预测 LDA 测试集与测试集的长度不同
Predicting LDA testing set different legnth than testing set
我在 lda 中预测时遇到问题。我想交叉验证我的数据集，所以我将数据分成训练集 (80%) 和测试集 (20%) 5 次。这给了我两个不同长度的数据帧。我可以使训练 lda 没问题，但是当我预测时，我没有得到新数据预测。它会自动进入训练集预测。对 R 新手有帮助吗？
df.test=structure(list(DEV.rabbit.Bi = c(0L, 1L, 1L, 0L, 0L, 0L, 1L,0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L), cytoP = c(0,0, 0, 0, 0, 0, 0, -2.1260048, 0, 0, 0, 2.83428136, 0, 0, NA,0, -2.33067135, -3.2528685, 0, 0, -3.9118235, 0, -2.12893162,0, -2.135834975, -3.38015, 0, 2.86341288, 0, -2.4050405, 0, -2.38829672,0, -2.24985834, 0, -2.2202064, -2.15253385, -2.2366473, -2.96851445,0, -0.743292433, 0, 0, 0, -2.61448215, 0, 0, 0, 0, -2.9443965,0, 0), GIP = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0.88683115, 0, 0, 0, 4.31335206, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.900614, 0, 1.4537355, 0,6.168443, 3.872625, 3.1133642, 0, 2.3501405), neuroP = c(0, 0,2.0428646, 0, 0, 0, 0, 0, 0, 0, 5.165785, 0, 0, 0, NA, 0, 0,0, 0, 2.5078381, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.317407, 0, 0, 0,0, 0, 0, 0, 1.9766362, 0, 0, 0, 0, 4.6628686, 0, 0, 0, 4.6432279,4.586727, 0, 0, 0, 7.039145), ProlifP = c(0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, NA, 0, 3.562687467, 0, 0, 0, 0, 0, 0, 0,-2.12833253, 4.947180667, -2.04286463, 0, 0, 0, -2.562395, 0,0, 0, -2.346905, 0, 0, 0, 0, 0, 0, 2.005820067, -3.0411488, -1.885536,-3.2384957, 0, 0, 0, 0, 5.6344196, 0, -4.767982), reproP = c(0.018018017,0.418918933, 0.040540533, 0.018018017, 0.454954967, 0, 0, 0.049549533,0, 0, 0, 0.3963964, 0.058558567, 0.040540533, NA, 0.054054067,0.441441433, 0, 0, 0.040540533, 0.063063067, 0, 0.35135135, 0.058558567,0.018018017, 0, 0.027027027, 0.040540533, 0.1036036, 0.4, 0.2,0.018018017, 0.130630633, 0.018018017, 0.1, 0.054054067, 0.031531533,0.081081067, 0.1036036, 0.040540533, 0.0900901, 0.369369367,0.036036033, -1.1009885, -0.673395133, NA, 0.045045033, 0, 0,0.1036036, -0.984343, 0)), .Names = c("DEV.rabbit.Bi", "cytoP","GIP", "neuroP", "ProlifP", "reproP"), row.names = c(12L, 23L,24L, 27L, 38L, 56L, 59L, 61L, 63L, 65L, 71L, 81L, 128L, 131L,141L, 154L, 163L, 168L, 170L, 184L, 186L, 205L, 210L, 217L, 233L,236L, 253L, 268L, 276L, 293L, 302L, 303L, 312L, 314L, 322L, 326L,335L, 339L, 343L, 361L, 377L, 385L, 392L, 394L, 399L, 402L, 418L,419L, 422L, 427L, 438L, 453L), class = "data.frame")
df.train= structure(list(DEV.rabbit.Bi = c(0L, 1L, 0L, 0L, 0L, 1L, 1L,1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L,0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L,0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L,0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L), cytoP = c(0,NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,NA, 0, 0, 0, -2.648429, 0, 0, -2.1260048, 0, 0, 0, 0, 0, 0, 0,2.83428136, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, -3.126005, 0, 0,0, 7.0318728, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 3.024976, 0,0, 0, -2.33067135, 0, 0, NA, 0, 0, -3.3048862, 3.2453672, 0,NA, 0, -3.9118235, NA, 0, 0, 0, 0, 0, -3.3074869, 0, 0, 0, 0,0, NA, 0, 0, 0, -3.64705195, 0, 0, -2.6801575, 0, -2.32687549,0, 0, -3.38015, 0, 0, NA, 0, -2.4122793, 0, 0, 0, 0, 0, 0, -2.434712735,2.86341288, 0, 0, 0, 0, 0, 0, 0, 0, -3.73306513, 0, 0, 0, 0,0, -2.38829672, 0, 0, 0, -0.823873667, 0, 0, 0, -2.24985834,0, 0, 0, 0, 0, -2.2202064, 0, -2.34696895, NA, NA, 0, -2.15253385,-2.1856675, -2.2366473, 2.017460955, -2.96851445, 0, 0, 0, -3.0842214,0, -3.50124325, -5.794065, 0, NA, 0, -3.1539793, -2.5736979,0, 0, -2.3865695, 0, -2.710736745, 0, -0.743292433, 0, 2.373366367,0, -2.75693455, NA, NA, -2.61448215, NA, 0, 0, 0, -2.2124975,0, 0, 0, 0, 0, 0, 0, 0, -3.053354, NA, 5.428529647, -2.9443965,-3.8878643, -2.2083998, 0, 0, 0, NA, 0, NA, -2.13583495, 0, 0,0), GIP = c(0, 0, NA, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0,5.820918, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 3.73598124, 0, 0,4.588133, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.0566821, 0,0, 0, 0, 0, 4.31335206, 0, 0, 0, 0, 0, 8.6651012, 0, 2.55087375,0, 0, 0, 0, 0, 0, 0, 0, 3.068526045, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 6.3948068, 0, 0,0, 0, 0, 0, 0, 0, 0, 3.3290915, 3.205779325, 0, 0, 0, 0, 0, 0,0, 0, 0, 1.01417725, 0, 0, 1.35015685, 0, 0, NA, 1.290875, 0,NA, 1.4537355, 0, 0, 0, 3.1133642, 0, 0, 0, 6.168443, 0, 6.26968469,3.872625, 0, 3.890076867, 0, 3.1133642, 2.250768067, 0, 0.97301535,4.8966569, 0, 8.487644, 0, 3.798781, 3.253654875, 4.960366, 0,2.3501405), neuroP = c(0, NA, NA, 0, 0, 0, 0, 0, 2.0428646, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11.03703, NA, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 5.165785, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,0, 0, 0, 0, 0, 3.583922, 0, 0, 0, 0, 0, 0, 2.0009107, 0, NA,NA, 0, 0, 0, 0, 0, 2.55936099, 0, 0, 0, NA, 0, 0, 0, 0, 0, NA,2.5078381, 0, NA, 0, 3.872625, 0, 0, 0, 0, 0, 0, 3.97424399,0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5064081, NA,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.16196, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 1.929947, 0, 2.000911, 0, 0, 0, 0, 0, 0, 0,0, 0, 2.247053, 0, 0, 0, NA, NA, 0, 0, 0, 1.9766362, 2.126448,0, 0, 0, 0, 4.130221, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0,2.599616, 0, 0, 0, 0, 0, NA, NA, 0, NA, 0, 0, 0, 0, 3.0913634,0, 0, 4.6432279, 4.586727, 0, 1.58651903, 0, 2.6652475, NA, 0,0, 0, 3.5208109, 4.2195317, 0, 0, NA, 10.5157265, NA, 0, 0, 2.8920614,7.039145), ProlifP = c(0, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, -1.945246, 0, 0, 0, 0, NA, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, -11.05227, 0, 0, 0,0, 3.562687467, 0, 0, NA, 0, 0, 0, -2.02585, 3.887923007, NA,0, 0, NA, 0, 0, 0, 0, 0, 3.7865502, 0, 0, 0, 0, 0, NA, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 4.947180667, 0, 0, NA, 0, 0, 0, 0,-2.04286463, -2.0343177, 0, 0, 5.591507567, 0, -2.0868461, 0,0, 0, 0, 0, 0, 5.151728643, 4.936735813, 0, 0, 0, 0, -2.562395,0, -2.009148, -7.564251, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.346905,3.207918667, 0, 0, -2.9254072, NA, NA, 0, 0, -2.5948795, 0, -2.060203,0, -4.14739583, -2.8027302, -4.487039, 0, 0, 0, NA, -2.8964375,5.003374, -2.263317, 0, 3.609647733, -2.6806902, 0, 3.505242133,0, 3.120921753, -3.445611, 0, 0, 5.147579867, 0, 0, NA, NA, -3.2384957,NA, 0, 0, 0, -2.798781, -1.6022584, 0, 0, 0, 0, 0, 0, 0, 4.713909533,4.4782686, -5.831885, 5.6344196, -6.8794451, 4.888960867, -3.1387679,-5.5994579, 0, NA, 0, NA, 0, 0, -4.6923589, -4.767982), reproP = c(0,0.58783785, 0.1486486, 0, 0.018018017, 0, 0.063063067, 0.418918933,0.040540533, 0, 0.4864865, 0.018018017, 0.0855856, 0.018018017,0.3963964, 0, 0, 0.0990991, 0.333333333, 0, 0, 0, 0, 0, 0, 0.076576567,0, 0.081081067, 0.049549533, 0.3873874, 0, 0, 0, 0.15, 0.06756755,0.0617284, 0.3963964, 0.383333333, 0.018018017, 0, 0.15, 0.031531533,0.3918919, 0.058558567, 0.0810811, 0, 0, 0.067567567, 0, 0, 0,0, 0, 0.06756755, 0, 0.516666667, NA, 0.058558567, 0.1621622,0.2567568, NA, NA, 0.1419753, 0, 0, 0.054054067, 0.040540533,0.018018017, 0.441441433, 0.031531533, 0, 0, 0, 0.1126126, 0.072072067,0, 0.35802469, 0.0472973, 0.040540533, 0.063063067, 0.16216215,0.083333333, 0.333333333, 0.018018017, 0.024691357, 0.0945946,0.0945946, 0.045045033, 0, 0.037037035, 0, 0.081081067, 0, 0.135135133,0.058558567, 0.081081067, 0.031531533, 0, 0.013513513, 0.063063067,0.333333333, 0.35802469, 0.1081081, 0.040540533, 0, 0.018018017,0.081081067, 0.075, 0.045045033, 0.067567567, 0.040540533, 0.031531533,0.027027027, 0.031531533, 0.036036033, 0.45, 0.018018017, 0.040540533,-0.7265556, 0.031531533, 0.4144144, 0.10185185, 0.067567567,0, 0.040540533, 0.018018017, 0.027027025, 0.0990991, 0.1036036,0.027027025, 0.054054067, 0.2, 0.018018017, 0, 0, 0.033333333,0, 0.031531533, 0.378378367, 0.130630633, 0.018018017, 0.1, 0,0, 0.1, 0, 0.054054067, 0.459459467, 0.031531533, 0.075, 0.5,0.364864867, 0.031531533, 0.06756755, 0.081081067, 0.6418919,0.1036036, 0.35135135, 0.054054067, -0.931616333, 0.3918919,0, 0.0855856, 0.1081081, 0.373873867, NA, 0.333333333, 0.0990991,-1.345913467, 0.040540533, 0.018018017, 0.081081067, 0.3963964,0.018018017, 0, 0.0900901, 0.2027027, 0.031531533, 0.3963964,0.364864867, 0.0743243, 0, -0.673395133, 0.06756755, NA, -0.316663167,0.031531533, 0, 0.031531533, 0.3873874, 0.0608108, 0.045045033,0, -1.004574, 0.018018017, 0, 0.4144144, 0.55405405, 0, 0.1036036,-1.646125933, -1.5806603, -0.9572768, -0.818359433, -0.984343,0.2, -4.2037963, 0, -1.2499105, 0.4, 0.0608108, 0)), .Names = c("DEV.rabbit.Bi","cytoP", "GIP", "neuroP", "ProlifP", "reproP"), row.names = c(2L,4L, 6L, 11L, 12L, 13L, 15L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,30L, 34L, 35L, 39L, 40L, 43L, 44L, 48L, 55L, 56L, 57L, 58L, 59L,60L, 61L, 62L, 63L, 65L, 71L, 72L, 75L, 79L, 81L, 84L, 85L, 86L,87L, 91L, 92L, 93L, 94L, 97L, 100L, 101L, 102L, 105L, 112L, 115L,118L, 119L, 120L, 121L, 126L, 128L, 129L, 132L, 136L, 141L, 144L,148L, 151L, 154L, 155L, 156L, 163L, 164L, 166L, 169L, 170L, 178L,179L, 180L, 181L, 183L, 184L, 186L, 188L, 190L, 191L, 193L, 194L,198L, 199L, 200L, 201L, 202L, 205L, 206L, 212L, 215L, 217L, 222L,223L, 224L, 228L, 229L, 230L, 231L, 232L, 235L, 236L, 238L, 239L,244L, 248L, 249L, 250L, 252L, 253L, 257L, 262L, 263L, 265L, 268L,271L, 272L, 275L, 279L, 282L, 285L, 286L, 287L, 289L, 290L, 291L,294L, 301L, 302L, 303L, 304L, 305L, 307L, 309L, 310L, 311L, 312L,314L, 315L, 317L, 319L, 322L, 323L, 326L, 327L, 329L, 331L, 333L,334L, 335L, 338L, 339L, 342L, 343L, 344L, 346L, 349L, 350L, 352L,353L, 354L, 356L, 359L, 360L, 363L, 365L, 366L, 368L, 370L, 371L,374L, 376L, 377L, 380L, 381L, 384L, 387L, 393L, 395L, 399L, 400L,402L, 403L, 408L, 409L, 414L, 415L, 417L, 418L, 419L, 420L, 421L,422L, 424L, 425L, 426L, 427L, 428L, 429L, 434L, 437L, 438L, 441L,442L, 443L, 444L, 448L, 451L, 453L), class = "data.frame")
lda.train= lda(df.train$DEV.rabbitBi~ df.train[,c(2)] +df.train[,c(3)]+df.train[,c(4)]+df.train[,c(5)]+df.train[,c(6)], data=df.train)
lda.pred= predict(lda.train, newdata=df.test)$class
预测调用不起作用，因为 lda 模型输出中的变量名称
不匹配新数据/测试数据中的变量名称。 （参见 newdata 参数说明
在 ?predict.lda ... 一个数据框中，其中的列与所使用的变量同名 )。
由于它们不匹配，预测方法会忽略 newdata 参数并根据原始数据进行预测。
您在模型和预测中使用了相同的变量
但重要的是如何将它们传递给 lda 模型。
使用 ?lda
中的示例
library(MASS)
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
train <- sample(1:150, 75)
names(Iris)
#[1] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W." "Sp"
z <- lda(Sp ~ . , Iris, prior = c(1,1,1)/3, subset = train)
z
#...
#...
# Coefficients of linear discriminants:
# LD1 LD2
# Sepal.L. 0.5863648 0.7580133
# Sepal.W. 2.0444073 -2.5613102
# Petal.L. -1.8827963 0.9356446
# Petal.W. -3.5895106 -3.4927051
#...
#...
模型中的变量名称与数据中的变量名称匹配，并且与 newdata 中的变量名称匹配，因此 predict 将按预期工作
pred <- predict(z, Iris[-train, ])
当您以不同的方式传递变量时，您可以 运行 解决问题。
z <- lda(Sp ~ Iris[,1] + Iris[,2] + Iris[,3] + Iris[,4] , Iris, prior = c(1,1,1)/3, subset = train)
z
# Coefficients of linear discriminants:
# LD1 LD2
# Iris[, 1] 0.5863648 0.7580133
# Iris[, 2] 2.0444073 -2.5613102
# Iris[, 3] -1.8827963 0.9356446
# Iris[, 4] -3.5895106 -3.4927051
注意系数名称。所以现在当你使用 predict 函数时，它期望调用的变量
Iris[,1], ... 等在 newdata 中，但事实并非如此，因此 predict 将忽略新数据。
至于~ .形式：这只是一种从数据中select所有变量的快速方法（由data=给出），
将等式左侧的那些 (lhs ~ rhs) 排除在模型中。 `See here。注意，你可以
仅将数据参数用于 select 某些列。
所以上面的第一个lda函数调用相当于手动写出所有名字
z <- lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L. + Petal.W. ,
Iris, prior = c(1,1,1)/3, subset = train)
请注意，对于大多数（所有？？）预测方法来说，任何新数据中的名称都需要与模型中的名称相匹配
我在 lda 中预测时遇到问题。我想交叉验证我的数据集，所以我将数据分成训练集 (80%) 和测试集 (20%) 5 次。这给了我两个不同长度的数据帧。我可以使训练 lda 没问题，但是当我预测时，我没有得到新数据预测。它会自动进入训练集预测。对 R 新手有帮助吗？
df.test=structure(list(DEV.rabbit.Bi = c(0L, 1L, 1L, 0L, 0L, 0L, 1L,0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L), cytoP = c(0,0, 0, 0, 0, 0, 0, -2.1260048, 0, 0, 0, 2.83428136, 0, 0, NA,0, -2.33067135, -3.2528685, 0, 0, -3.9118235, 0, -2.12893162,0, -2.135834975, -3.38015, 0, 2.86341288, 0, -2.4050405, 0, -2.38829672,0, -2.24985834, 0, -2.2202064, -2.15253385, -2.2366473, -2.96851445,0, -0.743292433, 0, 0, 0, -2.61448215, 0, 0, 0, 0, -2.9443965,0, 0), GIP = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0.88683115, 0, 0, 0, 4.31335206, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.900614, 0, 1.4537355, 0,6.168443, 3.872625, 3.1133642, 0, 2.3501405), neuroP = c(0, 0,2.0428646, 0, 0, 0, 0, 0, 0, 0, 5.165785, 0, 0, 0, NA, 0, 0,0, 0, 2.5078381, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.317407, 0, 0, 0,0, 0, 0, 0, 1.9766362, 0, 0, 0, 0, 4.6628686, 0, 0, 0, 4.6432279,4.586727, 0, 0, 0, 7.039145), ProlifP = c(0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, NA, 0, 3.562687467, 0, 0, 0, 0, 0, 0, 0,-2.12833253, 4.947180667, -2.04286463, 0, 0, 0, -2.562395, 0,0, 0, -2.346905, 0, 0, 0, 0, 0, 0, 2.005820067, -3.0411488, -1.885536,-3.2384957, 0, 0, 0, 0, 5.6344196, 0, -4.767982), reproP = c(0.018018017,0.418918933, 0.040540533, 0.018018017, 0.454954967, 0, 0, 0.049549533,0, 0, 0, 0.3963964, 0.058558567, 0.040540533, NA, 0.054054067,0.441441433, 0, 0, 0.040540533, 0.063063067, 0, 0.35135135, 0.058558567,0.018018017, 0, 0.027027027, 0.040540533, 0.1036036, 0.4, 0.2,0.018018017, 0.130630633, 0.018018017, 0.1, 0.054054067, 0.031531533,0.081081067, 0.1036036, 0.040540533, 0.0900901, 0.369369367,0.036036033, -1.1009885, -0.673395133, NA, 0.045045033, 0, 0,0.1036036, -0.984343, 0)), .Names = c("DEV.rabbit.Bi", "cytoP","GIP", "neuroP", "ProlifP", "reproP"), row.names = c(12L, 23L,24L, 27L, 38L, 56L, 59L, 61L, 63L, 65L, 71L, 81L, 128L, 131L,141L, 154L, 163L, 168L, 170L, 184L, 186L, 205L, 210L, 217L, 233L,236L, 253L, 268L, 276L, 293L, 302L, 303L, 312L, 314L, 322L, 326L,335L, 339L, 343L, 361L, 377L, 385L, 392L, 394L, 399L, 402L, 418L,419L, 422L, 427L, 438L, 453L), class = "data.frame")
df.train= structure(list(DEV.rabbit.Bi = c(0L, 1L, 0L, 0L, 0L, 1L, 1L,1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L,0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L,0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L,0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L), cytoP = c(0,NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,NA, 0, 0, 0, -2.648429, 0, 0, -2.1260048, 0, 0, 0, 0, 0, 0, 0,2.83428136, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, -3.126005, 0, 0,0, 7.0318728, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 3.024976, 0,0, 0, -2.33067135, 0, 0, NA, 0, 0, -3.3048862, 3.2453672, 0,NA, 0, -3.9118235, NA, 0, 0, 0, 0, 0, -3.3074869, 0, 0, 0, 0,0, NA, 0, 0, 0, -3.64705195, 0, 0, -2.6801575, 0, -2.32687549,0, 0, -3.38015, 0, 0, NA, 0, -2.4122793, 0, 0, 0, 0, 0, 0, -2.434712735,2.86341288, 0, 0, 0, 0, 0, 0, 0, 0, -3.73306513, 0, 0, 0, 0,0, -2.38829672, 0, 0, 0, -0.823873667, 0, 0, 0, -2.24985834,0, 0, 0, 0, 0, -2.2202064, 0, -2.34696895, NA, NA, 0, -2.15253385,-2.1856675, -2.2366473, 2.017460955, -2.96851445, 0, 0, 0, -3.0842214,0, -3.50124325, -5.794065, 0, NA, 0, -3.1539793, -2.5736979,0, 0, -2.3865695, 0, -2.710736745, 0, -0.743292433, 0, 2.373366367,0, -2.75693455, NA, NA, -2.61448215, NA, 0, 0, 0, -2.2124975,0, 0, 0, 0, 0, 0, 0, 0, -3.053354, NA, 5.428529647, -2.9443965,-3.8878643, -2.2083998, 0, 0, 0, NA, 0, NA, -2.13583495, 0, 0,0), GIP = c(0, 0, NA, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0,5.820918, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 3.73598124, 0, 0,4.588133, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.0566821, 0,0, 0, 0, 0, 4.31335206, 0, 0, 0, 0, 0, 8.6651012, 0, 2.55087375,0, 0, 0, 0, 0, 0, 0, 0, 3.068526045, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 6.3948068, 0, 0,0, 0, 0, 0, 0, 0, 0, 3.3290915, 3.205779325, 0, 0, 0, 0, 0, 0,0, 0, 0, 1.01417725, 0, 0, 1.35015685, 0, 0, NA, 1.290875, 0,NA, 1.4537355, 0, 0, 0, 3.1133642, 0, 0, 0, 6.168443, 0, 6.26968469,3.872625, 0, 3.890076867, 0, 3.1133642, 2.250768067, 0, 0.97301535,4.8966569, 0, 8.487644, 0, 3.798781, 3.253654875, 4.960366, 0,2.3501405), neuroP = c(0, NA, NA, 0, 0, 0, 0, 0, 2.0428646, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11.03703, NA, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 5.165785, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,0, 0, 0, 0, 0, 3.583922, 0, 0, 0, 0, 0, 0, 2.0009107, 0, NA,NA, 0, 0, 0, 0, 0, 2.55936099, 0, 0, 0, NA, 0, 0, 0, 0, 0, NA,2.5078381, 0, NA, 0, 3.872625, 0, 0, 0, 0, 0, 0, 3.97424399,0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5064081, NA,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.16196, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 1.929947, 0, 2.000911, 0, 0, 0, 0, 0, 0, 0,0, 0, 2.247053, 0, 0, 0, NA, NA, 0, 0, 0, 1.9766362, 2.126448,0, 0, 0, 0, 4.130221, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0,2.599616, 0, 0, 0, 0, 0, NA, NA, 0, NA, 0, 0, 0, 0, 3.0913634,0, 0, 4.6432279, 4.586727, 0, 1.58651903, 0, 2.6652475, NA, 0,0, 0, 3.5208109, 4.2195317, 0, 0, NA, 10.5157265, NA, 0, 0, 2.8920614,7.039145), ProlifP = c(0, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, -1.945246, 0, 0, 0, 0, NA, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, -11.05227, 0, 0, 0,0, 3.562687467, 0, 0, NA, 0, 0, 0, -2.02585, 3.887923007, NA,0, 0, NA, 0, 0, 0, 0, 0, 3.7865502, 0, 0, 0, 0, 0, NA, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 4.947180667, 0, 0, NA, 0, 0, 0, 0,-2.04286463, -2.0343177, 0, 0, 5.591507567, 0, -2.0868461, 0,0, 0, 0, 0, 0, 5.151728643, 4.936735813, 0, 0, 0, 0, -2.562395,0, -2.009148, -7.564251, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.346905,3.207918667, 0, 0, -2.9254072, NA, NA, 0, 0, -2.5948795, 0, -2.060203,0, -4.14739583, -2.8027302, -4.487039, 0, 0, 0, NA, -2.8964375,5.003374, -2.263317, 0, 3.609647733, -2.6806902, 0, 3.505242133,0, 3.120921753, -3.445611, 0, 0, 5.147579867, 0, 0, NA, NA, -3.2384957,NA, 0, 0, 0, -2.798781, -1.6022584, 0, 0, 0, 0, 0, 0, 0, 4.713909533,4.4782686, -5.831885, 5.6344196, -6.8794451, 4.888960867, -3.1387679,-5.5994579, 0, NA, 0, NA, 0, 0, -4.6923589, -4.767982), reproP = c(0,0.58783785, 0.1486486, 0, 0.018018017, 0, 0.063063067, 0.418918933,0.040540533, 0, 0.4864865, 0.018018017, 0.0855856, 0.018018017,0.3963964, 0, 0, 0.0990991, 0.333333333, 0, 0, 0, 0, 0, 0, 0.076576567,0, 0.081081067, 0.049549533, 0.3873874, 0, 0, 0, 0.15, 0.06756755,0.0617284, 0.3963964, 0.383333333, 0.018018017, 0, 0.15, 0.031531533,0.3918919, 0.058558567, 0.0810811, 0, 0, 0.067567567, 0, 0, 0,0, 0, 0.06756755, 0, 0.516666667, NA, 0.058558567, 0.1621622,0.2567568, NA, NA, 0.1419753, 0, 0, 0.054054067, 0.040540533,0.018018017, 0.441441433, 0.031531533, 0, 0, 0, 0.1126126, 0.072072067,0, 0.35802469, 0.0472973, 0.040540533, 0.063063067, 0.16216215,0.083333333, 0.333333333, 0.018018017, 0.024691357, 0.0945946,0.0945946, 0.045045033, 0, 0.037037035, 0, 0.081081067, 0, 0.135135133,0.058558567, 0.081081067, 0.031531533, 0, 0.013513513, 0.063063067,0.333333333, 0.35802469, 0.1081081, 0.040540533, 0, 0.018018017,0.081081067, 0.075, 0.045045033, 0.067567567, 0.040540533, 0.031531533,0.027027027, 0.031531533, 0.036036033, 0.45, 0.018018017, 0.040540533,-0.7265556, 0.031531533, 0.4144144, 0.10185185, 0.067567567,0, 0.040540533, 0.018018017, 0.027027025, 0.0990991, 0.1036036,0.027027025, 0.054054067, 0.2, 0.018018017, 0, 0, 0.033333333,0, 0.031531533, 0.378378367, 0.130630633, 0.018018017, 0.1, 0,0, 0.1, 0, 0.054054067, 0.459459467, 0.031531533, 0.075, 0.5,0.364864867, 0.031531533, 0.06756755, 0.081081067, 0.6418919,0.1036036, 0.35135135, 0.054054067, -0.931616333, 0.3918919,0, 0.0855856, 0.1081081, 0.373873867, NA, 0.333333333, 0.0990991,-1.345913467, 0.040540533, 0.018018017, 0.081081067, 0.3963964,0.018018017, 0, 0.0900901, 0.2027027, 0.031531533, 0.3963964,0.364864867, 0.0743243, 0, -0.673395133, 0.06756755, NA, -0.316663167,0.031531533, 0, 0.031531533, 0.3873874, 0.0608108, 0.045045033,0, -1.004574, 0.018018017, 0, 0.4144144, 0.55405405, 0, 0.1036036,-1.646125933, -1.5806603, -0.9572768, -0.818359433, -0.984343,0.2, -4.2037963, 0, -1.2499105, 0.4, 0.0608108, 0)), .Names = c("DEV.rabbit.Bi","cytoP", "GIP", "neuroP", "ProlifP", "reproP"), row.names = c(2L,4L, 6L, 11L, 12L, 13L, 15L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,30L, 34L, 35L, 39L, 40L, 43L, 44L, 48L, 55L, 56L, 57L, 58L, 59L,60L, 61L, 62L, 63L, 65L, 71L, 72L, 75L, 79L, 81L, 84L, 85L, 86L,87L, 91L, 92L, 93L, 94L, 97L, 100L, 101L, 102L, 105L, 112L, 115L,118L, 119L, 120L, 121L, 126L, 128L, 129L, 132L, 136L, 141L, 144L,148L, 151L, 154L, 155L, 156L, 163L, 164L, 166L, 169L, 170L, 178L,179L, 180L, 181L, 183L, 184L, 186L, 188L, 190L, 191L, 193L, 194L,198L, 199L, 200L, 201L, 202L, 205L, 206L, 212L, 215L, 217L, 222L,223L, 224L, 228L, 229L, 230L, 231L, 232L, 235L, 236L, 238L, 239L,244L, 248L, 249L, 250L, 252L, 253L, 257L, 262L, 263L, 265L, 268L,271L, 272L, 275L, 279L, 282L, 285L, 286L, 287L, 289L, 290L, 291L,294L, 301L, 302L, 303L, 304L, 305L, 307L, 309L, 310L, 311L, 312L,314L, 315L, 317L, 319L, 322L, 323L, 326L, 327L, 329L, 331L, 333L,334L, 335L, 338L, 339L, 342L, 343L, 344L, 346L, 349L, 350L, 352L,353L, 354L, 356L, 359L, 360L, 363L, 365L, 366L, 368L, 370L, 371L,374L, 376L, 377L, 380L, 381L, 384L, 387L, 393L, 395L, 399L, 400L,402L, 403L, 408L, 409L, 414L, 415L, 417L, 418L, 419L, 420L, 421L,422L, 424L, 425L, 426L, 427L, 428L, 429L, 434L, 437L, 438L, 441L,442L, 443L, 444L, 448L, 451L, 453L), class = "data.frame")
lda.train= lda(df.train$DEV.rabbitBi~ df.train[,c(2)] +df.train[,c(3)]+df.train[,c(4)]+df.train[,c(5)]+df.train[,c(6)], data=df.train)
lda.pred= predict(lda.train, newdata=df.test)$class
预测调用不起作用，因为 lda 模型输出中的变量名称
不匹配新数据/测试数据中的变量名称。 （参见 newdata 参数说明
在 ?predict.lda ... 一个数据框中，其中的列与所使用的变量同名 )。
由于它们不匹配，预测方法会忽略 newdata 参数并根据原始数据进行预测。
您在模型和预测中使用了相同的变量
但重要的是如何将它们传递给 lda 模型。
使用 ?lda
library(MASS)
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
train <- sample(1:150, 75)
names(Iris)
#[1] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W." "Sp"
z <- lda(Sp ~ . , Iris, prior = c(1,1,1)/3, subset = train)
z
#...
#...
# Coefficients of linear discriminants:
# LD1 LD2
# Sepal.L. 0.5863648 0.7580133
# Sepal.W. 2.0444073 -2.5613102
# Petal.L. -1.8827963 0.9356446
# Petal.W. -3.5895106 -3.4927051
#...
#...
模型中的变量名称与数据中的变量名称匹配，并且与 newdata 中的变量名称匹配，因此 predict 将按预期工作
pred <- predict(z, Iris[-train, ])
当您以不同的方式传递变量时，您可以 运行 解决问题。
z <- lda(Sp ~ Iris[,1] + Iris[,2] + Iris[,3] + Iris[,4] , Iris, prior = c(1,1,1)/3, subset = train)
z
# Coefficients of linear discriminants:
# LD1 LD2
# Iris[, 1] 0.5863648 0.7580133
# Iris[, 2] 2.0444073 -2.5613102
# Iris[, 3] -1.8827963 0.9356446
# Iris[, 4] -3.5895106 -3.4927051
注意系数名称。所以现在当你使用 predict 函数时，它期望调用的变量
Iris[,1], ... 等在 newdata 中，但事实并非如此，因此 predict 将忽略新数据。
至于~ .形式：这只是一种从数据中select所有变量的快速方法（由data=给出），
将等式左侧的那些 (lhs ~ rhs) 排除在模型中。 `See here。注意，你可以
仅将数据参数用于 select 某些列。
所以上面的第一个lda函数调用相当于手动写出所有名字
z <- lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L. + Petal.W. ,
Iris, prior = c(1,1,1)/3, subset = train)
请注意，对于大多数（所有？？）预测方法来说，任何新数据中的名称都需要与模型中的名称相匹配