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Data X:
210907 56 396 81 3 79 30 115 94 112285 24188 146283 144 145 120982 56 297 55 4 58 28 109 103 84786 18273 98364 103 101 176508 54 559 50 12 60 38 146 93 83123 14130 86146 98 98 179321 89 967 125 2 108 30 116 103 101193 32287 96933 135 132 123185 40 270 40 1 49 22 68 51 38361 8654 79234 61 60 52746 25 143 37 3 0 26 101 70 68504 9245 42551 39 38 385534 92 1562 63 0 121 25 96 91 119182 33251 195663 150 144 33170 18 109 44 0 1 18 67 22 22807 1271 6853 5 5 101645 63 371 88 0 20 11 44 38 17140 5279 21529 28 28 149061 44 656 66 5 43 26 100 93 116174 27101 95757 84 84 165446 33 511 57 0 69 25 93 60 57635 16373 85584 80 79 237213 84 655 74 0 78 38 140 123 66198 19716 143983 130 127 173326 88 465 49 7 86 44 166 148 71701 17753 75851 82 78 133131 55 525 52 7 44 30 99 90 57793 9028 59238 60 60 258873 60 885 88 3 104 40 139 124 80444 18653 93163 131 131 180083 66 497 36 9 63 34 130 70 53855 8828 96037 84 84 324799 154 1436 108 0 158 47 181 168 97668 29498 151511 140 133 230964 53 612 43 4 102 30 116 115 133824 27563 136368 151 150 236785 119 865 75 3 77 31 116 71 101481 18293 112642 91 91 135473 41 385 32 0 82 23 88 66 99645 22530 94728 138 132 202925 61 567 44 7 115 36 139 134 114789 15977 105499 150 136 215147 58 639 85 0 101 36 135 117 99052 35082 121527 124 124 344297 75 963 86 1 80 30 108 108 67654 16116 127766 119 118 153935 33 398 56 5 50 25 89 84 65553 15849 98958 73 70 132943 40 410 50 7 83 39 156 156 97500 16026 77900 110 107 174724 92 966 135 0 123 34 129 120 69112 26569 85646 123 119 174415 100 801 63 0 73 31 118 114 82753 24785 98579 90 89 225548 112 892 81 5 81 31 118 94 85323 17569 130767 116 112 223632 73 513 52 0 105 33 125 120 72654 23825 131741 113 108 124817 40 469 44 0 47 25 95 81 30727 7869 53907 56 52 221698 45 683 113 0 105 33 126 110 77873 14975 178812 115 112 210767 60 643 39 3 94 35 135 133 117478 37791 146761 119 116 170266 62 535 73 4 44 42 154 122 74007 9605 82036 129 123 260561 75 625 48 1 114 43 165 158 90183 27295 163253 127 125 84853 31 264 33 4 38 30 113 109 61542 2746 27032 27 27 294424 77 992 59 2 107 33 127 124 101494 34461 171975 175 162 101011 34 238 41 0 30 13 52 39 27570 8098 65990 35 32 215641 46 818 69 0 71 32 121 92 55813 4787 86572 64 64 325107 99 937 64 0 84 36 136 126 79215 24919 159676 96 92 7176 17 70 1 0 0 0 0 0 1423 603 1929 0 0 167542 66 507 59 2 59 28 108 70 55461 16329 85371 84 83 106408 30 260 32 1 33 14 46 37 31081 12558 58391 41 41 96560 76 503 129 0 42 17 54 38 22996 7784 31580 47 47 265769 146 927 37 2 96 32 124 120 83122 28522 136815 126 120 269651 67 1269 31 10 106 30 115 93 70106 22265 120642 105 105 149112 56 537 65 6 56 35 128 95 60578 14459 69107 80 79 175824 107 910 107 0 57 20 80 77 39992 14526 50495 70 65 152871 58 532 74 5 59 28 97 90 79892 22240 108016 73 70 111665 34 345 54 4 39 28 104 80 49810 11802 46341 57 55 116408 61 918 76 1 34 39 59 31 71570 7623 78348 40 39 362301 119 1635 715 2 76 34 125 110 100708 11912 79336 68 67 78800 42 330 57 2 20 26 82 66 33032 7935 56968 21 21 183167 66 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98866 18 397 140 0 25 13 49 39 23517 4977 32043 50 50 85439 33 350 43 0 32 28 104 78 56926 7785 54454 39 39 229242 247 719 128 4 63 31 120 119 91721 17939 78876 95 90 351619 139 1277 142 4 95 40 150 141 115168 23436 170745 169 166 84207 29 356 73 11 14 30 112 101 111194 325 6940 12 12 120445 118 457 72 0 36 16 59 56 51009 13539 49025 63 57 324598 110 1402 128 0 113 37 136 133 135777 34538 122037 134 133 131069 67 600 61 4 47 30 107 83 51513 12198 53782 69 69 204271 42 480 73 0 92 35 130 116 74163 26924 127748 119 119 165543 65 595 148 1 70 32 115 90 51633 12716 86839 119 119 141722 94 436 64 0 19 27 107 36 75345 8172 44830 75 65 116048 64 230 45 0 50 20 75 50 33416 10855 77395 63 61 250047 81 651 58 0 41 18 71 61 83305 11932 89324 55 49 299775 95 1367 97 9 91 31 120 97 98952 14300 103300 103 101 195838 67 564 50 1 111 31 116 98 102372 25515 112283 197 196 173260 63 716 37 3 41 21 79 78 37238 2805 10901 16 15 254488 83 747 50 10 120 39 150 117 103772 29402 120691 140 136 104389 45 467 105 5 135 41 156 148 123969 16440 58106 89 89 136084 30 671 69 0 27 13 51 41 27142 11221 57140 40 40 199476 70 861 46 2 87 32 118 105 135400 28732 122422 125 123 92499 32 319 57 0 25 18 71 55 21399 5250 25899 21 21 224330 83 612 52 1 131 39 144 132 130115 28608 139296 167 163 135781 31 433 98 2 45 14 47 44 24874 8092 52678 32 29 74408 67 434 61 4 29 7 28 21 34988 4473 23853 36 35 81240 66 503 89 0 58 17 68 50 45549 1572 17306 13 13 14688 10 85 0 0 4 0 0 0 6023 2065 7953 5 5 181633 70 564 48 2 47 30 110 73 64466 14817 89455 96 96 271856 103 824 91 1 109 37 147 86 54990 16714 147866 151 151 7199 5 74 0 0 7 0 0 0 1644 556 4245 6 6 46660 20 259 7 0 12 5 15 13 6179 2089 21509 13 13 17547 5 69 3 0 0 1 4 4 3926 2658 7670 3 3 133368 36 535 54 1 37 16 64 57 32755 10695 66675 57 56 95227 34 239 70 0 37 32 111 48 34777 1669 14336 23 23 152601 48 438 36 2 46 24 85 46 73224 16267 53608 61 57 98146 40 459 37 0 15 17 68 48 27114 7768 30059 21 14 79619 43 426 123 3 42 11 40 32 20760 7252 29668 43 43 59194 31 288 247 6 7 24 80 68 37636 6387 22097 20 20 139942 42 498 46 0 54 22 88 87 65461 18715 96841 82 72 118612 46 454 72 2 54 12 48 43 30080 7936 41907 90 87 72880 33 376 41 0 14 19 76 67 24094 8643 27080 25 21 65475 18 225 24 2 16 13 51 46 69008 7294 35885 60 56 99643 55 555 45 1 33 17 67 46 54968 4570 41247 61 59 71965 35 252 33 1 32 15 59 56 46090 7185 28313 85 82 77272 59 208 27 2 21 16 61 48 27507 10058 36845 43 43 49289 19 130 36 1 15 24 76 44 10672 2342 16548 25 25 135131 66 481 87 0 38 15 60 60 34029 8509 36134 41 38 108446 60 389 90 1 22 17 68 65 46300 13275 55764 26 25 89746 36 565 114 3 28 18 71 55 24760 6816 28910 38 38 44296 25 173 31 0 10 20 76 38 18779 1930 13339 12 12 77648 47 278 45 0 31 16 62 52 21280 8086 25319 29 29 181528 54 609 69 0 32 16 61 60 40662 10737 66956 49 47 134019 53 422 51 0 32 18 67 54 28987 8033 47487 46 45 124064 40 445 34 1 43 22 88 86 22827 7058 52785 41 40 92630 40 387 60 4 27 8 30 24 18513 6782 44683 31 30 121848 39 339 45 0 37 17 64 52 30594 5401 35619 41 41 52915 14 181 54 0 20 18 68 49 24006 6521 21920 26 25 81872 45 245 25 0 32 16 64 61 27913 10856 45608 23 23 58981 36 384 38 7 0 23 91 61 42744 2154 7721 14 14 53515 28 212 52 2 5 22 88 81 12934 6117 20634 16 16 60812 44 399 67 0 26 13 52 43 22574 5238 29788 25 26 56375 30 229 74 7 10 13 49 40 41385 4820 31931 21 21 65490 22 224 38 3 27 16 62 40 18653 5615 37754 32 27 80949 17 203 30 0 11 16 61 56 18472 4272 32505 9 9 76302 31 333 26 0 29 20 76 68 30976 8702 40557 35 33 104011 55 384 67 6 25 22 88 79 63339 15340 94238 42 42 98104 54 636 132 2 55 17 66 47 25568 8030 44197 68 68 67989 21 185 42 0 23 18 71 57 33747 9526 43228 32 32 30989 14 93 35 0 5 17 68 41 4154 1278 4103 6 6 135458 81 581 118 3 43 12 48 29 19474 4236 44144 68 67 73504 35 248 68 0 23 7 25 3 35130 3023 32868 33 33 63123 43 304 43 1 34 17 68 60 39067 7196 27640 84 77 61254 46 344 76 1 36 14 41 30 13310 3394 14063 46 46 74914 30 407 64 0 35 23 90 79 65892 6371 28990 30 30 31774 23 170 48 1 0 17 66 47 4143 1574 4694 0 0 81437 38 312 64 0 37 14 54 40 28579 9620 42648 36 36 87186 54 507 56 0 28 15 59 48 51776 6978 64329 47 46 50090 20 224 71 0 16 17 60 36 21152 4911 21928 20 18 65745 53 340 75 0 26 21 77 42 38084 8645 25836 50 48 56653 45 168 39 0 38 18 68 49 27717 8987 22779 30 29 158399 39 443 42 0 23 18 72 57 32928 5544 40820 30 28 46455 20 204 39 0 22 17 67 12 11342 3083 27530 34 34 73624 24 367 93 0 30 17 64 40 19499 6909 32378 33 33 38395 31 210 38 0 16 16 63 43 16380 3189 10824 34 34 91899 35 335 60 0 18 15 59 33 36874 6745 39613 37 33 139526 151 364 71 0 28 21 84 77 48259 16724 60865 83 80 52164 52 178 52 0 32 16 64 43 16734 4850 19787 32 32 51567 30 206 27 2 21 14 56 45 28207 7025 20107 30 30 70551 31 279 59 0 23 15 54 47 30143 6047 36605 43 41 84856 29 387 40 1 29 17 67 43 41369 7377 40961 41 41 102538 57 490 79 1 50 15 58 45 45833 9078 48231 51 51 86678 40 238 44 0 12 15 59 50 29156 4605 39725 19 18 85709 44 343 65 0 21 10 40 35 35944 3238 21455 37 34 34662 25 232 10 0 18 6 22 7 36278 8100 23430 33 31 150580 77 530 124 0 27 22 83 71 45588 9653 62991 41 39 99611 35 291 81 0 41 21 81 67 45097 8914 49363 54 54 19349 11 67 15 0 13 1 2 0 3895 786 9604 14 14 99373 63 397 92 1 12 18 72 62 28394 6700 24552 25 24 86230 44 467 42 0 21 17 61 54 18632 5788 31493 25 24 30837 19 178 10 0 8 4 15 4 2325 593 3439 8 8 31706 13 175 24 0 26 10 32 25 25139 4506 19555 26 26 89806 42 299 64 0 27 16 62 40 27975 6382 21228 20 19 62088 38 154 45 1 13 16 58 38 14483 5621 23177 11 11 40151 29 106 22 0 16 9 36 19 13127 3997 22094 14 14 27634 20 189 56 0 2 16 59 17 5839 520 2342 3 1 76990 27 194 94 0 42 17 68 67 24069 8891 38798 40 39 37460 20 135 19 0 5 7 21 14 3738 999 3255 5 5 54157 19 201 35 0 37 15 55 30 18625 7067 24261 38 37 49862 37 207 32 0 17 14 54 54 36341 4639 18511 32 32 84337 26 280 35 0 38 14 55 35 24548 5654 40798 41 38 64175 42 260 48 0 37 18 72 59 21792 6928 28893 46 47 59382 49 227 49 0 29 12 41 24 26263 1514 21425 47 47 119308 30 239 48 0 32 16 61 58 23686 9238 50276 37 37 76702 49 333 62 0 35 21 67 42 49303 8204 37643 51 51 103425 67 428 96 1 17 19 76 46 25659 5926 30377 49 45 70344 28 230 45 0 20 16 64 61 28904 5785 27126 21 21 43410 19 292 63 0 7 1 3 3 2781 4 13 1 1 104838 49 350 71 1 46 16 63 52 29236 5930 42097 44 42 62215 27 186 26 0 24 10 40 25 19546 3710 24451 26 26 69304 30 326 48 6 40 19 69 40 22818 705 14335 21 21 53117 22 155 29 3 3 12 48 32 32689 443 5084 4 4 19764 12 75 19 1 10 2 8 4 5752 2416 9927 10 10 86680 31 361 45 2 37 14 52 49 22197 7747 43527 43 43 84105 20 261 45 0 17 17 66 63 20055 5432 27184 34 34 77945 20 299 67 0 28 19 76 67 25272 4913 21610 32 31 89113 39 300 30 0 19 14 43 32 82206 2650 20484 20 19 91005 29 450 36 3 29 11 39 23 32073 2370 20156 34 34 40248 16 183 34 1 8 4 14 7 5444 775 6012 6 6 64187 27 238 36 0 10 16 61 54 20154 5576 18475 12 11 50857 21 165 34 0 15 20 71 37 36944 1352 12645 24 24 56613 19 234 37 1 15 12 44 35 8019 3080 11017 16 16 62792 35 176 46 0 28 15 60 51 30884 10205 37623 72 72 72535 14 329 44 0 17 16 64 39 19540 6095 35873 27 21
Names of X columns:
time_in_rfc logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1 feedback_messages_p120 totsize totrevisions totseconds tothyperlinks totblogs
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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