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Data X:
210907 56 81 94 1418 120982 56 55 103 869 176508 54 50 93 1530 179321 89 125 103 2172 123185 40 40 51 901 52746 25 37 70 463 385534 92 63 91 3201 33170 18 44 22 371 101645 63 88 38 1192 149061 44 66 93 1583 165446 33 57 60 1439 237213 84 74 123 1764 173326 88 49 148 1495 133131 55 52 90 1373 258873 60 88 124 2187 180083 66 36 70 1491 324799 154 108 168 4041 230964 53 43 115 1706 236785 119 75 71 2152 135473 41 32 66 1036 202925 61 44 134 1882 215147 58 85 117 1929 344297 75 86 108 2242 153935 33 56 84 1220 132943 40 50 156 1289 174724 92 135 120 2515 174415 100 63 114 2147 225548 112 81 94 2352 223632 73 52 120 1638 124817 40 44 81 1222 221698 45 113 110 1812 210767 60 39 133 1677 170266 62 73 122 1579 260561 75 48 158 1731 84853 31 33 109 807 294424 77 59 124 2452 101011 34 41 39 829 215641 46 69 92 1940 325107 99 64 126 2662 7176 17 1 0 186 167542 66 59 70 1499 106408 30 32 37 865 96560 76 129 38 1793 265769 146 37 120 2527 269651 67 31 93 2747 149112 56 65 95 1324 175824 107 107 77 2702 152871 58 74 90 1383 111665 34 54 80 1179 116408 61 76 31 2099 362301 119 715 110 4308 78800 42 57 66 918 183167 66 66 138 1831 277965 89 106 133 3373 150629 44 54 113 1713 168809 66 32 100 1438 24188 24 20 7 496 329267 259 71 140 2253 65029 17 21 61 744 101097 64 70 41 1161 218946 41 112 96 2352 244052 68 66 164 2144 341570 168 190 78 4691 103597 43 66 49 1112 233328 132 165 102 2694 256462 105 56 124 1973 206161 71 61 99 1769 311473 112 53 129 3148 235800 94 127 62 2474 177939 82 63 73 2084 207176 70 38 114 1954 196553 57 50 99 1226 174184 53 52 70 1389 143246 103 42 104 1496 187559 121 76 116 2269 187681 62 67 91 1833 119016 52 50 74 1268 182192 52 53 138 1943 73566 32 39 67 893 194979 62 50 151 1762 167488 45 77 72 1403 143756 46 57 120 1425 275541 63 73 115 1857 243199 75 34 105 1840 182999 88 39 104 1502 135649 46 46 108 1441 152299 53 63 98 1420 120221 37 35 69 1416 346485 90 106 111 2970 145790 63 43 99 1317 193339 78 47 71 1644 80953 25 31 27 870 122774 45 162 69 1654 130585 46 57 107 1054 112611 41 36 73 937 286468 144 263 107 3004 241066 82 78 93 2008 148446 91 63 129 2547 204713 71 54 69 1885 182079 63 63 118 1626 140344 53 77 73 1468 220516 62 79 119 2445 243060 63 110 104 1964 162765 32 56 107 1381 182613 39 56 99 1369 232138 62 43 90 1659 265318 117 111 197 2888 85574 34 71 36 1290 310839 92 62 85 2845 225060 93 56 139 1982 232317 54 74 106 1904 144966 144 60 50 1391 43287 14 43 64 602 155754 61 68 31 1743 164709 109 53 63 1559 201940 38 87 92 2014 235454 73 46 106 2143 220801 75 105 63 2146 99466 50 32 69 874 92661 61 133 41 1590 133328 55 79 56 1590 61361 77 51 25 1210 125930 75 207 65 2072 100750 72 67 93 1281 224549 50 47 114 1401 82316 32 34 38 834 102010 53 66 44 1105 101523 42 76 87 1272 243511 71 65 110 1944 22938 10 9 0 391 41566 35 42 27 761 152474 65 45 83 1605 61857 25 25 30 530 99923 66 115 80 1988 132487 41 97 98 1386 317394 86 53 82 2395 21054 16 2 0 387 209641 42 52 60 1742 22648 19 44 28 620 31414 19 22 9 449 46698 45 35 33 800 131698 65 74 59 1684 91735 35 103 49 1050 244749 95 144 115 2699 184510 49 60 140 1606 79863 37 134 49 1502 128423 64 89 120 1204 97839 38 42 66 1138 38214 34 52 21 568 151101 32 98 124 1459 272458 65 99 152 2158 172494 52 52 139 1111 108043 62 29 38 1421 328107 65 125 144 2833 250579 83 106 120 1955 351067 95 95 160 2922 158015 29 40 114 1002 98866 18 140 39 1060 85439 33 43 78 956 229242 247 128 119 2186 351619 139 142 141 3604 84207 29 73 101 1035 120445 118 72 56 1417 324598 110 128 133 3261 131069 67 61 83 1587 204271 42 73 116 1424 165543 65 148 90 1701 141722 94 64 36 1249 116048 64 45 50 946 250047 81 58 61 1926 299775 95 97 97 3352 195838 67 50 98 1641 173260 63 37 78 2035 254488 83 50 117 2312 104389 45 105 148 1369 136084 30 69 41 1577 199476 70 46 105 2201 92499 32 57 55 961 224330 83 52 132 1900 135781 31 98 44 1254 74408 67 61 21 1335 81240 66 89 50 1597 14688 10 0 0 207 181633 70 48 73 1645 271856 103 91 86 2429 7199 5 0 0 151 46660 20 7 13 474 17547 5 3 4 141 133368 36 54 57 1639 95227 34 70 48 872 152601 48 36 46 1318 98146 40 37 48 1018 79619 43 123 32 1383 59194 31 247 68 1314 139942 42 46 87 1335 118612 46 72 43 1403 72880 33 41 67 910 65475 18 24 46 616 99643 55 45 46 1407 71965 35 33 56 771 77272 59 27 48 766 49289 19 36 44 473 135131 66 87 60 1376 108446 60 90 65 1232 89746 36 114 55 1521 44296 25 31 38 572 77648 47 45 52 1059 181528 54 69 60 1544 134019 53 51 54 1230 124064 40 34 86 1206 92630 40 60 24 1205 121848 39 45 52 1255 52915 14 54 49 613 81872 45 25 61 721 58981 36 38 61 1109 53515 28 52 81 740 60812 44 67 43 1126 56375 30 74 40 728 65490 22 38 40 689 80949 17 30 56 592 76302 31 26 68 995 104011 55 67 79 1613 98104 54 132 47 2048 67989 21 42 57 705 30989 14 35 41 301 135458 81 118 29 1803 73504 35 68 3 799 63123 43 43 60 861 61254 46 76 30 1186 74914 30 64 79 1451 31774 23 48 47 628 81437 38 64 40 1161 87186 54 56 48 1463 50090 20 71 36 742 65745 53 75 42 979 56653 45 39 49 675 158399 39 42 57 1241 46455 20 39 12 676 73624 24 93 40 1049 38395 31 38 43 620 91899 35 60 33 1081 139526 151 71 77 1688 52164 52 52 43 736 51567 30 27 45 617 70551 31 59 47 812 84856 29 40 43 1051 102538 57 79 45 1656 86678 40 44 50 705 85709 44 65 35 945 34662 25 10 7 554 150580 77 124 71 1597 99611 35 81 67 982 19349 11 15 0 222 99373 63 92 62 1212 86230 44 42 54 1143 30837 19 10 4 435 31706 13 24 25 532 89806 42 64 40 882 62088 38 45 38 608 40151 29 22 19 459 27634 20 56 17 578 76990 27 94 67 826 37460 20 19 14 509 54157 19 35 30 717 49862 37 32 54 637 84337 26 35 35 857 64175 42 48 59 830 59382 49 49 24 652 119308 30 48 58 707 76702 49 62 42 954 103425 67 96 46 1461 70344 28 45 61 672 43410 19 63 3 778 104838 49 71 52 1141 62215 27 26 25 680 69304 30 48 40 1090 53117 22 29 32 616 19764 12 19 4 285 86680 31 45 49 1145 84105 20 45 63 733 77945 20 67 67 888 89113 39 30 32 849 91005 29 36 23 1182 40248 16 34 7 528 64187 27 36 54 642 50857 21 34 37 947 56613 19 37 35 819 62792 35 46 51 757 72535 14 44 39 894
Names of X columns:
time_in_RFC logins compendiumviews_pr feedbackmessages pageviews
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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Computing time
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R Server
Big Analytics Cloud Computing Center
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