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Data X:
14 501 501 11 11 20 20 91.81 91.81 77585 77585 1303.2 1303.2 2000 1 14 485 485 11 11 19 19 91.98 91.98 77585 77585 -58.7 -58.7 2000 1 15 464 464 11 11 18 18 91.72 91.72 77585 77585 -378.9 -378.9 2000 1 13 460 0 11 0 13 0 90.27 0 78302 0 175.6 0 2001 0 8 467 0 11 0 17 0 91.89 0 78302 0 233.7 0 2001 0 7 460 0 9 0 17 0 92.07 0 78302 0 706.8 0 2001 0 3 448 0 8 0 13 0 92.92 0 78224 0 -23.6 0 2001 0 3 443 0 6 0 14 0 93.34 0 78224 0 420.9 0 2001 0 4 436 0 7 0 13 0 93.6 0 78224 0 722.1 0 2001 0 4 431 0 8 0 17 0 92.41 0 78178 0 1401.3 0 2001 0 0 484 0 6 0 17 0 93.6 0 78178 0 -94.9 0 2001 0 -4 510 0 5 0 15 0 93.77 0 78178 0 1043.6 0 2001 0 -14 513 0 2 0 9 0 93.6 0 77988 0 1300.1 0 2001 0 -18 503 0 3 0 10 0 93.6 0 77988 0 721.1 0 2001 0 -8 471 0 3 0 9 0 93.51 0 77988 0 -45.6 0 2001 0 -1 471 0 7 0 14 0 92.66 0 77876 0 787.5 0 2002 0 1 476 0 8 0 18 0 94.2 0 77876 0 694.3 0 2002 0 2 475 0 7 0 18 0 94.37 0 77876 0 1054.7 0 2002 0 0 470 0 7 0 12 0 94.45 0 78432 0 821.9 0 2002 0 1 461 0 6 0 16 0 94.62 0 78432 0 1100.7 0 2002 0 0 455 0 6 0 12 0 94.37 0 78432 0 862.4 0 2002 0 -1 456 0 7 0 19 0 93.43 0 79025 0 1656.1 0 2002 0 -3 517 0 5 0 13 0 94.79 0 79025 0 -174 0 2002 0 -3 525 0 5 0 12 0 94.88 0 79025 0 1337.6 0 2002 0 -3 523 0 5 0 13 0 94.79 0 79407 0 1394.9 0 2002 0 -4 519 0 4 0 11 0 94.62 0 79407 0 915.7 0 2002 0 -8 509 0 4 0 10 0 94.71 0 79407 0 -481.1 0 2002 0 -9 512 0 4 0 16 0 93.77 0 79644 0 167.9 0 2003 0 -13 519 0 1 0 12 0 95.73 0 79644 0 208.2 0 2003 0 -18 517 0 -1 0 6 0 95.99 0 79644 0 382.2 0 2003 0 -11 510 0 3 0 8 0 95.82 0 79381 0 1004 0 2003 0 -9 509 0 4 0 6 0 95.47 0 79381 0 864.7 0 2003 0 -10 501 0 3 0 8 0 95.82 0 79381 0 1052.9 0 2003 0 -13 507 0 2 0 8 0 94.71 0 79536 0 1417.6 0 2003 0 -11 569 0 1 0 9 0 96.33 0 79536 0 -197.7 0 2003 0 -5 580 0 4 0 13 0 96.5 0 79536 0 1262.1 0 2003 0 -15 578 0 3 0 8 0 96.16 0 79813 0 1147.2 0 2003 0 -6 565 0 5 0 11 0 96.33 0 79813 0 700.2 0 2003 0 -6 547 0 6 0 8 0 96.33 0 79813 0 45.3 0 2003 0 -3 555 0 6 0 10 0 95.05 0 80332 0 458.5 0 2004 0 -1 562 0 6 0 15 0 96.84 0 80332 0 610.2 0 2004 0 -3 561 0 6 0 12 0 96.92 0 80332 0 786.4 0 2004 0 -4 555 0 6 0 13 0 97.44 0 81434 0 787.2 0 2004 0 -6 544 0 5 0 12 0 97.78 0 81434 0 1040 0 2004 0 0 537 0 6 0 15 0 97.69 0 81434 0 324.1 0 2004 0 -4 543 0 5 0 13 0 96.67 0 82167 0 1343 0 2004 0 -2 594 0 6 0 13 0 98.29 0 82167 0 -501.2 0 2004 0 -2 611 0 5 0 16 0 98.2 0 82167 0 800.4 0 2004 0 -6 613 0 7 0 14 0 98.71 0 82816 0 916.7 0 2004 0 -7 611 0 4 0 12 0 98.54 0 82816 0 695.8 0 2004 0 -6 594 0 5 0 15 0 98.2 0 82816 0 28 0 2004 0 -6 595 595 6 6 14 14 96.92 96.92 83000 83000 495.6 495.6 2005 1 -3 591 591 6 6 19 19 99.06 99.06 83000 83000 366.2 366.2 2005 1 -2 589 589 5 5 16 16 99.65 99.65 83000 83000 633 633 2005 1 -5 584 584 3 3 16 16 99.82 99.82 83251 83251 848.3 848.3 2005 1 -11 573 573 2 2 11 11 99.99 99.99 83251 83251 472.2 472.2 2005 1 -11 567 567 3 3 13 13 100.33 100.33 83251 83251 357.8 357.8 2005 1 -11 569 569 3 3 12 12 99.31 99.31 83591 83591 824.3 824.3 2005 1 -10 621 621 2 2 11 11 101.1 101.1 83591 83591 -880.1 -880.1 2005 1 -14 629 629 0 0 6 6 101.1 101.1 83591 83591 1066.8 1066.8 2005 1 -8 628 628 4 4 9 9 100.93 100.93 83910 83910 1052.8 1052.8 2005 1 -9 612 612 4 4 6 6 100.85 100.85 83910 83910 -32.1 -32.1 2005 1 -5 595 595 5 5 15 15 100.93 100.93 83910 83910 -1331.4 -1331.4 2005 1 -1 597 597 6 6 17 17 99.6 99.6 84599 84599 -767.1 -767.1 2006 1 -2 593 593 6 6 13 13 101.88 101.88 84599 84599 -236.7 -236.7 2006 1 -5 590 590 5 5 12 12 101.81 101.81 84599 84599 -184.9 -184.9 2006 1 -4 580 580 5 5 13 13 102.38 102.38 85275 85275 -143.4 -143.4 2006 1 -6 574 574 3 3 10 10 102.74 102.74 85275 85275 493.9 493.9 2006 1 -2 573 573 5 5 14 14 102.82 102.82 85275 85275 549.7 549.7 2006 1 -2 573 573 5 5 13 13 101.72 101.72 85608 85608 982.7 982.7 2006 1 -2 620 620 5 5 10 10 103.47 103.47 85608 85608 -856.3 -856.3 2006 1 -2 626 626 3 3 11 11 102.98 102.98 85608 85608 967 967 2006 1 2 620 620 6 6 12 12 102.68 102.68 86303 86303 659.4 659.4 2006 1 1 588 588 6 6 7 7 102.9 102.9 86303 86303 577.2 577.2 2006 1 -8 566 566 4 4 11 11 103.03 103.03 86303 86303 -213.1 -213.1 2006 1 -1 557 557 6 6 9 9 101.29 101.29 87115 87115 17.7 17.7 2007 1 1 561 561 5 5 13 13 103.69 103.69 87115 87115 390.1 390.1 2007 1 -1 549 549 4 4 12 12 103.68 103.68 87115 87115 509.3 509.3 2007 1 2 532 532 5 5 5 5 104.2 104.2 87931 87931 410 410 2007 1 2 526 526 5 5 13 13 104.08 104.08 87931 87931 212.5 212.5 2007 1 1 511 511 4 4 11 11 104.16 104.16 87931 87931 818 818 2007 1 -1 499 499 3 3 8 8 103.05 103.05 88164 88164 422.7 422.7 2007 1 -2 555 555 2 2 8 8 104.66 104.66 88164 88164 -158 -158 2007 1 -2 565 565 3 3 8 8 104.46 104.46 88164 88164 427.2 427.2 2007 1 -1 542 542 2 2 8 8 104.95 104.95 88792 88792 243.4 243.4 2007 1 -8 527 527 -1 -1 0 0 105.85 105.85 88792 88792 -419.3 -419.3 2007 1 -4 510 510 0 0 3 3 106.23 106.23 88792 88792 -1459.8 -1459.8 2007 1 -6 514 514 -2 -2 0 0 104.86 104.86 89263 89263 -1389.8 -1389.8 2008 1 -3 517 517 1 1 -1 -1 107.44 107.44 89263 89263 -2.1 -2.1 2008 1 -3 508 508 -2 -2 -1 -1 108.23 108.23 89263 89263 -938.6 -938.6 2008 1 -7 493 493 -2 -2 -4 -4 108.45 108.45 89881 89881 -839.9 -839.9 2008 1 -9 490 490 -2 -2 1 1 109.39 109.39 89881 89881 -297.6 -297.6 2008 1 -11 469 469 -6 -6 -1 -1 110.15 110.15 89881 89881 -376.3 -376.3 2008 1 -13 478 478 -4 -4 0 0 109.13 109.13 90120 90120 -79.4 -79.4 2008 1 -11 528 528 -2 -2 -1 -1 110.28 110.28 90120 90120 -2091.3 -2091.3 2008 1 -9 534 534 0 0 6 6 110.17 110.17 90120 90120 -1023 -1023 2008 1 -17 518 518 -5 -5 0 0 109.99 109.99 89703 89703 -765.6 -765.6 2008 1 -22 506 506 -4 -4 -3 -3 109.26 109.26 89703 89703 -1592.3 -1592.3 2008 1 -25 502 502 -5 -5 -3 -3 109.11 109.11 89703 89703 -1588.8 -1588.8 2008 1 -20 516 0 -1 0 4 0 107.06 0 87818 0 -1318 0 2009 0 -24 528 0 -2 0 1 0 109.53 0 87818 0 -402.4 0 2009 0 -24 533 0 -4 0 0 0 108.92 0 87818 0 -814.5 0 2009 0 -22 536 0 -1 0 -4 0 109.24 0 86273 0 -98.4 0 2009 0 -19 537 0 1 0 -2 0 109.12 0 86273 0 -305.9 0 2009 0 -18 524 0 1 0 3 0 109 0 86273 0 -18.4 0 2009 0 -17 536 0 -2 0 2 0 107.23 0 86316 0 610.3 0 2009 0 -11 587 0 1 0 5 0 109.49 0 86316 0 -917.3 0 2009 0 -11 597 0 1 0 6 0 109.04 0 86316 0 88.4 0 2009 0 -12 581 0 3 0 6 0 109.02 0 87234 0 -740.2 0 2009 0 -10 564 0 3 0 3 0 109.23 0 87234 0 29.3 0 2009 0 -15 558 0 1 0 4 0 109.46 0 87234 0 -893.2 0 2009 0 -15 575 0 1 0 7 0 107.9 0 87885 0 -1030.2 0 2010 0 -15 580 0 0 0 5 0 110.42 0 87885 0 -403.4 0 2010 0 -13 575 0 2 0 6 0 110.98 0 87885 0 -46.9 0 2010 0 -8 563 0 2 0 1 0 111.48 0 88003 0 -321.2 0 2010 0 -13 552 0 -1 0 3 0 111.88 0 88003 0 -239.9 0 2010 0 -9 537 0 1 0 6 0 111.89 0 88003 0 640.9 0 2010 0 -7 545 0 0 0 0 0 109.85 0 88910 0 511.6 0 2010 0 -4 601 0 1 0 3 0 112.1 0 88910 0 -665.1 0 2010 0 -4 604 0 1 0 4 0 112.24 0 88910 0 657.7 0 2010 0 -2 586 0 3 0 7 0 112.39 0 89397 0 -207.7 0 2010 0 0 564 0 2 0 6 0 112.52 0 89397 0 -885.2 0 2010 0 -2 549 0 0 0 6 0 113.16 0 89397 0 -1595.8 0 2010 0 -3 551 0 0 0 6 0 111.84 0 89813 0 -1374.9 0 2011 0 1 556 0 3 0 6 0 114.33 0 89813 0 -316.6 0 2011 0 -2 548 0 -2 0 2 0 114.82 0 89813 0 -283.4 0 2011 0 -1 540 0 0 0 2 0 115.2 0 90539 0 -175.8 0 2011 0 1 531 0 1 0 2 0 115.4 0 90539 0 -694.2 0 2011 0 -3 521 0 -1 0 3 0 115.74 0 90539 0 -249.9 0 2011 0 -4 519 0 -2 0 -1 0 114.19 0 90688 0 268.2 0 2011 0 -9 572 0 -1 0 -4 0 115.94 0 90688 0 -2105.1 0 2011 0 -9 581 0 -1 0 4 0 116.03 0 90688 0 -762.8 0 2011 0 -7 563 0 1 0 5 0 116.24 0 90691 0 -117.1 0 2011 0 -14 548 0 -2 0 3 0 116.66 0 90691 0 -1094.4 0 2011 0 -12 539 0 -5 0 -1 0 116.79 0 90691 0 -2095.2 0 2011 0 -16 541 0 -5 0 -4 0 115.48 0 90645 0 -1587.6 0 2012 0 -20 562 0 -6 0 0 0 118.16 0 90645 0 -528 0 2012 0 -12 559 0 -4 0 -1 0 118.38 0 90645 0 -324.2 0 2012 0 -12 546 0 -3 0 -1 0 118.51 0 90861 0 -276.1 0 2012 0 -10 536 0 -3 0 3 0 118.42 0 90861 0 -139.1 0 2012 0 -10 528 0 -1 0 2 0 118.24 0 90861 0 268 0 2012 0 -13 530 0 -2 0 -4 0 116.47 0 90401 0 570.5 0 2012 0 -16 582 0 -3 0 -3 0 118.96 0 90401 0 -316.5 0 2012 0
Names of X columns:
I W W_c F F_c S S_c C C_c B B_c H H_c T c
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') }
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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