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Data X:
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564 3.3673 6.55251 -0.2 2.8 0.7 71 23 72 0.69315 5.1299 -1.9 4.5 0.4 17 2 40 2.94444 5.18739 -7.3 1.9 0.5 85 3 118 4.17439 7.91608 0.1 2.1 2 77 18 49 2.83321 7.33954 -5.9 2.9 0.2 64.6 22 406 3.04452 7.0076 4.7 5.6 0 69 23 211 3.2581 7.87664 4.2 4.4 0.6 189.8 15 472 3.21888 7.23778 2.1 5.4 0.1 159 21 61 4.23411 7.64348 -0.5 2.1 -2 247.8 14 513 4.81218 7.08339 -3.7 1.1 0.1 192 22 101 4.39445 7.95718 6.5 6.2 -0.1 210.8 18 547 2.63906 6.42162 2.1 4.1 0.7 260 24 43 3.2581 7.29029 -3.1 1 -0.2 211 21 102 1.94591 5.42495 -12.8 0.8 1.2 55 6 91 2.94444 7.29029 3.8 4.3 0.5 210 22 105 2.56495 7.52348 3.4 6 0.2 191 14 482 3.21888 5.63479 -4.2 2.1 1.1 91 2 68 2.77259 7.97694 -1.7 5.2 0 76 8 124 4.61512 7.78197 5.4 3.6 -1.1 44 10 173 1.60944 5.743 3.4 4.9 0.7 243 2 140 2.07944 5.743 -0.2 5.6 0.2 16.6 2 553 3.8712 5.62762 2.1 1.5 2 227.8 5 533 3.17805 7.85477 -12.5 3.2 0.2 82.1 15 456 3.58352 7.56941 -4.1 4.3 -0.1 44.7 14 409 4.68213 7.08087 -9.8 0.5 0.2 131 21 83 2.56495 7.43603 -4.9 4.7 -0.1 79.6 21 441 2.30259 6.11368 -5.2 3.5 0 59 1 90 3.55535 7.97488 -1.6 3.5 0.5 34.5 18 480 3.46574 6.69084 6.4 1.2 0 188 6 207 2.48491 7.79523 2 2.8 1.6 242 18 127 4.21951 5.21494 -8.5 1.5 2.8 279 10 93 2.30259 7.65112 3.6 7.6 0.5 199.3 17 482 2.70805 6.12905 0.4 0.8 0.1 297 7 524 2.99573 8.08979 1.2 3.6 0.1 63.9 15 411 2.48491 5.76519 -3.5 3.2 0 240.2 2 504 3.13549 8.06401 3.6 2.3 -0.3 9.4 9 549 4.15888 7.39265 -14.9 1.9 0.8 65 20 91 1.79176 6.43294 1 2.5 -0.2 75 10 70 3.29584 7.62462 -1.7 1.6 -0.1 172 19 51 2.83321 5.22036 3.4 1.1 1.4 89 6 565 3.52636 6.35611 6.9 7 0.8 220 24 34 3.2581 4.47734 -4.8 2.5 0.4 84 3 101 3.98898 6.50279 4.2 3.9 0.6 78 24 197 1.60944 6.88959 1.5 4 0.1 240.4 22 523 3.49651 7.32909 -2.9 1.9 1.7 337.8 19 468 3.17805 7.3601 -8.2 4.7 -0.2 76.5 11 408 4.67283 7.77107 7 3.1 -0.3 73 10 185 3.43399 6.16121 8.1 1.9 -0.1 211.9 1 578 2.07944 7.97281 3.7 4.2 0.2 172 9 128 3.29584 7.90323 0.7 2.1 1.3 207.1 18 473 2.30259 8.16223 0.4 8.6 0.1 34 17 39 2.07944 4.83628 -10.4 1.9 1.1 75 7 86 0.69315 4.91998 6.5 4.5 0.4 206 2 57 3.13549 7.47591 9 4.6 -0.9 208.7 10 587 3.82864 7.52941 10.1 1.1 -3.1 255 11 188 3.8712 7.88796 -0.3 1.7 0.4 26.9 15 416 2.99573 4.47734 -4.4 2.6 0.8 85 4 38 3.2581 7.73281 1.7 1.5 1.1 345 19 158 3.3322 6.93245 -3.2 0.8 -0.1 260 22 68 3.49651 7.65634 -5.9 3.1 -0.1 77 16 85 1.94591 7.0193 3.7 3.6 0.1 195 22 107 4.57471 8.0762 9.9 2.2 0 92 16 177 3.91202 7.90175 7.4 4.3 -0.3 178 18 190 3.80666 6.89669 5.1 2.5 0.7 80 23 197 3.93183 4.30407 3.2 1.5 0.4 75 3 194 2.48491 5.15906 0 4.3 0.1 102.7 3 477 3.13549 7.82764 -5.6 6.1 -0.2 79.5 17 441 2.94444 7.02554 0.7 2.4 -0.2 232.5 23 542 3.4012 7.90175 15.1 2.9 0.2 113.9 19 567 1.94591 5.73334 -2.2 4.3 -0.1 72 5 124 3.17805 5.34711 -3.8 2.7 -0.2 80.5 3 511 3.21888 7.58477 4.2 6.5 0.4 214.3 11 474 2.63906 6.84055 -13.1 4.8 -0.2 52.8 11 462 3.3322 7.55381 -0.2 3.9 -0.2 79.6 12 430 2.70805 4.78749 1.5 3.2 0.1 153 4 61 3.78419 6.86485 0.8 1.5 -0.1 231.5 23 540 3.78419 7.81197 -6.6 2.2 1.3 87 7 122 1.60944 7.89469 2.1 4 -0.1 47.8 14 574 3.3673 7.76132 1.7 2.9 -0.1 81 14 60 3.04452 6.07764 -4.1 5.4 -0.1 66.5 1 427 4.45435 7.80954 18.2 3.7 -2.7 250.7 15 568 1.94591 5.4848 2.2 2 1.9 253.8 5 473 2.63906 5.27811 -7.8 4.6 -0.1 71.5 19 450 3.3322 7.76684 -5.4 3 -0.2 77 14 85 3.43399 5.5835 -4.5 4.1 0.3 59.4 1 507 3.89182 7.86634 8 6.1 0.1 197 19 212 2.19722 8.19644 2.7 3.8 0.2 139 8 128 4.59512 7.59186 -5.6 3.9 -0.2 41 13 116 3.49651 5.54126 4.6 0.9 0.5 108 5 190 2.83321 7.55799 12 4.4 0.2 274.2 20 584 2.19722 6.51471 4.4 2 0.7 205 6 33 2.89037 6.21461 4.5 3.5 0 55 9 196 3.4012 6.63988 0.2 0.5 1.4 84.8 24 483 2.99573 6.02345 -18.6 2.3 0.1 79.4 7 457 3.73767 7.62413 0.1 2.6 1.5 230 19 175 2.94444 5.84064 7.4 4.3 0.3 189.6 1 590 3.58352 8.32579 7.6 4.2 0 224.1 8 583 3.29584 7.32053 8.6 2.6 -0.1 189.2 22 577 4.8752 7.91571 -2.5 3.6 0 64 15 143 4.33073 7.7424 10.6 1.8 -1.6 245 14 177 3.29584 6.91771 -1.6 1.1 0 282 23 51 3.78419 7.78239 5 3.1 -0.4 230 16 545 3.61092 5.18739 5.2 4.4 0.8 206 4 126 2.83321 6.14204 5.3 2.3 1.2 153.1 1 587 3.7612 5.48064 -13.1 1.1 1.3 62.9 5 464 3.58352 5.2933 3.3 2.2 0.5 77.1 2 562 3.17805 5.4848 -0.8 3.5 0 176.8 5 522 3.52636 5.03044 -4 1.5 -0.1 108.2 2 521 4.21951 7.6406 1.7 2.3 -0.3 85 15 167 3.93183 8.29255 6.1 4.2 0.3 23 17 173 2.30259 5.70378 5.8 2.8 0 43 7 189 3.2581 5.4848 5.7 3.3 0.5 228 5 204 2.70805 7.02997 14.9 2.8 -5 255.5 10 594 3.55535 5.31321 -5.7 2.2 0.6 82 5 165 1.94591 6.14419 0.2 3.1 0.1 73 9 56 4.36945 5.14749 -9 1.3 0.4 77 2 88 4.70048 7.66388 16.8 6 -1.1 272 14 205 2.48491 5.8944 2.8 4.7 0 66 2 574 1.60944 7.61085 -0.1 6.6 -0.3 205.2 14 489 3.78419 7.52294 -12.5 0.8 -0.4 232 19 463 1.94591 4.82831 -7.8 3.7 0.1 58.4 4 407 3.09104 7.54908 0.9 1 -0.6 213.1 12 484 3.21888 7.75833 -4.2 4.6 -0.1 78 10 120 2.48491 7.50659 21.9 3.6 -2.5 264.1 19 608 2.19722 6.07764 -10.1 2.8 0 73 1 89 4.93447 7.01571 -9.4 1.2 0 73.8 23 437 4.04305 7.59488 11.2 1.7 -2 236 12 188 4.51086 8.20576 1.8 3.2 0.2 216.6 8 551 1.94591 4.59512 0.5 5.5 0.1 354 4 171 3.09104 8.23297 -2.2 1.6 -0.3 211.7 16 501 4.60517 8.31532 -0.1 5.2 -0.8 79 8 555 3.63759 7.57096 8.4 5.4 -1.8 240 11 163 3.46574 7.61036 0.9 3 -0.7 340 12 142 3.8712 8.21528 -2.9 1.4 -0.2 113.4 8 521 5.15329 8.19146 2.3 2.5 0.6 16 17 44 3.04452 7.67322 -1.4 4.4 -0.1 38.1 14 410 3.49651 7.78655 0.4 1.5 -0.1 110 13 124 2.56495 4.69135 -11.3 2.5 1.2 82 5 87 3.85015 7.629 0.1 3.9 -0.2 70.7 14 506 2.48491 5.49717 -5 4 -0.1 75.4 5 440 2.07944 6.57368 1.6 2 1.2 212.7 6 472 2.99573 7.90618 -2.8 2.3 -0.1 205 20 511 1.38629 6.55251 -3.5 3.9 -0.1 46.3 24 409 3.17805 8.22013 2 3.5 -0.2 82 8 64 2.19722 5.42935 2.4 3.6 0.1 125 5 128 3.55535 7.89096 7.1 3.4 -0.3 82.5 19 571 3.8712 8.02453 -2.7 1.4 1.6 66 9 73 3.4012 7.14283 -1.3 1.2 0.6 66 21 113 3.04452 7.97039 5.5 3.9 -0.5 231 14 131 4.34381 6.65801 5.5 1.1 0.9 138 24 199 2.3979 7.10003 -4 5.8 -0.4 78.4 11 399 2.63906 7.65681 -0.2 2.4 0 41 19 157 2.56495 4.70953 -2.8 1.8 1.4 198.9 3 537 3.3322 5.60212 3.8 3.9 -0.2 85 5 187 2.56495 7.85127 0.2 2.8 0.1 78 17 112 3.68888 7.89655 12.8 6.8 -0.6 227.7 15 583 2.3979 7.20638 -0.8 1.6 1.7 192.6 22 469 2.3979 4.89035 -10.8 1.5 0.2 57 3 460 4.68213 7.56735 -10.2 0.6 2.4 110.8 10 465 3.04452 7.61135 0.8 3.1 -0.1 65.6 19 429 3.04452 6.66823 4.1 1.4 0.2 139.8 24 572 2.19722 5.0689 4.3 4.9 0.1 82 2 212 3.17805 7.46107 -2.8 1.4 -0.1 243 20 102 4.48864 7.68018 -0.4 0.8 2.6 147 21 49 2.30259 7.80344 2.4 3.9 0.2 14.3 20 574 3.29584 8.26256 3.4 0.9 0.3 228 16 109 3.17805 7.6695 12.1 2.8 -3.2 256 13 203 4.06044 5.33272 -2.5 2 1.3 80.7 3 532 4.15888 6.30992 -4.5 2 0.3 90 24 100 3.09104 6.67456 2.2 7.1 0.1 37.9 6 557 4.41884 8.09316 -4.7 1.4 1.2 276.8 18 445 2.89037 6.56667 0.1 2.1 -0.2 247.4 6 543 1.09861 5.83481 -5.8 7.7 0.1 47 7 90 3.43399 7.794 3.3 4.3 0.8 217 17 105 3.52636 7.76089 2 1.7 1.1 234 15 121 3.13549 7.67276 1.3 4.2 0.2 169 10 61 2.19722 7.979 1.4 5 -0.1 40.7 9 480 3.13549 7.78406 6.1 1.9 0.4 171 21 210 2.07944 5.95842 -3.1 4.2 -0.1 52.5 2 426 3.63759 4.54329 -11.5 1.7 3.7 90.4 4 465 3.73767 5.62762 -6.6 1 -0.1 200 2 445 2.48491 5.66643 -3.8 2.1 0.6 73 3 77 1.94591 7.54539 6.5 9.4 -0.9 250 11 160 1.94591 6.23832 -0.4 3.3 0.3 215.1 24 470 2.77259 4.56435 -6.3 2 2.3 223 3 148 2.70805 6.58203 2.2 1.8 0.1 64.2 6 549 1.79176 5.31321 -4.9 4.2 0.3 353 2 117 2.30259 5.61677 -1.3 2.8 -0.1 65.2 1 486 4.11087 7.7111 -5.1 0.7 0.3 60 10 99 3.4012 6.2519 0.1 1 0.2 87 24 111 3.68888 7.85516 6.5 5.2 -0.2 69 19 196 4.17439 8.24512 8.6 1.6 -1 258.8 15 530
Names of X columns:
PartikelsFijnstof AutosPerUur TempOp2mHoogte Windsnelheid TempverschilTussen2mEn25m Windrichting Uur Dag
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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