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Data X:
210907 56 396 3 79 30 112285 120982 56 297 4 58 28 84786 176508 54 559 12 60 38 83123 179321 89 967 2 108 30 101193 123185 40 270 1 49 22 38361 52746 25 143 3 0 26 68504 385534 92 1562 0 121 25 119182 33170 18 109 0 1 18 22807 101645 63 371 0 20 11 17140 149061 44 656 5 43 26 116174 165446 33 511 0 69 25 57635 237213 84 655 0 78 38 66198 173326 88 465 7 86 44 71701 133131 55 525 7 44 30 57793 258873 60 885 3 104 40 80444 180083 66 497 9 63 34 53855 324799 154 1436 0 158 47 97668 230964 53 612 4 102 30 133824 236785 119 865 3 77 31 101481 135473 41 385 0 82 23 99645 202925 61 567 7 115 36 114789 215147 58 639 0 101 36 99052 344297 75 963 1 80 30 67654 153935 33 398 5 50 25 65553 132943 40 410 7 83 39 97500 174724 92 966 0 123 34 69112 174415 100 801 0 73 31 82753 225548 112 892 5 81 31 85323 223632 73 513 0 105 33 72654 124817 40 469 0 47 25 30727 221698 45 683 0 105 33 77873 210767 60 643 3 94 35 117478 170266 62 535 4 44 42 74007 260561 75 625 1 114 43 90183 84853 31 264 4 38 30 61542 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207176 70 711 8 56 32 87011 196553 57 503 2 41 29 95260 174184 53 382 0 72 25 55183 143246 103 464 5 67 27 106671 187559 121 717 8 75 36 73511 187681 62 690 2 114 28 92945 119016 52 462 5 118 23 78664 182192 52 657 12 77 40 70054 73566 32 385 6 22 23 22618 194979 62 577 7 66 40 74011 167488 45 619 2 69 28 83737 143756 46 479 0 105 34 69094 275541 63 817 4 116 33 93133 243199 75 752 3 88 28 95536 182999 88 430 6 73 34 225920 135649 46 451 2 99 30 62133 152299 53 537 0 62 33 61370 120221 37 519 1 53 22 43836 346485 90 1000 0 118 38 106117 145790 63 637 5 30 26 38692 193339 78 465 2 100 35 84651 80953 25 437 0 49 8 56622 122774 45 711 0 24 24 15986 130585 46 299 5 67 29 95364 112611 41 248 0 46 20 26706 286468 144 1162 1 57 29 89691 241066 82 714 0 75 45 67267 148446 91 905 1 135 37 126846 204713 71 649 1 68 33 41140 182079 63 512 2 124 33 102860 140344 53 472 6 33 25 51715 220516 62 905 1 98 32 55801 243060 63 786 4 58 29 111813 162765 32 489 2 68 28 120293 182613 39 479 3 81 28 138599 232138 62 617 0 131 31 161647 265318 117 925 10 110 52 115929 85574 34 351 0 37 21 24266 310839 92 1144 9 130 24 162901 225060 93 669 7 93 41 109825 232317 54 707 0 118 33 129838 144966 144 458 0 39 32 37510 43287 14 214 4 13 19 43750 155754 61 599 4 74 20 40652 164709 109 572 0 81 31 87771 201940 38 897 0 109 31 85872 235454 73 819 0 151 32 89275 220801 75 720 1 51 18 44418 99466 50 273 0 28 23 192565 92661 61 508 1 40 17 35232 133328 55 506 0 56 20 40909 61361 77 451 0 27 12 13294 125930 75 699 4 37 17 32387 100750 72 407 0 83 30 140867 224549 50 465 4 54 31 120662 82316 32 245 4 27 10 21233 102010 53 370 3 28 13 44332 101523 42 316 0 59 22 61056 243511 71 603 0 133 42 101338 22938 10 154 0 12 1 1168 41566 35 229 5 0 9 13497 152474 65 577 0 106 32 65567 61857 25 192 4 23 11 25162 99923 66 617 0 44 25 32334 132487 41 411 0 71 36 40735 317394 86 975 1 116 31 91413 21054 16 146 0 4 0 855 209641 42 705 5 62 24 97068 22648 19 184 0 12 13 44339 31414 19 200 0 18 8 14116 46698 45 274 0 14 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36278 150580 77 530 0 27 22 45588 99611 35 291 0 41 21 45097 19349 11 67 0 13 1 3895 99373 63 397 1 12 18 28394 86230 44 467 0 21 17 18632 30837 19 178 0 8 4 2325 31706 13 175 0 26 10 25139 89806 42 299 0 27 16 27975 62088 38 154 1 13 16 14483 40151 29 106 0 16 9 13127 27634 20 189 0 2 16 5839 76990 27 194 0 42 17 24069 37460 20 135 0 5 7 3738 54157 19 201 0 37 15 18625 49862 37 207 0 17 14 36341 84337 26 280 0 38 14 24548 64175 42 260 0 37 18 21792 59382 49 227 0 29 12 26263 119308 30 239 0 32 16 23686 76702 49 333 0 35 21 49303 103425 67 428 1 17 19 25659 70344 28 230 0 20 16 28904 43410 19 292 0 7 1 2781 104838 49 350 1 46 16 29236 62215 27 186 0 24 10 19546 69304 30 326 6 40 19 22818 53117 22 155 3 3 12 32689 19764 12 75 1 10 2 5752 86680 31 361 2 37 14 22197 84105 20 261 0 17 17 20055 77945 20 299 0 28 19 25272 89113 39 300 0 19 14 82206 91005 29 450 3 29 11 32073 40248 16 183 1 8 4 5444 64187 27 238 0 10 16 20154 50857 21 165 0 15 20 36944 56613 19 234 1 15 12 8019 62792 35 176 0 28 15 30884 72535 14 329 0 17 16 19540
Names of X columns:
time_in_rfc logins compendium_views_info shared_compendiums blogged_computations compendiums_reviewed 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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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