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Data X:
101645 63 20 17140 101011 34 30 27570 7176 17 0 1423 96560 76 42 22996 175824 107 57 39992 341570 168 94 117105 103597 43 27 23789 112611 41 46 26706 85574 34 37 24266 220801 75 51 44418 92661 61 40 35232 133328 55 56 40909 61361 77 27 13294 125930 75 37 32387 82316 32 27 21233 102010 53 28 44332 101523 42 59 61056 41566 35 0 13497 99923 66 44 32334 22648 19 12 44339 46698 45 14 10288 131698 65 60 65622 91735 35 7 16563 79863 37 29 29011 108043 62 45 34553 98866 18 25 23517 120445 118 36 51009 116048 64 50 33416 250047 81 41 83305 136084 30 27 27142 92499 32 25 21399 135781 31 45 24874 74408 67 29 34988 81240 66 58 45549 133368 36 37 32755 98146 40 15 27114 79619 43 42 20760 59194 31 7 37636 139942 42 54 65461 118612 46 54 30080 72880 33 14 24094 65475 18 16 69008 99643 55 33 54968 71965 35 32 46090 77272 59 21 27507 49289 19 15 10672 135131 66 38 34029 108446 60 22 46300 89746 36 28 24760 44296 25 10 18779 77648 47 31 21280 181528 54 32 40662 134019 53 32 28987 124064 40 43 22827 92630 40 27 18513 121848 39 37 30594 52915 14 20 24006 81872 45 32 27913 58981 36 0 42744 53515 28 5 12934 60812 44 26 22574 56375 30 10 41385 65490 22 27 18653 80949 17 11 18472 76302 31 29 30976 104011 55 25 63339 98104 54 55 25568 67989 21 23 33747 30989 14 5 4154 135458 81 43 19474 73504 35 23 35130 63123 43 34 39067 61254 46 36 13310 74914 30 35 65892 31774 23 0 4143 81437 38 37 28579 87186 54 28 51776 50090 20 16 21152 65745 53 26 38084 56653 45 38 27717 158399 39 23 32928 46455 20 22 11342 73624 24 30 19499 38395 31 16 16380 91899 35 18 36874 139526 151 28 48259 52164 52 32 16734 51567 30 21 28207 70551 31 23 30143 84856 29 29 41369 102538 57 50 45833 86678 40 12 29156 85709 44 21 35944 34662 25 18 36278 150580 77 27 45588 99611 35 41 45097 19349 11 13 3895 99373 63 12 28394 86230 44 21 18632 30837 19 8 2325 31706 13 26 25139 89806 42 27 27975 62088 38 13 14483 40151 29 16 13127 27634 20 2 5839 76990 27 42 24069 37460 20 5 3738 54157 19 37 18625 49862 37 17 36341 84337 26 38 24548 64175 42 37 21792 59382 49 29 26263 119308 30 32 23686 76702 49 35 49303 103425 67 17 25659 70344 28 20 28904 43410 19 7 2781 104838 49 46 29236 62215 27 24 19546 69304 30 40 22818 53117 22 3 32689 19764 12 10 5752 86680 31 37 22197 84105 20 17 20055 77945 20 28 25272 89113 39 19 82206 91005 29 29 32073 40248 16 8 5444 64187 27 10 20154 50857 21 15 36944 56613 19 15 8019 210907 56 79 112285 120982 56 58 84786 176508 54 60 83123 179321 89 108 101193 123185 40 49 38361 52746 25 0 68504 385534 92 121 119182 33170 18 1 22807 149061 44 43 116174 165446 33 69 57635 237213 84 78 66198 173326 88 86 71701 133131 55 44 57793 258873 60 104 80444 180083 66 63 53855 324799 154 158 97668 230964 53 102 133824 236785 119 77 101481 135473 41 82 99645 202925 61 115 114789 215147 58 101 99052 344297 75 80 67654 153935 33 50 65553 132943 40 83 97500 174724 92 123 69112 174415 100 73 82753 225548 112 81 85323 223632 73 105 72654 124817 40 47 30727 221698 45 105 77873 210767 60 94 117478 170266 62 44 74007 260561 75 114 90183 84853 31 38 61542 294424 77 107 101494 215641 46 71 55813 325107 99 84 79215 167542 66 59 55461 106408 30 33 31081 265769 146 96 83122 269651 67 106 70106 149112 56 56 60578 152871 58 59 79892 111665 34 39 49810 116408 61 34 71570 362301 119 76 100708 78800 42 20 33032 183167 66 91 82875 277965 89 115 139077 150629 44 85 71595 168809 66 76 72260 24188 24 8 5950 329267 259 79 115762 65029 17 21 32551 101097 64 30 31701 218946 41 76 80670 244052 68 101 143558 233328 132 92 120733 256462 105 123 105195 206161 71 75 73107 311473 112 128 132068 235800 94 105 149193 177939 82 55 46821 207176 70 56 87011 196553 57 41 95260 174184 53 72 55183 143246 103 67 106671 187559 121 75 73511 187681 62 114 92945 119016 52 118 78664 182192 52 77 70054 73566 32 22 22618 194979 62 66 74011 167488 45 69 83737 143756 46 105 69094 275541 63 116 93133 243199 75 88 95536 182999 88 73 225920 135649 46 99 62133 152299 53 62 61370 120221 37 53 43836 346485 90 118 106117 145790 63 30 38692 193339 78 100 84651 80953 25 49 56622 122774 45 24 15986 130585 46 67 95364 286468 144 57 89691 241066 82 75 67267 148446 91 135 126846 204713 71 68 41140 182079 63 124 102860 140344 53 33 51715 220516 62 98 55801 243060 63 58 111813 162765 32 68 120293 182613 39 81 138599 232138 62 131 161647 265318 117 110 115929 310839 92 130 162901 225060 93 93 109825 232317 54 118 129838 144966 144 39 37510 43287 14 13 43750 155754 61 74 40652 164709 109 81 87771 201940 38 109 85872 235454 73 151 89275 99466 50 28 192565 100750 72 83 140867 224549 50 54 120662 243511 71 133 101338 22938 10 12 1168 152474 65 106 65567 61857 25 23 25162 132487 41 71 40735 317394 86 116 91413 21054 16 4 855 209641 42 62 97068 31414 19 18 14116 244749 95 98 76643 184510 49 64 110681 128423 64 32 92696 97839 38 25 94785 38214 34 16 8773 151101 32 48 83209 272458 65 100 93815 172494 52 46 86687 328107 65 129 105547 250579 83 130 103487 351067 95 136 213688 158015 29 59 71220 85439 33 32 56926 229242 247 63 91721 351619 139 95 115168 84207 29 14 111194 324598 110 113 135777 131069 67 47 51513 204271 42 92 74163 165543 65 70 51633 141722 94 19 75345 299775 95 91 98952 195838 67 111 102372 173260 63 41 37238 254488 83 120 103772 104389 45 135 123969 199476 70 87 135400 224330 83 131 130115 14688 10 4 6023 181633 70 47 64466 271856 103 109 54990 7199 5 7 1644 46660 20 12 6179 17547 5 0 3926 95227 34 37 34777 152601 48 46 73224
Names of X columns:
TimeRfc Logins Bloggedcomp 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
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2
3
4
5
6
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8
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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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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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