========================================

2 2

Panel xyplot Lattice
by Fernando Souza 05 Dez '17

05 Dez '17

Caros colegas Criei esses gráficos utilizando o xyplot {lattice}, utilizei o argumento aspect="xy" os gráficos ficaram como eu queria, porém a relação a área da imagem eles ficaram muito pequenos, como posso reduzir essa diferença? Segue o CMR, anexo inserir a imagem para vcs verem o problema. Como resolver? #install.packages("tidyr") #install.packages("lattice") #install.packages("RColorBrewer") library(tidyr) library(lattice) library(RColorBrewer) DADOS.CURTO <- read.csv(" https://www.dropbox.com/s/b4cckybgrcg86me/galinhas.csv?raw=1",head = TRUE) head(DADOS.CURTO) colnames(DADOS.CURTO) <- c("0","1","2","3","4","5","6","TRAT") DADOS<- DADOS.CURTO %>% gather('0','1','2','3','4','5','6',key ="SEMANA",value = "PESO", -TRAT) head(DADOS) str(DADOS) ID <-rep(seq(1,370), times=7) DADOS<-cbind(ID,DADOS) DADOS <- transform(DADOS, SEMANA = as.numeric(SEMANA),ID=factor(ID)) cores = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)] parset <-list(plot.symbol = list(col = "black",fill = "black"),strip.background=list(col="lightgrey"),superpose.line = list(col = cores[1:375]),superpose.symbol = list(col = cores[1:375])) xyplot(PESO~SEMANA|TRAT,groups=ID,data=DADOS,ylab = "PESO (Kg)",pch = 16,par.settings =parset ,type=c("l","p","g"),layout = c(3,1),aspect = "xy") -- ========================================= Fernando Souza Zootecnista, DSc. Produção e Alimentação Animal Celular: (31)99796-8781 (Vivo) E-mail:nandodesouza@gmail.com <e-mail%3Anandodesouza(a)gmail.com> Lattes: http://lattes.cnpq.br/6519538815038307 Blog: https://producaoanimalcomr.wordpress.com/ ==========================================

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Re: [R-br] Sobreposição de BoxPlots
by Leonard Assis 05 Dez '17

05 Dez '17

Jasmire, Acredito que faltou uma coisa básica: voce explicar o que você espera obter como resultado(visualizar) para que alguém possa propor uma solução. Leonard Em 1 de dez de 2017 6:34 PM, "Jasmine Moreira via R-br" < r-br(a)listas.c3sl.ufpr.br> escreveu: Pessoas queridas, estou encontrando um problema de sobreposição ao utilizar um boxplot com o parâmetro barwidth (usei alpha=0.6 para mostrar a sobreposição). Obrigada pela ajuda, Jasmine library(ggplot2) data(mtcars) ggplot(mtcars, aes(x=factor(cyl), y=mpg)) + geom_boxplot(aes(fill=factor(gear)), alpha=0.6, varwidth = TRUE) Resultado: Sem o parâmetro barwidth: _______________________________________________ R-br mailing list R-br(a)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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Sobreposição de BoxPlots
by Jasmine Moreira 01 Dez '17

01 Dez '17

Pessoas queridas, estou encontrando um problema de sobreposição ao utilizar um boxplot com o parâmetro barwidth (usei alpha=0.6 para mostrar a sobreposição). Obrigada pela ajuda, Jasmine library(ggplot2) data(mtcars) ggplot(mtcars, aes(x=factor(cyl), y=mpg)) + geom_boxplot(aes(fill=factor(gear)), alpha=0.6, varwidth = TRUE) Resultado: Sem o parâmetro barwidth:

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Modelo de regressão separando por sexo
by Luciane Maria Pilotto 01 Dez '17

01 Dez '17

Olá, estou tentando rodar análises de regressão separando por sexo (data=reg[reg$sexo == 1, ]) e resulta no seguinte erro: GLM.2 <- glm(dent ~ idcat2, family=binomial(logit), data=reg[reg$sexo == 1, ]) Error in model.frame.default(formula = dent ~ idcat2, data = reg[reg$sexo == : comprimentos das variáveis diferem (encontradas em 'idcat2') Alguma dica? _________________________________________ Luciane Maria Pilotto

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editar eixo no ggplo2
by Ze Henrique 01 Dez '17

01 Dez '17

Boa noite pessoal, desde já agradeço a ajuda de vcs. Estou tentando fazer uma figura usando o ggplot2 mas os nomes em volta cortão, alguém poderia me ajudar, em qual função devo alterar para isso não acontecer mais, tentei varias cosias mas não consegui. Desde já obrigado. [image: Imagem inline 1] -- *José Henrique Soler Guilhen* Doutorando em Genética e Melhoramento Universidade Federal do Espírito Santo - CCAUFES

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Peso espacial em X usando spreg- regressão espacial
by Edimeire Alexandra Pinto 21 Nov '17

21 Nov '17

Boa tarde, pessoal. Alguém por acaso sabe com se trabalha com modelos "parecidos" com CLIFF-ORD usando spreg do pacote sphet? Sei que o Stata faz, mas gostaria de fazer no R. Estou trabalhando com vários tipos de modelos espaciais (na realidade, quase todos do pacote sphet) e esse me gerou muita dúvida, pois preciso de que uma das variaveis explicativas com e sem peso espacial da matriz W. Por exemplo, acrescentando peso espacial a uma das variaveis explicativas do modelo. library(spdep)data(columbus)listw<-nb2listw(col.gal.nb)res1<- spreg(CRIME~HOVAL + INC+ INC, data=columbus , listw= listw, het = TRUE, verbose = FALSE,model="sarar")res2<- spreg(CRIME~HOVAL + INC+ WINC, data=columbus , listw= listw, het = TRUE, verbose = FALSE,model="sarar") ### WINC é o peso espacial multiplicado #pelo variavel INC, especie de W*INC ou mesmo res3<- spreg(CRIME~HOVAL + INC+WINC, data=columbus , listw= listw, het = TRUE, verbose = FALSE,model="error")

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Reshape
by Edson Lira 17 Nov '17

17 Nov '17

Bom dia caros amigos, estou trabalhando com a base de dados abaixo(somente 6 linhas): id UF x2002 x2003 x2004 x2005 x2006 x2007 x2008 x2009 x2011 x2012 x2013 x2014 1 1 Acre 192 189 311 311 333 327 338 354 357 381 337 373 2 2 Alagoas 1251 1269 1292 1357 1413 1410 1355 1401 1344 1391 1429 1417 3 3 Amapá 204 198 239 262 252 294 317 299 302 311 332 355 4 4 Amazonas 958 1050 1433 1528 1502 1488 1531 1673 1689 1720 1771 1908 5 5 Bahia 6885 6901 7167 7336 7292 7338 7665 7792 7602 7416 7530 7879 6 6 Ceará 3705 3871 3944 4120 4143 4138 4314 4380 4121 4184 4215 4319 Estou usando o reshape para tentar transformar as linhas em colunas, ou seja, cada uma UF seria uma coluna, e assim com as demaisComo gostaria que ficasse: Acre Alagoas Amapá Amazonas Bahia Ceará ano 192 1251 3705 2002 189 1269 3871 2003 311 1293 3944 2004 ... .... Estou usando a rotina: pea<-reshape(pea1, varying=c("x2002","x2003","x2004","x2005","x2006","x2007", "x2008","x2009","x2011","x2012","x2013","x2014"), v.names="Medida", timevar="UF", times=c("Acre","Alagoas","Amapá","Amazonas","Bahia", "Ceará","Distrito Federal","Espírito Santo", "Goiás","Maranhão","Mato Grosso","Mato Grosso do Sul", "Minas Gerais","Pará","Paraíba","Paraná"," Pernambuco", "Piauí","Rio de Janeiro","Rio Grande do Norte", "Rio Grande do Sul","Rondônia","Roraima", "Santa Catarina","São Paulo","Sergipe","Tocantins"), new.row.names=1:27, direction="wide") que está me dando o erro: Erro em varying[, i] : número incorreto de dimensões Alguém tem alguma sugesstão? [ ]'s Prof. Edson Lira, Me Estatístico Manaus-Amazonas

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usar separador de milhar
by Adriele Giaretta Biase 16 Nov '17

16 Nov '17

Bom dia a todos, tenho uma tabela, em que os números não estão separados por milhar, uma tabela com muitas linhas, mas para exemplificar, tomei como exemplo essa tabela pequena abaixo: Ex: teste metodo_1 metodo_2 A 30000000 530000000 B 40000034 550003600 C 30000020 570000002 D 50000000 830000000 E 20000000 730000000 F 30000400 530000500 Eu faço graficos de barras usando cada linha de forma que os valores apareçam em cima de cada barra. A dúvida é como fazer com que esses valores (da tabela) fiquem separados por milhar. A minha intenção é fazer tanto em padrão ingles como em padrão português. Exemplo, tomando o padrão português, o teste A da tabela acima, no metodo1 queria que o R lesse 3.000.0000 e no metodo2 53.000.0000 Se tomasse o padrão ingles, o teste A no metodo1 seria 3,000,0000 e no metodo2 53,000,0000 Porém, a tabela não tem nem ponto e nem virgula para poder trocar de uma nomenclatura para outra.

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Corrigir ausência de dados
by Edson Lira 15 Nov '17

15 Nov '17

Caros amigos, tenho uma série de dados de pea e pnea que têm uma ausência de dados no ano de 2010. Como faço para corrigir essa ausência? TrêsDuas dúvidas: 1. Como organizar a planilha visto que devo corrigir a ausência por estado para corrigir a ausência de pea e pnea para o ano de 2010 por estado?2. Qual a técnica que me aconselham a usar?3. E como fazê-lo com uma única rotina? Abaixo o link da planilha Download pea pnea 02 14.csv | | | Download pea pnea 02 14.csv | | | [ ]'s Prof. Edson Lira, Me Estatístico Manaus-Amazonas

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