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Data X:
0 0 1 501 134 0 368 0 6.70 0.00 8.50 0.00 8.70 0 2 1 2 485 124 124 361 361 6.80 6.80 8.40 8.40 8.60 8.6 3 1 3 464 113 113 351 351 6.70 6.70 8.40 8.40 8.60 8.6 4 1 4 460 109 109 351 351 6.60 6.60 8.30 8.30 8.50 8.5 5 1 5 467 109 109 358 358 6.40 6.40 8.20 8.20 8.50 8.5 6 1 6 460 106 106 354 354 6.30 6.30 8.20 8.20 8.50 8.5 7 1 7 448 101 101 347 347 6.30 6.30 8.10 8.10 8.50 8.5 8 1 8 443 98 98 345 345 6.50 6.50 8.10 8.10 8.50 8.5 0 0 9 436 93 0 343 0 6.50 0.00 8.10 0.00 8.50 0 0 0 10 431 91 0 340 0 6.40 0.00 8.10 0.00 8.50 0 0 0 11 484 122 0 362 0 6.20 0.00 8.10 0.00 8.50 0 0 0 12 510 139 0 370 0 6.20 0.00 8.10 0.00 8.60 0 0 0 13 513 140 0 373 0 6.50 0.00 8.10 0.00 8.60 0 14 1 14 503 132 132 371 371 7.00 7.00 8.20 8.20 8.60 8.6 15 1 15 471 117 117 354 354 7.20 7.20 8.20 8.20 8.70 8.7 16 1 16 471 114 114 357 357 7.30 7.30 8.30 8.30 8.70 8.7 17 1 17 476 113 113 363 363 7.40 7.40 8.20 8.20 8.70 8.7 18 1 18 475 110 110 364 364 7.40 7.40 8.30 8.30 8.80 8.8 19 1 19 470 107 107 363 363 7.40 7.40 8.30 8.30 8.80 8.8 20 1 20 461 103 103 358 358 7.30 7.30 8.40 8.40 8.90 8.9 0 0 21 455 98 0 357 0 7.40 0.00 8.50 0.00 8.90 0 0 0 22 456 98 0 357 0 7.40 0.00 8.50 0.00 8.90 0 0 0 23 517 137 0 380 0 7.60 0.00 8.60 0.00 9.00 0 0 0 24 525 148 0 378 0 7.60 0.00 8.60 0.00 9.00 0 0 0 25 523 147 0 376 0 7.70 0.00 8.70 0.00 9.00 0 26 1 26 519 139 139 380 380 7.70 7.70 8.70 8.70 9.00 9 27 1 27 509 130 130 379 379 7.80 7.80 8.80 8.80 9.00 9 28 1 28 512 128 128 384 384 7.80 7.80 8.80 8.80 9.00 9 29 1 29 519 127 127 392 392 8.00 8.00 8.90 8.90 9.10 9.1 30 1 30 517 123 123 394 394 8.10 8.10 9.00 9.00 9.10 9.1 31 1 31 510 118 118 392 392 8.10 8.10 9.00 9.00 9.10 9.1 32 1 32 509 114 114 396 396 8.20 8.20 9.00 9.00 9.10 9.1 0 0 33 501 108 0 392 0 8.10 0.00 9.00 0.00 9.10 0 0 0 34 507 111 0 396 0 8.10 0.00 9.10 0.00 9.10 0 0 0 35 569 151 0 419 0 8.10 0.00 9.10 0.00 9.10 0 0 0 36 580 159 0 421 0 8.10 0.00 9.00 0.00 9.10 0 0 0 37 578 158 0 420 0 8.20 0.00 9.10 0.00 9.10 0 38 1 38 565 148 148 418 418 8.20 8.20 9.00 9.00 9.10 9.1 39 1 39 547 138 138 410 410 8.30 8.30 9.10 9.10 9.10 9.1 40 1 40 555 137 137 418 418 8.40 8.40 9.10 9.10 9.20 9.2 41 1 41 562 136 136 426 426 8.60 8.60 9.20 9.20 9.30 9.3 42 1 42 561 133 133 428 428 8.60 8.60 9.20 9.20 9.30 9.3 43 1 43 555 126 126 430 430 8.40 8.40 9.20 9.20 9.30 9.3 44 1 44 544 120 120 424 424 8.00 8.00 9.20 9.20 9.20 9.2 0 0 45 537 114 0 423 0 7.90 0.00 9.20 0.00 9.20 0 0 0 46 543 116 0 427 0 8.10 0.00 9.30 0.00 9.20 0 0 0 47 594 153 0 441 0 8.50 0.00 9.30 0.00 9.20 0 0 0 48 611 162 0 449 0 8.80 0.00 9.30 0.00 9.20 0 0 0 49 613 161 0 452 0 8.80 0.00 9.30 0.00 9.20 0 50 1 50 611 149 149 462 462 8.50 8.50 9.30 9.30 9.20 9.2 51 1 51 594 139 139 455 455 8.30 8.30 9.40 9.40 9.20 9.2 52 1 52 595 135 135 461 461 8.30 8.30 9.40 9.40 9.20 9.2 53 1 53 591 130 130 461 461 8.30 8.30 9.30 9.30 9.20 9.2 54 1 54 589 127 127 463 463 8.40 8.40 9.30 9.30 9.20 9.2 55 1 55 584 122 122 462 462 8.50 8.50 9.30 9.30 9.20 9.2 56 1 56 573 117 117 456 456 8.50 8.50 9.30 9.30 9.20 9.2 0 0 57 567 112 0 455 0 8.60 0.00 9.20 0.00 9.10 0 0 0 58 569 113 0 456 0 8.50 0.00 9.20 0.00 9.10 0 0 0 59 621 149 0 472 0 8.60 0.00 9.20 0.00 9.00 0 0 0 60 629 157 0 472 0 8.60 0.00 9.10 0.00 8.90 0 0 0 61 628 157 0 471 0 8.60 0.00 9.10 0.00 8.90 0 62 1 62 612 147 147 465 465 8.50 8.50 9.10 9.10 9.00 9 63 1 63 595 137 137 459 459 8.40 8.40 9.10 9.10 8.90 8.9 64 1 64 597 132 132 465 465 8.40 8.40 9.00 9.00 8.80 8.8 65 1 65 593 125 125 468 468 8.50 8.50 8.90 8.90 8.70 8.7 66 1 66 590 123 123 467 467 8.50 8.50 8.80 8.80 8.60 8.6 67 1 67 580 117 117 463 463 8.50 8.50 8.70 8.70 8.50 8.5 68 1 68 574 114 114 460 460 8.60 8.60 8.60 8.60 8.50 8.5 0 0 69 573 111 0 462 0 8.60 0.00 8.60 0.00 8.40 0 0 0 70 573 112 0 461 0 8.40 0.00 8.50 0.00 8.30 0 0 0 71 620 144 0 476 0 8.20 0.00 8.40 0.00 8.20 0 0 0 72 626 150 0 476 0 8.00 0.00 8.40 0.00 8.20 0 0 0 73 620 149 0 471 0 8.00 0.00 8.30 0.00 8.10 0 74 1 74 588 134 134 453 453 8.00 8.00 8.20 8.20 8.00 8 75 1 75 566 123 123 443 443 8.00 8.00 8.20 8.20 7.90 7.9 76 1 76 557 116 116 442 442 7.90 7.90 8.00 8.00 7.80 7.8 77 1 77 561 117 117 444 444 7.90 7.90 7.90 7.90 7.60 7.6 78 1 78 549 111 111 438 438 7.90 7.90 7.80 7.80 7.50 7.5 79 1 79 532 105 105 427 427 7.90 7.90 7.70 7.70 7.40 7.4 80 1 80 526 102 102 424 424 8.00 8.00 7.60 7.60 7.30 7.3 0 0 81 511 95 0 416 0 7.90 0.00 7.60 0.00 7.30 0 0 0 82 499 93 0 406 0 7.40 0.00 7.60 0.00 7.20 0 0 0 83 555 124 0 431 0 7.20 0.00 7.60 0.00 7.20 0 0 0 84 565 130 0 434 0 7.00 0.00 7.60 0.00 7.20 0 0 0 85 542 124 0 418 0 6.90 0.00 7.50 0.00 7.10 0 86 1 86 527 115 115 412 412 7.10 7.10 7.50 7.50 7.00 7 87 1 87 510 106 106 404 404 7.20 7.20 7.40 7.40 7.00 7 88 1 88 514 105 105 409 409 7.20 7.20 7.40 7.40 6.90 6.9 89 1 89 517 105 105 412 412 7.10 7.10 7.40 7.40 6.90 6.9 90 1 90 508 101 101 406 406 6.90 6.90 7.30 7.30 6.80 6.8 91 1 91 493 95 95 398 398 6.80 6.80 7.30 7.30 6.80 6.8 92 1 92 490 93 93 397 397 6.80 6.80 7.40 7.40 6.80 6.8 0 0 93 469 84 0 385 0 6.80 0.00 7.50 0.00 6.90 0 0 0 94 478 87 0 390 0 6.90 0.00 7.60 0.00 7.00 0 0 0 95 528 116 0 413 0 7.10 0.00 7.60 0.00 7.00 0 0 0 96 534 120 0 413 0 7.20 0.00 7.70 0.00 7.10 0 0 0 97 518 117 0 401 0 7.20 0.00 7.70 0.00 7.20 0 98 1 98 506 109 109 397 397 7.10 7.10 7.90 7.90 7.30 7.3 99 1 99 502 105 105 397 397 7.10 7.10 8.10 8.10 7.50 7.5 100 1 100 516 107 107 409 409 7.20 7.20 8.40 8.40 7.70 7.7 101 1 101 528 109 109 419 419 7.50 7.50 8.70 8.70 8.10 8.1 102 1 102 533 109 109 424 424 7.70 7.70 9.00 9.00 8.40 8.4 103 1 103 536 108 108 428 428 7.80 7.80 9.30 9.30 8.60 8.6 104 1 104 537 107 107 430 430 7.70 7.70 9.40 9.40 8.80 8.8 0 0 105 524 99 0 424 0 7.70 0.00 9.50 0.00 8.90 0 0 0 106 536 103 0 433 0 7.80 0.00 9.60 0.00 9.10 0 0 0 107 587 131 0 456 0 8.00 0.00 9.80 0.00 9.20 0 0 0 108 597 137 0 459 0 8.10 0.00 9.80 0.00 9.30 0 0 0 109 581 135 0 446 0 8.10 0.00 9.90 0.00 9.40 0 110 1 110 564 124 124 441 441 8.00 8.00 10.00 10.00 9.40 9.4 111 1 111 558 118 118 439 439 8.10 8.10 10.00 10.00 9.50 9.5 112 1 112 575 121 121 454 454 8.20 8.20 10.10 10.10 9.50 9.5 113 1 113 580 121 121 460 460 8.40 8.40 10.10 10.10 9.70 9.7 114 1 114 575 118 118 457 457 8.50 8.50 10.10 10.10 9.70 9.7 115 1 115 563 113 113 451 451 8.50 8.50 10.10 10.10 9.70 9.7 116 1 116 552 107 107 444 444 8.50 8.50 10.20 10.20 9.70 9.7 0 0 117 537 100 0 437 0 8.50 0.00 10.20 0.00 9.70 0 0 0 118 545 102 0 443 0 8.50 0.00 10.10 0.00 9.60 0 0 0 119 601 130 0 471 0 8.40 0.00 10.10 0.00 9.60 0 0 0 120 604 136 0 469 0 8.30 0.00 10.10 0.00 9.60 0 0 0 121 586 133 0 454 0 8.20 0.00 10.10 0.00 9.60 0 122 1 122 564 120 120 444 444 8.10 8.10 10.10 10.10 9.60 9.6 123 1 123 549 112 112 436 436 7.90 7.90 10.10 10.10 9.60 9.6 124 1 124 551 109 109 442 442 7.60 7.60 10.10 10.10 9.60 9.6 125 1 125 556 110 110 446 446 7.30 7.30 10.00 10.00 9.50 9.5 126 1 126 548 106 106 442 442 7.10 7.10 9.90 9.90 9.50 9.5 127 1 127 540 102 102 438 438 7.00 7.00 9.90 9.90 9.40 9.4 128 1 128 531 98 98 433 433 7.10 7.10 9.90 9.90 9.40 9.4 0 0 129 521 92 0 428 0 7.10 0.00 9.90 0.00 9.50 0 0 0 130 519 92 0 426 0 7.10 0.00 10.00 0.00 9.50 0 0 0 131 572 120 0 452 0 7.30 0.00 10.10 0.00 9.60 0 0 0 132 581 127 0 455 0 7.30 0.00 10.20 0.00 9.70 0 0 0 133 563 124 0 439 0 7.30 0.00 10.30 0.00 9.80 0 134 1 134 548 114 114 434 434 7.20 7.20 10.50 10.50 9.90 9.9 135 1 135 539 108 108 431 431 7.20 7.20 10.60 10.60 10.00 10 136 1 136 541 106 106 435 435 7.10 7.10 10.70 10.70 10.00 10 137 1 137 562 111 111 450 450 7.10 7.10 10.80 10.80 10.10 10.1 138 1 138 559 110 110 449 449 7.10 7.10 10.90 10.90 10.20 10.2 139 1 139 546 104 104 442 442 7.20 7.20 11.00 11.00 10.30 10.3 140 1 140 536 100 100 437 437 7.30 7.30 11.20 11.20 10.30 10.3 0 0 141 528 96 0 431 0 7.40 0.00 11.30 0.00 10.40 0 0 0 142 530 98 0 433 0 7.40 0.00 11.40 0.00 10.50 0 0 0 143 582 122 0 460 0 7.50 0.00 11.50 0.00 10.50 0 0 0 144 599 134 0 465 0 7.40 0.00 11.50 0.00 10.60 0 0 0 145 584 133 0 451 0 7.40 0.00 11.60 0.00 10.60 0
Names of X columns:
S_t s t Totale_werkloosheid Jonger_dan_25 Jonger_dan_25_s Vanaf_25 Vanaf_25_s Belgie Belgie_s Euroraad Euroraad_s EU-27 EU-27_s
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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