Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
1 162556 1081 213118 6282929 1 29790 309 81767 4324047 1 87550 458 153198 4108272 0 84738 588 -26007 -1212617 1 54660 299 126942 1485329 1 42634 156 157214 1779876 0 40949 481 129352 1367203 1 42312 323 234817 2519076 1 37704 452 60448 912684 1 16275 109 47818 1443586 0 25830 115 245546 1220017 0 12679 110 48020 984885 1 18014 239 -1710 1457425 0 43556 247 32648 -572920 1 24524 497 95350 929144 0 6532 103 151352 1151176 0 7123 109 288170 790090 1 20813 502 114337 774497 1 37597 248 37884 990576 0 17821 373 122844 454195 1 12988 119 82340 876607 1 22330 84 79801 711969 0 13326 102 165548 702380 0 16189 295 116384 264449 0 7146 105 134028 450033 0 15824 64 63838 541063 1 26088 267 74996 588864 0 11326 129 31080 -37216 0 8568 37 32168 783310 0 14416 361 49857 467359 1 3369 28 87161 688779 1 11819 85 106113 608419 1 6620 44 80570 696348 1 4519 49 102129 597793 0 2220 22 301670 821730 0 18562 155 102313 377934 0 10327 91 88577 651939 1 5336 81 112477 697458 1 2365 79 191778 700368 0 4069 145 79804 225986 0 7710 816 128294 348695 0 13718 61 96448 373683 0 4525 226 93811 501709 0 6869 105 117520 413743 0 4628 62 69159 379825 1 3653 24 101792 336260 1 1265 26 210568 636765 1 7489 322 136996 481231 0 4901 84 121920 469107 0 2284 33 76403 211928 1 3160 108 108094 563925 1 4150 150 134759 511939 1 7285 115 188873 521016 1 1134 162 146216 543856 1 4658 158 156608 329304 0 2384 97 61348 423262 0 3748 9 50350 509665 0 5371 66 87720 455881 0 1285 107 99489 367772 1 9327 101 87419 406339 1 5565 47 94355 493408 0 1528 38 60326 232942 1 3122 34 94670 416002 1 7317 84 82425 337430 0 2675 79 59017 361517 0 13253 947 90829 360962 0 880 74 80791 235561 1 2053 53 100423 408247 0 1424 94 131116 450296 1 4036 63 100269 418799 1 3045 58 27330 247405 0 5119 49 39039 378519 0 1431 34 106885 326638 0 554 11 79285 328233 0 1975 35 118881 386225 1 1286 17 77623 283662 0 1012 47 114768 370225 0 810 43 74015 269236 0 1280 117 69465 365732 1 666 171 117869 420383 0 1380 26 60982 345811 1 4608 73 90131 431809 0 876 59 138971 418876 0 814 18 39625 297476 0 514 15 102725 416776 1 5692 72 64239 357257 0 3642 86 90262 458343 0 540 14 103960 388386 0 2099 64 106611 358934 0 567 11 103345 407560 0 2001 52 95551 392558 1 2949 41 82903 373177 0 2253 99 63593 428370 1 6533 75 126910 369419 0 1889 45 37527 358649 1 3055 43 60247 376641 0 272 8 112995 467427 1 1414 198 70184 364885 0 2564 22 130140 436230 1 1383 11 73221 329118 1 1261 33 76114 317365 0 975 23 90534 286849 0 3366 80 108479 376685 0 576 18 113761 407198 0 1306 28 68696 377772 0 746 23 71561 271483 1 3192 60 59831 153661 1 2045 20 97890 513294 0 5477 59 101481 324881 1 1932 36 72954 264512 0 936 30 67939 420968 1 3437 47 48022 129302 0 5131 71 86111 191521 1 2397 14 74020 268673 1 1389 9 57530 353179 0 1503 39 56364 354624 0 402 26 84990 363713 0 2239 21 88590 456657 1 2234 16 77200 211742 0 837 69 61262 338381 0 10579 92 110309 418530 0 875 14 67000 351483 0 1395 103 93099 372928 1 1659 29 107577 485538 1 2647 37 62920 279268 1 3294 23 75832 219060 0 0 0 60720 325560 0 94 7 60793 325314 0 422 28 57935 322046 0 0 0 60720 325560 0 34 8 60630 325599 0 1558 63 55637 377028 0 0 0 60720 325560 0 43 3 60887 323850 0 0 0 60720 325560 0 316 9 60505 331514 0 115 13 60945 325632 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 389 14 58990 322265 0 0 0 60720 325560 0 1002 15 56750 325906 0 36 3 60894 325985 0 460 15 63346 346145 0 309 11 56535 325898 0 0 0 60720 325560 0 9 6 60835 325356 0 0 0 60720 325560 0 14 1 61016 325930 0 520 10 58650 318020 0 1766 73 60438 326389 0 0 0 60720 325560 0 458 11 58625 302925 0 20 3 60938 325540 0 0 0 60720 325560 0 0 0 60720 325560 0 98 2 61490 326736 0 405 7 60845 340580 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 483 27 60830 331828 0 454 51 63261 323299 0 0 0 60720 325560 0 0 0 60720 325560 0 757 19 45689 387722 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 36 4 61564 324598 0 0 0 60720 325560 0 203 9 61938 328726 0 0 0 60720 325560 0 90 8 60951 325043 0 0 0 60720 325560 1 71 1 60745 325806 0 0 0 60720 325560 0 0 0 60720 325560 0 972 34 71642 387732 1 531 10 71641 349729 0 604 38 55792 332202 1 283 10 71873 305442 1 23 5 62555 329537 1 638 14 60370 327055 1 699 16 64873 356245 0 149 5 62041 328451 0 226 5 65745 307062 0 0 0 60720 325560 0 275 4 59500 331345 0 0 0 60720 325560 0 141 6 61630 331824 0 0 0 60720 325560 0 28 2 60890 325685 1 0 0 60720 325560 1 2566 80 113521 404480 1 0 0 60720 325560 1 0 0 60720 325560 1 472 20 80045 318314 1 0 0 60720 325560 1 0 0 60720 325560 1 0 0 60720 325560 1 203 27 50804 311807 1 496 17 87390 337724 1 10 2 61656 326431 1 63 4 65688 327556 1 0 0 60720 325560 1 1136 32 48522 356850 1 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 267 32 57640 322741 0 474 20 61977 310902 0 534 7 62620 324295 0 0 0 60720 325560 0 15 8 60831 326156 0 397 28 60646 326960 0 0 0 60720 325560 0 1061 20 56225 333411 0 288 4 60510 297761 0 0 0 60720 325560 0 3 2 60698 325536 0 0 0 60720 325560 0 20 2 60805 325762 0 278 26 61404 327957 0 0 0 60720 325560 0 0 0 60720 325560 0 192 4 65276 318521 0 0 0 60720 325560 0 317 9 63915 319775 0 0 0 60720 325560 0 0 0 60720 325560 0 368 17 61686 332128 0 0 0 60720 325560 0 2 1 60743 325486 0 0 0 60720 325560 0 53 6 60349 325838 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 94 3 61360 331767 0 0 0 60720 325560 0 24 8 59818 324523 0 2332 4 72680 339995 1 0 0 60720 325560 1 0 0 60720 325560 0 131 11 61808 319582 1 0 0 60720 325560 1 0 0 60720 325560 0 206 9 53110 307245 1 0 0 60720 325560 0 167 2 64245 317967 0 622 73 73007 331488 0 885 85 82732 335452 0 0 0 60720 325560 0 365 8 54820 334184 0 364 35 47705 313213 1 0 0 60720 325560 0 0 0 60720 325560 1 0 0 60720 325560 1 0 0 60720 325560 0 226 12 72835 348678 0 307 15 58856 328727 1 0 0 60720 325560 0 0 0 60720 325560 1 0 0 60720 325560 0 188 11 77655 387978 1 0 0 60720 325560 0 138 6 69817 336704 1 0 0 60720 325560 1 0 0 60720 325560 1 0 0 60720 325560 0 125 12 60798 322076 0 0 0 60720 325560 0 282 30 62452 334272 0 335 33 64175 338197 0 0 0 60720 325560 1 813 82 67440 321024 0 176 28 68136 322145 1 0 0 60720 325560 0 0 0 60720 325560 0 249 72 56726 323351 0 0 0 60720 325560 0 333 13 70811 327748 0 0 0 60720 325560 1 0 0 60720 325560 0 30 4 62045 328157 1 0 0 60720 325560 0 249 62 54323 311594 1 0 0 60720 325560 0 165 24 62841 335962 0 453 21 81125 372426 0 0 0 60720 325560 0 53 14 59506 319844 1 382 21 59365 355822 0 0 0 60720 325560 1 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 1 30 4 60798 324047 0 290 2 58790 311464 0 0 0 60720 325560 1 0 0 60720 325560 0 366 53 61808 353417 0 2 9 60735 325590 1 0 0 60720 325560 1 209 13 64016 328576 0 384 22 54683 326126 1 0 0 60720 325560 1 0 0 60720 325560 0 365 83 87192 369376 1 0 0 60720 325560 1 49 8 64107 332013 0 3 4 60761 325871 0 133 14 65990 342165 0 32 1 59988 324967 0 368 17 61167 314832 0 1 6 60719 325557 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 1 0 0 60720 325560 0 0 0 60720 325560 0 22 2 60722 322649 1 0 0 60720 325560 1 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 96 5 60379 324598 0 1 2 60727 325567 0 0 0 60720 325560 0 81 7 60925 324005 0 0 0 60720 325560 0 26 1 60896 325748 0 125 13 59734 323385 0 304 15 62969 315409 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 119 6 59118 312275 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 312 14 58598 320576 0 60 10 61124 325246 0 587 12 59595 332961 0 135 2 62065 323010 0 0 0 60720 325560 0 0 0 60720 325560 0 514 52 78780 345253 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 1 4 60722 325559 0 0 0 60720 325560 0 0 0 60720 325560 1 58 3 61600 319634 0 180 11 59635 319951 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 448 40 59781 318519 0 227 9 76644 343222 0 174 1 64820 317234 0 0 0 60720 325560 0 0 0 60720 325560 0 121 24 56178 314025 0 607 11 60436 320249 0 0 0 60720 325560 0 0 0 60720 325560 0 0 0 60720 325560 0 530 60 73433 349365 0 571 80 41477 289197 0 0 0 60720 325560 0 78 16 62700 329245 0 2489 40 67804 240869 0 131 6 59661 327182 0 923 8 58620 322876 0 72 3 60398 323117 0 572 16 58580 306351 0 397 10 62710 335137 0 450 8 59325 308271 0 622 7 60950 301731 0 694 8 68060 382409 1 3425 12 83620 279230 0 562 13 58456 298731 0 4917 42 52811 243650 1 1442 118 121173 532682 0 529 9 63870 319771 1 2126 138 21001 171493 0 1061 5 70415 347262 0 776 9 64230 343945 0 611 8 59190 311874 1 1526 25 69351 302211 0 592 7 64270 316708 0 1182 13 70694 333463 0 621 16 68005 344282 0 989 11 58930 319635 0 438 11 58320 301186 0 726 3 69980 300381 0 1303 61 69863 318765 1 6341 24 63255 286146 1 1164 17 57320 306844 1 3310 33 75230 307705 0 1366 7 79420 312448 0 965 3 73490 299715 0 3256 66 35250 373399 1 1135 17 62285 299446 0 1270 26 69206 325586 0 661 3 65920 291221 0 1013 2 69770 261173 0 2844 67 72683 255027 1 11528 70 -14545 -78375 0 6526 26 55830 -58143 0 2264 24 55174 227033 1 4461 94 67038 235098 0 3999 30 51252 21267 0 35624 223 157278 238675 0 9252 48 79510 197687 0 15236 90 77440 418341 0 18073 180 27284 -297706
Names of X columns:
Group Costs Trades Dividends Wealth
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
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation