Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
36 27 71 8.1 3.34 11.4 81.5 3243 8.8 42.6 11.7 21 15 59 59 921 35 23 72 11.1 3.14 11.0 78.8 4281 3.6 50.7 14.4 8 10 39 57 997 44 29 74 10.4 3.21 9.8 81.6 4260 0.8 39.4 12.4 6 6 33 54 962 47 45 79 6.5 3.41 11.1 77.5 3125 27.1 50.2 20.6 18 8 24 56 982 43 35 77 7.6 3.44 9.6 84.6 6441 24.4 43.7 14.3 43 38 206 55 107 53 45 80 7.7 3.45 10.2 66.8 3325 38.5 43.1 25.5 30 32 72 54 103 43 30 74 10.9 3.23 12.1 83.9 4679 3.5 49.2 11.3 21 32 62 56 934 45 30 73 9.3 3.29 10.6 86.0 2140 5.3 40.4 10.5 6 4 4 56 899 36 24 70 9.0 3.31 10.5 83.2 6582 8.1 42.5 12.6 18 12 37 61 100 36 27 72 9.5 3.36 10.7 79.3 4213 6.7 41.0 13.2 12 7 20 59 912 52 42 79 7.7 3.39 9.6 69.2 2302 22.2 41.3 24.2 18 8 27 56 101 33 26 76 8.6 3.20 10.9 83.4 6122 16.3 44.9 10.7 88 63 278 58 102 40 34 77 9.2 3.21 10.2 77.0 4101 13.0 45.7 15.1 26 26 146 57 970 35 28 71 8.8 3.29 11.1 86.3 3042 14.7 44.6 11.4 31 21 64 60 985 37 31 75 8.0 3.26 11.9 78.4 4259 13.1 49.6 13.9 23 9 15 58 958 35 46 85 7.1 3.22 11.8 79.9 1441 14.8 51.2 16.1 1 1 1 54 860 36 30 75 7.5 3.35 11.4 81.9 4029 12.4 44.0 12.0 6 4 16 58 936 15 30 73 8.2 3.15 12.2 84.2 4824 4.7 53.1 12.7 17 8 28 38 871 31 27 74 7.2 3.44 10.8 87.0 4834 15.8 43.5 13.6 52 35 124 59 959 30 24 72 6.5 3.53 10.8 79.5 3694 13.1 33.8 12.4 11 4 11 61 941 31 45 85 7.3 3.22 11.4 80.7 1844 11.5 48.1 18.5 1 1 1 53 891 31 24 72 9.0 3.37 10.9 82.8 3226 5.1 45.2 12.3 5 3 10 61 871 42 40 77 6.1 3.45 10.4 71.8 2269 22.7 41.4 19.5 8 3 5 53 971 43 27 72 9.0 3.25 11.5 87.1 2909 7.2 51.6 9.5 7 3 10 56 887 46 55 84 5.6 3.35 11.4 79.7 2647 21.0 46.9 17.9 6 5 1 59 952 39 29 76 8.7 3.23 11.4 78.6 4412 15.6 46.6 13.2 13 7 33 60 968 35 31 81 9.2 3.10 12.0 78.3 3262 12.6 48.6 13.9 7 4 4 55 919 43 32 74 10.1 3.38 9.5 79.2 3214 2.9 43.7 12.0 11 7 32 54 844 11 53 68 9.2 2.99 12.1 90.6 4700 7.8 48.9 12.3 648 319 130 47 861 30 35 71 8.3 3.37 9.9 77.4 4474 13.1 42.6 17.7 38 37 193 57 989 50 42 82 7.3 3.49 10.4 72.5 3497 36.7 43.3 26.4 15 10 34 59 100 60 67 82 10.0 2.98 11.5 88.6 4657 13.6 47.3 22.4 3 1 1 60 861 30 20 69 8.8 3.26 11.1 85.4 2934 5.8 44.0 9.4 33 23 125 64 929 25 12 73 9.2 3.28 12.1 83.1 2095 2.0 51.9 9.8 20 11 26 50 857 45 40 80 8.3 3.32 10.1 70.3 2682 21.0 46.1 24.1 17 14 78 56 961 46 30 72 10.2 3.16 11.3 83.2 3327 8.8 45.3 12.2 4 3 8 58 923 54 54 81 7.4 3.36 9.7 72.8 3172 31.4 45.5 24.2 20 17 1 62 111 42 33 77 9.7 3.03 10.7 83.5 7462 11.3 48.7 12.4 41 26 108 58 994 42 32 76 9.1 3.32 10.5 87.5 6092 17.5 45.3 13.2 29 32 161 54 101 36 29 72 9.5 3.32 10.6 77.6 3437 8.1 45.5 13.8 45 59 263 56 991 37 38 67 11.3 2.99 12.0 81.5 3387 3.6 50.3 13.5 56 21 44 73 893 42 29 72 10.7 3.19 10.1 79.5 3508 2.2 38.3 15.7 6 4 18 56 938 41 33 77 11.2 3.08 9.6 79.9 4843 2.7 38.6 14.1 11 11 89 54 946 44 39 78 8.2 3.32 11.0 79.9 3768 28.6 49.5 17.5 12 9 48 53 102 32 25 72 10.9 3.21 11.1 82.5 4355 5.0 46.4 10.8 7 4 18 60 874 34 32 79 9.3 3.23 9.7 76.8 5160 17.2 45.1 15.3 31 15 68 57 953 10 55 70 7.3 3.11 12.1 88.9 3033 5.9 51.0 14.0 144 66 20 61 839 18 48 63 9.2 2.92 12.2 87.7 4253 13.7 51.2 12.0 311 171 86 71 911 13 49 68 7.0 3.36 12.2 90.7 2702 3.0 51.9 9.7 105 32 3 71 790 35 40 64 9.6 3.02 12.2 82.5 3626 5.7 54.3 10.1 20 7 20 72 899 45 28 74 10.6 3.21 11.1 82.6 1883 3.4 41.9 12.3 5 4 20 56 904 38 24 72 9.8 3.34 11.4 78.0 4923 3.8 50.5 11.1 8 5 25 61 950 31 26 73 9.3 3.22 10.7 81.3 3249 9.5 43.9 13.6 11 7 25 59 972 40 23 71 11.3 3.28 10.3 73.8 1671 2.5 47.4 13.5 5 2 11 60 912 41 37 78 6.2 3.25 12.3 89.5 5308 25.9 59.7 10.3 65 28 102 52 967 28 32 81 7.0 3.27 12.1 81.0 3665 7.5 51.6 13.2 4 2 1 54 823 45 33 76 7.7 3.39 11.3 82.2 3152 12.1 47.3 10.9 14 11 42 56 100 45 24 70 11.8 3.25 11.1 79.8 3678 1.0 44.8 14.0 7 3 8 56 895 42 83 76 9.7 3.22 9.0 76.2 9699 4.8 42.2 14.5 8 8 49 54 911 38 28 72 8.9 3.48 10.7 79.8 3451 11.7 37.5 13.0 14 13 39 58 954
Names of X columns:
Gem_jaarlijkse_neerslag Gem_temp_januari Gem_temp_juli Omvang_bevolking_>65jaar #leden_per_huishouden #jaren_onderwijs_personen>22j huishoudens_met_volledig_uitgeruste_keuken bevolking_per_mijl² omvang_niet-blanke_bevolking #kantoormedewerkers #gezinnen_inkomen<$3000 index_olievervuiling index_stikstofoxidevervuiling index_zwaveldioxidevervuiling luchtvochtigheidgraad sterftecijfer
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