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Data X:
162556 807 213118 6282154 1 5626 37 29790 444 81767 4321023 2 13337 138 87550 412 153198 4111912 3 8541 45 84738 428 -26007 223193 415 39 0 54660 315 126942 1491348 6 4276 24 42634 168 157214 1629616 5 9164 34 40949 263 129352 1398893 9 2493 29 45187 267 234817 1926517 4 4891 38 37704 228 60448 983660 14 1734 21 16275 129 47818 1443586 7 11409 76 25830 104 245546 1073089 10 7592 34 12679 122 48020 984885 13 7135 62 18014 393 -1710 1405225 8 5043 67 43556 190 32648 227132 414 110 1 24811 280 95350 929118 15 1444 29 6575 63 151352 1071292 11 5480 133 7123 102 288170 638830 28 4026 62 21950 265 114337 856956 17 1266 30 37597 234 37884 992426 12 3195 21 17821 277 122844 444477 46 655 14 12988 73 82340 857217 16 5523 51 22330 67 79801 711969 20 6095 23 13326 103 165548 702380 21 4925 38 16189 290 116384 358589 89 538 10 7146 83 134028 297978 367 933 14 15824 56 63838 585715 30 6027 24 27664 236 74996 657954 24 1624 17 11920 73 31080 209458 419 52 1 8568 34 32168 786690 18 15856 68 14416 139 49857 439798 48 664 17 3369 26 87161 688779 23 17456 145 11819 70 106113 574339 31 4404 32 6984 40 80570 741409 19 12031 78 4519 42 102129 597793 29 8118 88 2220 12 301670 644190 26 20190 200 18562 211 102313 377934 72 1148 10 10327 74 88577 640273 27 4838 43 5336 80 112477 697458 22 6141 93 2365 83 191778 550608 32 4438 148 4069 131 79804 207393 420 51 2 8636 203 128294 301607 359 119 12 13718 56 96448 345783 100 2390 11 4525 89 93811 501749 37 1335 67 6869 88 117520 379983 68 1714 26 4628 39 69159 387475 64 3024 41 3689 25 101792 377305 74 7092 48 4891 49 210568 370837 79 787 35 7489 149 136996 430866 50 717 31 4901 58 121920 469107 41 3204 55 2284 41 76403 194493 423 -167 -2 3160 90 108094 530670 33 3062 105 4150 136 134759 518365 35 2122 77 7285 97 188873 491303 39 2533 40 1134 63 146216 527021 34 2019 288 4658 114 156608 233773 411 214 7 2384 77 61348 405972 58 2123 86 3748 6 50350 652925 25 50325 121 5371 47 87720 446211 45 3730 46 1285 51 99489 341340 106 1321 110 9327 85 87419 387699 63 1858 20 5565 43 94355 493408 38 6243 53 1528 32 60326 146494 426 -1408 -35 3122 25 94670 414462 54 6308 69 7561 77 82425 364304 86 1889 22 2675 54 59017 355178 93 1964 58 13253 251 90829 357760 90 167 12 880 15 80791 261216 399 827 70 2053 44 100423 397144 60 3720 96 1424 73 131116 374943 77 1861 123 4036 85 100269 424898 53 3570 56 3045 49 27330 202055 422 35 1 5119 38 39039 378525 69 3643 35 1431 35 106885 310768 330 3258 77 554 9 79285 325738 125 11431 227 1975 34 118881 394510 61 5557 98 1765 20 77623 247060 404 2353 27 1012 29 114768 368078 82 3576 166 810 11 74015 236761 409 855 45 1280 52 69465 312378 326 960 88 666 13 117869 339836 108 818 210 1380 29 60982 347385 98 5669 107 4677 66 90131 426280 52 3017 48 876 33 138971 352850 96 2591 174 814 15 39625 301881 358 5660 125 514 15 102725 377516 73 11834 346 5692 68 64239 357312 91 2185 28 3642 100 90262 458343 42 3004 71 540 13 103960 354228 94 11016 286 2099 45 106611 308636 339 1697 52 567 14 103345 386212 65 16928 329 2001 36 95551 393343 62 3718 97 2949 40 82903 378509 70 4354 61 2253 68 63593 452469 43 2550 112 6533 29 126910 364839 85 2198 25 1889 43 37527 358649 88 3526 84 3055 30 60247 376641 75 4108 58 272 9 112995 429112 51 28639 844 1414 22 70184 330546 119 659 92 2564 19 130140 403560 59 9253 79 1383 9 73221 317892 151 10717 85 1261 31 76114 307528 343 3258 85 975 19 90534 235133 410 1528 36 3366 55 108479 299243 362 1241 29 576 8 113761 314073 313 6337 198 1686 28 68696 368186 81 4205 100 746 29 71561 269661 396 3029 93 3192 48 59831 125390 427 -1243 -23 2045 16 97890 510834 36 15542 152 5702 47 101481 321896 138 1998 21 1932 20 72954 249898 403 1386 26 936 22 67939 408881 56 6963 223 3437 33 48022 158492 425 -883 -12 5131 44 86111 292154 379 1298 18 2397 13 74020 289513 385 6394 37 1389 6 57530 378049 71 19783 128 1503 35 56364 343466 104 3679 95 402 8 84990 332743 115 5106 330 2239 17 88590 442882 47 11566 108 2234 11 77200 214215 418 888 6 837 21 61262 315688 165 1677 138 10579 92 110309 375195 76 1904 17 875 12 67000 334280 112 9591 153 1585 112 93099 355864 92 1457 98 1659 25 107577 480382 40 9668 169 2647 17 62920 353058 95 4137 58 3294 23 75832 217193 416 748 5 94 10 60793 314533 308 16362 1212 422 23 57935 318056 150 4216 280 34 7 60630 314353 309 14294 3340 1558 25 55637 369448 80 2690 109 43 20 60887 312846 323 37615 2613 645 4 60720 312075 329 22415 174 316 4 60505 315009 299 12779 364 115 10 60945 318903 145 9146 1037 5 1 60720 314887 304 57443 24814 897 4 60720 314913 303 22983 128 389 8 58990 325506 127 8965 322 1002 11 56750 298568 364 6571 98 36 4 60894 315834 162 38611 3215 460 15 63346 329784 122 8652 282 309 9 56535 312878 322 10262 365 9 7 60835 314987 300 19165 12458 271 2 60720 325249 129 62625 463 14 0 61016 315877 161 115877 8041 520 7 58650 291650 383 9165 176 1766 46 60438 305959 349 1451 60 458 7 58625 297765 368 8888 213 20 2 60938 315245 297 38415 5674 98 2 61490 315236 298 57618 1179 405 5 60845 336425 111 19489 337 483 7 60830 306268 348 3936 220 454 24 63261 302187 357 2004 225 47 1 60720 314882 305 38294 2432 757 18 45689 382712 66 9616 242 4655 55 60720 341570 105 360 30 36 3 61564 312412 325 28103 3146 203 9 61938 309596 334 12177 541 126 8 60951 315547 171 11555 921 400 113 60720 313267 319 745 283 71 0 60745 316176 159 116176 1646 972 19 71642 359335 87 4686 164 531 11 71641 330068 120 13007 245 2461 25 55792 314289 310 2005 46 378 16 71873 297413 369 1873 258 23 5 62555 314806 306 22961 4974 638 11 60370 333210 114 9515 209 2300 23 64873 352108 97 5245 66 149 6 62041 313332 318 22666 761 226 5 65745 291787 382 18357 406 275 7 59500 318745 146 29686 431 141 7 61630 315366 295 19228 819 28 3 60890 315688 166 57844 4148 4980 89 113521 409642 55 2304 42 472 19 80045 269587 397 3479 148 203 12 50804 300962 361 3739 498 496 12 87390 325479 128 7381 253 10 5 61656 316155 160 58077 11639 63 2 65688 318574 148 29643 1891 1136 26 48522 343613 103 4488 126 265 3 60720 306948 346 3450 404 267 11 57640 330059 121 4064 487 474 10 61977 288985 386 4449 188 534 5 62620 304485 351 14926 196 15 6 60831 315688 164 14461 7687 397 7 60646 317736 152 4205 297 1866 28 56225 322331 135 4218 66 288 3 60510 296656 373 24164 335 3 1 60698 315354 296 57677 36505 468 20 60720 312161 328 5341 239 20 1 60805 315576 169 57788 5924 278 22 61404 314922 302 4420 413 61 9 60720 314551 307 8182 1874 192 2 65276 312339 327 28085 586 317 7 63915 298700 363 10967 311 738 9 60720 321376 139 12138 164 368 13 61686 303230 355 6072 281 2 0 60743 315487 172 115487 52257 53 6 60349 315793 163 19299 2197 94 3 61360 312887 321 37629 1200 24 7 59818 315637 168 14455 4772 2332 2 72680 324385 131 31096 53 131 15 61808 308989 338 9908 834 206 9 53110 296702 372 10745 469 167 1 64245 307322 345 53661 642 622 38 73007 304376 352 1430 168 2328 57 82732 253588 400 570 23 365 7 54820 309560 335 13695 300 364 26 47705 298466 365 2813 271 226 13 72835 343929 102 11994 636 307 10 58856 331955 116 8797 430 188 9 77655 381180 67 16471 964 138 26 69817 331420 117 21903 954 125 19 60798 310201 332 9183 883 282 12 62452 320016 141 4001 425 335 23 64175 320398 140 3648 359 1324 29 67440 291841 381 785 69 176 8 68136 310670 331 3952 629 249 26 56726 313491 317 1576 455 333 9 70811 331323 118 10102 394 601 5 60720 319210 144 19868 198 30 3 62045 318098 149 29525 3912 249 13 54323 292754 378 1496 373 165 12 62841 325176 130 5216 759 453 19 81125 365959 84 7903 366 53 10 59506 302409 356 7315 1932 382 9 59365 340968 107 6713 369 30 4 60798 313164 320 28291 3808 290 1 58790 301164 360 50582 348 366 14 61808 344425 101 2725 394 2 12 60735 315394 174 12822 63057 209 19 64016 316647 157 8973 558 384 17 54683 309836 333 4993 286 365 32 87192 346611 99 1766 402 49 14 64107 322031 137 15254 2500 3 8 60761 315656 167 28914 37920 133 4 65990 339445 109 9960 1052 32 0 59988 314964 301 114964 3603 368 20 61167 297141 370 5714 264 1 5 60719 315372 293 19229 84213 22 1 60722 312502 324 56251 5154 96 4 60379 313729 316 22746 1188 1 1 60727 315388 175 57694 117743 314 4 60720 315371 294 23074 367 844 20 60925 296139 375 1233 114 26 1 60896 313880 315 113880 4328 125 10 59734 317698 153 9054 944 304 12 62969 295580 376 6372 314 621 13 60720 308256 341 2255 174 119 3 59118 303677 354 17280 872 1595 10 60720 319369 143 7022 75 312 3 58598 318690 147 8478 381 60 7 61124 314049 314 11405 1916 587 10 59595 325699 126 10475 214 135 1 62065 314210 311 57105 847 514 15 78780 322378 133 2353 238 1 4 60722 315398 173 28849 172235 1763 28 61600 308336 340 4514 61 180 9 59635 316386 158 10581 647 218 7 60720 315553 170 5503 531 448 7 59781 323361 132 3084 275 227 7 76644 336639 110 15182 603 174 3 64820 307424 344 107424 618 121 11 56178 295370 377 3974 788 607 7 60436 322340 134 11122 201 2212 10 60720 319864 142 8562 54 530 18 73433 317291 155 1955 221 571 14 41477 280398 390 1005 141 78 12 62700 317330 154 7333 1511 2489 29 67804 238125 408 953 15 131 3 59661 327071 123 21179 969 923 6 58620 309038 337 13630 118 72 3 60398 314210 312 38070 1591 572 8 58580 307930 342 6746 189 397 10 62710 322327 136 12233 308 450 6 59325 292136 380 11517 205 622 8 60950 263276 398 9039 102 694 6 68060 367655 83 20957 241 3425 9 83620 283910 388 6993 25 562 8 58456 283587 389 6430 149 4917 26 52811 243650 406 1039 9 1442 239 121173 438493 49 2021 165 529 7 63870 296261 374 10696 182 2126 41 21001 230621 413 222 14 1061 3 70415 304252 353 20850 98 776 8 64230 333505 113 14834 172 611 6 59190 296919 371 12115 159 1526 21 69351 278990 391 3160 52 592 7 64270 276898 392 10985 130 1182 11 70694 327007 124 9770 107 621 11 68005 317046 156 7315 188 989 12 58930 304555 350 9505 106 438 9 58320 298096 366 8918 224 726 3 69980 231861 412 10620 44 1303 57 69863 309422 336 1794 84 7419 21 63255 286963 387 2999 12 1164 15 57320 269753 395 4103 60 3310 32 75230 448243 44 7523 75 1920 11 79420 165404 424 -2306 -18 965 2 73490 204325 421 1442 4 3256 23 35250 407159 57 3139 64 1135 20 62285 290476 384 5322 80 1270 24 69206 275311 393 2897 59 661 1 65920 246541 405 15514 70 1013 1 69770 253468 402 26734 53 2844 74 72683 240897 407 610 14 11528 68 -14545 -83265 431 -4047 -25 6526 20 55830 -42143 430 -9313 -37 2264 20 55174 272713 394 3030 32 5109 82 67038 215362 417 158 3 3999 21 51252 42754 428 -5242 -39 35624 244 157278 306275 347 477 3 9252 32 79510 253537 401 1115 6 15236 86 77440 372631 78 1918 11 18073 69 27284 -7170 429 -1151 -11
Names of X columns:
Costs Orders Dividends Wealth Wrank Profit/Trades Profit/Cost
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
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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