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Data X:
3 210907 56 79 94 30 4 120982 56 58 103 28 12 176508 54 60 93 38 2 179321 89 108 103 30 1 123185 40 49 51 22 3 52746 25 0 70 26 0 385534 92 121 91 25 0 33170 18 1 22 18 0 101645 63 20 38 11 5 149061 44 43 93 26 0 165446 33 69 60 25 0 237213 84 78 123 38 7 173326 88 86 148 44 7 133131 55 44 90 30 3 258873 60 104 124 40 9 180083 66 63 70 34 0 324799 154 158 168 47 4 230964 53 102 115 30 3 236785 119 77 71 31 0 135473 41 82 66 23 7 202925 61 115 134 36 0 215147 58 101 117 36 1 344297 75 80 108 30 5 153935 33 50 84 25 7 132943 40 83 156 39 0 174724 92 123 120 34 0 174415 100 73 114 31 5 225548 112 81 94 31 0 223632 73 105 120 33 0 124817 40 47 81 25 0 221698 45 105 110 33 3 210767 60 94 133 35 4 170266 62 44 122 42 1 260561 75 114 158 43 4 84853 31 38 109 30 2 294424 77 107 124 33 0 101011 34 30 39 13 0 215641 46 71 92 32 0 325107 99 84 126 36 0 7176 17 0 0 0 2 167542 66 59 70 28 1 106408 30 33 37 14 0 96560 76 42 38 17 2 265769 146 96 120 32 10 269651 67 106 93 30 6 149112 56 56 95 35 0 175824 107 57 77 20 5 152871 58 59 90 28 4 111665 34 39 80 28 1 116408 61 34 31 39 2 362301 119 76 110 34 2 78800 42 20 66 26 0 183167 66 91 138 39 8 277965 89 115 133 39 3 150629 44 85 113 33 0 168809 66 76 100 28 0 24188 24 8 7 4 8 329267 259 79 140 39 5 65029 17 21 61 18 3 101097 64 30 41 14 1 218946 41 76 96 29 5 244052 68 101 164 44 1 341570 168 94 78 21 1 103597 43 27 49 16 5 233328 132 92 102 28 0 256462 105 123 124 35 12 206161 71 75 99 28 8 311473 112 128 129 38 8 235800 94 105 62 23 8 177939 82 55 73 36 8 207176 70 56 114 32 2 196553 57 41 99 29 0 174184 53 72 70 25 5 143246 103 67 104 27 8 187559 121 75 116 36 2 187681 62 114 91 28 5 119016 52 118 74 23 12 182192 52 77 138 40 6 73566 32 22 67 23 7 194979 62 66 151 40 2 167488 45 69 72 28 0 143756 46 105 120 34 4 275541 63 116 115 33 3 243199 75 88 105 28 6 182999 88 73 104 34 2 135649 46 99 108 30 0 152299 53 62 98 33 1 120221 37 53 69 22 0 346485 90 118 111 38 5 145790 63 30 99 26 2 193339 78 100 71 35 0 80953 25 49 27 8 0 122774 45 24 69 24 5 130585 46 67 107 29 0 112611 41 46 73 20 1 286468 144 57 107 29 0 241066 82 75 93 45 1 148446 91 135 129 37 1 204713 71 68 69 33 2 182079 63 124 118 33 6 140344 53 33 73 25 1 220516 62 98 119 32 4 243060 63 58 104 29 2 162765 32 68 107 28 3 182613 39 81 99 28 0 232138 62 131 90 31 10 265318 117 110 197 52 0 85574 34 37 36 21 9 310839 92 130 85 24 7 225060 93 93 139 41 0 232317 54 118 106 33 0 144966 144 39 50 32 4 43287 14 13 64 19 4 155754 61 74 31 20 0 164709 109 81 63 31 0 201940 38 109 92 31 0 235454 73 151 106 32 1 220801 75 51 63 18 0 99466 50 28 69 23 1 92661 61 40 41 17 0 133328 55 56 56 20 0 61361 77 27 25 12 4 125930 75 37 65 17 0 100750 72 83 93 30 4 224549 50 54 114 31 4 82316 32 27 38 10 3 102010 53 28 44 13 0 101523 42 59 87 22 0 243511 71 133 110 42 0 22938 10 12 0 1 5 41566 35 0 27 9 0 152474 65 106 83 32 4 61857 25 23 30 11 0 99923 66 44 80 25 0 132487 41 71 98 36 1 317394 86 116 82 31 0 21054 16 4 0 0 5 209641 42 62 60 24 0 22648 19 12 28 13 0 31414 19 18 9 8 0 46698 45 14 33 13 0 131698 65 60 59 19 0 91735 35 7 49 18 2 244749 95 98 115 33 7 184510 49 64 140 40 1 79863 37 29 49 22 8 128423 64 32 120 38 2 97839 38 25 66 24 0 38214 34 16 21 8 2 151101 32 48 124 35 0 272458 65 100 152 43 0 172494 52 46 139 43 1 108043 62 45 38 14 3 328107 65 129 144 41 0 250579 83 130 120 38 3 351067 95 136 160 45 0 158015 29 59 114 31 0 98866 18 25 39 13 0 85439 33 32 78 28 4 229242 247 63 119 31 4 351619 139 95 141 40 11 84207 29 14 101 30 0 120445 118 36 56 16 0 324598 110 113 133 37 4 131069 67 47 83 30 0 204271 42 92 116 35 1 165543 65 70 90 32 0 141722 94 19 36 27 0 116048 64 50 50 20 0 250047 81 41 61 18 9 299775 95 91 97 31 1 195838 67 111 98 31 3 173260 63 41 78 21 10 254488 83 120 117 39 5 104389 45 135 148 41 0 136084 30 27 41 13 2 199476 70 87 105 32 0 92499 32 25 55 18 1 224330 83 131 132 39 2 135781 31 45 44 14 4 74408 67 29 21 7 0 81240 66 58 50 17 0 14688 10 4 0 0 2 181633 70 47 73 30 1 271856 103 109 86 37 0 7199 5 7 0 0 0 46660 20 12 13 5 0 17547 5 0 4 1 1 133368 36 37 57 16 0 95227 34 37 48 32 2 152601 48 46 46 24 0 98146 40 15 48 17 3 79619 43 42 32 11 6 59194 31 7 68 24 0 139942 42 54 87 22 2 118612 46 54 43 12 0 72880 33 14 67 19 2 65475 18 16 46 13 1 99643 55 33 46 17 1 71965 35 32 56 15 2 77272 59 21 48 16 1 49289 19 15 44 24 0 135131 66 38 60 15 1 108446 60 22 65 17 3 89746 36 28 55 18 0 44296 25 10 38 20 0 77648 47 31 52 16 0 181528 54 32 60 16 0 134019 53 32 54 18 1 124064 40 43 86 22 4 92630 40 27 24 8 0 121848 39 37 52 17 0 52915 14 20 49 18 0 81872 45 32 61 16 7 58981 36 0 61 23 2 53515 28 5 81 22 0 60812 44 26 43 13 7 56375 30 10 40 13 3 65490 22 27 40 16 0 80949 17 11 56 16 0 76302 31 29 68 20 6 104011 55 25 79 22 2 98104 54 55 47 17 0 67989 21 23 57 18 0 30989 14 5 41 17 3 135458 81 43 29 12 0 73504 35 23 3 7 1 63123 43 34 60 17 1 61254 46 36 30 14 0 74914 30 35 79 23 1 31774 23 0 47 17 0 81437 38 37 40 14 0 87186 54 28 48 15 0 50090 20 16 36 17 0 65745 53 26 42 21 0 56653 45 38 49 18 0 158399 39 23 57 18 0 46455 20 22 12 17 0 73624 24 30 40 17 0 38395 31 16 43 16 0 91899 35 18 33 15 0 139526 151 28 77 21 0 52164 52 32 43 16 2 51567 30 21 45 14 0 70551 31 23 47 15 1 84856 29 29 43 17 1 102538 57 50 45 15 0 86678 40 12 50 15 0 85709 44 21 35 10 0 34662 25 18 7 6 0 150580 77 27 71 22 0 99611 35 41 67 21 0 19349 11 13 0 1 1 99373 63 12 62 18 0 86230 44 21 54 17 0 30837 19 8 4 4 0 31706 13 26 25 10 0 89806 42 27 40 16 1 62088 38 13 38 16 0 40151 29 16 19 9 0 27634 20 2 17 16 0 76990 27 42 67 17 0 37460 20 5 14 7 0 54157 19 37 30 15 0 49862 37 17 54 14 0 84337 26 38 35 14 0 64175 42 37 59 18 0 59382 49 29 24 12 0 119308 30 32 58 16 0 76702 49 35 42 21 1 103425 67 17 46 19 0 70344 28 20 61 16 0 43410 19 7 3 1 1 104838 49 46 52 16 0 62215 27 24 25 10 6 69304 30 40 40 19 3 53117 22 3 32 12 1 19764 12 10 4 2 2 86680 31 37 49 14 0 84105 20 17 63 17 0 77945 20 28 67 19 0 89113 39 19 32 14 3 91005 29 29 23 11 1 40248 16 8 7 4 0 64187 27 10 54 16 0 50857 21 15 37 20 1 56613 19 15 35 12 0 62792 35 28 51 15 0 72535 14 17 39 16
Names of X columns:
shared_compendiums time_in_rfc logins blogged_computations feedback_messages_p120 compendiums_reviewed
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') }
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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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