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Data X:
1 210907 56 79 30 1 120982 56 58 28 1 176508 54 60 38 0 179321 89 108 30 1 123185 40 49 22 1 52746 25 0 26 1 385534 92 121 25 0 33170 18 1 18 1 101645 63 20 11 0 149061 44 43 26 0 165446 33 69 25 0 237213 84 78 38 0 173326 88 86 44 0 133131 55 44 30 1 258873 60 104 40 0 180083 66 63 34 1 324799 154 158 47 1 230964 53 102 30 0 236785 119 77 31 0 135473 41 82 23 0 202925 61 115 36 1 215147 58 101 36 0 344297 75 80 30 1 153935 33 50 25 0 132943 40 83 39 0 174724 92 123 34 1 174415 100 73 31 1 225548 112 81 31 1 223632 73 105 33 0 124817 40 47 25 1 221698 45 105 33 0 210767 60 94 35 1 170266 62 44 42 0 260561 75 114 43 1 84853 31 38 30 0 294424 77 107 33 1 101011 34 30 13 1 215641 46 71 32 0 325107 99 84 36 0 7176 17 0 0 1 167542 66 59 28 1 106408 30 33 14 0 96560 76 42 17 1 265769 146 96 32 1 269651 67 106 30 1 149112 56 56 35 0 175824 107 57 20 0 152871 58 59 28 1 111665 34 39 28 0 116408 61 34 39 1 362301 119 76 34 0 78800 42 20 26 1 183167 66 91 39 0 277965 89 115 39 1 150629 44 85 33 0 168809 66 76 28 0 24188 24 8 4 0 329267 259 79 39 0 65029 17 21 18 1 101097 64 30 14 0 218946 41 76 29 0 244052 68 101 44 0 341570 168 94 21 1 103597 43 27 16 0 233328 132 92 28 1 256462 105 123 35 1 206161 71 75 28 1 311473 112 128 38 0 235800 94 105 23 0 177939 82 55 36 0 207176 70 56 32 1 196553 57 41 29 1 174184 53 72 25 1 143246 103 67 27 1 187559 121 75 36 0 187681 62 114 28 0 119016 52 118 23 0 182192 52 77 40 0 73566 32 22 23 1 194979 62 66 40 1 167488 45 69 28 1 143756 46 105 34 1 275541 63 116 33 0 243199 75 88 28 0 182999 88 73 34 1 135649 46 99 30 1 152299 53 62 33 1 120221 37 53 22 1 346485 90 118 38 1 145790 63 30 26 1 193339 78 100 35 0 80953 25 49 8 0 122774 45 24 24 0 130585 46 67 29 0 112611 41 46 20 0 286468 144 57 29 0 241066 82 75 45 1 148446 91 135 37 1 204713 71 68 33 0 182079 63 124 33 1 140344 53 33 25 1 220516 62 98 32 0 243060 63 58 29 1 162765 32 68 28 0 182613 39 81 28 0 232138 62 131 31 1 265318 117 110 52 1 85574 34 37 21 1 310839 92 130 24 1 225060 93 93 41 0 232317 54 118 33 0 144966 144 39 32 0 43287 14 13 19 1 155754 61 74 20 0 164709 109 81 31 0 201940 38 109 31 1 235454 73 151 32 0 220801 75 51 18 1 99466 50 28 23 0 92661 61 40 17 0 133328 55 56 20 0 61361 77 27 12 1 125930 75 37 17 1 100750 72 83 30 0 224549 50 54 31 0 82316 32 27 10 1 102010 53 28 13 0 101523 42 59 22 0 243511 71 133 42 1 22938 10 12 1 1 41566 35 0 9 1 152474 65 106 32 1 61857 25 23 11 0 99923 66 44 25 0 132487 41 71 36 0 317394 86 116 31 0 21054 16 4 0 1 209641 42 62 24 0 22648 19 12 13 1 31414 19 18 8 1 46698 45 14 13 0 131698 65 60 19 1 91735 35 7 18 1 244749 95 98 33 1 184510 49 64 40 1 79863 37 29 22 1 128423 64 32 38 0 97839 38 25 24 0 38214 34 16 8 0 151101 32 48 35 1 272458 65 100 43 1 172494 52 46 43 1 108043 62 45 14 1 328107 65 129 41 0 250579 83 130 38 1 351067 95 136 45 0 158015 29 59 31 1 98866 18 25 13 1 85439 33 32 28 1 229242 247 63 31 1 351619 139 95 40 1 84207 29 14 30 1 120445 118 36 16 1 324598 110 113 37 0 131069 67 47 30 1 204271 42 92 35 0 165543 65 70 32 0 141722 94 19 27 0 116048 64 50 20 1 250047 81 41 18 0 299775 95 91 31 1 195838 67 111 31 1 173260 63 41 21 1 254488 83 120 39 0 104389 45 135 41 1 136084 30 27 13 1 199476 70 87 32 0 92499 32 25 18 0 224330 83 131 39 1 135781 31 45 14 1 74408 67 29 7 1 81240 66 58 17 0 14688 10 4 0 1 181633 70 47 30 1 271856 103 109 37 1 7199 5 7 0 1 46660 20 12 5 0 17547 5 0 1 1 133368 36 37 16 0 95227 34 37 32 0 152601 48 46 24 0 98146 40 15 17 0 79619 43 42 11 1 59194 31 7 24 1 139942 42 54 22 1 118612 46 54 12 1 72880 33 14 19 0 65475 18 16 13 0 99643 55 33 17 0 71965 35 32 15 0 77272 59 21 16 1 49289 19 15 24 1 135131 66 38 15 0 108446 60 22 17 1 89746 36 28 18 1 44296 25 10 20 0 77648 47 31 16 1 181528 54 32 16 0 134019 53 32 18 1 124064 40 43 22 1 92630 40 27 8 0 121848 39 37 17 1 52915 14 20 18 1 81872 45 32 16 0 58981 36 0 23 1 53515 28 5 22 0 60812 44 26 13 1 56375 30 10 13 1 65490 22 27 16 0 80949 17 11 16 1 76302 31 29 20 0 104011 55 25 22 1 98104 54 55 17 1 67989 21 23 18 0 30989 14 5 17 1 135458 81 43 12 1 73504 35 23 7 0 63123 43 34 17 1 61254 46 36 14 1 74914 30 35 23 1 31774 23 0 17 1 81437 38 37 14 1 87186 54 28 15 0 50090 20 16 17 0 65745 53 26 21 0 56653 45 38 18 0 158399 39 23 18 1 46455 20 22 17 1 73624 24 30 17 1 38395 31 16 16 0 91899 35 18 15 1 139526 151 28 21 0 52164 52 32 16 1 51567 30 21 14 0 70551 31 23 15 0 84856 29 29 17 1 102538 57 50 15 1 86678 40 12 15 0 85709 44 21 10 1 34662 25 18 6 0 150580 77 27 22 0 99611 35 41 21 0 19349 11 13 1 1 99373 63 12 18 0 86230 44 21 17 0 30837 19 8 4 0 31706 13 26 10 0 89806 42 27 16 0 62088 38 13 16 1 40151 29 16 9 0 27634 20 2 16 0 76990 27 42 17 0 37460 20 5 7 0 54157 19 37 15 0 49862 37 17 14 1 84337 26 38 14 0 64175 42 37 18 1 59382 49 29 12 1 119308 30 32 16 1 76702 49 35 21 0 103425 67 17 19 0 70344 28 20 16 0 43410 19 7 1 0 104838 49 46 16 1 62215 27 24 10 0 69304 30 40 19 1 53117 22 3 12 0 19764 12 10 2 0 86680 31 37 14 0 84105 20 17 17 0 77945 20 28 19 1 89113 39 19 14 0 91005 29 29 11 0 40248 16 8 4 0 64187 27 10 16 0 50857 21 15 20 1 56613 19 15 12 1 62792 35 28 15 0 72535 14 17 16
Names of X columns:
Geslacht Time_in_RFC Logins Blogged_computations Reviewed_compendiums
Sample Range:
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From:
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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
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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') }
Compute
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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