10
我該如何處理這個錯誤？ 我的代碼是：plot.window（...）中的錯誤：需要有限'xlim'值
rm(list=ls())
library(e1071)
library(hydroGOF)
donnees <- read.csv("F:/new work with shahab/Code-SVR/SVR/MainData.csv")
summary(donnees)
#partitioning into training and testing set
donnees.train <- donnees[donnees$subset=="train",2:ncol(donnees)]
donnees.test <- donnees[donnees$subset=="test",2:ncol(donnees)]
#use the mean of the dependent variable as a predictor
def.pred <- mean(donnees.train$y)
#error sum of squares of the default model on the test set
def.rss <- sum((donnees.test$y-def.pred)^2)
print(def.rss)
plot(donnees.train)
#*****************
#linear regression
#*****************
#Linear Models
reg <- lm(y ~., data = donnees.train)
print(summary(reg))
#error sum of squares of the model on the test set
reg.pred <- predict(reg,newdata = donnees.test)
reg.rss <- sum((donnees.test$y-reg.pred)^2)
print(reg.rss)
#pseudo-r-squared
print(1.0-reg.rss/def.rss)
#**********************************
#rbf epsilon-svr with cost = 1.0
#**********************************
epsilon.svr <- svm(y ~.,data = donnees.train, scale = T, type = "eps-regression",
kernel = "radial", cost = 1.0, epsilon=0.1,tolerance=0.001, shrinking=T,
fitted=T)
print(epsilon.svr)
#prédiction
esvr.pred <- predict(epsilon.svr,newdata = donnees.test)
esvr.rss <- sum((donnees.test$y-esvr.pred)^2)
#pseudo-R2
print(1.0-esvr.rss/def.rss)
esvr.rmse=rmse(donnees.test$y,esvr.pred)
print(esvr.rmse)
#****************************************************
#detect the "best" cost parameter for rbf epsilon-svr
#****************************************************
costs <- seq(from=0.05,to=3.0,by=0.005)
pseudor2 <- double(length(costs))
for (c in 1:length(costs)){
epsilon.svr <- svm(y ~.,data = donnees.train, scale = T, type = "eps-regression",
kernel = "radial", cost = costs[c], epsilon=0.1,tolerance=0.001, shrinking=T,
fitted=T)
#prédiction
esvr.pred <- predict(epsilon.svr,newdata = donnees.test)
esvr.rss <- sum((donnees.test$y-esvr.pred)^2)
pseudor2[c] <- 1.0-esvr.rss/def.rss
}
#graphical representation
plot(costs,pseudor2,type="l")
#show the max. of pseudo-r2 and the corresponding cost parameter
print(max(pseudor2))
k <- which.max(pseudor2)
print(costs[k])
而且我在Excel工作表maindata是：
subset x1 x2 y
train 18 1088 9.77
train 0 831 5.96
train 0 785 5.36
train 0 762 5.08
train 0 749 4.92
train 0.5 731 4.69
train 0 727 4.64
train 2 743 4.84
train 5 818 5.83
train 12 942 7.49
train 13 973 7.98
train 89.5 1292 12.94
train 46.5 1086 9.61
train 5.5 877 6.59
train 1 826 5.89
train 0.5 780 5.3
train 3.5 756 5
train 4 764 5.1
train 28.5 851 6.26
train 10 866 6.45
train 20.5 839 6.09
train 7 759 5.03
train 0.5 722 4.57
train 0 708 4.4
train 0 694 4.22
train 0 689 4.16
train 0 679 4.03
train 11 769 5.2
train 0.5 697 4.26
train 10.5 702 4.33
train 1.5 692 4.2
train 3 743 4.86
train 16 958 7.98
train 14 835 6.05
train 0 713 4.46
train 0.5 671 3.94
train 0 659 3.79
train 0 646 3.63
train 0.5 636 3.52
train 0 627 3.43
train 0 629 3.44
train 1 682 4.1
train 8.5 735 4.81
train 1 729 4.67
train 0 649 3.66
train 56 774 5.29
train 1.5 663 3.84
train 5.5 787 5.49
train 50 839 6.14
train 6.5 699 4.29
train 1.5 756 5.03
train 11.5 669 3.91
train 5 684 4.1
train 0 653 3.71
train 0.5 669 3.94
train 0 638 3.53
train 0.5 647 3.65
train 12.5 715 4.56
train 7.5 921 7.37
train 50 1149 10.95
train 10.5 772 5.21
train 23.5 1205 11.93
train 23.5 1171 11.01
train 8.5 927 7.26
train 0.5 1009 8.45
train 4 1019 8.62
train 0 968 7.88
train 2 862 6.38
train 22 1349 14.15
train 16.5 1029 8.74
train 8.5 846 6.15
train 0.5 853 6.26
train 9.5 819 5.81
train 19.5 775 5.24
train 23 746 4.88
train 46.5 723 4.58
train 1 733 4.72
train 26.5 731 4.69
train 34.5 814 5.81
train 2 743 4.84
train 0 715 4.49
train 4 680 4.05
train 8 816 5.85
train 20 823 5.91
train 0.5 824 5.93
train 2.5 746 4.88
train 0 817 5.87
train 0 732 4.7
train 6 682 4.07
train 0 685 4.12
train 1 719 4.56
train 10.5 701 4.31
train 23.5 1002 8.74
train 23.5 947 7.71
train 8.5 808 5.66
train 0.5 835 6.06
train 4 811 5.71
train 0 709 4.42
train 2 696 4.25
train 22 913 7.21
train 16.5 860 6.42
train 8.5 902 7.15
train 0.5 781 5.32
train 9.5 862 6.45
train 19.5 833 6.02
train 23 803 5.63
train 46.5 903 7.06
train 1 822 5.86
train 26.5 1040 9.19
train 34.5 939 7.55
train 2 793 5.48
train 0 730 4.68
train 4 719 4.53
train 8 706 4.38
train 20 829 5.99
train 0.5 724 4.6
train 2.5 697 4.26
train 0 669 3.91
train 0 657 3.76
train 6 724 4.66
train 0 657 3.76
train 1 676 4.02
train 23.5 968 8.24
train 0 696 4.25
train 12 727 4.73
train 0.5 651 3.69
train 3.5 685 4.12
train 0.5 668 3.9
train 0 626 3.4
train 0 619 3.32
train 1 697 4.34
train 0.5 624 3.37
train 13.5 683 4.14
train 0 651 3.68
train 0 621 3.33
train 0 612 3.24
train 3 668 3.91
train 0 626 3.39
train 0.5 614 3.27
train 0 614 3.26
train 2.5 630 3.45
train 0.5 617 3.3
train 0 616 3.3
train 8 684 4.14
train 0.5 612 3.24
train 0 598 3.09
train 0 588 2.99
train 0 590 3
train 6 648 3.71
train 0 598 3.1
train 2 614 3.29
train 33 804 5.9
train 0 619 3.32
train 0 588 2.98
train 0 577 2.87
train 0 571 2.81
train 0.5 572 2.82
train 4.5 607 3.2
train 0 579 2.89
train 0 562 2.72
train 0 565 2.74
train 0 554 2.63
train 0 543 2.51
train 0 536 2.44
train 0 531 2.39
train 0 532 2.4
train 0.5 529 2.36
train 0 527 2.35
train 0 528 2.36
train 0 523 2.31
train 0 521 2.29
train 0 523 2.31
train 0.5 541 2.49
train 0 522 2.3
train 0.5 533 2.42
train 2 529 2.37
train 10 638 3.65
train 0.5 544 2.52
train 5 627 3.52
train 0 535 2.43
train 0 516 2.24
train 0 520 2.27
train 32 841 6.55
train 11.5 838 6.29
train 0 595 3.06
train 0.5 592 3.03
train 0 558 2.67
train 0 540 2.48
train 0 534 2.42
train 2 539 2.46
train 13 623 3.42
train 0 553 2.62
train 0 561 2.71
train 0 546 2.55
train 0 512 2.2
train 2 518 2.26
train 32 702 4.46
train 27 731 4.76
train 1 604 3.15
train 0 584 2.94
train 0 548 2.57
train 0 519 2.26
train 29.5 735 4.91
train 0 564 2.74
train 12 606 3.23
train 0 542 2.51
train 0 516 2.24
train 0 508 2.15
train 0 500 2.07
train 0 495 2.03
train 0 496 2.04
train 0 492 1.99
train 0 496 2.04
train 0 490 1.98
train 0 494 2.02
train 0 490 1.99
train 3 548 2.62
train 17 546 2.61
train 9.5 737 4.95
train 1.5 584 2.96
train 0 521 2.27
train 0.5 526 2.34
train 0 539 2.48
train 24.5 699 4.45
train 41 740 4.97
train 3 569 2.8
train 1 525 2.32
train 0 511 2.18
train 0 498 2.05
train 2 597 3.22
train 0.5 520 2.27
train 66 909 7.77
train 23 716 4.54
train 0.5 564 2.74
train 4.5 582 2.94
train 0 577 2.88
train 0 527 2.34
train 0 512 2.19
train 0 503 2.09
train 8.5 561 2.73
train 0 533 2.4
train 24.5 640 3.77
train 0 515 2.21
train 0 496 2.03
train 0 485 1.93
train 0 480 1.88
train 0 476 1.85
train 0 480 1.88
train 24 689 4.34
train 0 568 2.79
train 0 506 2.12
train 8.5 680 4.19
train 12 657 3.87
train 5.5 635 3.61
train 19.5 761 5.18
train 1.5 567 2.77
train 3.5 678 4.1
train 4 574 2.84
train 7 628 3.5
train 6 656 3.77
train 0 551 2.6
train 0.5 526 2.33
train 0.5 555 2.64
train 8.5 666 4.01
train 1 564 2.74
train 0 534 2.41
train 0 521 2.27
train 7.5 599 3.15
train 4.5 585 2.96
train 3 647 3.65
train 0 547 2.56
train 0 531 2.38
train 0 508 2.15
train 0 500 2.08
train 0 503 2.09
train 0 492 1.99
train 0.5 492 1.99
train 5 647 3.92
train 0 513 2.19
train 6.5 523 2.3
train 2 527 2.35
train 2 522 2.3
train 22.5 817 6.14
train 18.5 808 5.86
train 8.5 775 5.37
train 4.5 705 4.37
train 58 891 6.96
train 7 642 3.58
train 7 614 3.29
train 10.5 772 5.29
train 7.5 714 4.54
train 3.5 613 3.25
train 6 575 2.85
train 24.5 680 4.19
train 18.5 801 5.64
train 0 640 3.55
train 6.5 610 3.23
train 0.5 592 3.03
train 36.5 835 6.2
test 0 673 3.97 2.97 2.49
test 0.5 571 2.81 3.74 2.3
test 0 553 2.62 3.56 3.1
test 6 597 3.17 3.52 3.46
test 7 584 2.97 3.75 3.6
test 4.5 649 3.74 3.76 3.5
test 9.5 636 3.56 5.27 5.4
test 14.5 629 3.52 3.69 3.65
test 6.5 648 3.75 3.01 3
test 18 653 3.76 4.07 4.1
test 25.5 767 5.27 3.52 3.46
test 16 650 3.69 5.49 5.1
test 0.5 589 3.01 5.79 5.3
test 18.5 676 4.07 5.29 5.12
test 10 635 3.52 3.4 3.2
test 64 784 5.49 4.11 4.3
test 35.5 812 5.79 2.91 3
test 17.5 775 5.29 2.66 2.9
test 0.5 627 3.4 2.88 2.4
test 7 680 4.11 4.46 4.26
test 0 581 2.91 7.43 6.6
test 0 557 2.66 10.73 9.08
test 0 578 2.88 10.87 9.4
test 21 707 4.46 10.3 9.1
test 40 911 7.43 11.52 10.7
test 61 1151 10.73 11.33 10.4
test 42 1144 10.87 10.61 10.8
test 13 1121 10.3 13.26 13.29
test 6.5 1208 11.52 16.74 15.2
test 7.5 1206 11.33 13.26 12.7
test 0.5 1158 10.61 13.36 12.9
test 30.5 1328 13.26 11.22 11.19
test 84 1529 16.74 10.68 13.1
test 18.5 1332 13.26 13.22 13.8
test 8 1338 13.36 8.68 9.1
test 0.5 1199 11.22 8.13 10.05
test 19.5 1163 10.68 7.51 7.8
test 36.5 1313 13.22 7.05 9.6
test 1.5 1026 8.68 6.99 10.7
test 1 988 8.13 6.39 6.18
test 0 945 7.51 6.71 6.12
test 0 912 7.05 8.51 8.28
test 2 907 6.99 7.69 7.95
test 0.5 864 6.39 7.66 7.2
test 4 887 6.71 6.73 6.9
test 20 1012 8.51 6.86 6.4
test 21.5 957 7.69 8.88 8.1
test 17.5 955 7.66 7.26 7.4
test 1 889 6.73 6.35 6.32
test 11 898 6.86 6.25 6.18
test 9.5 1039 8.88 6.32 6.2
test 2.5 927 7.26 7.46 7.7
test 2.5 859 6.35 5.7 5.4
test 5 853 6.25 7.5 7.9
test 4 858 6.32 6.51 6.3
test 8 936 7.46 7.51 7.39
test 4 811 5.7 9.02 9.01
test 9 937 7.5 6.16 6.12
test 9 871 6.51 5.35 5.6
test 9 943 7.51 5.61 5.9
test 5 1047 9.02 8.56 8.3
test 6.5 846 6.16 7.3 7.1
test 2 784 5.35 6.4 6.2
test 3.5 804 5.61 5.46 5.43
test 0 726 4.63 5.3 5.32
test 37 917 7.3 7.2 7.12
test 12 864 6.4 6.1 6.01
那麼應該怎麼辦？我該如何解決這個錯誤？ plot.window（...）中的錯誤：需要有限的'xlim'值 另外：警告消息： 1：在min（x）中：沒有非缺少參數min;返回Inf 2：在max（x）中：沒有非缺失參數爲max;返回-Inf 如果可能，請更正我的代碼。 我對Rstudio和R不是很熟悉。
您需要隔離產生此錯誤的代碼行。它很簡單。你已經要求一個無限的繪圖區域。看看你的成本和僞對象，看看它們是否有任何不是NA的實際值。 – Thomas
我不知道更多關於R的信息，我在這裏輸入的數據並不完整，因爲我看到了，當我編輯它沒有完成。它有4列，多2列：列車有NA值，測試有數值。 – user3231184
我輸入了我的工作的完整maindata。所以我該怎麼做？ 另一個問題是關於rmse。我如何計算這個？怎麼樣？ – user3231184