我应该怎么做这个错误？ 我的代码是：
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])
我在excel工作表中的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
那我现在该怎么办？我该如何解决这个错误？
plot.window（...）出错：需要有限的'xlim'值
另外：警告信息：
1：在min（x）中：min没有非缺失参数;返回Inf
2：在max（x）中：max没有非缺失参数;返回-Inf
如果可能，请更正我的代码。 我对Rstudio和R不是很熟悉。
答案 0 :(得分：23)
问题是你（可能）试图绘制一个仅包含缺失（NA）值的向量。这是一个例子：
> x=rep(NA,100)
> y=rnorm(100)
> plot(x,y)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
在您的示例中，这意味着在您的行plot(costs,pseudor2,type="l")中，costs完全是NA。你必须弄清楚这是为什么，但这是你的错误的解释。
答案 1 :(得分：1)
我有同样的问题。我的解决方案是使所有向量均为数字。
答案 2 :(得分：1)
当列包含字符时会出现此错误，如果您检查数据类型将是'chr'类型，则将该列转换为'Factor'将解决此问题。
例如如果您将“城市”与“销售”相对比，则必须将“城市”列转换为“因子”
答案 3 :(得分：0)
我有同样的问题。我将字符串转换为因数时解决了。在您的情况下，请检查变量的类并检查它们是否为数字，并且“训练和测试”应为因素。