提前为问题的长度道歉，但本页的大部分内容包含分步编码，以说明我在尝试解决错误消息时的思考过程。我已使用“插入符号”将数据分为训练（70％）和测试（30％）集，用于三种监督机器学习算法，称为线性判别分析（LDA）、朴素贝叶斯（NB）和分类树（CT） R中的包（数据的可重现示例，代码如下）。因此，每个算法都使用重复的 10 倍交叉验证和 100 次运行进行训练。这是一个探索性分类练习，响应变量有 2 个类别，即家庭（即“G8”和“V4”）、12 个预测变量和 80 个观察值。如果有人对如何解决这些问题有任何见解并帮助理解错误消息，那么非常感谢。这将不胜感激，因为我对 R 比较陌生。
目标
我的目标是使用“caret”包中的“confusionMatrix”函数生成混淆矩阵。我的目标是使用这个函数来获得 Kappa 系数、分类准确度、敏感性、特异性、单向假设检验和相关统计数据。从混淆矩阵中，我打算计算归一化互信息（NMI）系数来评估模型性能。我的数据称为“mydat”（可以在本页底部找到）。
问题
(1) 对于混淆矩阵，输出错误信息为：
Error in sort.list(y) : 'x' must be atomic for 'sort.list'
Have you called 'sort' on a list?
我尝试了不同的代码组合来解决这个问题，但无法理解这个问题
(2) 我的 NMI 分数相差很远，应该介于 0 到 1 之间。我想知道我的编码中是否有任何警告，或者反过来，是否有一种更简单、更直接的方法可以从混淆矩阵中计算 NMI (方程式如下）？
这个想法是使用“caret”包中的函数“confusionMatrix”为LDA、NB和CT输出三个单独的混淆矩阵的混淆矩阵统计数据（下面的例子）：
#The example below was randomly extracted
from `A short introduction to the "caret"
package' by Max Kuhn (2015) to highlight
what I am attempting to achieve with my data.
The only text changed is the grouping factors,
(i.e G8 and v4).
Confusion Matrix and Statistics
Reference
Prediction G8 V4
G8 20 7
V4 7 17
Kappa : 0.4491
Mcnemar's Test P-Value : 1.000000
Sensitivity : 0.7407
Specificity : 0.7083
Pos Pred Value : 0.7407
eg Pred Value : 0.7083
Prevalence : 0.5294
etection Rate : 0.3922
Detection Prevalence : 0.5294
Balanced Accuracy : 0.7245
'Positive' Class : M
算法和混淆矩阵的代码
library(pROC)
library(MASS)
library(caret)
library(e1071)
# Randomly permute the data before subsetting
mydat$Family <- factor(mydat$Family, levels = c("G8", "v4"))
mydat_idx <- sample(1:nrow(mydat), replace = FALSE)
mydat1 <- mydat[mydat_idx, ]
#Produce 70 % training and 30 % test set
mydat_resampled_idx <- createDataPartition(mydat_idx, times = 1, p = 0.7, list = FALSE)
mydat_resampled_train <- mydat1[mydat_resampled_idx, ] # Training portion of the data
mydat_resampled_test <- mydat1[-mydat_resampled_idx, ] # Test portion
set.seed(1234)
重复 10 倍线性判别分析
lda_mod <-train(x = mydat_resampled_train[, 2:13],
y = as.factor(mydat_resampled_train[, 1]),
method = "lda",
trControl = trainControl(method = "repeatedcv", number=10, repeats=100, classProbs = TRUE))
# Generate model predictions
lda_pred<- predict(lda_mod, newdata = mydat_resampled_train[ , 2:13], type = "prob")
# Store the predictions with the data set
mydat_resampled_train['lda_pred'] <- lda_pred["G8"] # Here we only want the probability associated
# with the class (Y = 1), or in this case, `G8'
重复 10 倍朴素贝叶斯
nb_tune <- data.frame(usekernel =TRUE, fL = 0)
nb_mod <- train(x = mydat_resampled_train[, 2:13],
y = as.factor(mydat_resampled_train[, 1]),
method = "nb",
trControl = trainControl(method = "repeatedcv", number=10, repeats=100, classProbs = TRUE),
tuneGrid = nb_tune)
# Model predictions
nb_pred <- predict(nb_mod, newdata = mydat_resampled_train[ , 2:13], type = "prob")
mydat_resampled_train['nb_pred'] <- nb_pred["G8"]
重复 10 折分类树
ct_mod <- train(x = mydat_resampled_train[, 2:13],
y = as.factor(mydat_resampled_train[, 1]),
method = "rpart",
trControl = trainControl(method = "repeatedcv", number=10, repeats=100, classProbs = TRUE))
ct_pred <- predict(ct_mod, newdata = mydat_resampled_train[ , 2:13], type = "prob")
mydat_resampled_train['ct_pred'] <- ct_pred["G8"]
混淆矩阵代码和伴随的错误信息
#LDA
confusionMatrix(lda_pred, mydat_resampled_test$Family)
#NB
confusionMatrix(nb_pred, mydat_resampled_test$Family)
#CT
confusionMatrix(ct_pred, mydat_resampled_test$Family)
每个模型都返回相同的错误消息，在网上和教程中搜索后，我仍然无法解决问题。
Error in sort.list(y) : 'x' must be atomic for 'sort.list'
Have you called 'sort' on a list?
归一化互信息 (NMI)
• a is the number of correct predictions that an instance is negative,
• b is the number of incorrect predictions that an instance is positive,
• c is the number of incorrect of predictions that an instance is negative,
• d is the number of correct predictions that an instance is positive,
• N is the count total
参考文献：Fielding, AH & Bell, JF (1997) 评估保护存在/不存在模型中预测误差的方法综述。环境保护, 24, 38–49。
#Equation to calculate the NMI after Fielding and Bell (1997)
NMI= -a*In(a)-b*In(b)-c*In(c)-d*In(d)+(a+b)*In(a+b)+(c+d)*In(c+d)
-----------------------------------------------------------
n*In(n)-((a+c)*In(a+c)+(b+d)*In(b+d))
#This is how I approached this problem
值 ad 取自 Kuhn (2015) 的上述混淆矩阵示例。
a<-20
b<-7
c<-7
d<-17
N<-80
NMI 编码
#I believe the function `solve' is used to inverse a value. Is this correct?
NMI_top.row<-((-a)*(solve(a))-((b*solve(b))-(c*solve(c))-(d*solve(d)))+((a+b)*(solve(a+b)+(c+d)*(solve(c+d)))))
NMI_bottom.row<-(N*solve(N))-(((a+c))*(solve(a+c)+(b+d))*solve((b+d)))
NMI<-(NMI_top.row/NMI_bottom.row)
Answer: This answer is way off since it should lie between 0 to 1
[,1]
[1,] -1.0752
#
数据
mydat <- structure(list(Family = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("G8", "v4"), class="factor"),
Swimming = c(-0.4805568, 0.12600625, 0.06823834, 0.67480139,
0.64591744, 0.21265812, -0.01841352, 0.12600625, -0.2206012,
0.27042603, 0.03935439, -0.45167284, -0.04729748, -0.10506539,
0.0971223, -0.07618143, 0.29930998, 0.01047043, -0.24948516,
-0.04729748, -0.01841352, -0.19171725, -0.4805568, 0.01047043,
-0.42278889, -0.45167284, -0.30725307, 0.24154207, 1.45466817,
-0.01841352, 0.38596185, 0.15489021, -0.04729748, 0.27042603,
-0.07618143, -0.10506539, -0.01841352, 0.01047043, 0.06823834,
-0.16283329, -0.01841352, -0.39390493, -0.04729748, 0.01047043,
0.01047043, 0.06823834, -0.04729748, -0.2206012, -0.16283329,
-0.07618143, -0.2206012, -0.19171725, -0.16283329, -0.2206012,
-0.13394934, -0.27836911, -0.04729748, 0.01047043, 0.12600625,
0.06823834, 0.06823834, 0.32819394, 0.32819394, -0.27836911,
0.18377416, 0.55926557, -0.19171725, -0.19171725, 0.01047043,
-0.19171725, -0.01841352, -0.07618143, -0.13394934, -0.39390493,
-0.04729748, -0.27836911, 0.70368535, 0.29930998, -0.13394934,
0.21265812), Not.Swimming = c(-0.0862927, -0.074481895, -0.056765686,
-0.050860283, -0.050860283, -0.068576492, -0.068576492, 0.05543697,
0.114491, -0.021333268, -0.04495488, 0.008193747, -0.056765686,
0.008193747, 0.037720761, 0.01409915, 0.108585597, -0.074481895,
0.002288344, 0.049531567, 0.043626164, 0.049531567, 0.020004552,
0.008193747, 0.025909955, 0.031815358, 0.049531567, -0.039049477,
-0.003617059, 0.002288344, 0.084963985, -0.080387298, 0.067247776,
0.031815358, 0.037720761, 0.025909955, 0.126301805, 0.031815358,
0.037720761, -0.050860283, -0.039049477, -0.003617059, 0.008193747,
-0.039049477, -0.003617059, 0.008193747, 0.01409915, -0.015427865,
0.020004552, 0.031815358, 0.020004552, -0.033144074, -0.039049477,
-0.009522462, -0.003617059, -0.04495488, -0.050860283, -0.04495488,
-0.068576492, -0.033144074, -0.027238671, -0.068576492, 0.01409915,
0.002288344, 0.025909955, -0.009522462, -0.009522462, 0.025909955,
0.15582882, 0.002288344, -0.04495488, -0.015427865, 0.008193747,
0.037720761, 0.008193747, -0.015427865, -0.056765686, 0.079058582,
-0.056765686, 0.025909955), Running = c(-0.157157188, 0.057316151,
0.064711783, 0.153459372, 0.072107416, 0.057316151, -0.053618335,
0.012942357, -0.03882707, 0.049920519, 0.012942357, -0.075805232,
0.035129254, -0.046222702, 0.109085578, -0.03882707, 0.057316151,
0.020337989, 0.035129254, 0.057316151, 0.005546724, -0.016640173,
-0.142365923, 0.220020063, -0.149761556, -0.134970291, 0.042524886,
0.072107416, 0.064711783, 0.020337989, 0.049920519, 0.020337989,
0.138668107, 0.049920519, 0.020337989, -0.083200864, -0.024035805,
-0.016640173, -0.03882707, -0.03882707, 0.005546724, -0.090596497,
-0.00924454, -0.016640173, -0.075805232, -0.090596497, 0.012942357,
-0.075805232, -0.061013967, -0.03882707, -0.112783394, -0.068409599,
-0.090596497, -0.053618335, -0.075805232, -0.090596497, 0.064711783,
0.012942357, 0.042524886, -0.061013967, -0.061013967, 0.064711783,
0.175646269, -0.068409599, 0.027733621, 0.042524886, -0.03882707,
-0.00924454, 0.027733621, -0.031431438, -0.046222702, -0.031431438,
-0.068409599, -0.120179026, 0.035129254, -0.061013967, 0.39751524,
0.138668107, 0.020337989, 0.035129254), Not.Running = c(-0.438809944,
-0.539013927, -0.539013927, -0.539013927, -0.472211271, -0.071395338,
-0.071395338, 0.296019267, 0.563229889, -0.03799401, 0.195815284,
-0.171599321, -0.305204632, 0.062209973, -0.104796666, 0.095611301,
0.028808645, -0.071395338, 0.329420595, 0.296019267, -0.171599321,
-0.071395338, 0.596631217, 0.062209973, 0.028808645, -0.138197994,
0.095611301, -0.104796666, 0.296019267, 0.028808645, -0.03799401,
-0.33860596, 0.129012629, 0.195815284, -0.03799401, 0.396223251,
0.362821923, -0.138197994, 0.26261794, -0.405408616, -0.205000649,
0.129012629, 0.195815284, -0.205000649, -0.004592683, -0.205000649,
-0.071395338, -0.171599321, -0.104796666, -0.138197994, -0.104796666,
-0.071395338, -0.104796666, -0.03799401, -0.004592683, -0.238401977,
0.028808645, -0.305204632, -0.305204632, -0.271803305, -0.03799401,
-0.372007288, 0.095611301, 0.195815284, 0.162413956, 0.229216612,
0.229216612, 0.396223251, 0.630032545, 0.463025906, 0.496427234,
0.062209973, -0.071395338, 0.229216612, -0.071395338, -0.071395338,
-0.205000649, 0.229216612, -0.305204632, 0.396223251), Fighting = c(-0.67708172,
-0.58224128, -0.11436177, -0.34830152, -0.84568695, -0.32933343,
0.35984044, -0.3251183, 1.51478626, 0.11114773, 0.27975296,
-0.89626852, 0.12379312, 0.66965255, 1.56536783, 0.56427428,
-0.71291033, -0.75927677, -0.75295407, -1.00164679, -1.03958296,
0.82139726, -1.07541157, -1.0311527, -0.98900139, -1.06908888,
-1.20186549, 0.58324237, -0.9700333, 0.22917139, 0.41042201,
-1.11545531, -0.19023412, 0.25446217, -0.05324237, 0.09007207,
1.21129685, 0.62539368, 1.32932051, 0.40199175, 0.44625062,
0.60221046, 0.33665722, -0.63493041, -0.282967, -0.32722587,
-0.11646933, -0.10171637, 0.13643851, -0.57802615, 0.05002833,
-0.1607282, -0.29139726, 0.13222338, -0.41152848, 0.68229794,
-0.24292325, -0.11646933, -0.21341734, -0.24292325, -0.24292325,
0.09007207, -0.34197883, -0.30825778, -0.08696342, -0.8119659,
0.49683219, -0.13754498, -0.4831857, 0.39988418, 0.90148474,
0.28396809, 1.05322945, 1.24923303, 0.47154141, 1.27873894,
0.05002833, 1.54218461, 0.74763247, 0.11747042), Not.Fighting = c(-0.097624192,
-0.160103675, -0.092996082, -0.234153433, -0.136963126, -0.15778962,
-0.15778962, -0.023574435, 0.00188017, -0.224897213, -0.109194467,
-0.069855533, -0.123078796, -0.111508522, -0.143905291, -0.099938247,
-0.118450687, 1.519900201, 0.177748344, 0.108326696, 0.652129604,
0.638245274, -0.072169588, 0.087500202, -0.18093017, -0.146219346,
-0.049029039, -0.125392851, -0.134649071, -0.060599313, -0.086053918,
-0.197128554, -0.083739863, -0.092996082, 0.844196163, 0.055103433,
1.971140911, -0.111508522, -0.224897213, -0.187872334, -0.160103675,
-0.194814499, -0.053657149, -0.206384774, 0.108326696, -0.164731785,
0.187004564, 0.025020719, 0.057417488, 0.434608441, 0.057417488,
0.073615872, -0.035144709, -0.051343094, -0.134649071, -0.185558279,
0.013450444, -0.134649071, -0.215640993, -0.185558279, -0.005061995,
-0.238781543, -0.099938247, -0.16704584, -0.208698829, 0.048161268,
0.048161268, -0.037458764, 0.16154996, 0.031962884, -0.102252302,
-0.123078796, -0.139277181, -0.208698829, -0.118450687, -0.072169588,
-0.044400929, -0.030516599, -0.132335016, -0.037458764),
Resting = c(0.01081204879, -0.03398160805, 0.057108797, -0.04063432116,
-0.13084281035, -0.02997847693, 0.12732080268, -0.1028170581,
0.08155320398, -0.17932134171, -0.14338902206, -0.02058415581,
-0.11528274705, -0.11764091337, 0.04389156236, 0.01399844913,
-0.05755560242, 0.04711630687, 0.0158428036, 0.093485909,
0.09677967302, 0.02053612974, -0.03608286844, 0.07805238146,
-9.686695e-05, -0.02285413055, -0.00424187149, 0.01446241356,
0.03187450017, 0.11323315542, -0.01171898422, -0.06499053655,
-0.07758659568, -0.07399758157, -0.11503350996, 0.02167111711,
0.01904454162, 0.05768779393, 0.05555202379, -0.01031175326,
-0.00458313459, 0.17430774591, 0.00481502094, -0.00928412956,
0.09047589183, 0.08917985896, -0.05671203072, -0.05333390954,
0.08541446168, 0.10140397965, -0.02509342995, -0.0369877908,
0.04609635201, 0.06524159499, 0.0845977309, -0.03239032508,
-0.03208740616, 0.06264952925, 0.05241547086, -0.03437271856,
-0.03437271856, -0.06747523863, -0.01270059491, 0.10014629095,
-0.02872845706, -0.00950652573, 0.04867308008, 0.02486518629,
-0.05951115497, -0.02353665674, -0.01967923345, -0.10148651548,
-0.00480936518, -0.00098261723, -0.13970798195, -0.00286148145,
-0.05492902692, 0.10732815358, 0.11660744219, -0.02016620439
), Not.Resting = c(-0.77046287, 0.773856776, -2.593072768,
-2.837675606, -1.680828329, -0.947623773, -0.947623773, -2.607366431,
-0.637055341, -1.818396455, 2.170944974, -0.658126752, -0.808243774,
2.377766908, 2.111220276, -0.322326312, 2.218858946, 3.920878638,
-0.304945754, 1.038591535, 1.752268128, 0.907465624, 1.137774798,
-3.663486997, 2.350924346, 0.067293462, -1.898454393, -2.497647463,
-4.471716512, -1.465081244, -0.232806371, -3.043893581, -2.323908986,
1.437404886, 1.079056696, 1.110865131, 1.404724068, -1.706664294,
0.736746935, -0.005516985, 1.727170333, 1.685228831, 1.836016918,
0.46617392, 1.697173771, 1.057314221, 0.933704227, 0.482480775,
0.680713089, 0.090780703, 0.680713089, -0.982921741, -2.281900378,
0.97208909, 0.027767791, -0.1628815, -0.530221948, -0.385741863,
-0.972251823, 0.002267358, -1.134447998, 0.626424009, -0.722750217,
-0.382722075, -0.356550578, -1.851614124, -1.851614124, 1.731465143,
0.254319006, 2.043778341, -0.28991392, 1.386940871, 0.054207713,
0.594212936, 1.551821303, 3.100704184, 0.327263666, -1.055195336,
-1.134447998, 1.730726972), Hunting = c(-0.67708172, -0.58224128,
-0.11436177, -0.34830152, -0.84568695, -0.32933343, 0.35984044,
-0.3251183, 1.51478626, 0.11114773, 0.27975296, -0.89626852,
0.12379312, 0.66965255, 1.56536783, 0.56427428, -0.71291033,
-0.75927677, -0.75295407, -1.00164679, -1.03958296, 0.82139726,
-1.07541157, -1.0311527, -0.98900139, -1.06908888, -1.20186549,
0.58324237, -0.9700333, 0.22917139, 0.41042201, -1.11545531,
-0.19023412, 0.25446217, -0.05324237, 0.09007207, 1.21129685,
0.62539368, 1.32932051, 0.40199175, 0.44625062, 0.60221046,
0.33665722, -0.63493041, -0.282967, -0.32722587, -0.11646933,
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"Swimming", "Not.Swimming", "Running", "Not.Running", "Fighting",
"Not.Fighting", "Resting", "Not.Resting", "Hunting", "Not.Hunting",
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