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Data X:
210907 81 94 112285 24188 120982 55 103 84786 18273 176508 50 93 83123 14130 179321 125 103 101193 32287 123185 40 51 38361 8654 52746 37 70 68504 9245 385534 63 91 119182 33251 33170 44 22 22807 1271 101645 88 38 17140 5279 149061 66 93 116174 27101 165446 57 60 57635 16373 237213 74 123 66198 19716 173326 49 148 71701 17753 133131 52 90 57793 9028 258873 88 124 80444 18653 180083 36 70 53855 8828 324799 108 168 97668 29498 230964 43 115 133824 27563 236785 75 71 101481 18293 135473 32 66 99645 22530 202925 44 134 114789 15977 215147 85 117 99052 35082 344297 86 108 67654 16116 153935 56 84 65553 15849 132943 50 156 97500 16026 174724 135 120 69112 26569 174415 63 114 82753 24785 225548 81 94 85323 17569 223632 52 120 72654 23825 124817 44 81 30727 7869 221698 113 110 77873 14975 210767 39 133 117478 37791 170266 73 122 74007 9605 260561 48 158 90183 27295 84853 33 109 61542 2746 294424 59 124 101494 34461 101011 41 39 27570 8098 215641 69 92 55813 4787 325107 64 126 79215 24919 7176 1 0 1423 603 167542 59 70 55461 16329 106408 32 37 31081 12558 96560 129 38 22996 7784 265769 37 120 83122 28522 269651 31 93 70106 22265 149112 65 95 60578 14459 175824 107 77 39992 14526 152871 74 90 79892 22240 111665 54 80 49810 11802 116408 76 31 71570 7623 362301 715 110 100708 11912 78800 57 66 33032 7935 183167 66 138 82875 18220 277965 106 133 139077 19199 150629 54 113 71595 19918 168809 32 100 72260 21884 24188 20 7 5950 2694 329267 71 140 115762 15808 65029 21 61 32551 3597 101097 70 41 31701 5296 218946 112 96 80670 25239 244052 66 164 143558 29801 341570 190 78 117105 18450 103597 66 49 23789 7132 233328 165 102 120733 34861 256462 56 124 105195 35940 206161 61 99 73107 16688 311473 53 129 132068 24683 235800 127 62 149193 46230 177939 63 73 46821 10387 207176 38 114 87011 21436 196553 50 99 95260 30546 174184 52 70 55183 19746 143246 42 104 106671 15977 187559 76 116 73511 22583 187681 67 91 92945 17274 119016 50 74 78664 16469 182192 53 138 70054 14251 73566 39 67 22618 3007 194979 50 151 74011 16851 167488 77 72 83737 21113 143756 57 120 69094 17401 275541 73 115 93133 23958 243199 34 105 95536 23567 182999 39 104 225920 13065 135649 46 108 62133 15358 152299 63 98 61370 14587 120221 35 69 43836 12770 346485 106 111 106117 24021 145790 43 99 38692 9648 193339 47 71 84651 20537 80953 31 27 56622 7905 122774 162 69 15986 4527 130585 57 107 95364 30495 112611 36 73 26706 7117 286468 263 107 89691 17719 241066 78 93 67267 27056 148446 63 129 126846 33473 204713 54 69 41140 9758 182079 63 118 102860 21115 140344 77 73 51715 7236 220516 79 119 55801 13790 243060 110 104 111813 32902 162765 56 107 120293 25131 182613 56 99 138599 30910 232138 43 90 161647 35947 265318 111 197 115929 29848 85574 71 36 24266 6943 310839 62 85 162901 42705 225060 56 139 109825 31808 232317 74 106 129838 26675 144966 60 50 37510 8435 43287 43 64 43750 7409 155754 68 31 40652 14993 164709 53 63 87771 36867 201940 87 92 85872 33835 235454 46 106 89275 24164 220801 105 63 44418 12607 99466 32 69 192565 22609 92661 133 41 35232 5892 133328 79 56 40909 17014 61361 51 25 13294 5394 125930 207 65 32387 9178 100750 67 93 140867 6440 224549 47 114 120662 21916 82316 34 38 21233 4011 102010 66 44 44332 5818 101523 76 87 61056 18647 243511 65 110 101338 20556 22938 9 0 1168 238 41566 42 27 13497 70 152474 45 83 65567 22392 61857 25 30 25162 3913 99923 115 80 32334 12237 132487 97 98 40735 8388 317394 53 82 91413 22120 21054 2 0 855 338 209641 52 60 97068 11727 22648 44 28 44339 3704 31414 22 9 14116 3988 46698 35 33 10288 3030 131698 74 59 65622 13520 91735 103 49 16563 1421 244749 144 115 76643 20923 184510 60 140 110681 20237 79863 134 49 29011 3219 128423 89 120 92696 3769 97839 42 66 94785 12252 38214 52 21 8773 1888 151101 98 124 83209 14497 272458 99 152 93815 28864 172494 52 139 86687 21721 108043 29 38 34553 4821 328107 125 144 105547 33644 250579 106 120 103487 15923 351067 95 160 213688 42935 158015 40 114 71220 18864 98866 140 39 23517 4977 85439 43 78 56926 7785 229242 128 119 91721 17939 351619 142 141 115168 23436 84207 73 101 111194 325 120445 72 56 51009 13539 324598 128 133 135777 34538 131069 61 83 51513 12198 204271 73 116 74163 26924 165543 148 90 51633 12716 141722 64 36 75345 8172 116048 45 50 33416 10855 250047 58 61 83305 11932 299775 97 97 98952 14300 195838 50 98 102372 25515 173260 37 78 37238 2805 254488 50 117 103772 29402 104389 105 148 123969 16440 136084 69 41 27142 11221 199476 46 105 135400 28732 92499 57 55 21399 5250 224330 52 132 130115 28608 135781 98 44 24874 8092 74408 61 21 34988 4473 81240 89 50 45549 1572 14688 0 0 6023 2065 181633 48 73 64466 14817 271856 91 86 54990 16714 7199 0 0 1644 556 46660 7 13 6179 2089 17547 3 4 3926 2658 133368 54 57 32755 10695 95227 70 48 34777 1669 152601 36 46 73224 16267 98146 37 48 27114 7768 79619 123 32 20760 7252 59194 247 68 37636 6387 139942 46 87 65461 18715 118612 72 43 30080 7936 72880 41 67 24094 8643 65475 24 46 69008 7294 99643 45 46 54968 4570 71965 33 56 46090 7185 77272 27 48 27507 10058 49289 36 44 10672 2342 135131 87 60 34029 8509 108446 90 65 46300 13275 89746 114 55 24760 6816 44296 31 38 18779 1930 77648 45 52 21280 8086 181528 69 60 40662 10737 134019 51 54 28987 8033 124064 34 86 22827 7058 92630 60 24 18513 6782 121848 45 52 30594 5401 52915 54 49 24006 6521 81872 25 61 27913 10856 58981 38 61 42744 2154 53515 52 81 12934 6117 60812 67 43 22574 5238 56375 74 40 41385 4820 65490 38 40 18653 5615 80949 30 56 18472 4272 76302 26 68 30976 8702 104011 67 79 63339 15340 98104 132 47 25568 8030 67989 42 57 33747 9526 30989 35 41 4154 1278 135458 118 29 19474 4236 73504 68 3 35130 3023 63123 43 60 39067 7196 61254 76 30 13310 3394 74914 64 79 65892 6371 31774 48 47 4143 1574 81437 64 40 28579 9620 87186 56 48 51776 6978 50090 71 36 21152 4911 65745 75 42 38084 8645 56653 39 49 27717 8987 158399 42 57 32928 5544 46455 39 12 11342 3083 73624 93 40 19499 6909 38395 38 43 16380 3189 91899 60 33 36874 6745 139526 71 77 48259 16724 52164 52 43 16734 4850 51567 27 45 28207 7025 70551 59 47 30143 6047 84856 40 43 41369 7377 102538 79 45 45833 9078 86678 44 50 29156 4605 85709 65 35 35944 3238 34662 10 7 36278 8100 150580 124 71 45588 9653 99611 81 67 45097 8914 19349 15 0 3895 786 99373 92 62 28394 6700 86230 42 54 18632 5788 30837 10 4 2325 593 31706 24 25 25139 4506 89806 64 40 27975 6382 62088 45 38 14483 5621 40151 22 19 13127 3997 27634 56 17 5839 520 76990 94 67 24069 8891 37460 19 14 3738 999 54157 35 30 18625 7067 49862 32 54 36341 4639 84337 35 35 24548 5654 64175 48 59 21792 6928 59382 49 24 26263 1514 119308 48 58 23686 9238 76702 62 42 49303 8204 103425 96 46 25659 5926 70344 45 61 28904 5785 43410 63 3 2781 4 104838 71 52 29236 5930 62215 26 25 19546 3710 69304 48 40 22818 705 53117 29 32 32689 443 19764 19 4 5752 2416 86680 45 49 22197 7747 84105 45 63 20055 5432 77945 67 67 25272 4913 89113 30 32 82206 2650 91005 36 23 32073 2370 40248 34 7 5444 775 64187 36 54 20154 5576 50857 34 37 36944 1352 56613 37 35 8019 3080 62792 46 51 30884 10205 72535 44 39 19540 6095
Names of X columns:
time_in_rfc compendium_views_pr feedback_messages_p120 totsize totrevisions
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
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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