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])
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
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