为什么当我尝试将数值变量转换为因子时会出现此问题？-解网

问：

为了构建更准确的模型，我将分类变量从数值转换为因子。但是，每当我为此做一个变量时，它都会将其从 0 秒和 1 秒切换到 1 秒和 2 秒。我希望变量保持二进制，但从数值切换到因子。我能做些什么来解决这个问题？

代码如下：

heartfail$DEATH_EVENT <- factor(heartfail$DEATH_EVENT, levels = c(0, 1), labels = c("Survived", "Dead"))

我尝试了许多不同的方法来做到这一点，但没有一个代码有效。这导致我在对逻辑回归进行向后消除时出现错误。

这是 dput 结果（对不起，它很大）：

    structure(list(age = c(75, 55, 65, 50, 65, 90, 75, 60, 65, 80,
75, 62, 45, 50, 49, 82, 87, 45, 70, 48, 65, 65, 68, 53, 75, 80,
95, 70, 58, 82, 94, 85, 50, 50, 65, 69, 90, 82, 60, 60, 70, 50,
70, 72, 60, 50, 51, 60, 80, 57, 68, 53, 60, 70, 60, 95, 70, 60,
49, 72, 45, 50, 55, 45, 45, 60, 42, 72, 70, 65, 41, 58, 85, 65,
69, 60, 70, 42, 75, 55, 70, 67, 60, 79, 59, 51, 55, 65, 44, 57,
70, 60, 42, 60, 58, 58, 63, 70, 60, 63, 65, 75, 80, 42, 60, 72,
55, 45, 63, 45, 85, 55, 50, 70, 60, 58, 60, 85, 65, 86, 60, 66,
60, 60, 60, 43, 46, 58, 61, 53, 53, 60, 46, 63, 81, 75, 65, 68,
62, 50, 80, 46, 50, 61, 72, 50, 52, 64, 75, 60, 72, 62, 50, 50,
65, 60, 52, 50, 85, 59, 66, 45, 63, 50, 45, 80, 53, 59, 65, 70,
51, 52, 70, 50, 65, 60, 69, 49, 63, 55, 40, 59, 65, 75, 58, 60.667,
50, 60, 60.667, 40, 80, 64, 50, 73, 45, 77, 45, 65, 50, 60, 63,
45, 70, 60, 78, 50, 40, 85, 60, 49, 70, 50, 78, 48, 65, 73, 70,
54, 68, 55, 73, 65, 42, 47, 58, 75, 58, 55, 65, 72, 60, 70, 40,
53, 53, 77, 75, 70, 65, 55, 70, 65, 40, 73, 54, 61, 55, 64, 40,
53, 50, 55, 50, 70, 53, 52, 65, 58, 45, 53, 55, 62, 65, 68, 61,
50, 55, 56, 45, 40, 44, 51, 67, 42, 60, 45, 70, 70, 50, 55, 70,
70, 42, 65, 50, 55, 60, 45, 65, 90, 45, 60, 52, 63, 62, 55, 45,
45, 50), anaemia = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
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1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
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1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L), levels = c("False", "True"), class = "factor"),
    creatinine_phosphokinase = c(582L, 7861L, 146L, 111L, 160L,
    47L, 246L, 315L, 157L, 123L, 81L, 231L, 981L, 168L, 80L,
    379L, 149L, 582L, 125L, 582L, 52L, 128L, 220L, 63L, 582L,
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    60L, 855L, 2656L, 235L, 582L, 124L, 571L, 127L, 588L, 582L,
    1380L, 582L, 553L, 129L, 577L, 91L, 3964L, 69L, 260L, 371L,
    75L, 607L, 789L, 364L, 7702L, 318L, 109L, 582L, 582L, 68L,
    250L, 110L, 161L, 113L, 148L, 582L, 5882L, 224L, 582L, 47L,
    92L, 102L, 203L, 336L, 69L, 582L, 76L, 55L, 280L, 78L, 47L,
    68L, 84L, 115L, 66L, 897L, 582L, 154L, 144L, 133L, 514L,
    59L, 156L, 61L, 305L, 582L, 898L, 5209L, 53L, 328L, 748L,
    1876L, 936L, 292L, 129L, 60L, 369L, 143L, 754L, 400L, 96L,
    102L, 113L, 582L, 737L, 68L, 96L, 582L, 582L, 358L, 168L,
    200L, 248L, 270L, 1808L, 1082L, 719L, 193L, 4540L, 582L,
    59L, 646L, 281L, 1548L, 805L, 291L, 482L, 84L, 943L, 185L,
    132L, 1610L, 582L, 2261L, 233L, 30L, 115L, 1846L, 335L, 231L,
    58L, 250L, 910L, 129L, 72L, 130L, 582L, 2334L, 2442L, 776L,
    196L, 66L, 582L, 835L, 582L, 3966L, 171L, 115L, 198L, 95L,
    1419L, 69L, 122L, 835L, 478L, 176L, 395L, 99L, 145L, 104L,
    582L, 1896L, 151L, 244L, 582L, 62L, 121L, 231L, 582L, 418L,
    582L, 167L, 582L, 1211L, 1767L, 308L, 97L, 59L, 64L, 167L,
    101L, 212L, 2281L, 972L, 212L, 582L, 224L, 131L, 135L, 582L,
    1202L, 427L, 1021L, 582L, 582L, 118L, 86L, 582L, 582L, 675L,
    57L, 2794L, 56L, 211L, 166L, 93L, 129L, 707L, 582L, 109L,
    119L, 232L, 720L, 180L, 81L, 582L, 90L, 1185L, 582L, 80L,
    2017L, 143L, 624L, 207L, 2522L, 572L, 245L, 88L, 446L, 191L,
    326L, 132L, 66L, 56L, 66L, 655L, 258L, 157L, 582L, 298L,
    1199L, 135L, 582L, 582L, 582L, 582L, 213L, 64L, 257L, 582L,
    618L, 582L, 1051L, 84L, 2695L, 582L, 64L, 1688L, 54L, 170L,
    253L, 582L, 892L, 337L, 615L, 320L, 190L, 103L, 61L, 1820L,
    2060L, 2413L, 196L), diabetes = structure(c(1L, 1L, 1L, 1L,
    2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
    1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
    1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
    1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
    2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
    1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
    1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
    1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
    2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
    1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
    2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
    1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
    2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
    1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L), levels = c("False",
    "True"), class = "factor"), ejection_fraction = c(20L, 38L,
    20L, 20L, 20L, 40L, 15L, 60L, 65L, 35L, 38L, 25L, 30L, 38L,
    30L, 50L, 38L, 14L, 25L, 55L, 25L, 30L, 35L, 60L, 30L, 38L,
    40L, 45L, 38L, 30L, 38L, 45L, 35L, 30L, 50L, 35L, 50L, 50L,
    30L, 38L, 20L, 30L, 45L, 50L, 60L, 38L, 25L, 38L, 20L, 30L,
    25L, 20L, 62L, 50L, 38L, 30L, 35L, 40L, 20L, 20L, 25L, 40L,
    35L, 35L, 80L, 20L, 15L, 25L, 25L, 25L, 40L, 35L, 35L, 50L,
    20L, 20L, 60L, 40L, 38L, 45L, 40L, 50L, 25L, 50L, 25L, 50L,
    35L, 60L, 40L, 25L, 45L, 45L, 60L, 25L, 38L, 60L, 25L, 60L,
    25L, 40L, 25L, 45L, 25L, 30L, 50L, 30L, 45L, 35L, 38L, 35L,
    60L, 35L, 25L, 60L, 40L, 40L, 60L, 60L, 60L, 38L, 60L, 38L,
    38L, 30L, 40L, 50L, 17L, 60L, 30L, 35L, 60L, 45L, 40L, 60L,
    35L, 40L, 60L, 25L, 35L, 30L, 38L, 35L, 30L, 40L, 25L, 30L,
    30L, 60L, 30L, 35L, 45L, 60L, 45L, 35L, 35L, 25L, 35L, 25L,
    50L, 45L, 40L, 35L, 40L, 35L, 30L, 38L, 60L, 20L, 40L, 35L,
    35L, 40L, 60L, 20L, 35L, 60L, 40L, 50L, 60L, 40L, 30L, 25L,
    25L, 38L, 25L, 30L, 50L, 25L, 40L, 45L, 35L, 60L, 40L, 30L,
    20L, 45L, 38L, 30L, 20L, 35L, 45L, 60L, 60L, 25L, 40L, 45L,
    40L, 38L, 40L, 35L, 17L, 62L, 50L, 30L, 35L, 35L, 50L, 70L,
    35L, 35L, 20L, 50L, 35L, 25L, 25L, 60L, 25L, 35L, 25L, 25L,
    30L, 35L, 35L, 38L, 45L, 50L, 50L, 30L, 40L, 45L, 35L, 30L,
    35L, 40L, 38L, 38L, 25L, 25L, 35L, 40L, 30L, 35L, 45L, 35L,
    60L, 30L, 38L, 38L, 25L, 50L, 40L, 40L, 25L, 60L, 38L, 35L,
    20L, 38L, 38L, 35L, 30L, 40L, 38L, 40L, 30L, 38L, 35L, 38L,
    30L, 38L, 40L, 40L, 30L, 38L, 40L, 40L, 35L, 55L, 35L, 38L,
    55L, 35L, 38L, 35L, 38L, 38L, 60L, 38L, 45L), high_blood_pressure = structure(c(2L,
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
    1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
    1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
    2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
    1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
    2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
    2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
    1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
    2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), levels = c("False",
    "True"), class = "factor"), platelets = c(265000, 263358.03,
    162000, 210000, 327000, 204000, 127000, 454000, 263358.03,
    388000, 368000, 253000, 136000, 276000, 427000, 47000, 262000,
    166000, 237000, 87000, 276000, 297000, 289000, 368000, 263358.03,
    149000, 196000, 284000, 153000, 2e+05, 263358.03, 360000,
    319000, 302000, 188000, 228000, 226000, 321000, 305000, 329000,
    263358.03, 153000, 185000, 218000, 194000, 310000, 271000,
    451000, 140000, 395000, 166000, 418000, 263358.03, 351000,
    255000, 461000, 223000, 216000, 319000, 254000, 390000, 216000,
    254000, 385000, 263358.03, 119000, 213000, 274000, 244000,
    497000, 374000, 122000, 243000, 149000, 266000, 204000, 317000,
    237000, 283000, 324000, 293000, 263358.03, 196000, 172000,
    302000, 406000, 173000, 304000, 235000, 181000, 249000, 297000,
    263358.03, 210000, 327000, 219000, 254000, 255000, 318000,
    221000, 298000, 263358.03, 149000, 226000, 286000, 621000,
    263000, 226000, 304000, 850000, 306000, 228000, 252000, 351000,
    328000, 164000, 271000, 507000, 203000, 263358.03, 210000,
    162000, 228000, 127000, 217000, 237000, 271000, 3e+05, 267000,
    227000, 249000, 250000, 263358.03, 295000, 231000, 263358.03,
    172000, 305000, 221000, 211000, 263358.03, 348000, 329000,
    229000, 338000, 266000, 218000, 242000, 225000, 228000, 235000,
    244000, 184000, 263358.03, 235000, 194000, 277000, 262000,
    235000, 362000, 242000, 174000, 448000, 75000, 334000, 192000,
    220000, 70000, 270000, 305000, 263358.03, 325000, 176000,
    189000, 281000, 337000, 105000, 132000, 267000, 279000, 303000,
    221000, 265000, 224000, 219000, 389000, 153000, 365000, 201000,
    275000, 350000, 309000, 260000, 160000, 126000, 223000, 263358.03,
    259000, 279000, 263358.03, 73000, 377000, 220000, 212000,
    277000, 362000, 226000, 186000, 283000, 268000, 389000, 147000,
    481000, 244000, 290000, 203000, 358000, 151000, 271000, 371000,
    263358.03, 194000, 365000, 130000, 504000, 265000, 189000,
    141000, 237000, 274000, 62000, 185000, 255000, 330000, 305000,
    406000, 248000, 173000, 257000, 263358.03, 533000, 249000,
    255000, 220000, 264000, 282000, 314000, 246000, 301000, 223000,
    404000, 231000, 274000, 236000, 263358.03, 334000, 294000,
    253000, 233000, 308000, 203000, 283000, 198000, 208000, 147000,
    362000, 263358.03, 133000, 302000, 222000, 263358.03, 221000,
    215000, 189000, 150000, 422000, 327000, 25100, 232000, 451000,
    241000, 51000, 215000, 263358.03, 279000, 336000, 279000,
    543000, 263358.03, 390000, 222000, 133000, 382000, 179000,
    155000, 270000, 742000, 140000, 395000), serum_creatinine = c(1.9,
    1.1, 1.3, 1.9, 2.7, 2.1, 1.2, 1.1, 1.5, 9.4, 4, 0.9, 1.1,
    1.1, 1, 1.3, 0.9, 0.8, 1, 1.9, 1.3, 1.6, 0.9, 0.8, 1.83,
    1.9, 1, 1.3, 5.8, 1.2, 1.83, 3, 1, 1.2, 1, 3.5, 1, 1, 2.3,
    3, 1.83, 1.2, 1.2, 1, 1.1, 1.9, 0.9, 0.6, 4.4, 1, 1, 1.4,
    6.8, 1, 2.2, 2, 2.7, 0.6, 1.1, 1.3, 1, 2.3, 1.1, 1, 1.18,
    2.9, 1.3, 1, 1.2, 1.83, 0.8, 0.9, 1, 1.3, 1.2, 0.7, 0.8,
    1.2, 0.6, 0.9, 1.7, 1.18, 2.5, 1.8, 1, 0.7, 1.1, 0.8, 0.7,
    1.1, 0.8, 1, 1.18, 1.7, 0.7, 1, 1.3, 1.1, 1.2, 1.1, 1.1,
    1.18, 1.1, 1, 2.3, 1.7, 1.3, 0.9, 1.1, 1.3, 1.2, 1.2, 1.6,
    1.3, 1.2, 1, 0.7, 3.2, 0.9, 1.83, 1.5, 1, 0.75, 0.9, 3.7,
    1.3, 2.1, 0.8, 0.7, 3.4, 0.7, 6.1, 1.18, 1.3, 1.18, 1.18,
    0.9, 2.1, 1, 0.8, 1.1, 0.9, 0.9, 0.9, 1.7, 0.7, 0.7, 1, 1.83,
    0.9, 2.5, 0.9, 0.9, 1.18, 0.8, 1.7, 1.4, 1, 1.3, 1.1, 1.2,
    0.8, 0.9, 0.9, 1.1, 1.3, 0.7, 2.4, 1, 0.8, 1.5, 0.9, 1.1,
    0.8, 0.9, 1, 1, 1, 1.2, 0.7, 0.9, 1, 1.2, 2.5, 1.2, 1.5,
    0.6, 2.1, 1, 0.9, 2.1, 1.5, 0.7, 1.18, 1.6, 1.8, 1.18, 0.8,
    1, 1.8, 0.7, 1, 0.9, 3.5, 0.7, 1, 0.8, 0.9, 1, 0.8, 1, 0.8,
    1.4, 1.6, 0.8, 1.3, 0.9, 9, 1.1, 0.7, 1.83, 1.1, 1.1, 0.8,
    1, 1.4, 1.3, 1, 5, 1.2, 1.7, 1.1, 0.9, 1.4, 1.1, 1.1, 1.1,
    1.2, 1, 1.18, 1.3, 1.3, 1.1, 0.9, 1.8, 1.4, 1.1, 2.4, 1,
    1.2, 0.5, 0.8, 1, 1.2, 1, 1, 1.7, 1, 0.8, 0.7, 1, 0.7, 1.4,
    1, 1.2, 0.9, 1.83, 1.7, 0.9, 1, 1.6, 0.9, 1.2, 0.7, 1, 0.8,
    1.1, 1.1, 0.7, 1.3, 1, 2.7, 3.8, 1.1, 0.8, 1.2, 1.7, 1, 1.1,
    0.9, 0.8, 1.4, 1, 0.9, 1.1, 1.2, 0.8, 1.4, 1.6), serum_sodium = c(130L,
    136L, 129L, 137L, 116L, 132L, 137L, 131L, 138L, 133L, 131L,
    140L, 137L, 137L, 138L, 136L, 140L, 127L, 140L, 121L, 137L,
    136L, 140L, 135L, 134L, 144L, 138L, 136L, 134L, 132L, 134L,
    132L, 128L, 138L, 140L, 134L, 134L, 145L, 137L, 142L, 134L,
    136L, 139L, 134L, 142L, 135L, 130L, 138L, 133L, 140L, 138L,
    139L, 146L, 134L, 132L, 132L, 138L, 138L, 136L, 136L, 139L,
    131L, 139L, 145L, 137L, 127L, 136L, 140L, 142L, 135L, 140L,
    139L, 132L, 137L, 134L, 139L, 140L, 140L, 131L, 140L, 136L,
    137L, 132L, 133L, 141L, 140L, 137L, 140L, 139L, 144L, 136L,
    133L, 137L, 135L, 142L, 141L, 134L, 136L, 137L, 140L, 141L,
    137L, 144L, 140L, 143L, 138L, 137L, 138L, 133L, 142L, 132L,
    135L, 136L, 137L, 126L, 139L, 136L, 138L, 140L, 134L, 135L,
    136L, 140L, 145L, 134L, 135L, 124L, 137L, 136L, 145L, 138L,
    131L, 137L, 145L, 137L, 137L, 137L, 130L, 136L, 138L, 134L,
    140L, 132L, 141L, 139L, 141L, 136L, 137L, 134L, 136L, 135L,
    139L, 134L, 137L, 136L, 140L, 136L, 136L, 134L, 139L, 134L,
    139L, 137L, 142L, 139L, 135L, 133L, 134L, 138L, 133L, 136L,
    140L, 145L, 139L, 137L, 138L, 135L, 140L, 145L, 140L, 136L,
    136L, 136L, 134L, 137L, 136L, 134L, 144L, 136L, 140L, 134L,
    135L, 130L, 142L, 135L, 145L, 137L, 138L, 134L, 113L, 137L,
    136L, 138L, 136L, 137L, 136L, 141L, 136L, 141L, 130L, 136L,
    140L, 138L, 130L, 134L, 134L, 141L, 137L, 134L, 140L, 134L,
    145L, 139L, 134L, 138L, 125L, 132L, 140L, 130L, 134L, 127L,
    134L, 137L, 137L, 137L, 137L, 148L, 132L, 136L, 137L, 139L,
    136L, 136L, 141L, 134L, 137L, 138L, 135L, 142L, 130L, 139L,
    143L, 133L, 132L, 139L, 142L, 139L, 139L, 135L, 135L, 138L,
    133L, 129L, 140L, 141L, 140L, 134L, 140L, 140L, 132L, 130L,
    134L, 133L, 140L, 137L, 137L, 142L, 140L, 136L, 136L, 137L,
    136L, 128L, 138L, 141L, 135L, 140L, 132L, 142L, 144L, 141L,
    139L, 140L, 136L, 143L, 139L, 138L, 140L, 136L), sex = structure(c(2L,
    2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
    2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
    2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
    2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
    1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
    1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
    2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
    1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
    2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
    2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
    2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
    2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
    2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
    1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
    1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L), levels = c("Female",
    "Male"), class = "factor"), smoking = structure(c(1L, 1L,
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
    1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
    2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
    1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
    1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
    1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
    2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L), levels = c("False",
    "True"), class = "factor"), time = c(4L, 6L, 7L, 7L, 8L,
    8L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 12L, 13L, 14L,
    14L, 15L, 15L, 16L, 20L, 20L, 22L, 23L, 23L, 24L, 26L, 26L,
    26L, 27L, 28L, 28L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 31L,
    32L, 33L, 33L, 33L, 35L, 38L, 40L, 41L, 42L, 43L, 43L, 43L,
    44L, 45L, 50L, 54L, 54L, 55L, 59L, 60L, 60L, 60L, 61L, 63L,
    64L, 65L, 65L, 66L, 67L, 68L, 71L, 72L, 72L, 73L, 73L, 74L,
    74L, 74L, 74L, 75L, 76L, 77L, 78L, 78L, 79L, 79L, 79L, 79L,
    79L, 80L, 80L, 82L, 82L, 83L, 83L, 83L, 85L, 85L, 86L, 87L,
    87L, 87L, 87L, 87L, 88L, 88L, 88L, 88L, 88L, 90L, 90L, 90L,
    90L, 91L, 91L, 94L, 94L, 94L, 95L, 95L, 95L, 95L, 95L, 96L,
    97L, 100L, 104L, 104L, 105L, 106L, 107L, 107L, 107L, 107L,
    107L, 107L, 108L, 108L, 108L, 109L, 109L, 109L, 110L, 111L,
    112L, 112L, 113L, 113L, 115L, 115L, 117L, 118L, 119L, 120L,
    120L, 120L, 120L, 121L, 121L, 121L, 121L, 123L, 126L, 129L,
    130L, 134L, 135L, 140L, 145L, 145L, 146L, 146L, 146L, 146L,
    146L, 147L, 147L, 147L, 147L, 148L, 150L, 154L, 162L, 170L,
    171L, 172L, 172L, 172L, 174L, 174L, 174L, 175L, 180L, 180L,
    180L, 185L, 186L, 186L, 186L, 186L, 186L, 186L, 187L, 187L,
    187L, 187L, 187L, 187L, 187L, 188L, 192L, 192L, 193L, 194L,
    195L, 196L, 196L, 197L, 197L, 198L, 200L, 201L, 201L, 205L,
    205L, 205L, 206L, 207L, 207L, 207L, 208L, 209L, 209L, 209L,
    209L, 209L, 210L, 210L, 211L, 212L, 212L, 212L, 213L, 213L,
    213L, 214L, 214L, 214L, 214L, 214L, 215L, 215L, 215L, 215L,
    216L, 220L, 230L, 230L, 231L, 233L, 233L, 235L, 237L, 237L,
    240L, 241L, 244L, 244L, 244L, 244L, 244L, 245L, 245L, 245L,
    245L, 245L, 246L, 246L, 246L, 247L, 250L, 250L, 250L, 250L,
    250L, 250L, 250L, 256L, 256L, 257L, 258L, 258L, 270L, 270L,
    271L, 278L, 280L, 285L), DEATH_EVENT = structure(c(2L, 2L,
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
    2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
    2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
    1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
    1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 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), levels = c("Survived",
    "Dead"), class = "factor")), row.names = c(NA, 299L), class = "data.frame")

R 因子

0赞 jay.sf 4/1/2023

在我看来是正确的，试试.factor(c(0, 1, 1, 0, 1), levels=c(0, 1), labels=c("Survived", "Dead"))

0赞 Shawn Hemelstrand 4/1/2023

公平地说，如果没有可重现的数据集，其他人很难重现此错误。如果您运行并将输出粘贴到您的问题中，其他人可能更容易看到出现问题的原因。dput(heartfail)

0赞 jay.sf 4/2/2023

@rua 谢谢你。您可以使用头部使其变小。然而，奇怪的是，DEATH_EVENT看起来已经是目标因子格式了？dputdput(head(heartfail))

答：

0赞 DuckPyjamas 4/3/2023 #1

您无法让因子从零开始计数。R 将因子存储为带有标签的整数。R（与许多其他语言不同）从 1 开始计数，因此当您进行因式分解时，第一级始终是。1

很容易看出你是否是一个因子变量：unclass

unclass(iris$Species)
#>   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#>  [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
#> [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [149] 3 3
#> attr(,"levels")
#> [1] "setosa"     "versicolor" "virginica"

即使您从 0 和 1 创建因子，您仍将获得以下 1 索引存储：

binary_data <- c(0, 0, 1, 1, 0, 0, 1)

binary_to_factor <- factor(binary_data, levels = c(0, 1), labels = c("No", "Yes"))

unclass(binary_to_factor)
#> [1] 1 1 2 2 1 1 2
#> attr(,"levels")
#> [1] "No"  "Yes"

当然，你可以通过获取基础数字并从中减去 1 来将整数从因子中取出，但如果你这样做，那么为什么要花时间将它们转换为因子呢？

back_to_binary <- as.integer(binary_to_factor) - 1L

back_to_binary
#> [1] 0 0 1 1 0 0 1

class(back_to_binary)
#> [1] "integer"

^{创建于 2023-04-03 with reprex v2.0.2}

上一个：如何将数值列分解为用户定义的区间

下一个：为什么 as.factor（）函数在 Shiny 反应式表达式中不起作用，但在 Shiny 之外的 R 基础中工作正常？

为什么当我尝试将数值变量转换为因子时会出现此问题？

Why is it when I try to convert numerical variable into factor I get this problem?

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