Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
210907 115 112285 145 120982 109 84786 101 176508 146 83123 98 179321 116 101193 132 123185 68 38361 60 52746 101 68504 38 385534 96 119182 144 33170 67 22807 5 101645 44 17140 28 149061 100 116174 84 165446 93 57635 79 237213 140 66198 127 173326 166 71701 78 133131 99 57793 60 258873 139 80444 131 180083 130 53855 84 324799 181 97668 133 230964 116 133824 150 236785 116 101481 91 135473 88 99645 132 202925 139 114789 136 215147 135 99052 124 344297 108 67654 118 153935 89 65553 70 132943 156 97500 107 174724 129 69112 119 174415 118 82753 89 225548 118 85323 112 223632 125 72654 108 124817 95 30727 52 221698 126 77873 112 210767 135 117478 116 170266 154 74007 123 260561 165 90183 125 84853 113 61542 27 294424 127 101494 162 101011 52 27570 32 215641 121 55813 64 325107 136 79215 92 7176 0 1423 0 167542 108 55461 83 106408 46 31081 41 96560 54 22996 47 265769 124 83122 120 269651 115 70106 105 149112 128 60578 79 175824 80 39992 65 152871 97 79892 70 111665 104 49810 55 116408 59 71570 39 362301 125 100708 67 78800 82 33032 21 183167 149 82875 127 277965 149 139077 152 150629 122 71595 113 168809 118 72260 99 24188 12 5950 7 329267 144 115762 141 65029 67 32551 21 101097 52 31701 35 218946 108 80670 109 244052 166 143558 133 341570 80 117105 123 103597 60 23789 26 233328 107 120733 230 256462 127 105195 166 206161 107 73107 68 311473 146 132068 147 235800 84 149193 179 177939 141 46821 61 207176 123 87011 101 196553 111 95260 108 174184 98 55183 90 143246 105 106671 114 187559 135 73511 103 187681 107 92945 142 119016 85 78664 79 182192 155 70054 88 73566 88 22618 25 194979 155 74011 83 167488 104 83737 113 143756 132 69094 118 275541 127 93133 110 243199 108 95536 129 182999 129 225920 51 135649 116 62133 93 152299 122 61370 76 120221 85 43836 49 346485 147 106117 118 145790 99 38692 38 193339 87 84651 141 80953 28 56622 58 122774 90 15986 27 130585 109 95364 91 112611 78 26706 48 286468 111 89691 63 241066 158 67267 56 148446 141 126846 144 204713 122 41140 73 182079 124 102860 168 140344 93 51715 64 220516 124 55801 97 243060 112 111813 117 162765 108 120293 100 182613 99 138599 149 232138 117 161647 187 265318 199 115929 127 85574 78 24266 37 310839 91 162901 245 225060 158 109825 87 232317 126 129838 177 144966 122 37510 49 43287 71 43750 49 155754 75 40652 73 164709 115 87771 177 201940 119 85872 94 235454 124 89275 117 220801 72 44418 60 99466 91 192565 55 92661 45 35232 39 133328 78 40909 64 61361 39 13294 26 125930 68 32387 64 100750 119 140867 58 224549 117 120662 95 82316 39 21233 25 102010 50 44332 26 101523 88 61056 76 243511 155 101338 129 22938 0 1168 11 41566 36 13497 2 152474 123 65567 101 61857 32 25162 28 99923 99 32334 36 132487 136 40735 89 317394 117 91413 193 21054 0 855 4 209641 88 97068 84 22648 39 44339 23 31414 25 14116 39 46698 52 10288 14 131698 75 65622 78 91735 71 16563 14 244749 124 76643 101 184510 151 110681 82 79863 71 29011 24 128423 145 92696 36 97839 87 94785 75 38214 27 8773 16 151101 131 83209 55 272458 162 93815 131 172494 165 86687 131 108043 54 34553 39 328107 159 105547 144 250579 147 103487 139 351067 170 213688 211 158015 119 71220 78 98866 49 23517 50 85439 104 56926 39 229242 120 91721 90 351619 150 115168 166 84207 112 111194 12 120445 59 51009 57 324598 136 135777 133 131069 107 51513 69 204271 130 74163 119 165543 115 51633 119 141722 107 75345 65 116048 75 33416 61 250047 71 83305 49 299775 120 98952 101 195838 116 102372 196 173260 79 37238 15 254488 150 103772 136 104389 156 123969 89 136084 51 27142 40 199476 118 135400 123 92499 71 21399 21 224330 144 130115 163 135781 47 24874 29 74408 28 34988 35 81240 68 45549 13 14688 0 6023 5 181633 110 64466 96 271856 147 54990 151 7199 0 1644 6 46660 15 6179 13 17547 4 3926 3 133368 64 32755 56 95227 111 34777 23 152601 85 73224 57 98146 68 27114 14 79619 40 20760 43 59194 80 37636 20 139942 88 65461 72 118612 48 30080 87 72880 76 24094 21 65475 51 69008 56 99643 67 54968 59 71965 59 46090 82 77272 61 27507 43 49289 76 10672 25 135131 60 34029 38 108446 68 46300 25 89746 71 24760 38 44296 76 18779 12 77648 62 21280 29 181528 61 40662 47 134019 67 28987 45 124064 88 22827 40 92630 30 18513 30 121848 64 30594 41 52915 68 24006 25 81872 64 27913 23 58981 91 42744 14 53515 88 12934 16 60812 52 22574 26 56375 49 41385 21 65490 62 18653 27 80949 61 18472 9 76302 76 30976 33 104011 88 63339 42 98104 66 25568 68 67989 71 33747 32 30989 68 4154 6 135458 48 19474 67 73504 25 35130 33 63123 68 39067 77 61254 41 13310 46 74914 90 65892 30 31774 66 4143 0 81437 54 28579 36 87186 59 51776 46 50090 60 21152 18 65745 77 38084 48 56653 68 27717 29 158399 72 32928 28 46455 67 11342 34 73624 64 19499 33 38395 63 16380 34 91899 59 36874 33 139526 84 48259 80 52164 64 16734 32 51567 56 28207 30 70551 54 30143 41 84856 67 41369 41 102538 58 45833 51 86678 59 29156 18 85709 40 35944 34 34662 22 36278 31 150580 83 45588 39 99611 81 45097 54 19349 2 3895 14 99373 72 28394 24 86230 61 18632 24 30837 15 2325 8 31706 32 25139 26 89806 62 27975 19 62088 58 14483 11 40151 36 13127 14 27634 59 5839 1 76990 68 24069 39 37460 21 3738 5 54157 55 18625 37 49862 54 36341 32 84337 55 24548 38 64175 72 21792 47 59382 41 26263 47 119308 61 23686 37 76702 67 49303 51 103425 76 25659 45 70344 64 28904 21 43410 3 2781 1 104838 63 29236 42 62215 40 19546 26 69304 69 22818 21 53117 48 32689 4 19764 8 5752 10 86680 52 22197 43 84105 66 20055 34 77945 76 25272 31 89113 43 82206 19 91005 39 32073 34 40248 14 5444 6 64187 61 20154 11 50857 71 36944 24 56613 44 8019 16 62792 60 30884 72 72535 64 19540 21
Names of X columns:
time_in_rfc feedbackmessages totsize totblogs
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