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Data X:
3.166464608 869 28 84786 58 5 3.286476783 1530 38 83123 60 5 3.29159842 2172 30 101193 108 0 3.172097125 901 22 38361 49 0 2.917880593 463 26 68504 0 0 3.549331417 3201 25 119182 121 5 2.787585412 371 18 22807 1 0 3.112617164 1192 11 17140 20 0 3.232225797 1583 26 116174 43 5 3.265595013 1439 25 57635 69 5 3.38355752 1764 38 66198 78 6 3.280593845 1495 44 71701 86 0 3.196447267 1373 30 57793 44 5 3.41280065 2187 40 80444 104 0 3.292973325 1491 34 53855 63 0 3.489912189 4041 47 97668 158 5 3.374673049 1706 30 133824 102 5 3.382955786 2152 31 101481 77 5 3.201941772 1036 23 99645 82 6 3.331930651 1882 36 114789 115 0 3.351177732 1929 36 99052 101 7 3.510007237 2242 30 67654 80 7 3.242484174 1220 25 65553 50 5 3.196002436 1289 39 97500 83 0 3.283190383 2515 34 69112 123 4 3.282618086 2147 31 82753 73 6 3.366795815 2352 31 85323 81 7 3.363968236 1638 33 72654 105 5 3.17621148 1222 25 30727 47 7 3.361091858 1812 33 77873 105 0 3.344396201 1677 35 117478 94 6 3.274843895 1579 42 74007 44 5 3.414985875 1731 43 90183 114 0 3.057754304 807 30 61542 38 0 3.456327842 2452 33 101494 107 6 3.110699706 829 13 27570 30 0 3.351934761 1940 32 55813 71 0 3.490237979 2662 36 79215 84 6 2.397445642 186 0 1423 0 6 3.26964639 1499 28 55461 59 0 3.126687047 865 14 31081 33 6 3.096924276 1793 17 22996 42 6 3.421648446 2527 32 83122 96 5 3.426538534 2747 30 70106 106 0 3.232334693 1324 35 60578 56 6 3.285220306 2702 20 39992 57 5 3.24027 1383 28 79892 59 7 3.141571707 1179 28 49810 39 5 3.154468489 2099 39 71570 34 0 3.527671345 4308 34 100708 76 6 3.035548408 918 26 33032 20 0 3.298484868 1831 39 82875 91 5 3.436801551 3373 39 139077 115 0 3.235558571 1713 33 71595 85 0 3.272073282 1438 28 72260 76 5 2.702223892 496 4 5950 8 7 3.494611213 2253 39 115762 79 6 2.978664445 744 18 32551 21 0 3.110960438 1161 14 31701 30 0 3.356959651 2352 29 80670 76 7 3.393042179 2144 44 143558 101 6 3.507259418 4691 21 117105 94 5 3.118453789 1112 16 23789 27 7 3.378059245 2694 28 120733 92 0 3.409657073 1973 35 105195 123 7 3.337126301 1769 28 73107 75 0 3.475542679 3148 38 132068 128 0 3.381567219 2474 23 149193 105 6 3.28909133 2084 36 46821 55 0 3.338740819 1954 32 87011 56 0 3.321477767 1226 29 95260 41 6 3.282189656 1389 25 55183 72 7 3.219583634 1496 27 106671 67 6 3.306191303 2269 36 73511 75 6 3.306403039 1833 28 92945 114 4 3.161359411 1268 23 78664 118 0 3.296751508 1943 40 70054 77 0 3.01507042 893 23 22618 22 6 3.318848699 1762 40 74011 66 0 3.269542588 1403 28 83737 69 7 3.220710738 1425 34 69094 105 5 3.433838202 1857 33 93133 116 0 3.391872373 1840 28 95536 88 7 3.298186791 1502 34 225920 73 7 3.202351214 1441 30 62133 99 0 3.239073925 1420 33 61370 62 6 3.16449742 1416 22 43836 53 0 3.51219779 2970 38 106117 118 6 3.225170333 1317 26 38692 30 0 3.316088958 1644 35 84651 100 7 3.043617742 870 8 56622 49 0 3.171053229 1654 24 15986 24 3 3.190374416 1054 29 95364 67 7 3.144182912 937 20 26706 46 6 3.447015513 3004 29 89691 57 5 3.388930904 2008 45 67267 75 0 3.230909983 2547 37 126846 135 5 3.334810581 1885 33 41140 68 0 3.296550075 1626 33 102860 124 7 3.213100481 1468 25 51715 33 5 3.359322749 2445 32 55801 98 5 3.391681398 1964 29 111813 58 4 3.260344897 1381 28 120293 68 7 3.297500987 1369 28 138599 81 0 3.376358574 1659 31 161647 131 7 3.42107616 2888 52 115929 110 5 3.060303396 1290 21 24266 37 6 3.474845309 2845 24 162901 130 5 3.3660777 1982 41 109825 93 6 3.37661489 1904 33 129838 118 5 3.223370493 1391 32 37510 39 7 2.86163539 602 19 43750 13 0 3.246237736 1743 20 40652 74 0 3.264159459 1559 31 87771 81 6 3.330334326 2014 31 85872 109 0 3.381078218 2143 32 89275 151 0 3.359750092 2146 18 44418 51 6 3.10598121 874 23 192565 28 6 3.084378504 1590 17 35232 40 7 3.196912787 1590 20 40909 56 6 2.96168116 1210 12 13294 27 6 3.178989684 2072 17 32387 37 0 3.109907189 1281 30 140867 83 5 3.365324233 1401 31 120662 54 0 3.048626753 834 10 21233 27 0 3.113716178 1105 13 44332 28 7 3.112249029 1272 22 61056 59 6 3.392300679 1944 42 101338 133 5 2.688139407 391 1 1168 12 6 2.850224414 761 9 13497 0 0 3.239440287 1605 32 65567 106 6 2.964030376 530 11 25162 23 0 3.10738375 1988 25 32334 44 6 3.194921123 1386 36 40735 71 6 3.481994422 2395 31 91413 116 5 2.665545273 387 0 855 4 5 3.3426323 1742 24 97068 62 4 2.684773093 620 13 44339 12 7 2.772692669 449 8 14116 18 6 2.883091932 800 13 10288 14 6 3.193042217 1684 19 65622 60 6 3.081329075 1050 18 16563 7 0 3.393995313 2699 33 76643 98 4 3.30085889 1606 40 110681 64 0 3.039556987 1502 22 29011 29 0 3.185133106 1204 38 92696 32 4 3.100940311 1138 24 94785 25 7 2.826719943 568 8 8773 16 0 3.23655568 1459 35 83209 48 0 3.430035081 2158 43 93815 100 7 3.279039575 1111 43 86687 46 6 3.13138611 1421 14 34553 45 4 3.493396784 2833 41 105547 129 6 3.40187326 1955 38 103487 130 0 3.516745024 2922 45 213688 136 6 3.250848673 1002 31 71220 59 5 3.104130954 1060 13 23517 25 0 3.059827581 956 28 56926 32 0 3.372186728 2186 31 91721 63 6 3.51728922 3604 40 115168 95 0 3.055453733 1035 30 111194 14 7 3.165077624 1417 16 51009 36 5 3.489699429 3261 37 135777 113 6 3.191537047 1587 30 51513 47 6 3.334100768 1424 35 74163 92 6 3.265783524 1701 32 51633 70 0 3.216193893 1249 27 75345 19 0 3.153506379 946 20 33416 50 4 3.401161271 1926 18 83305 41 5 3.462464297 3352 31 98952 91 4 3.320285857 1641 31 102372 111 7 3.280470795 2035 21 37238 41 5 3.407063377 2312 39 103772 120 6 3.120793693 1369 41 123969 135 0 3.203361134 1577 13 27142 27 0 3.326310128 2201 32 135400 87 0 3.083847007 961 18 21399 25 5 3.365000845 1900 39 130115 131 7 3.20265798 1254 14 24874 45 7 3.018451651 1335 7 34988 29 7 3.04467875 1597 17 45549 58 7 2.57267948 207 0 6023 4 0 3.295753939 1645 30 64466 47 5 3.429287942 2429 37 54990 109 6 2.398201326 151 0 1644 7 0 2.882860791 474 5 6179 12 0 2.618139172 141 1 3926 0 0 3.197007233 1639 16 32755 37 0 3.092687321 872 32 34777 37 6 3.239705923 1318 24 73224 46 0 3.101897238 1018 17 27114 15 5 3.038641134 1383 11 20760 42 0 2.951212462 1314 24 37636 7 6 3.212192889 1335 22 65461 54 6 3.160300918 1403 12 30080 54 4 3.01228975 910 19 24094 14 4 2.980670224 616 13 69008 16 6 3.106525114 1407 17 54968 33 0 3.008543881 771 15 46090 32 6 3.029700059 766 16 27507 21 0 2.89846558 473 24 10672 15 0 3.20114478 1376 15 34029 38 4 3.132534498 1232 17 46300 22 6 3.074684105 1521 18 24760 28 0 2.868136691 572 20 18779 10 0 3.031148789 1059 16 21280 31 0 3.295566252 1544 16 40662 32 5 3.198540761 1230 18 28987 32 5 3.174319192 1206 22 22827 43 0 3.084276863 1205 8 18513 27 0 3.168689696 1255 17 30594 37 6 2.918800006 613 18 24006 20 0 3.047003322 721 16 27913 32 5 2.950164898 1109 23 42744 0 6 2.922042953 740 22 12934 5 5 2.959060874 1126 13 22574 26 0 2.937064306 728 13 41385 10 5 2.980737469 689 16 18653 27 7 3.04360293 592 16 18472 11 0 3.025933091 995 20 30976 29 5 3.119678925 1613 22 63339 25 6 3.101766482 2048 17 25568 55 6 2.991751124 705 18 33747 23 0 2.768975577 301 17 4154 5 3 3.201906854 1803 12 19474 43 6 3.014820069 799 7 35130 23 0 2.969950443 861 17 39067 34 4 2.961172129 1186 14 13310 36 0 3.020467046 1451 23 65892 35 6 2.775805966 628 17 4143 0 6 3.045405083 1161 14 28579 37 6 3.065933296 1463 15 51776 28 0 2.903070932 742 17 21152 16 0 2.981878508 979 21 38084 26 5 2.938487555 675 18 27717 38 4 3.251625863 1241 18 32928 23 7 2.881610915 676 17 11342 22 0 3.015304447 1049 17 19499 30 6 2.828035732 620 16 16380 16 0 3.081871163 1081 15 36874 18 6 3.211251212 1688 21 48259 28 7 2.914693877 736 16 16734 32 0 2.911391551 617 14 28207 21 7 3.002669872 812 15 30143 23 0 3.05776495 1051 17 41369 29 0 3.115299727 1656 15 45833 50 6 3.064169308 705 15 29156 12 6 3.060778535 945 10 35944 21 0 2.799690732 554 6 36278 18 0 3.235454896 1597 22 45588 27 7 3.106426846 982 21 45097 41 7 2.643467633 222 1 3895 13 0 3.105695081 1212 18 28394 12 6 3.062605914 1143 17 18632 21 7 2.767635006 435 4 2325 8 7 2.775220343 532 10 25139 26 5 3.074886491 882 16 27975 27 5 2.965118675 608 16 14483 13 0 2.840518675 459 9 13127 16 0 2.737903319 578 16 5839 2 0 3.028609336 826 17 24069 42 0 2.821177538 509 7 3738 5 0 2.925476806 717 15 18625 37 0 2.901766844 637 14 36341 17 0 3.055917972 857 14 24548 38 0 2.974788928 830 18 21792 37 4 2.952134253 652 12 26263 29 5 3.162122445 707 16 23686 32 6 3.027491682 954 21 49303 35 6 3.117943498 1461 19 25659 17 0 3.001801066 672 16 28904 20 0 2.862435185 778 1 2781 7 0 3.12211313 1141 16 29236 46 0 2.96571545 680 10 19546 24 0 2.997400864 1090 19 22818 40 0 2.919895484 616 12 32689 3 0 2.648998252 285 2 5752 10 7 3.064176271 1145 14 22197 37 0 3.055089032 733 17 20055 17 6 3.032288667 888 19 25272 28 0 3.072541479 849 14 82206 19 0 3.078905472 1182 11 32073 29 0 2.841193783 528 4 5444 8 0 2.974843706 642 16 20154 10 6 2.907418992 947 20 36944 15 0 2.938283159 819 12 8019 15 0 2.968413029 757 15 30884 28 0 3.010882379 894 16 19540 17 7
Names of X columns:
time_in_rfc pageviews compendiums_reviewed totsize blogged_computations Q1_28
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
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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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