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Data X:
1818 279055 73 504 95 3 96 42 159 130 140824 32033 186099 165 165 1412 209884 73 502 68 4 75 38 149 143 110459 20654 113854 135 132 2049 233446 82 709 64 16 70 46 178 118 105079 16346 99776 121 121 2733 222117 106 1154 139 2 134 42 164 146 112098 35926 106194 148 145 1330 179751 54 402 51 1 72 30 100 73 43929 10621 100792 73 71 631 70849 28 179 46 3 8 35 129 89 76173 10024 47552 49 47 5185 568125 131 2452 118 0 169 40 156 146 187326 43068 250931 185 177 381 33186 19 111 46 0 1 18 67 22 22807 1271 6853 5 5 2150 227332 62 763 79 7 88 38 148 132 144408 34416 115466 125 124 1978 258676 47 647 76 0 98 37 132 92 66485 20318 110896 93 92 2388 341549 117 922 82 0 101 46 169 147 79089 24409 169351 154 149 2352 260484 129 728 66 7 122 60 230 203 81625 20648 94853 98 93 2029 202918 79 760 60 10 57 37 122 113 68788 12347 72591 70 70 3010 367799 83 1185 117 4 139 55 191 171 103297 21857 101345 148 148 2265 269455 88 724 50 10 87 44 162 87 69446 11034 113713 100 100 5081 394578 186 1751 133 0 176 63 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138 50 195 179 139165 46201 184531 155 152 2116 252512 82 728 84 6 50 50 186 149 86652 10971 114198 165 159 2483 367819 101 880 56 1 141 51 197 187 112302 34811 198299 161 159 1187 115602 46 410 45 4 48 42 157 153 69652 3029 33750 31 31 3566 430118 103 1293 87 4 141 44 168 163 119442 38941 189723 199 185 2764 273950 56 1186 87 0 83 42 159 127 69867 4958 100826 78 78 3753 428028 126 1348 77 0 112 44 161 151 101629 32344 188355 121 117 2061 251349 91 689 72 2 79 40 153 100 70168 19433 104470 112 109 947 115658 33 284 36 1 33 17 55 46 31081 12558 58391 41 41 3699 388812 207 1304 51 2 149 43 166 156 103925 36524 164808 158 149 3397 343783 84 1556 44 10 126 41 151 128 92622 26041 134097 123 123 1902 198635 74 742 75 10 81 41 148 111 79011 16637 80238 104 103 1922 214344 81 676 87 5 84 40 129 119 93487 28395 133252 94 87 1819 182398 65 487 97 6 68 49 181 148 64520 16747 54518 73 71 2598 157164 84 1051 90 1 50 52 93 65 93473 9105 121850 52 51 5568 459440 155 2088 860 2 101 42 150 134 114360 11941 79367 71 70 918 78800 42 330 57 2 20 26 82 66 33032 7935 56968 21 21 2404 217575 82 691 99 0 101 59 229 201 96125 19499 106314 155 155 4144 368086 122 1410 120 10 150 50 193 177 151911 22938 191889 174 172 2536 210554 66 1057 76 3 116 50 176 156 89256 25314 104864 136 133 2164 244640 79 688 56 0 99 47 179 158 95676 28527 160792 128 125 496 24188 24 218 20 0 8 4 12 7 5950 2694 15049 7 7 2688 399093 331 862 94 8 88 51 181 175 149695 20867 191179 165 158 744 65029 17 255 21 5 21 18 67 61 32551 3597 25109 21 21 1161 101097 64 454 70 3 30 14 52 41 31701 5296 45824 35 35 3215 297973 62 1185 133 1 97 41 148 133 100087 32982 129711 137 133 2954 369627 90 785 86 5 163 61 230 228 169707 38975 210012 174 169 3968 367127 204 1208 224 6 132 40 148 140 150491 42721 194679 257 256 2798 374143 150 1096 65 0 161 44 160 155 120192 41455 197680 207 190 2324 270099 88 887 86 12 89 40 155 141 95893 23923 81180 103 100 4076 391871 150 1335 70 10 160 51 198 181 151715 26719 197765 171 171 3293 315924 121 1190 148 12 139 29 104 75 176225 53405 214738 279 267 3132 291391 124 1257 72 11 104 43 169 97 59900 12526 96252 83 80 2808 286417 92 1008 59 8 100 42 163 142 104767 26584 124527 130 126 1745 276201 78 658 67 3 66 41 151 136 114799 37062 153242 131 132 2109 267432 71 542 58 0 163 30 116 87 72128 25696 145707 126 121 2140 215924 140 651 60 6 93 39 153 140 143592 24634 113963 158 156 2945 252767 156 877 105 10 85 51 195 169 89626 27269 134904 138 133 2536 260919 86 913 84 2 150 40 149 129 131072 25270 114268 200 199 1737 182961 73 637 63 5 143 29 106 92 126817 24634 94333 104 98 2679 256967 73 900 67 13 107 47 179 160 81351 17828 102204 111 109 893 73566 32 385 39 6 22 23 88 67 22618 3007 23824 26 25 2389 272362 93 784 60 7 85 48 185 179 88977 20065 111563 115 113 2061 216802 60 872 94 2 86 38 133 90 92059 24648 91313 127 126 2220 228835 68 779 67 4 131 42 164 144 81897 21588 89770 140 137 2369 371391 90 1001 96 4 140 46 169 144 108146 25217 100125 121 121 3207 393845 102 1253 54 3 156 40 153 144 126372 30927 165278 183 178 1974 220401 109 586 54 6 81 45 166 134 249771 18487 181712 68 63 2487 225825 69 755 62 2 137 42 164 146 71154 18050 80906 112 109 2126 217623 70 737 71 0 102 41 146 121 71571 17696 75881 103 101 2075 199011 52 760 50 1 72 37 141 112 55918 17326 83963 63 61 4228 483074 131 1272 117 1 161 47 183 145 160141 39361 175721 166 157 1370 145943 70 653 45 5 30 26 99 99 38692 9648 68580 38 38 2448 295224 108 703 61 2 120 48 134 96 102812 26759 136323 163 159 870 80953 25 437 31 0 49 8 28 27 56622 7905 55792 59 58 2169 171206 60 905 175 0 63 27 101 77 15986 4527 25157 27 27 1573 179344 61 459 70 6 76 38 139 137 123534 41517 100922 108 108 4045 415550 221 1586 284 1 85 41 159 151 108535 21261 118845 88 83 3116 369093 128 1053 95 0 146 61 222 126 93879 36099 170492 92 88 3098 180679 106 1051 72 1 165 45 171 159 144551 39039 81716 170 164 2605 298696 103 843 63 1 89 41 154 101 56750 13841 115750 98 96 2404 292260 84 732 75 3 168 42 154 144 127654 23841 105590 205 192 1931 199481 67 632 90 10 48 35 129 102 65594 8589 92795 96 94 3146 282361 77 1128 89 1 149 36 140 135 59938 15049 82390 107 107 2598 329281 89 971 138 4 75 40 156 147 146975 39038 135599 150 144 2058 231266 47 693 68 5 103 40 156 155 165904 36774 127667 138 136 2193 297995 67 738 80 7 116 38 138 138 169265 40076 163073 177 171 2262 305984 88 820 65 0 165 43 153 113 183500 43840 211381 213 210 4197 416463 162 1369 130 12 155 65 251 248 165986 43146 189944 208 193 4022 412530 117 1493 85 13 165 33 126 116 184923 50099 226168 307 297 2841 297080 141 893 83 9 121 51 198 176 140358 40312 117495 125 125 2493 318283 70 902 89 0 156 45 168 140 149959 32616 195894 208 204 2208 202726 195 719 116 0 81 36 138 59 57224 11338 80684 73 70 602 43287 14 214 43 4 13 19 71 64 43750 7409 19630 49 49 2434 223456 86 795 87 4 113 25 90 40 48029 18213 88634 82 82 2513 258249 158 874 80 0 112 44 167 98 104978 45873 139292 206 205 2900 299566 57 1275 132 0 133 45 172 139 100046 39844 128602 112 111 2786 321797 95 1079 59 0 169 44 162 135 101047 28317 135848 139 135 1343 170299 87 426 50 0 28 35 129 97 197426 24797 178377 60 59 3313 169545 100 976 87 0 121 46 179 142 160902 7471 106330 70 70 2106 354041 77 677 62 5 82 44 163 155 147172 27259 178303 112 108 2331 303273 90 696 70 1 148 45 164 115 109432 23201 116938 142 141 398 23623 11 156 9 0 12 1 0 0 1168 238 5841 11 11 2217 195880 76 779 54 0 146 40 155 103 83248 28830 106020 130 130 530 61857 25 192 25 4 23 11 32 30 25162 3913 24610 31 28 1946 207339 53 606 113 0 84 51 189 130 45724 9935 74151 132 101 3199 431443 122 1234 63 1 163 38 140 102 110529 27738 232241 219 216 387 21054 16 146 2 0 4 0 0 0 855 338 6622 4 4 2137 252805 52 866 67 5 81 30 111 77 101382 13326 127097 102 97 492 31961 22 200 22 0 18 8 25 9 14116 3988 13155 39 39 3808 354622 123 1343 157 3 118 43 159 150 89506 24347 160501 125 119 2183 251240 76 735 79 7 76 48 183 163 135356 27111 91502 121 118 1789 187003 95 522 113 14 55 49 184 148 116066 3938 24469 42 41 1863 172481 56 716 50 3 57 32 119 94 144244 17416 88229 111 107 568 38214 34 276 52 0 16 8 27 21 8773 1888 13983 16 16 2504 256082 52 836 113 3 93 43 163 151 102153 18700 80716 70 69 2819 358276 84 1031 115 0 137 52 198 187 117440 36809 157384 162 160 1463 211775 66 511 78 0 50 53 205 171 104128 24959 122975 173 158 3927 445926 89 1708 135 4 152 49 191 170 134238 37343 191469 171 161 2554 348017 99 884 120 0 163 48 187 145 134047 21849 231257 172 165 3506 441946 133 1201 122 3 142 56 210 198 279488 49809 258287 254 246 1458 208962 41 547 54 0 77 45 166 152 79756 21654 122531 90 89 1213 105332 44 422 63 0 42 40 145 112 66089 8728 61394 50 49 3060 315219 358 1002 162 4 94 48 187 173 102070 20920 86480 113 107 4494 460249 196 1558 162 5 126 50 186 177 146760 27195 195791 187 182 1844 160740 60 569 107 16 63 43 164 153 154771 1037 18284 16 16 4365 412099 138 1812 146 5 127 46 172 161 165933 42570 147581 175 173 2037 173802 83 755 77 5 59 40 147 115 64593 17672 72558 90 90 1981 284582 52 648 87 2 117 45 167 147 92280 34245 147341 140 140 2564 283913 100 905 192 1 110 46 158 124 67150 16786 114651 145 142 1996 234262 120 661 75 0 44 37 144 57 128692 20954 100187 141 126 4097 386740 124 1611 131 9 95 45 169 144 124089 16378 130332 125 123 2339 246963 92 811 67 1 128 39 145 126 125386 31852 134218 241 239 2035 173260 63 716 37 3 41 21 79 78 37238 2805 10901 16 15 3241 346748 108 1034 61 11 146 50 194 153 140015 38086 145758 175 170 1974 176654 58 732 127 5 147 55 212 196 150047 21166 75767 132 123 2770 264767 92 1060 58 2 121 40 148 130 154451 34672 134969 154 151 2748 314070 112 852 71 1 185 48 171 159 156349 36171 169216 198 194 2 1 0 0 0 9 0 0 0 0 0 0 0 0 0 207 14688 10 85 0 0 4 0 0 0 6023 2065 7953 5 5 5 98 1 0 0 0 0 0 0 0 0 0 0 0 0 8 455 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2415 284420 92 809 72 2 85 46 141 94 84601 19354 105406 125 122 3462 410509 164 1134 123 3 157 52 204 129 68946 22124 174586 174 173 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 203 4 0 0 0 0 0 0 0 0 0 0 0 0 151 7199 5 74 0 0 7 0 0 0 1644 556 4245 6 6 474 46660 20 259 7 0 12 5 15 13 6179 2089 21509 13 13 141 17547 5 69 3 0 0 1 4 4 3926 2658 7670 3 3 1145 121550 46 309 106 0 37 48 172 89 52789 1813 15673 35 35 29 969 2 0 0 0 0 0 0 0 0 0 0 0 0 2066 242258 73 690 53 2 62 34 125 71 100350 17372 75882 80 72
Names of X columns:
P TRFC LOG CCV CCVComp Shared B NRC SUBMESP LARGPEER ComNrW CRRev COMTime CompHyp CompBlog
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 Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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