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