在單個 ROC plot 上繪制線性判別分析、分類樹和朴素貝葉斯曲線
[英]Plotting a linear discriminant analysis, classification tree and Naive Bayes Curve on a single ROC plot
問題描述
數據位於頁面的最底部，稱為 LDA.scores'。 這是一個分類任務，我對數據集執行了三種監督機器學習分類技術。 提供所有編碼以顯示如何生成這些 ROC 曲線。 很抱歉問了一個問題，但近兩周來我一直在嘗試使用不同的代碼組合來解決這些問題，所以如果有人能幫助我，那么謝謝你。 主要問題是朴素貝葉斯曲線顯示滿分 1，這顯然是錯誤的，我無法解決如何將線性判別分析曲線合並到單個 ROC plot 中以與提供的編碼進行比較。
- 在“MASS”package 中執行的線性判別分析 (LDA)
- “kLAR”中的朴素貝葉斯 (NB) package
- “rpart”中的分類樹 (CT) package
目標
- 單個 ROC plot 展示了用於比較每種分類技術的 ROC 曲線，並附有圖例。
- 計算每種分類技術的曲線下面積
問題
- 每種分類技術都在不同的 R package 中執行，我無法將這些 ROC 曲線合並到一個 plot 中。所有錯誤消息都顯示在頁面底部
- LDA 和 NB 的 ROC 曲線看起來都是假的
- 我無法應用圖例並遇到錯誤消息
我提供了所有三種技術的編碼，因此任何人都可以逐步評估我的邏輯
線性判別分析
library(MASS)
predictors<-as.matrix(LDA.scores[,2:13])
response<-as.factor(LDA.scores[,1])
#Perform LDA
Family.lda<-lda(response~predictors, CV=TRUE)
predict.Family <-predict(Family.lda)
tab <- table(response, Family.lda$class)
構建混淆矩陣以預測類別
conCV1 <- rbind(tab[1, ]/sum(tab[1, ]), tab[2, ]/sum(tab[2, ]))
dimnames(conCV1) <- list(Actual = c("No", "Yes"), "Predicted (cv)"= c("No", "Yes"))
print(round(conCV1, 3))
Plot 判別分數
library(lattice)
windows(width=10, height=7)
densityplot(~predict.Family$x, groups=LDA.scores$Family)
Function 計算混淆矩陣
confusion <- function(actual, predicted, names = NULL, printit = TRUE, prior = NULL) {
if (is.null(names))
names <- levels(actual)
tab <- table(actual, predicted)
acctab <- t(apply(tab, 1, function(x) x/sum(x)))
dimnames(acctab) <- list(Actual = names, "Predicted (cv)" = names)
if (is.null(prior)) {
relnum <- table(actual)
prior <- relnum/sum(relnum)
acc <- sum(tab[row(tab) == col(tab)])/sum(tab)
}
else {
acc <- sum(prior * diag(acctab))
names(prior) <- names
}
if (printit)
print(round(c("Overall accuracy" = acc, "Prior frequency" = prior),
+ 4))
if (printit) {
cat("\nConfusion matrix", "\n")
print(round(acctab, 4))
}
invisible(acctab)
}
更改比例以創建訓練和測試集 (70:30)
prior <- c(0.7, 0.3)
lda.70.30 <- lda(response~predictors, CV=TRUE, prior=prior)
confusion(response, lda.70.30$class, prior = c(0.7, 0.3))
A function 創建ROC曲線
truepos <- numeric(19)
falsepos <- numeric(19)
p1 <- (1:19)/20
for (i in 1:19) {
p <- p1[i]
Family.ROC <- lda(response~predictors, CV = TRUE, prior = c(p, 1 - p))
confmat <- confusion(LDA.scores$Family, Family.ROC$class, printit = FALSE)
falsepos[i] <- confmat[1, 2]
truepos[i] <- confmat[2, 2]
}
Plot ROC曲線
windows(width=10, height=7)
LDA.ROC<-plot(truepos~falsepos, type = "l", lwd=2,
xlab = "False positive rate (Specificity)",
ylab = "True positive rate (Sensitivity)" col ="green")
abline(a=0,b=1, col="red")
圖1
分類樹
生成測試和訓練集 70:30
index<-1:nrow(LDA.scores)
trainindex.LDA=sample(index, trunc(length(index)*0.70), replace=FALSE)
LDA.70.trainset<-LDA.scores[trainindex,]
LDA.30.testset<-LDA.scores[-trainindex,]
使用 70% 的訓練集種植樹木
#Grow Tree the tree with the 70 % training set
library(rpart)
tree.split3<-rpart(Family~., data=LDA.70.trainset3, method="class")
summary(tree.split3)
print(tree.split3)
plot(tree.split3)
text(tree.split3,use.n=T,digits=0)
printcp(tree.split3)
使用測試和訓練集進行分類樹預測 (70:30)
res3=predict(tree.split3,newdata=LDA.30.testset3)
res4=as.data.frame(res3)
為分類分組因子的二項分布創建二進制系統（0 或 1）
res4$actual2 = NA
res4$actual2[res4$actual=="G8"]= 1
res4$actual2[res4$actual=="V4"]= 0
繪制 ROC 曲線
roc_pred <- prediction(re4$Predicted.prob, res4$actual2)
perf <- performance(roc_pred, "tpr", "fpr")
plot(perf, col="blue", lwd=2)
abline(0,1,col="grey")
圖 2
朴素貝葉斯
library(klaR)
library(caret)
生成測試和訓練集 70:30
trainIndex <- createDataPartition(LDA.scores$Family, p=0.70, list=FALSE)
sig.train=LDA.scores[trainIndex,]
sig.test=LDA.scores[-trainIndex,]
構建 NB model 並對測試集進行預測
sig.train$Family<-as.factor(sig.train$Family)
sig.test$Family<-as.factor(sig.test$Family)
nbmodel<-NaiveBayes(Family~., data=sig.train)
prediction<-predict(nbmodel, sig.test[2:13])
NB<-as.data.frame(prediction)
colnames(NB)<-c("Family", "Actual", "Predicted")
為分類因子的二項分布創建二元系統（0 或 1）
NB$actual2 = NA
NB$actual2[NB$Family=="V4"]=1
NB$actual2[NB$Family=="G8"]=0
NB2<-as.data.frame(NB)
PLOT ROC 曲線 - 這條曲線看起來很可疑
library(ROCR)
windows(width=10, height=7)
roc_pred.NB<- prediction(NB2$Predicted, NB2$actual2)
perf.NB <- performance(roc_pred.NB, "tpr", "fpr")
plot(perf.NB, col="orange", lwd=2)
abline(0,1,col="grey")
圖 3：這條 ROC 曲線顯然是錯誤的
將所有 ROC 曲線繪制到單個 plot
windows(width=10, height=7)
plot(fit.perf, col="blue", lwd=2); #CT
plot(LDA.ROC, col="green", lwd=2, add=T); #LDA
plot(perf.NB,lwd=2,col="orange", lwd=2, add=T);NB
abline(0,1,col="red", lwd=2)
錯誤信息
Warning in min(x) : no non-missing arguments to min; returning Inf
Warning in max(x) : no non-missing arguments to max; returning -Inf
Warning in min(x) : no non-missing arguments to min; returning Inf
Warning in max(x) : no non-missing arguments to max; returning -Inf
Warning in plot.window(...) : "add" is not a graphical parameter
Error in plot.window(...) : need finite 'xlim' values
plot(fit.NB,lwd=2,col="orange", lwd=2, add=T); #NB
Error in plot(fit.NB, lwd = 2, col = "orange", lwd = 2, add = T) :
error in evaluating the argument 'x' in selecting a method for function
Warning in plot.window(...) : "add" is not a graphical parameter
Error in plot.window(...) : need finite 'xlim' values
曲線下面積
auc1<-performance(fit.pred,"auc")#CT
auc2<-performance(fit.NB, "auc")#NB
我不確定如何計算提供的 LDA 代碼的曲線下面積
傳奇的制作
此代碼產生錯誤消息
legend(c('fit.pred',fit.NB','LDA.ROC'), col=c('blue',orange','green'),lwd=3)
名為 LDA.scores 的數據
Family Swimming Not.Swimming Running Not.Running
1 v4 -0.48055680 -0.086292700 -0.157157188 -0.438809944
2 v4 0.12600625 -0.074481895 0.057316151 -0.539013927
3 v4 0.06823834 -0.056765686 0.064711783 -0.539013927
4 v4 0.67480139 -0.050860283 0.153459372 -0.539013927
5 v4 0.64591744 -0.050860283 0.072107416 -0.472211271
6 v4 0.21265812 -0.068576492 0.057316151 -0.071395338
7 v4 -0.01841352 -0.068576492 -0.053618335 -0.071395338
8 v4 0.12600625 0.055436970 0.012942357 0.296019267
9 v4 -0.22060120 0.114491000 -0.038827070 0.563229889
10 v4 0.27042603 -0.021333268 0.049920519 -0.037994010
11 v4 0.03935439 -0.044954880 0.012942357 0.195815284
12 v4 -0.45167284 0.008193747 -0.075805232 -0.171599321
13 v4 -0.04729748 -0.056765686 0.035129254 -0.305204632
14 v4 -0.10506539 0.008193747 -0.046222702 0.062209973
15 v4 0.09712230 0.037720761 0.109085578 -0.104796666
16 v4 -0.07618143 0.014099150 -0.038827070 0.095611301
17 v4 0.29930998 0.108585597 0.057316151 0.028808645
18 v4 0.01047043 -0.074481895 0.020337989 -0.071395338
19 v4 -0.24948516 0.002288344 0.035129254 0.329420595
20 v4 -0.04729748 0.049531567 0.057316151 0.296019267
21 v4 -0.01841352 0.043626164 0.005546724 -0.171599321
22 v4 -0.19171725 0.049531567 -0.016640173 -0.071395338
23 v4 -0.48055680 0.020004552 -0.142365923 0.596631217
24 v4 0.01047043 0.008193747 0.220020063 0.062209973
25 v4 -0.42278889 0.025909955 -0.149761556 0.028808645
26 v4 -0.45167284 0.031815358 -0.134970291 -0.138197994
27 v4 -0.30725307 0.049531567 0.042524886 0.095611301
28 v4 0.24154207 -0.039049477 0.072107416 -0.104796666
29 v4 1.45466817 -0.003617059 0.064711783 0.296019267
30 v4 -0.01841352 0.002288344 0.020337989 0.028808645
31 G8 0.38596185 0.084963985 0.049920519 -0.037994010
32 G8 0.15489021 -0.080387298 0.020337989 -0.338605960
33 G8 -0.04729748 0.067247776 0.138668107 0.129012629
34 G8 0.27042603 0.031815358 0.049920519 0.195815284
35 G8 -0.07618143 0.037720761 0.020337989 -0.037994010
36 G8 -0.10506539 0.025909955 -0.083200864 0.396223251
37 G8 -0.01841352 0.126301805 -0.024035805 0.362821923
38 G8 0.01047043 0.031815358 -0.016640173 -0.138197994
39 G8 0.06823834 0.037720761 -0.038827070 0.262617940
40 G8 -0.16283329 -0.050860283 -0.038827070 -0.405408616
41 G8 -0.01841352 -0.039049477 0.005546724 -0.205000649
42 G8 -0.39390493 -0.003617059 -0.090596497 0.129012629
43 G8 -0.04729748 0.008193747 -0.009244540 0.195815284
44 G8 0.01047043 -0.039049477 -0.016640173 -0.205000649
45 G8 0.01047043 -0.003617059 -0.075805232 -0.004592683
46 G8 0.06823834 0.008193747 -0.090596497 -0.205000649
47 G8 -0.04729748 0.014099150 0.012942357 -0.071395338
48 G8 -0.22060120 -0.015427865 -0.075805232 -0.171599321
49 G8 -0.16283329 0.020004552 -0.061013967 -0.104796666
50 G8 -0.07618143 0.031815358 -0.038827070 -0.138197994
51 G8 -0.22060120 0.020004552 -0.112783394 -0.104796666
52 G8 -0.19171725 -0.033144074 -0.068409599 -0.071395338
53 G8 -0.16283329 -0.039049477 -0.090596497 -0.104796666
54 G8 -0.22060120 -0.009522462 -0.053618335 -0.037994010
55 G8 -0.13394934 -0.003617059 -0.075805232 -0.004592683
56 G8 -0.27836911 -0.044954880 -0.090596497 -0.238401977
57 G8 -0.04729748 -0.050860283 0.064711783 0.028808645
58 G8 0.01047043 -0.044954880 0.012942357 -0.305204632
59 G8 0.12600625 -0.068576492 0.042524886 -0.305204632
60 G8 0.06823834 -0.033144074 -0.061013967 -0.271803305
61 G8 0.06823834 -0.027238671 -0.061013967 -0.037994010
62 G8 0.32819394 -0.068576492 0.064711783 -0.372007288
63 G8 0.32819394 0.014099150 0.175646269 0.095611301
64 G8 -0.27836911 0.002288344 -0.068409599 0.195815284
65 G8 0.18377416 0.025909955 0.027733621 0.162413956
66 G8 0.55926557 -0.009522462 0.042524886 0.229216612
67 G8 -0.19171725 -0.009522462 -0.038827070 0.229216612
68 G8 -0.19171725 0.025909955 -0.009244540 0.396223251
69 G8 0.01047043 0.155828820 0.027733621 0.630032545
70 G8 -0.19171725 0.002288344 -0.031431438 0.463025906
71 G8 -0.01841352 -0.044954880 -0.046222702 0.496427234
72 G8 -0.07618143 -0.015427865 -0.031431438 0.062209973
73 G8 -0.13394934 0.008193747 -0.068409599 -0.071395338
74 G8 -0.39390493 0.037720761 -0.120179026 0.229216612
75 G8 -0.04729748 0.008193747 0.035129254 -0.071395338
76 G8 -0.27836911 -0.015427865 -0.061013967 -0.071395338
77 G8 0.70368535 -0.056765686 0.397515240 -0.205000649
78 G8 0.29930998 0.079058582 0.138668107 0.229216612
79 G8 -0.13394934 -0.056765686 0.020337989 -0.305204632
80 G8 0.21265812 0.025909955 0.035129254 0.396223251
Family Fighting Not.Fighting Resting Not.Resting
1 v4 -0.67708172 -0.097624192 0.01081204879 -0.770462870
2 v4 -0.58224128 -0.160103675 -0.03398160805 0.773856776
3 v4 -0.11436177 -0.092996082 0.05710879700 -2.593072768
4 v4 -0.34830152 -0.234153433 -0.04063432116 -2.837675606
5 v4 -0.84568695 -0.136963126 -0.13084281035 -1.680828329
6 v4 -0.32933343 -0.157789620 -0.02997847693 -0.947623773
7 v4 0.35984044 -0.157789620 0.12732080268 -0.947623773
8 v4 -0.32511830 -0.023574435 -0.10281705810 -2.607366431
9 v4 1.51478626 0.001880170 0.08155320398 -0.637055341
10 v4 0.11114773 -0.224897213 -0.17932134171 -1.818396455
11 v4 0.27975296 -0.109194467 -0.14338902206 2.170944974
12 v4 -0.89626852 -0.069855533 -0.02058415581 -0.658126752
13 v4 0.12379312 -0.123078796 -0.11528274705 -0.808243774
14 v4 0.66965255 -0.111508522 -0.11764091337 2.377766908
15 v4 1.56536783 -0.143905291 0.04389156236 2.111220276
16 v4 0.56427428 -0.099938247 0.01399844913 -0.322326312
17 v4 -0.71291033 -0.118450687 -0.05755560242 2.218858946
18 v4 -0.75927677 1.519900201 0.04711630687 3.920878638
19 v4 -0.75295407 0.177748344 0.01584280360 -0.304945754
20 v4 -1.00164679 0.108326696 0.09348590900 1.038591535
21 v4 -1.03958296 0.652129604 0.09677967302 1.752268128
22 v4 0.82139726 0.638245274 0.02053612974 0.907465624
23 v4 -1.07541157 -0.072169588 -0.03608286844 1.137774798
24 v4 -1.03115270 0.087500202 0.07805238146 -3.663486997
25 v4 -0.98900139 -0.180930170 -0.00009686695 2.350924346
26 v4 -1.06908888 -0.146219346 -0.02285413055 0.067293462
27 v4 -1.20186549 -0.049029039 -0.00424187149 -1.898454393
28 v4 0.58324237 -0.125392851 0.01446241356 -2.497647463
29 v4 -0.97003330 -0.134649071 0.03187450017 -4.471716512
30 v4 0.22917139 -0.060599313 0.11323315542 -1.465081244
31 G8 0.41042201 -0.086053918 -0.01171898422 -0.232806371
32 G8 -1.11545531 -0.197128554 -0.06499053655 -3.043893581
33 G8 -0.19023412 -0.083739863 -0.07758659568 -2.323908986
34 G8 0.25446217 -0.092996082 -0.07399758157 1.437404886
35 G8 -0.05324237 0.844196163 -0.11503350996 1.079056696
36 G8 0.09007207 0.055103433 0.02167111711 1.110865131
37 G8 1.21129685 1.971140911 0.01904454162 1.404724068
38 G8 0.62539368 -0.111508522 0.05768779393 -1.706664294
39 G8 1.32932051 -0.224897213 0.05555202379 0.736746935
40 G8 0.40199175 -0.187872334 -0.01031175326 -0.005516985
41 G8 0.44625062 -0.160103675 -0.00458313459 1.727170333
42 G8 0.60221046 -0.194814499 0.17430774591 1.685228831
43 G8 0.33665722 -0.053657149 0.00481502094 1.836016918
44 G8 -0.63493041 -0.206384774 -0.00928412956 0.466173920
45 G8 -0.28296700 0.108326696 0.09047589183 1.697173771
46 G8 -0.32722587 -0.164731785 0.08917985896 1.057314221
47 G8 -0.11646933 0.187004564 -0.05671203072 0.933704227
48 G8 -0.10171637 0.025020719 -0.05333390954 0.482480775
49 G8 0.13643851 0.057417488 0.08541446168 0.680713089
50 G8 -0.57802615 0.434608441 0.10140397965 0.090780703
51 G8 0.05002833 0.057417488 -0.02509342995 0.680713089
52 G8 -0.16072820 0.073615872 -0.03698779080 -0.982921741
53 G8 -0.29139726 -0.035144709 0.04609635201 -2.281900378
54 G8 0.13222338 -0.051343094 0.06524159499 0.972089090
55 G8 -0.41152848 -0.134649071 0.08459773090 0.027767791
56 G8 0.68229794 -0.185558279 -0.03239032508 -0.162881500
57 G8 -0.24292325 0.013450444 -0.03208740616 -0.530221948
58 G8 -0.11646933 -0.134649071 0.06264952925 -0.385741863
59 G8 -0.21341734 -0.215640993 0.05241547086 -0.972251823
60 G8 -0.24292325 -0.185558279 -0.03437271856 0.002267358
61 G8 -0.24292325 -0.005061995 -0.03437271856 -1.134447998
62 G8 0.09007207 -0.238781543 -0.06747523863 0.626424009
63 G8 -0.34197883 -0.099938247 -0.01270059491 -0.722750217
64 G8 -0.30825778 -0.167045840 0.10014629095 -0.382722075
65 G8 -0.08696342 -0.208698829 -0.02872845706 -0.356550578
66 G8 -0.81196590 0.048161268 -0.00950652573 -1.851614124
67 G8 0.49683219 0.048161268 0.04867308008 -1.851614124
68 G8 -0.13754498 -0.037458764 0.02486518629 1.731465143
69 G8 -0.48318570 0.161549960 -0.05951115497 0.254319006
70 G8 0.39988418 0.031962884 -0.02353665674 2.043778341
71 G8 0.90148474 -0.102252302 -0.01967923345 -0.289913920
72 G8 0.28396809 -0.123078796 -0.10148651548 1.386940871
73 G8 1.05322945 -0.139277181 -0.00480936518 0.054207713
74 G8 1.24923303 -0.208698829 -0.00098261723 0.594212936
75 G8 0.47154141 -0.118450687 -0.13970798195 1.551821303
76 G8 1.27873894 -0.072169588 -0.00286148145 3.100704184
77 G8 0.05002833 -0.044400929 -0.05492902692 0.327263666
78 G8 1.54218461 -0.030516599 0.10732815358 -1.055195336
79 G8 0.74763247 -0.132335016 0.11660744219 -1.134447998
80 G8 0.11747042 -0.037458764 -0.02016620439 1.730726972
Family Fighting Hunting Not.Hunting Grooming
1 v4 -0.67708172 0.114961983 0.2644238 0.105443109
2 v4 -0.58224128 0.556326739 -1.9467488 -0.249016133
3 v4 -0.11436177 0.326951992 2.1597867 -0.563247851
4 v4 -0.34830152 0.795734469 2.1698228 -0.611969290
5 v4 -0.84568695 0.770046573 0.2554708 -0.230476117
6 v4 -0.32933343 0.736574466 0.1225477 -0.270401826
7 v4 0.35984044 0.215724268 0.1225477 1.057451389
8 v4 -0.32511830 -0.200731013 0.2593696 -0.260830004
9 v4 1.51478626 -2.160535836 0.8687508 1.030589923
10 v4 0.11114773 0.660462182 1.7955299 -0.809959417
11 v4 0.27975296 -0.293709087 -0.8377330 -0.292132450
12 v4 -0.89626852 0.565754284 1.3339454 -0.573854465
13 v4 0.12379312 -0.499644710 -0.5100101 -0.372285683
14 v4 0.66965255 0.080624964 -2.6852985 -0.470590886
15 v4 1.56536783 -4.076143639 -0.8432925 1.657328707
16 v4 0.56427428 -0.127040484 -0.8662526 -0.161145079
17 v4 -0.71291033 0.661240603 -2.1990933 -0.381900622
18 v4 -0.75927677 0.294950237 -3.5062302 -0.121909231
19 v4 -0.75295407 0.548369546 -1.3326746 -0.338568723
20 v4 -1.00164679 0.137622686 -1.7580862 -0.312742050
21 v4 -1.03958296 0.019302681 -2.2730277 0.708985315
22 v4 0.82139726 -0.043057497 -3.1829838 -0.378408200
23 v4 -1.07541157 0.351515502 -0.3762928 -0.304161903
24 v4 -1.03115270 -0.007163636 1.3605877 -0.431053223
25 v4 -0.98900139 0.253780410 -1.1388134 -0.554883286
26 v4 -1.06908888 0.700680605 0.6629041 0.113074697
27 v4 -1.20186549 0.340704098 0.9979915 -0.693545361
28 v4 0.58324237 -1.727041782 1.5589254 0.180163686
29 v4 -0.97003330 0.209410408 1.7613786 -0.258156792
30 v4 0.22917139 -2.441026901 1.3929340 0.276959818
31 G8 0.41042201 0.383257784 -0.5374467 0.165978418
32 G8 -1.11545531 -1.098682982 2.9654839 0.148947473
33 G8 -0.19023412 0.873144122 2.5120581 -0.846910101
34 G8 0.25446217 0.968889915 -0.4130434 -0.938661624
35 G8 -0.05324237 0.936455703 -2.5993065 -0.949914982
36 G8 0.09007207 -0.467815937 -1.0766479 1.474170593
37 G8 1.21129685 -1.239490708 -4.1335895 1.357023559
38 G8 0.62539368 0.177235670 2.4989896 1.393241265
39 G8 1.32932051 -4.736158229 -0.5718146 2.467225606
40 G8 0.40199175 0.342693397 0.5675981 0.648320657
41 G8 0.44625062 0.488950070 -1.6998195 0.709588943
42 G8 0.60221046 -0.415575233 -1.4313741 0.728473890
43 G8 0.33665722 0.353937257 -2.2985148 0.379706002
44 G8 -0.63493041 0.262083568 0.2245685 -0.367629121
45 G8 -0.28296700 0.574316915 -1.0020637 0.280710938
46 G8 -0.32722587 0.323665326 -1.1559252 0.119455912
47 G8 -0.11646933 0.786566398 0.1746772 -0.858206576
48 G8 -0.10171637 0.718065343 -0.2673407 -0.552555005
49 G8 0.13643851 0.584868846 -0.1203383 -0.335378116
50 G8 -0.57802615 -0.053955393 0.6359729 0.057885811
51 G8 0.05002833 0.738563765 -0.1203383 -0.188308359
52 G8 -0.16072820 0.778263240 2.1906890 -0.545138998
53 G8 -0.29139726 0.751018502 1.6039070 0.198100074
54 G8 0.13222338 0.297804447 -0.5217068 -0.514310832
55 G8 -0.41152848 0.102161281 0.3866610 -0.036323341
56 G8 0.68229794 0.371667959 1.6179863 -0.176365139
57 G8 -0.24292325 0.631574111 1.4206594 -0.269668849
58 G8 -0.11646933 -0.004568899 1.6827511 0.003731717
59 G8 -0.21341734 0.214080935 1.0590019 0.036586351
60 G8 -0.24292325 0.796339908 1.2727184 -0.615289246
61 G8 -0.24292325 0.796339908 2.6745838 -0.615289246
62 G8 0.09007207 -0.396720145 0.2644238 0.290800156
63 G8 -0.34197883 0.441985331 1.4545220 -0.520648930
64 G8 -0.30825778 -2.489721464 1.3587105 1.711267220
65 G8 -0.08696342 0.407907785 0.8136610 -0.273333736
66 G8 -0.81196590 0.554423932 1.3666527 -0.594420949
67 G8 0.49683219 0.697912886 1.3666527 -0.446661330
68 G8 -0.13754498 0.491198842 -1.3307974 -0.333825929
69 G8 -0.48318570 0.604848320 -0.1305910 -0.601492025
70 G8 0.39988418 0.773938679 -0.5078441 -0.712559657
71 G8 0.90148474 0.734412186 -0.1166561 -0.548803885
72 G8 0.28396809 1.145505011 -1.3062489 -0.921846260
73 G8 1.05322945 0.616784110 0.9039851 -0.165629176
74 G8 1.24923303 0.329287256 0.3647117 0.111867440
75 G8 0.47154141 -0.016764163 -1.1586689 -0.476713403
76 G8 1.27873894 0.007799347 -3.0386529 0.215087903
77 G8 0.05002833 0.209496900 -1.5080522 0.324560232
78 G8 1.54218461 -5.031179821 1.6811626 2.366893936
79 G8 0.74763247 -0.325105405 1.6851337 1.351590903
80 G8 0.11747042 -0.756350687 -1.3315194 0.375911766
Family Not.Grooming
1 v4 0.019502286
2 v4 -0.290451956
3 v4 0.359948884
4 v4 0.557840914
5 v4 0.117453376
6 v4 0.126645924
7 v4 0.126645924
8 v4 0.196486873
9 v4 0.152780228
10 v4 0.354469789
11 v4 -0.261430968
12 v4 0.176448238
13 v4 -0.007374708
14 v4 -0.557848621
15 v4 -0.213674557
16 v4 -0.005819262
17 v4 -0.470070992
18 v4 -0.786078864
19 v4 0.006063789
20 v4 -0.271842650
21 v4 -0.349418792
22 v4 -0.338096262
23 v4 -0.165119403
24 v4 0.346566439
25 v4 -0.344191931
26 v4 0.074321265
27 v4 0.179825379
28 v4 0.278407054
29 v4 0.593125727
30 v4 0.199177375
31 G8 -0.058900625
32 G8 0.633875622
33 G8 0.428150308
34 G8 -0.206023441
35 G8 -0.436958199
36 G8 -0.291839246
37 G8 -0.907641911
38 G8 0.448567295
39 G8 -0.127186127
40 G8 0.024715134
41 G8 -0.416345030
42 G8 -0.330697382
43 G8 -0.469720666
44 G8 -0.047494017
45 G8 -0.301732446
46 G8 -0.138901021
47 G8 0.098101379
48 G8 -0.002063769
49 G8 -0.028324190
50 G8 0.071630763
51 G8 -0.028324190
52 G8 0.295110588
53 G8 0.347112947
54 G8 -0.083577573
55 G8 -0.036886152
56 G8 0.189045953
57 G8 0.467596992
58 G8 0.303378276
59 G8 0.218879697
60 G8 0.092005711
61 G8 0.270111340
62 G8 -0.012909856
63 G8 0.262292068
64 G8 0.107125772
65 G8 0.123422927
66 G8 0.299426602
67 G8 0.299426602
68 G8 -0.326871824
69 G8 -0.022088391
70 G8 -0.428508341
71 G8 -0.014675497
72 G8 -0.114462294
73 G8 0.087227267
74 G8 -0.031519161
75 G8 -0.159318008
76 G8 -0.397875854
77 G8 0.101520559
78 G8 0.244481505
79 G8 0.529968994
80 G8 -0.326619590
2 個解決方案
解決方案1
2 已采納 2015-09-21 20:08:03
首先，關於將數據細分為訓練和測試子集的最重要問題之一是在子集設置之前，必須將數據隨機化，否則在訓練和測試數據子集中您的類別划分會不平等。
以下代碼的一些注釋。 為了簡化模型擬合方法，我使用了caret包。
library(pROC)
library(MASS)
library(caret)
set.seed(1234)
mydat <- read.table("~/Desktop/family.txt", header = TRUE, stringsAsFactors= FALSE)
mydat$Family <- factor(mydat$Family, levels = c("v4", "G8"))
# Randomly permute the data before subsetting
mydat_idx <- sample(1:nrow(mydat), replace = FALSE)
mydat <- mydat[mydat_idx, ]
mydat_resampled_idx <- createDataPartition(mydat_idx, times = 1, p = 0.7, list = FALSE)
mydat_resampled <- mydat[mydat_resampled_idx, ] # Training portion of the data
線性判別分析
lda_mod <-train(x = mydat_resampled[, 2:9], y = as.factor(mydat_resampled[, 1]),
method = "lda", trControl = trainControl(method = "cv", classProbs = TRUE))
# Generate model predictions
lda_pred <- predict(lda_mod, newdata = mydat[ , 2:9], type = "prob")
# Store the predictions with the data set
mydat['lda_pred'] <- lda_pred["G8"] # Here we only want the probability associated
# with the class (Y = 1), or in this case, G8
朴素貝葉斯
nb_tune <- data.frame(usekernel =TRUE, fL = 0)
nb_mod <- train(x = mydat_resampled[, 2:9], y = as.factor(mydat_resampled[, 1]),
method = "nb", trControl = trainControl(method = "cv", classProbs = TRUE), tuneGrid = nb_tune)
# Model predictions
nb_pred <- predict(nb_mod, newdata = mydat[ , 2:9], type = "prob")
mydat['nb_pred'] <- nb_pred["G8"]
分類樹
ct_mod <- train(x = mydat_resampled[, 2:9], y = as.factor(mydat_resampled[, 1]),
method = "rpart", trControl = trainControl(method = "cv", classProbs = TRUE))
ct_pred <- predict(ct_mod, newdata = mydat[ , 2:9], type = "prob")
mydat['ct_pred'] <- ct_pred["G8"]
數據訓練和測試部分的ROC曲線
編輯：更改了計算和AUC曲線圖以使用pROC軟件包
mydat$binary_response <- as.numeric(mydat$Family) - 1 # convert factor to 0, 1 with G8 = 1
lda_train_roc <- roc(binary_response ~ lda_pred, data = mydat[mydat_resampled_idx, ], ci = TRUE)
nb_train_roc <- roc(binary_response ~ nb_pred, data = mydat[mydat_resampled_idx, ], ci = TRUE)
ct_train_roc <- roc(binary_response ~ ct_pred, data = mydat[mydat_resampled_idx, ], ci = TRUE)
lda_test_roc <- roc(binary_response ~ lda_pred, data = mydat[-mydat_resampled_idx, ], ci = TRUE)
nb_test_roc <- roc(binary_response ~ nb_pred, data = mydat[-mydat_resampled_idx, ], ci = TRUE)
ct_test_roc <- roc(binary_response ~ ct_pred, data = mydat[-mydat_resampled_idx, ], ci = TRUE)
par(mfrow = c(2, 1))
plot(lda_train_roc, las = 1, main = "Training data")
plot(nb_train_roc, add = TRUE, col = "red")
plot(ct_train_roc, add = TRUE, col = "blue")
legend(0.4, 0.4, legend = c("lda", "nb", "ct"), lty = c(1,1,1), col = c("black", "red", "blue"))
plot(lda_test_roc, las = 1, main = "Testing data")
plot(nb_test_roc, add = TRUE, col = "red")
plot(ct_test_roc, add = TRUE, col = "blue")
legend(0.4, 0.4, legend = c("lda", "nb", "ct"), lty = c(1,1,1), col = c("black", "red", "blue"))
par(mfrow = c(1, 1))
# AUC with 95% CL
lda_train_roc$ci[c(2, 1, 3)] # 0.8353741 0.7235472 0.9472011
nb_train_roc$ci[c(2, 1, 3)] # 0.9714286 [0.9303684, 1.0000000]
ct_train_roc$ci[c(2, 1, 3)] # 0.7619048 [0.6524637, 0.8713458]
lda_test_roc$ci[c(2, 1, 3)] # 0.6148148 [0.3555396, 0.8740900]
nb_test_roc$ci[c(2, 1, 3)] # 0.7407407 [0.5345984, 0.9468831]
ct_test_roc$ci[c(2, 1, 3)] # 0.6000000 [0.4139795, 0.7860205]
解決方案2
0 2022-04-05 00:11:26
這是數據
LDA.scores <- structure(list(ID = 1:80, 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,
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插入符號和標准化互信息（NMI）中的混淆矩陣：線性判別分析，朴素貝葉斯和分類樹
[英]Confusion matrix in `caret' and normalised mutual information (NMI): Linear discriminant analysis, Naive Bayes and Classification Trees
如何在R中進行朴素貝葉斯分類算法的roc曲線分析？
[英]How can I implement roc curve analysis for naive bayes classification algorithm in R?
R：在ggplot2中繪制線性判別分析的后驗分類概率
[英]R: plotting posterior classification probabilities of a linear discriminant analysis in ggplot2
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