plot.window(...) 中的错误:需要有限的“xlim"值 [英] Error in plot.window(...) : need finite 'xlim' values
问题描述
对于这个错误我应该怎么做?我的代码是:
What should i do for this error? My code is :
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工作表中的主要数据是:
And my maindata in excel worksheet is :
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?
So what should i do now? How can i solve this error?
plot.window(...) 中的错误:需要有限的 'xlim' 值
Error in plot.window(...) : need finite 'xlim' values
此外:警告消息:
1:在 min(x) 中:没有非缺失的 min 参数;返回信息
1: In min(x) : no non-missing arguments to min; returning Inf
2:在 max(x) 中:没有非缺失的 max 参数;返回 -Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
如果可能,请更正我的代码.我对 Rstudio 和 R 不是很熟悉.
If it is possible , please correct my code . I am not very familiar with Rstudio and R.
推荐答案
问题是您(可能)试图绘制一个完全由缺失 (NA
) 值组成的向量.举个例子:
The problem is that you're (probably) trying to plot a vector that consists exclusively of missing (NA
) values. Here's an example:
> 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
.你必须弄清楚为什么会这样,但这就是你的错误的解释.
In your example this means that in your line plot(costs,pseudor2,type="l")
, costs
is completely NA
. You have to figure out why this is, but that's the explanation of your error.
来自评论:
斯科特 C 威尔逊:此消息的另一个可能原因(不是在这种情况下,而是在其他情况下)是尝试将字符值用作 X 或 Y 数据.您可以使用类函数来检查您的 x 和 Y 值,以确定您是否认为这可能是您的问题.
Scott C Wilson: Another possible cause of this message (not in this case, but in others) is attempting to use character values as X or Y data. You can use the class function to check your x and Y values to be sure if you think this might be your issue.
stevec:这是一个快速简便地解决该问题(基本上将 x 包裹在 as.factor(x)
中)
stevec: Here is a quick and easy solution to that problem (basically wrap x in as.factor(x)
)
这篇关于plot.window(...) 中的错误:需要有限的“xlim"值的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!