Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
30 210907 79 112285 81 28 120982 58 84786 55 38 176508 60 83123 50 30 179321 108 101193 125 22 123185 49 38361 40 26 52746 0 68504 37 25 385534 121 119182 63 18 33170 1 22807 44 11 101645 20 17140 88 26 149061 43 116174 66 25 165446 69 57635 57 38 237213 78 66198 74 44 173326 86 71701 49 30 133131 44 57793 52 40 258873 104 80444 88 34 180083 63 53855 36 47 324799 158 97668 108 30 230964 102 133824 43 31 236785 77 101481 75 23 135473 82 99645 32 36 202925 115 114789 44 36 215147 101 99052 85 30 344297 80 67654 86 25 153935 50 65553 56 39 132943 83 97500 50 34 174724 123 69112 135 31 174415 73 82753 63 31 225548 81 85323 81 33 223632 105 72654 52 25 124817 47 30727 44 33 221698 105 77873 113 35 210767 94 117478 39 42 170266 44 74007 73 43 260561 114 90183 48 30 84853 38 61542 33 33 294424 107 101494 59 13 101011 30 27570 41 32 215641 71 55813 69 36 325107 84 79215 64 0 7176 0 1423 1 28 167542 59 55461 59 14 106408 33 31081 32 17 96560 42 22996 129 32 265769 96 83122 37 30 269651 106 70106 31 35 149112 56 60578 65 20 175824 57 39992 107 28 152871 59 79892 74 28 111665 39 49810 54 39 116408 34 71570 76 34 362301 76 100708 715 26 78800 20 33032 57 39 183167 91 82875 66 39 277965 115 139077 106 33 150629 85 71595 54 28 168809 76 72260 32 4 24188 8 5950 20 39 329267 79 115762 71 18 65029 21 32551 21 14 101097 30 31701 70 29 218946 76 80670 112 44 244052 101 143558 66 21 341570 94 117105 190 16 103597 27 23789 66 28 233328 92 120733 165 35 256462 123 105195 56 28 206161 75 73107 61 38 311473 128 132068 53 23 235800 105 149193 127 36 177939 55 46821 63 32 207176 56 87011 38 29 196553 41 95260 50 25 174184 72 55183 52 27 143246 67 106671 42 36 187559 75 73511 76 28 187681 114 92945 67 23 119016 118 78664 50 40 182192 77 70054 53 23 73566 22 22618 39 40 194979 66 74011 50 28 167488 69 83737 77 34 143756 105 69094 57 33 275541 116 93133 73 28 243199 88 95536 34 34 182999 73 225920 39 30 135649 99 62133 46 33 152299 62 61370 63 22 120221 53 43836 35 38 346485 118 106117 106 26 145790 30 38692 43 35 193339 100 84651 47 8 80953 49 56622 31 24 122774 24 15986 162 29 130585 67 95364 57 20 112611 46 26706 36 29 286468 57 89691 263 45 241066 75 67267 78 37 148446 135 126846 63 33 204713 68 41140 54 33 182079 124 102860 63 25 140344 33 51715 77 32 220516 98 55801 79 29 243060 58 111813 110 28 162765 68 120293 56 28 182613 81 138599 56 31 232138 131 161647 43 52 265318 110 115929 111 21 85574 37 24266 71 24 310839 130 162901 62 41 225060 93 109825 56 33 232317 118 129838 74 32 144966 39 37510 60 19 43287 13 43750 43 20 155754 74 40652 68 31 164709 81 87771 53 31 201940 109 85872 87 32 235454 151 89275 46 18 220801 51 44418 105 23 99466 28 192565 32 17 92661 40 35232 133 20 133328 56 40909 79 12 61361 27 13294 51 17 125930 37 32387 207 30 100750 83 140867 67 31 224549 54 120662 47 10 82316 27 21233 34 13 102010 28 44332 66 22 101523 59 61056 76 42 243511 133 101338 65 1 22938 12 1168 9 9 41566 0 13497 42 32 152474 106 65567 45 11 61857 23 25162 25 25 99923 44 32334 115 36 132487 71 40735 97 31 317394 116 91413 53 0 21054 4 855 2 24 209641 62 97068 52 13 22648 12 44339 44 8 31414 18 14116 22 13 46698 14 10288 35 19 131698 60 65622 74 18 91735 7 16563 103 33 244749 98 76643 144 40 184510 64 110681 60 22 79863 29 29011 134 38 128423 32 92696 89 24 97839 25 94785 42 8 38214 16 8773 52 35 151101 48 83209 98 43 272458 100 93815 99 43 172494 46 86687 52 14 108043 45 34553 29 41 328107 129 105547 125 38 250579 130 103487 106 45 351067 136 213688 95 31 158015 59 71220 40 13 98866 25 23517 140 28 85439 32 56926 43 31 229242 63 91721 128 40 351619 95 115168 142 30 84207 14 111194 73 16 120445 36 51009 72 37 324598 113 135777 128 30 131069 47 51513 61 35 204271 92 74163 73 32 165543 70 51633 148 27 141722 19 75345 64 20 116048 50 33416 45 18 250047 41 83305 58 31 299775 91 98952 97 31 195838 111 102372 50 21 173260 41 37238 37 39 254488 120 103772 50 41 104389 135 123969 105 13 136084 27 27142 69 32 199476 87 135400 46 18 92499 25 21399 57 39 224330 131 130115 52 14 135781 45 24874 98 7 74408 29 34988 61 17 81240 58 45549 89 0 14688 4 6023 0 30 181633 47 64466 48 37 271856 109 54990 91 0 7199 7 1644 0 5 46660 12 6179 7 1 17547 0 3926 3 16 133368 37 32755 54 32 95227 37 34777 70 24 152601 46 73224 36 17 98146 15 27114 37 11 79619 42 20760 123 24 59194 7 37636 247 22 139942 54 65461 46 12 118612 54 30080 72 19 72880 14 24094 41 13 65475 16 69008 24 17 99643 33 54968 45 15 71965 32 46090 33 16 77272 21 27507 27 24 49289 15 10672 36 15 135131 38 34029 87 17 108446 22 46300 90 18 89746 28 24760 114 20 44296 10 18779 31 16 77648 31 21280 45 16 181528 32 40662 69 18 134019 32 28987 51 22 124064 43 22827 34 8 92630 27 18513 60 17 121848 37 30594 45 18 52915 20 24006 54 16 81872 32 27913 25 23 58981 0 42744 38 22 53515 5 12934 52 13 60812 26 22574 67 13 56375 10 41385 74 16 65490 27 18653 38 16 80949 11 18472 30 20 76302 29 30976 26 22 104011 25 63339 67 17 98104 55 25568 132 18 67989 23 33747 42 17 30989 5 4154 35 12 135458 43 19474 118 7 73504 23 35130 68 17 63123 34 39067 43 14 61254 36 13310 76 23 74914 35 65892 64 17 31774 0 4143 48 14 81437 37 28579 64 15 87186 28 51776 56 17 50090 16 21152 71 21 65745 26 38084 75 18 56653 38 27717 39 18 158399 23 32928 42 17 46455 22 11342 39 17 73624 30 19499 93 16 38395 16 16380 38 15 91899 18 36874 60 21 139526 28 48259 71 16 52164 32 16734 52 14 51567 21 28207 27 15 70551 23 30143 59 17 84856 29 41369 40 15 102538 50 45833 79 15 86678 12 29156 44 10 85709 21 35944 65 6 34662 18 36278 10 22 150580 27 45588 124 21 99611 41 45097 81 1 19349 13 3895 15 18 99373 12 28394 92 17 86230 21 18632 42 4 30837 8 2325 10 10 31706 26 25139 24 16 89806 27 27975 64 16 62088 13 14483 45 9 40151 16 13127 22 16 27634 2 5839 56 17 76990 42 24069 94 7 37460 5 3738 19 15 54157 37 18625 35 14 49862 17 36341 32 14 84337 38 24548 35 18 64175 37 21792 48 12 59382 29 26263 49 16 119308 32 23686 48 21 76702 35 49303 62 19 103425 17 25659 96 16 70344 20 28904 45 1 43410 7 2781 63 16 104838 46 29236 71 10 62215 24 19546 26 19 69304 40 22818 48 12 53117 3 32689 29 2 19764 10 5752 19 14 86680 37 22197 45 17 84105 17 20055 45 19 77945 28 25272 67 14 89113 19 82206 30 11 91005 29 32073 36 4 40248 8 5444 34 16 64187 10 20154 36 20 50857 15 36944 34 12 56613 15 8019 37 15 62792 28 30884 46 16 72535 17 19540 44
Names of X columns:
compendiums_reviewed time_in_rfc blogged_computations totsize compendium_views_pr
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