Ошибка в plot.window(...): нужны конечные значения "xlim"

Что я должен делать для этой ошибки? Мой код:

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])

И мои основные данные в листе excel:

    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

Итак, что мне теперь делать? Как я могу решить эту ошибку? Ошибка в plot.window(...): нужны конечные значения "xlim" Кроме того: Предупреждающие сообщения: 1: In min (x): отсутствие непустых аргументов до min; возвращение Inf 2: В max (x): нет аргументов без пропуска до max; возвращение -Inf Если это возможно, исправьте мой код. Я не очень хорошо знаком с Rstudio и R.

Ответ 1

Проблема в том, что вы (вероятно) пытаетесь построить вектор, который состоит исключительно из отсутствующих значений (NA). Вот пример:

> 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

В вашем примере это означает, что в вашей строке plot(costs,pseudor2,type="l"), costs полностью NA. Вы должны понять, почему это так, но это объяснение вашей ошибки.

Ответ 2

Я столкнулся с одной и той же проблемой, и в моем решении было решение - я пытался создать пустую переменную.