Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
112285 1418 210907 56 79 144 84786 869 120982 56 58 103 119182 3201 385534 92 121 150 116174 1583 149061 44 43 84 133824 1706 230964 53 102 151 99645 1036 135473 41 82 138 99052 1929 215147 58 101 124 65553 1220 153935 33 50 73 85323 2352 225548 112 81 116 117478 1677 210767 60 94 119 74007 1579 170266 62 44 129 101494 2452 294424 77 107 175 31081 865 106408 30 33 41 22996 1793 96560 76 42 47 60578 1324 149112 56 56 80 79892 1383 152871 58 59 73 82875 1831 183167 66 91 127 23789 1112 103597 43 27 26 149193 2474 235800 94 105 190 106671 1496 143246 103 67 116 92945 1833 187681 62 114 143 83737 1403 167488 45 69 113 69094 1425 143756 46 105 120 95536 1840 243199 75 88 134 95364 1054 130585 46 67 91 102860 1626 182079 63 124 181 115929 2888 265318 117 110 138 162901 2845 310839 92 130 254 109825 1982 225060 93 93 87 37510 1391 144966 144 39 51 192565 874 99466 50 28 56 44332 1105 102010 53 28 26 32334 1988 99923 66 44 36 91413 2395 317394 86 116 195 44339 620 22648 19 12 24 14116 449 31414 19 18 39 92696 1204 128423 64 32 37 94785 1138 97839 38 25 77 105547 2833 328107 65 129 153 71220 1002 158015 29 59 79 51009 1417 120445 118 36 63 135777 3261 324598 110 113 134 51513 1587 131069 67 47 69 74163 1424 204271 42 92 119 33416 946 116048 64 50 63 102372 1641 195838 67 111 197 103772 2312 254488 83 120 140 130115 1900 224330 83 131 167 24874 1254 135781 31 45 32 45549 1597 81240 66 58 13 4143 628 31774 23 0 0 28207 617 51567 30 21 30 45833 1656 102538 57 50 51 28394 1212 99373 63 12 25 18632 1143 86230 44 21 25 2325 435 30837 19 8 8 21792 830 64175 42 37 46 26263 652 59382 49 29 47 23686 707 119308 30 32 37 49303 954 76702 49 35 51 20055 733 84105 20 17 34 83123 1530 176508 54 60 98 57635 1439 165446 33 69 80 66198 1764 237213 84 78 130 57793 1373 133131 55 44 60 97668 4041 324799 154 158 140 101481 2152 236785 119 77 91 67654 2242 344297 75 80 119 69112 2515 174724 92 123 123 82753 2147 174415 100 73 90 72654 1638 223632 73 105 113 30727 1222 124817 40 47 56 79215 2662 325107 99 84 96 1423 186 7176 17 0 0 83122 2527 265769 146 96 126 39992 2702 175824 107 57 70 49810 1179 111665 34 39 57 100708 4308 362301 119 76 68 72260 1438 168809 66 76 102 5950 496 24188 24 8 7 115762 2253 329267 259 79 148 143558 2144 244052 68 101 137 117105 4691 341570 168 94 135 105195 1973 256462 105 123 181 95260 1226 196553 57 41 107 55183 1389 174184 53 72 94 73511 2269 187559 121 75 106 22618 893 73566 32 22 26 225920 1502 182999 88 73 54 61370 1420 152299 53 62 78 106117 2970 346485 90 118 121 84651 1644 193339 78 100 145 15986 1654 122774 45 24 27 26706 937 112611 41 46 48 89691 3004 286468 144 57 68 126846 2547 148446 91 135 150 51715 1468 140344 53 33 65 55801 2445 220516 62 98 97 111813 1964 243060 63 58 121 120293 1381 162765 32 68 99 161647 1659 232138 62 131 188 24266 1290 85574 34 37 40 129838 1904 232317 54 118 178 87771 1559 164709 109 81 176 44418 2146 220801 75 51 66 35232 1590 92661 61 40 39 40909 1590 133328 55 56 66 13294 1210 61361 77 27 27 140867 1281 100750 72 83 58 61056 1272 101523 42 59 77 101338 1944 243511 71 133 130 1168 391 22938 10 12 11 65567 1605 152474 65 106 101 40735 1386 132487 41 71 120 855 387 21054 16 4 4 97068 1742 209641 42 62 89 10288 800 46698 45 14 14 65622 1684 131698 65 60 78 76643 2699 244749 95 98 106 93815 2158 272458 65 100 132 34553 1421 108043 62 45 40 213688 2922 351067 95 136 220 91721 2186 229242 247 63 95 111194 1035 84207 29 14 12 83305 1926 250047 81 41 55 98952 3352 299775 95 91 103 37238 2035 173260 63 41 16 21399 961 92499 32 25 21 34988 1335 74408 67 29 36 64466 1645 181633 70 47 96 28579 1161 81437 38 37 36 38084 979 65745 53 26 50 27717 675 56653 45 38 30 32928 1241 158399 39 23 30 19499 1049 73624 24 30 33 36874 1081 91899 35 18 37 48259 1688 139526 151 28 83 29156 705 86678 40 12 19 45588 1597 150580 77 27 41 45097 982 99611 35 41 54 25139 532 31706 13 26 26 27975 882 89806 42 27 20 5752 285 19764 12 10 10 20154 642 64187 27 10 12 19540 894 72535 14 17 27 101193 2172 179321 89 108 135 38361 901 123185 40 49 61 68504 463 52746 25 0 39 22807 371 33170 18 1 5 17140 1192 101645 63 20 28 71701 1495 173326 88 86 82 80444 2187 258873 60 104 131 53855 1491 180083 66 63 84 114789 1882 202925 61 115 150 97500 1289 132943 40 83 110 77873 1812 221698 45 105 115 90183 1731 260561 75 114 127 61542 807 84853 31 38 27 27570 829 101011 34 30 35 55813 1940 215641 46 71 64 55461 1499 167542 66 59 84 70106 2747 269651 67 106 105 71570 2099 116408 61 34 40 33032 918 78800 42 20 21 139077 3373 277965 89 115 154 71595 1713 150629 44 85 116 32551 744 65029 17 21 21 120733 2694 233328 132 92 230 73107 1769 206161 71 75 71 132068 3148 311473 112 128 147 46821 2084 177939 82 55 64 87011 1954 207176 70 56 105 78664 1268 119016 52 118 81 70054 1943 182192 52 77 89 74011 1762 194979 62 66 84 93133 1857 275541 63 116 110 62133 1441 135649 46 99 96 43836 1416 120221 37 53 51 38692 1317 145790 63 30 38 56622 870 80953 25 49 59 67267 2008 241066 82 75 58 41140 1885 204713 71 68 74 138599 1369 182613 39 81 152 43750 602 43287 14 13 49 40652 1743 155754 61 74 73 85872 2014 201940 38 109 94 89275 2143 235454 73 151 120 32387 2072 125930 75 37 65 120662 1401 224549 50 54 98 21233 834 82316 32 27 25 13497 761 41566 35 0 2 25162 530 61857 25 23 31 16563 1050 91735 35 7 15 110681 1606 184510 49 64 83 29011 1502 79863 37 29 24 8773 568 38214 34 16 16 83209 1459 151101 32 48 56 86687 1111 172494 52 46 144 103487 1955 250579 83 130 143 23517 1060 98866 18 25 50 56926 956 85439 33 32 39 115168 3604 351619 139 95 169 51633 1701 165543 65 70 119 75345 1249 141722 94 19 75 123969 1369 104389 45 135 89 27142 1577 136084 30 27 40 135400 2201 199476 70 87 125 6023 207 14688 10 4 5 51776 1463 87186 54 28 47 21152 742 50090 20 16 20 11342 676 46455 20 22 34 16380 620 38395 31 16 34 16734 736 52164 52 32 32 30143 812 70551 31 23 43 41369 1051 84856 29 29 41 35944 945 85709 44 21 37 36278 554 34662 25 18 33 3895 222 19349 11 13 14 14483 608 62088 38 13 11 13127 459 40151 29 16 14 5839 578 27634 20 2 3 24069 826 76990 27 42 40 3738 509 37460 20 5 5 18625 717 54157 19 37 38 36341 637 49862 37 17 32 24548 857 84337 26 38 41 25659 1461 103425 67 17 49 28904 672 70344 28 20 21 2781 778 43410 19 7 1 29236 1141 104838 49 46 44 19546 680 62215 27 24 26 22818 1090 69304 30 40 21 32689 616 53117 22 3 4 22197 1145 86680 31 37 43 25272 888 77945 20 28 32 82206 849 89113 39 19 20 32073 1182 91005 29 29 34 5444 528 40248 16 8 6 36944 947 50857 21 15 24 8019 819 56613 19 15 16 30884 757 62792 35 28 72
Names of X columns:
totsize pageviews time_in_rfc logins blogged_computations tothyperlinks
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation