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Data X:
1418 210907 56 396 3 115 112285 869 120982 56 297 4 109 84786 1530 176508 54 559 12 146 83123 2172 179321 89 967 2 116 101193 901 123185 40 270 1 68 38361 463 52746 25 143 3 101 68504 3201 385534 92 1562 0 96 119182 371 33170 18 109 0 67 22807 1192 101645 63 371 0 44 17140 1583 149061 44 656 5 100 116174 1439 165446 33 511 0 93 57635 1764 237213 84 655 0 140 66198 1495 173326 88 465 7 166 71701 1373 133131 55 525 7 99 57793 2187 258873 60 885 3 139 80444 1491 180083 66 497 9 130 53855 4041 324799 154 1436 0 181 97668 1706 230964 53 612 4 116 133824 2152 236785 119 865 3 116 101481 1036 135473 41 385 0 88 99645 1882 202925 61 567 7 139 114789 1929 215147 58 639 0 135 99052 2242 344297 75 963 1 108 67654 1220 153935 33 398 5 89 65553 1289 132943 40 410 7 156 97500 2515 174724 92 966 0 129 69112 2147 174415 100 801 0 118 82753 2352 225548 112 892 5 118 85323 1638 223632 73 513 0 125 72654 1222 124817 40 469 0 95 30727 1812 221698 45 683 0 126 77873 1677 210767 60 643 3 135 117478 1579 170266 62 535 4 154 74007 1731 260561 75 625 1 165 90183 807 84853 31 264 4 113 61542 2452 294424 77 992 2 127 101494 829 101011 34 238 0 52 27570 1940 215641 46 818 0 121 55813 2662 325107 99 937 0 136 79215 186 7176 17 70 0 0 1423 1499 167542 66 507 2 108 55461 865 106408 30 260 1 46 31081 1793 96560 76 503 0 54 22996 2527 265769 146 927 2 124 83122 2747 269651 67 1269 10 115 70106 1324 149112 56 537 6 128 60578 2702 175824 107 910 0 80 39992 1383 152871 58 532 5 97 79892 1179 111665 34 345 4 104 49810 2099 116408 61 918 1 59 71570 4308 362301 119 1635 2 125 100708 918 78800 42 330 2 82 33032 1831 183167 66 557 0 149 82875 3373 277965 89 1178 8 149 139077 1713 150629 44 740 3 122 71595 1438 168809 66 452 0 118 72260 496 24188 24 218 0 12 5950 2253 329267 259 764 8 144 115762 744 65029 17 255 5 67 32551 1161 101097 64 454 3 52 31701 2352 218946 41 866 1 108 80670 2144 244052 68 574 5 166 143558 4691 341570 168 1276 1 80 117105 1112 103597 43 379 1 60 23789 2694 233328 132 825 5 107 120733 1973 256462 105 798 0 127 105195 1769 206161 71 663 12 107 73107 3148 311473 112 1069 8 146 132068 2474 235800 94 921 8 84 149193 2084 177939 82 858 8 141 46821 1954 207176 70 711 8 123 87011 1226 196553 57 503 2 111 95260 1389 174184 53 382 0 98 55183 1496 143246 103 464 5 105 106671 2269 187559 121 717 8 135 73511 1833 187681 62 690 2 107 92945 1268 119016 52 462 5 85 78664 1943 182192 52 657 12 155 70054 893 73566 32 385 6 88 22618 1762 194979 62 577 7 155 74011 1403 167488 45 619 2 104 83737 1425 143756 46 479 0 132 69094 1857 275541 63 817 4 127 93133 1840 243199 75 752 3 108 95536 1502 182999 88 430 6 129 225920 1441 135649 46 451 2 116 62133 1420 152299 53 537 0 122 61370 1416 120221 37 519 1 85 43836 2970 346485 90 1000 0 147 106117 1317 145790 63 637 5 99 38692 1644 193339 78 465 2 87 84651 870 80953 25 437 0 28 56622 1654 122774 45 711 0 90 15986 1054 130585 46 299 5 109 95364 937 112611 41 248 0 78 26706 3004 286468 144 1162 1 111 89691 2008 241066 82 714 0 158 67267 2547 148446 91 905 1 141 126846 1885 204713 71 649 1 122 41140 1626 182079 63 512 2 124 102860 1468 140344 53 472 6 93 51715 2445 220516 62 905 1 124 55801 1964 243060 63 786 4 112 111813 1381 162765 32 489 2 108 120293 1369 182613 39 479 3 99 138599 1659 232138 62 617 0 117 161647 2888 265318 117 925 10 199 115929 1290 85574 34 351 0 78 24266 2845 310839 92 1144 9 91 162901 1982 225060 93 669 7 158 109825 1904 232317 54 707 0 126 129838 1391 144966 144 458 0 122 37510 602 43287 14 214 4 71 43750 1743 155754 61 599 4 75 40652 1559 164709 109 572 0 115 87771 2014 201940 38 897 0 119 85872 2143 235454 73 819 0 124 89275 2146 220801 75 720 1 72 44418 874 99466 50 273 0 91 192565 1590 92661 61 508 1 45 35232 1590 133328 55 506 0 78 40909 1210 61361 77 451 0 39 13294 2072 125930 75 699 4 68 32387 1281 100750 72 407 0 119 140867 1401 224549 50 465 4 117 120662 834 82316 32 245 4 39 21233 1105 102010 53 370 3 50 44332 1272 101523 42 316 0 88 61056 1944 243511 71 603 0 155 101338 391 22938 10 154 0 0 1168 761 41566 35 229 5 36 13497 1605 152474 65 577 0 123 65567 530 61857 25 192 4 32 25162 1988 99923 66 617 0 99 32334 1386 132487 41 411 0 136 40735 2395 317394 86 975 1 117 91413 387 21054 16 146 0 0 855 1742 209641 42 705 5 88 97068 620 22648 19 184 0 39 44339 449 31414 19 200 0 25 14116 800 46698 45 274 0 52 10288 1684 131698 65 502 0 75 65622 1050 91735 35 382 0 71 16563 2699 244749 95 964 2 124 76643 1606 184510 49 537 7 151 110681 1502 79863 37 438 1 71 29011 1204 128423 64 369 8 145 92696 1138 97839 38 417 2 87 94785 568 38214 34 276 0 27 8773 1459 151101 32 514 2 131 83209 2158 272458 65 822 0 162 93815 1111 172494 52 389 0 165 86687 1421 108043 62 466 1 54 34553 2833 328107 65 1255 3 159 105547 1955 250579 83 694 0 147 103487 2922 351067 95 1024 3 170 213688 1002 158015 29 400 0 119 71220 1060 98866 18 397 0 49 23517 956 85439 33 350 0 104 56926 2186 229242 247 719 4 120 91721 3604 351619 139 1277 4 150 115168 1035 84207 29 356 11 112 111194 1417 120445 118 457 0 59 51009 3261 324598 110 1402 0 136 135777 1587 131069 67 600 4 107 51513 1424 204271 42 480 0 130 74163 1701 165543 65 595 1 115 51633 1249 141722 94 436 0 107 75345 946 116048 64 230 0 75 33416 1926 250047 81 651 0 71 83305 3352 299775 95 1367 9 120 98952 1641 195838 67 564 1 116 102372 2035 173260 63 716 3 79 37238 2312 254488 83 747 10 150 103772 1369 104389 45 467 5 156 123969 1577 136084 30 671 0 51 27142 2201 199476 70 861 2 118 135400 961 92499 32 319 0 71 21399 1900 224330 83 612 1 144 130115 1254 135781 31 433 2 47 24874 1335 74408 67 434 4 28 34988 1597 81240 66 503 0 68 45549 207 14688 10 85 0 0 6023 1645 181633 70 564 2 110 64466 2429 271856 103 824 1 147 54990 151 7199 5 74 0 0 1644 474 46660 20 259 0 15 6179 141 17547 5 69 0 4 3926 1639 133368 36 535 1 64 32755 872 95227 34 239 0 111 34777 1318 152601 48 438 2 85 73224 1018 98146 40 459 0 68 27114 1383 79619 43 426 3 40 20760 1314 59194 31 288 6 80 37636 1335 139942 42 498 0 88 65461 1403 118612 46 454 2 48 30080 910 72880 33 376 0 76 24094
Names of X columns:
pageviews time_in_rfc logins compendium_views_info shared_compendiums feedback_messages_p1 totsize
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 Output
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