ROC 曲線單獨繪制，不進入 plot 網格，R 中的 plot_grid() function 不起作用

Question

I'm just trying to plot the ROC curves in a plot grid in Rstudio, the neural net models are all working but the ROC curves just won't go in a grid layout, they plot individually but there's a lot more than this and the markdown 文件將有 20 頁長。 我已將所有模型減少為兩個神經元作為虛擬數據，以使其快速運行，只需要調整 setwd() 和數據線中的讀取。

這是空的 plot 網格：

但是在這里你可以看到它們工作並且將 plot 單獨：

library(Rcpp)
library(RSNNS)
library(ggplot2)
library(plotROC)
library(tidyr)
library(cowplot)

setwd("**set working directory or copy paste sample**")
data <- read.csv("WDBC.csv", header=T)
cancerdata <- data[,2:11] #numeric data
cancerdata <- scale(cancerdata)  # normalizes the cancerdata

numHneurons2 = 2
numHneurons3 = 3
numHneurons10 = 10
numHneurons20 = 20
numHneurons50 = 50

DecTargets = decodeClassLabels(data[,12]) #non-numeric decision data
head(DecTargets) #decoding based on Diagnosis, M or B for malignant or benign

train.test3 <- splitForTrainingAndTest(cancerdata, DecTargets,ratio = 0.50) # split
train.test10 <- splitForTrainingAndTest(cancerdata, DecTargets,ratio = 0.50) # split
train.test20 <- splitForTrainingAndTest(cancerdata, DecTargets,ratio = 0.50) # split
train.test50 <- splitForTrainingAndTest(cancerdata, DecTargets,ratio = 0.50) # split

model3_02 <- mlp(train.test3$inputsTrain, train.test3$targetsTrain,  # build model3
                 size = numHneurons2, learnFuncParams = c(0.02),maxit = 10000, 
                 inputsTest = train.test3$inputsTest, 
                 targetsTest = train.test3$targetsTest)

model10_02 <- mlp(train.test10$inputsTrain, train.test10$targetsTrain, # build model10
                  size = numHneurons2, learnFuncParams = c(0.02),maxit = 10000, 
                  inputsTest = train.test10$inputsTest, 
                  targetsTest = train.test10$targetsTest)

model20_02 <- mlp(train.test20$inputsTrain, train.test20$targetsTrain, # build model20
                  size = numHneurons2, learnFuncParams = c(0.02),maxit = 10000, 
                  inputsTest = train.test20$inputsTest, 
                  targetsTest = train.test20$targetsTest)

model50_02 <- mlp(train.test50$inputsTrain, train.test50$targetsTrain, # build model50
                  size = numHneurons2, learnFuncParams = c(0.02),maxit = 10000, 
                  inputsTest = train.test50$inputsTest, 
                  targetsTest = train.test50$targetsTest)

#--------------------------------------
#            ROC curves:
#--------------------------------------
roc3_02 <- plotROC(fitted.values(model3_02), train.test3$targetsTrain)
roc10_02 <- plotROC(fitted.values(model10_02), train.test10$targetsTrain) 
roc20_02 <- plotROC(fitted.values(model20_02), train.test10$targetsTrain) 
roc50_02 <- plotROC(fitted.values(model50_02), train.test10$targetsTrain) 

plotsROC02 <- plot_grid(roc3_02, roc10_02, roc20_02, roc50_02, labels = c("N3", "N10", "N20", "N50"), label_size = 16)
title <- ggplot() + 
  labs(title = "ANN Models with 0.02 Learning Rate",
       subtitle = "Models with 3, 10, 20, and 50 Neurons") +
  theme_minimal()
plot_grid(title, plotsROC02, ncol = 1, rel_heights = c(0.1, 0.9))

這是癌症數據的樣本：

     PatientID radius texture perimeter   area smoothness compactness concavity concavePoints symmetry fractalDimension Diagnosis
1     842302 17.990   10.38    122.80 1001.0    0.11840     0.27760   0.30010       0.14710   0.2419          0.07871         M
2     842517 20.570   17.77    132.90 1326.0    0.08474     0.07864   0.08690       0.07017   0.1812          0.05667         M
3   84300903 19.690   21.25    130.00 1203.0    0.10960     0.15990   0.19740       0.12790   0.2069          0.05999         M
4   84348301 11.420   20.38     77.58  386.1    0.14250     0.28390   0.24140       0.10520   0.2597          0.09744         M
5   84358402 20.290   14.34    135.10 1297.0    0.10030     0.13280   0.19800       0.10430   0.1809          0.05883         M
6     843786 12.450   15.70     82.57  477.1    0.12780     0.17000   0.15780       0.08089   0.2087          0.07613         M
7     844359 18.250   19.98    119.60 1040.0    0.09463     0.10900   0.11270       0.07400   0.1794          0.05742         M
8   84458202 13.710   20.83     90.20  577.9    0.11890     0.16450   0.09366       0.05985   0.2196          0.07451         M
9     844981 13.000   21.82     87.50  519.8    0.12730     0.19320   0.18590       0.09353   0.2350          0.07389         M
10  84501001 12.460   24.04     83.97  475.9    0.11860     0.23960   0.22730       0.08543   0.2030          0.08243         M
11    845636 16.020   23.24    102.70  797.8    0.08206     0.06669   0.03299       0.03323   0.1528          0.05697         M
12  84610002 15.780   17.89    103.60  781.0    0.09710     0.12920   0.09954       0.06606   0.1842          0.06082         M
13    846226 19.170   24.80    132.40 1123.0    0.09740     0.24580   0.20650       0.11180   0.2397          0.07800         M
14    846381 15.850   23.95    103.70  782.7    0.08401     0.10020   0.09938       0.05364   0.1847          0.05338         M
15  84667401 13.730   22.61     93.60  578.3    0.11310     0.22930   0.21280       0.08025   0.2069          0.07682         M
16  84799002 14.540   27.54     96.73  658.8    0.11390     0.15950   0.16390       0.07364   0.2303          0.07077         M
17    848406 14.680   20.13     94.74  684.5    0.09867     0.07200   0.07395       0.05259   0.1586          0.05922         M
18  84862001 16.130   20.68    108.10  798.8    0.11700     0.20220   0.17220       0.10280   0.2164          0.07356         M
19    849014 19.810   22.15    130.00 1260.0    0.09831     0.10270   0.14790       0.09498   0.1582          0.05395         M
20   8510426 13.540   14.36     87.46  566.3    0.09779     0.08129   0.06664       0.04781   0.1885          0.05766         B
21   8510653 13.080   15.71     85.63  520.0    0.10750     0.12700   0.04568       0.03110   0.1967          0.06811         B
22   8510824  9.504   12.44     60.34  273.9    0.10240     0.06492   0.02956       0.02076   0.1815          0.06905         B
23   8511133 15.340   14.26    102.50  704.4    0.10730     0.21350   0.20770       0.09756   0.2521          0.07032         M
24    851509 21.160   23.04    137.20 1404.0    0.09428     0.10220   0.10970       0.08632   0.1769          0.05278         M
25    852552 16.650   21.38    110.00  904.6    0.11210     0.14570   0.15250       0.09170   0.1995          0.06330         M
26    852631 17.140   16.40    116.00  912.7    0.11860     0.22760   0.22290       0.14010   0.3040          0.07413         M
27    852763 14.580   21.53     97.41  644.8    0.10540     0.18680   0.14250       0.08783   0.2252          0.06924         M
28    852781 18.610   20.25    122.10 1094.0    0.09440     0.10660   0.14900       0.07731   0.1697          0.05699         M
29    852973 15.300   25.27    102.40  732.4    0.10820     0.16970   0.16830       0.08751   0.1926          0.06540         M
30    853201 17.570   15.05    115.00  955.1    0.09847     0.11570   0.09875       0.07953   0.1739          0.06149         M

Answer 1

我將根據pROC package（我創建了一個維護）給你一個答案。

首先，我正在創建一個 ROC 曲線列表：

library(pROC)
roc3_02 <- roc(as.vector(train.test3$targetsTrain), as.vector(fitted.values(model3_02)))
roc10_02 <- roc(as.vector(train.test3$targetsTrain), as.vector(fitted.values(model10_02)))
roc20_02 <- roc(as.vector(train.test3$targetsTrain), as.vector(fitted.values(model20_02)))
roc50_02 <- roc(as.vector(train.test3$targetsTrain), as.vector(fitted.values(model50_02)))

roc_list <- list(
    roc3_02=roc3_02,
    roc10_02=roc10_02,
    roc20_02=roc20_02,
    roc50_02=roc50_02)

然后我可以將ggroc function 與facet_wrap結合使用

 library(ggplot)
 g.group <- ggroc(roc_list, aes="group")
 g.group
 g.group + facet_wrap(.~name)

然后，您可以像自定義 ggplot2 plot 一樣自定義 plot。