Error in plot window need finite xlim values

Question

What should i do for this error  My code is   library e1071  library hydroGOF  donnees  lt - read csv  quot F  new work with shahab Code-SVR SVR MainData csv quot   summary donnees    partitioning into training and testing set donnees train  lt - donnees donnees subset   quot train quot  2 ncol donnees   donnees test  lt - donnees donnees subset   quot test quot  2 ncol donnees     use the mean of the dependent variable as a predictor def pred  lt - mean donnees train y    error sum of squares of the default model on the test set def rss  lt - sum  donnees test y-def pred  2  print def rss  plot donnees train                      linear regression                     Linear Models reg  lt - lm y     data   donnees train  print summary reg    error sum of squares of the model on the test set reg pred  lt - predict reg newdata   donnees test  reg rss  lt - 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  lt - svm y    data   donnees train  scale   T  type    quot eps-regression quot                      kernel    quot radial quot   cost   1 0  epsilon 0 1 tolerance 0 001  shrinking T                     fitted T  print epsilon svr   pr  diction esvr pred  lt - predict epsilon svr newdata   donnees test  esvr rss  lt - 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  quot best quot  cost parameter for rbf epsilon-svr                                                       costs  lt - seq from 0 05 to 3 0 by 0 005  pseudor2  lt - double length costs   for  c in 1 length costs      epsilon svr  lt - svm y    data   donnees train  scale   T  type    quot eps-regression quot                        kernel    quot radial quot   cost   costs c   epsilon 0 1 tolerance 0 001  shrinking T                       fitted T     pr  diction   esvr pred  lt - predict epsilon svr newdata   donnees test    esvr rss  lt - sum  donnees test y-esvr pred  2    pseudor2 c   lt - 1 0-esvr rss def rss     graphical representation plot costs pseudor2 type  quot l quot    show the max  of pseudo-r2 and the corresponding cost parameter print max pseudor2   k  lt - which max pseudor2  print costs k    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  So what should i do now  How can i solve this error   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  If it is possible   please correct my code   I am not very familiar with Rstudio and R

User · Answer

I had the same problem  I solve it when I convert string to factor  In your case  check the class of variable and check if they are numeric and  train and test  should be factor

User · Answer

I had the same problem  My solution was to make all vectors numeric

User · Answer

This error appears when the column contains character  if you check the data type it would be of type  chr  converting the column to  Factor  would solve this issue   For e g  In case you plot  City  against  Sales   you have to convert column  City  to type  Factor

User · Answer

The problem is that you re  probably  trying to plot a vector that consists exclusively of missing  NA  values  Here s an example    gt  x rep NA 100   gt  y rnorm 100   gt  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   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     From comments   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  Here is a quick and easy solution to that problem  basically wrap x in as factor x

[r] Error in plot.window(...) : need finite 'xlim' values

Examples related to r

Examples related to plot