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Type 'q()' to quit R. > x <- array(list(124.9 + ,11554.5 + ,132 + ,13182.1 + ,151.4 + ,14800.1 + ,108.9 + ,12150.7 + ,121.3 + ,14478.2 + ,123.4 + ,13253.9 + ,90.3 + ,12036.8 + ,79.3 + ,12653.2 + ,117.2 + ,14035.4 + ,116.9 + ,14571.4 + ,120.8 + ,15400.9 + ,96.1 + ,14283.2 + ,100.8 + ,14485.3 + ,105.3 + ,14196.3 + ,116.1 + ,15559.1 + ,112.8 + ,13767.4 + ,114.5 + ,14634 + ,117.2 + ,14381.1 + ,77.1 + ,12509.9 + ,80.1 + ,12122.3 + ,120.3 + ,13122.3 + ,133.4 + ,13908.7 + ,109.4 + ,13456.5 + ,93.2 + ,12441.6 + ,91.2 + ,12953 + ,99.2 + ,13057.2 + ,108.2 + ,14350.1 + ,101.5 + ,13830.2 + ,106.9 + ,13755.5 + ,104.4 + ,13574.4 + ,77.9 + ,12802.6 + ,60 + ,11737.3 + ,99.5 + ,13850.2 + ,95 + ,15081.8 + ,105.6 + ,13653.3 + ,102.5 + ,14019.1 + ,93.3 + ,13962 + ,97.3 + ,13768.7 + ,127 + ,14747.1 + ,111.7 + ,13858.1 + ,96.4 + ,13188 + ,133 + ,13693.1 + ,72.2 + ,12970 + ,95.8 + ,11392.8 + ,124.1 + ,13985.2 + ,127.6 + ,14994.7 + ,110.7 + ,13584.7 + ,104.6 + ,14257.8 + ,112.7 + ,13553.4 + ,115.3 + ,14007.3 + ,139.4 + ,16535.8 + ,119 + ,14721.4 + ,97.4 + ,13664.6 + ,154 + ,16405.9 + ,81.5 + ,13829.4 + ,88.8 + ,13735.6 + ,127.7 + ,15870.5 + ,105.1 + ,15962.4 + ,114.9 + ,15744.1 + ,106.4 + ,16083.7 + ,104.5 + ,14863.9 + ,121.6 + ,15533.1 + ,141.4 + ,17473.1 + ,99 + ,15925.5 + ,126.7 + ,15573.7 + ,134.1 + ,17495 + ,81.3 + ,14155.8 + ,88.6 + ,14913.9 + ,132.7 + ,17250.4 + ,132.9 + ,15879.8 + ,134.4 + ,17647.8 + ,103.7 + ,17749.9) + ,dim=c(2 + ,72) + ,dimnames=list(c('transport' + ,'Invoer') + ,1:72)) > y <- array(NA,dim=c(2,72),dimnames=list(c('transport','Invoer'),1:72)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > 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 transport Invoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 124.9 11554.5 1 0 0 0 0 0 0 0 0 0 0 2 132.0 13182.1 0 1 0 0 0 0 0 0 0 0 0 3 151.4 14800.1 0 0 1 0 0 0 0 0 0 0 0 4 108.9 12150.7 0 0 0 1 0 0 0 0 0 0 0 5 121.3 14478.2 0 0 0 0 1 0 0 0 0 0 0 6 123.4 13253.9 0 0 0 0 0 1 0 0 0 0 0 7 90.3 12036.8 0 0 0 0 0 0 1 0 0 0 0 8 79.3 12653.2 0 0 0 0 0 0 0 1 0 0 0 9 117.2 14035.4 0 0 0 0 0 0 0 0 1 0 0 10 116.9 14571.4 0 0 0 0 0 0 0 0 0 1 0 11 120.8 15400.9 0 0 0 0 0 0 0 0 0 0 1 12 96.1 14283.2 0 0 0 0 0 0 0 0 0 0 0 13 100.8 14485.3 1 0 0 0 0 0 0 0 0 0 0 14 105.3 14196.3 0 1 0 0 0 0 0 0 0 0 0 15 116.1 15559.1 0 0 1 0 0 0 0 0 0 0 0 16 112.8 13767.4 0 0 0 1 0 0 0 0 0 0 0 17 114.5 14634.0 0 0 0 0 1 0 0 0 0 0 0 18 117.2 14381.1 0 0 0 0 0 1 0 0 0 0 0 19 77.1 12509.9 0 0 0 0 0 0 1 0 0 0 0 20 80.1 12122.3 0 0 0 0 0 0 0 1 0 0 0 21 120.3 13122.3 0 0 0 0 0 0 0 0 1 0 0 22 133.4 13908.7 0 0 0 0 0 0 0 0 0 1 0 23 109.4 13456.5 0 0 0 0 0 0 0 0 0 0 1 24 93.2 12441.6 0 0 0 0 0 0 0 0 0 0 0 25 91.2 12953.0 1 0 0 0 0 0 0 0 0 0 0 26 99.2 13057.2 0 1 0 0 0 0 0 0 0 0 0 27 108.2 14350.1 0 0 1 0 0 0 0 0 0 0 0 28 101.5 13830.2 0 0 0 1 0 0 0 0 0 0 0 29 106.9 13755.5 0 0 0 0 1 0 0 0 0 0 0 30 104.4 13574.4 0 0 0 0 0 1 0 0 0 0 0 31 77.9 12802.6 0 0 0 0 0 0 1 0 0 0 0 32 60.0 11737.3 0 0 0 0 0 0 0 1 0 0 0 33 99.5 13850.2 0 0 0 0 0 0 0 0 1 0 0 34 95.0 15081.8 0 0 0 0 0 0 0 0 0 1 0 35 105.6 13653.3 0 0 0 0 0 0 0 0 0 0 1 36 102.5 14019.1 0 0 0 0 0 0 0 0 0 0 0 37 93.3 13962.0 1 0 0 0 0 0 0 0 0 0 0 38 97.3 13768.7 0 1 0 0 0 0 0 0 0 0 0 39 127.0 14747.1 0 0 1 0 0 0 0 0 0 0 0 40 111.7 13858.1 0 0 0 1 0 0 0 0 0 0 0 41 96.4 13188.0 0 0 0 0 1 0 0 0 0 0 0 42 133.0 13693.1 0 0 0 0 0 1 0 0 0 0 0 43 72.2 12970.0 0 0 0 0 0 0 1 0 0 0 0 44 95.8 11392.8 0 0 0 0 0 0 0 1 0 0 0 45 124.1 13985.2 0 0 0 0 0 0 0 0 1 0 0 46 127.6 14994.7 0 0 0 0 0 0 0 0 0 1 0 47 110.7 13584.7 0 0 0 0 0 0 0 0 0 0 1 48 104.6 14257.8 0 0 0 0 0 0 0 0 0 0 0 49 112.7 13553.4 1 0 0 0 0 0 0 0 0 0 0 50 115.3 14007.3 0 1 0 0 0 0 0 0 0 0 0 51 139.4 16535.8 0 0 1 0 0 0 0 0 0 0 0 52 119.0 14721.4 0 0 0 1 0 0 0 0 0 0 0 53 97.4 13664.6 0 0 0 0 1 0 0 0 0 0 0 54 154.0 16405.9 0 0 0 0 0 1 0 0 0 0 0 55 81.5 13829.4 0 0 0 0 0 0 1 0 0 0 0 56 88.8 13735.6 0 0 0 0 0 0 0 1 0 0 0 57 127.7 15870.5 0 0 0 0 0 0 0 0 1 0 0 58 105.1 15962.4 0 0 0 0 0 0 0 0 0 1 0 59 114.9 15744.1 0 0 0 0 0 0 0 0 0 0 1 60 106.4 16083.7 0 0 0 0 0 0 0 0 0 0 0 61 104.5 14863.9 1 0 0 0 0 0 0 0 0 0 0 62 121.6 15533.1 0 1 0 0 0 0 0 0 0 0 0 63 141.4 17473.1 0 0 1 0 0 0 0 0 0 0 0 64 99.0 15925.5 0 0 0 1 0 0 0 0 0 0 0 65 126.7 15573.7 0 0 0 0 1 0 0 0 0 0 0 66 134.1 17495.0 0 0 0 0 0 1 0 0 0 0 0 67 81.3 14155.8 0 0 0 0 0 0 1 0 0 0 0 68 88.6 14913.9 0 0 0 0 0 0 0 1 0 0 0 69 132.7 17250.4 0 0 0 0 0 0 0 0 1 0 0 70 132.9 15879.8 0 0 0 0 0 0 0 0 0 1 0 71 134.4 17647.8 0 0 0 0 0 0 0 0 0 0 1 72 103.7 17749.9 0 0 0 0 0 0 0 0 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Invoer M1 M2 M3 M4 54.998510 0.003113 7.354995 13.340835 27.098109 10.110324 M5 M6 M7 M8 M9 M10 11.287110 26.616549 -15.570302 -12.612771 19.540853 16.588908 M11 14.545097 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -23.5311 -6.6445 -0.3655 5.3755 26.5819 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 54.998510 17.758212 3.097 0.002990 ** Invoer 0.003113 0.001155 2.695 0.009156 ** M1 7.354995 6.922864 1.062 0.292374 M2 13.340835 6.842687 1.950 0.055976 . M3 27.098109 6.830554 3.967 0.000200 *** M4 10.110324 6.829354 1.480 0.144081 M5 11.287110 6.806381 1.658 0.102562 M6 26.616549 6.772161 3.930 0.000225 *** M7 -15.570302 7.069044 -2.203 0.031542 * M8 -12.612771 7.172870 -1.758 0.083866 . M9 19.540853 6.773582 2.885 0.005460 ** M10 16.588908 6.778843 2.447 0.017395 * M11 14.545097 6.773321 2.147 0.035880 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 11.73 on 59 degrees of freedom Multiple R-squared: 0.6818, Adjusted R-squared: 0.6171 F-statistic: 10.54 on 12 and 59 DF, p-value: 1.020e-10 > 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 + } [,1] [,2] [,3] [1,] 0.8737519 0.2524962 0.12624809 [2,] 0.7832286 0.4335428 0.21677139 [3,] 0.6786228 0.6427544 0.32137721 [4,] 0.5841885 0.8316229 0.41581146 [5,] 0.4660140 0.9320281 0.53398597 [6,] 0.3681592 0.7363184 0.63184081 [7,] 0.3786488 0.7572977 0.62135117 [8,] 0.5203749 0.9592501 0.47962507 [9,] 0.4781174 0.9562348 0.52188260 [10,] 0.5636580 0.8726839 0.43634197 [11,] 0.6285440 0.7429119 0.37145596 [12,] 0.7779196 0.4441608 0.22208040 [13,] 0.7159580 0.5680840 0.28404198 [14,] 0.6753387 0.6493227 0.32466134 [15,] 0.7530164 0.4939672 0.24698358 [16,] 0.6851090 0.6297820 0.31489098 [17,] 0.7942564 0.4114872 0.20574360 [18,] 0.8594528 0.2810944 0.14054719 [19,] 0.9496551 0.1006897 0.05034486 [20,] 0.9389546 0.1220908 0.06104538 [21,] 0.9153597 0.1692806 0.08464031 [22,] 0.9115538 0.1768924 0.08844619 [23,] 0.9305084 0.1389831 0.06949156 [24,] 0.9066722 0.1866555 0.09332777 [25,] 0.8733359 0.2533281 0.12666407 [26,] 0.8879102 0.2241795 0.11208976 [27,] 0.8699622 0.2600756 0.13003780 [28,] 0.8345616 0.3308767 0.16543836 [29,] 0.8426760 0.3146480 0.15732400 [30,] 0.7927726 0.4144549 0.20722743 [31,] 0.7677030 0.4645941 0.23229703 [32,] 0.7043697 0.5912606 0.29563028 [33,] 0.6274985 0.7450030 0.37250151 [34,] 0.5760687 0.8478626 0.42393128 [35,] 0.4768441 0.9536881 0.52315594 [36,] 0.3935977 0.7871954 0.60640230 [37,] 0.4655652 0.9311304 0.53443478 [38,] 0.5405198 0.9189603 0.45948015 [39,] 0.6841147 0.6317706 0.31588530 [40,] 0.5356492 0.9287016 0.46435082 [41,] 0.3785806 0.7571612 0.62141940 > postscript(file="/var/www/html/rcomp/tmp/1umfm1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2iqfp1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3li861229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4v3w71229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/58oi51229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 72 Frequency = 1 1 2 3 4 5 6 26.58193394 22.63002271 23.23655918 5.97087210 9.94950486 0.53082459 7 8 9 10 11 12 13.40602291 -2.47011605 -1.02597860 -0.04238778 3.31951930 -3.35642809 13 14 15 16 17 18 -6.64048032 -7.22677835 -14.42590582 4.83872827 2.66456145 -9.17770050 19 20 21 22 23 24 -1.26654915 -0.01763559 4.91613838 18.52033363 -2.02833716 -0.52426004 25 26 27 28 29 30 -11.47104011 -9.78121329 -18.56276988 -6.65674314 -2.20101760 -19.46676438 31 32 33 34 35 36 -1.37760779 -18.91928378 -18.14952469 -23.53105990 -6.44089726 3.86561013 37 38 39 40 41 42 -12.51165563 -13.89582969 -0.99847291 3.45641526 -10.93461590 8.76376975 43 44 45 46 47 48 -7.59865738 17.95300764 6.03027402 9.34004774 -1.12737275 5.22263201 49 50 51 52 53 54 8.16015359 3.36150345 5.83401570 8.06930586 -11.41808206 21.31990275 55 56 57 58 59 60 -0.97362763 3.66080342 3.76208527 -16.17201732 -3.64872575 1.33933182 61 62 63 64 65 66 -4.11891148 4.91229515 4.91657374 -15.67857834 11.93964925 -1.97003221 67 68 69 70 71 72 -2.18958096 -0.20677564 4.46700562 11.88508362 9.92581362 -6.54688582 > postscript(file="/var/www/html/rcomp/tmp/6ssq41229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 72 Frequency = 1 lag(myerror, k = 1) myerror 0 26.58193394 NA 1 22.63002271 26.58193394 2 23.23655918 22.63002271 3 5.97087210 23.23655918 4 9.94950486 5.97087210 5 0.53082459 9.94950486 6 13.40602291 0.53082459 7 -2.47011605 13.40602291 8 -1.02597860 -2.47011605 9 -0.04238778 -1.02597860 10 3.31951930 -0.04238778 11 -3.35642809 3.31951930 12 -6.64048032 -3.35642809 13 -7.22677835 -6.64048032 14 -14.42590582 -7.22677835 15 4.83872827 -14.42590582 16 2.66456145 4.83872827 17 -9.17770050 2.66456145 18 -1.26654915 -9.17770050 19 -0.01763559 -1.26654915 20 4.91613838 -0.01763559 21 18.52033363 4.91613838 22 -2.02833716 18.52033363 23 -0.52426004 -2.02833716 24 -11.47104011 -0.52426004 25 -9.78121329 -11.47104011 26 -18.56276988 -9.78121329 27 -6.65674314 -18.56276988 28 -2.20101760 -6.65674314 29 -19.46676438 -2.20101760 30 -1.37760779 -19.46676438 31 -18.91928378 -1.37760779 32 -18.14952469 -18.91928378 33 -23.53105990 -18.14952469 34 -6.44089726 -23.53105990 35 3.86561013 -6.44089726 36 -12.51165563 3.86561013 37 -13.89582969 -12.51165563 38 -0.99847291 -13.89582969 39 3.45641526 -0.99847291 40 -10.93461590 3.45641526 41 8.76376975 -10.93461590 42 -7.59865738 8.76376975 43 17.95300764 -7.59865738 44 6.03027402 17.95300764 45 9.34004774 6.03027402 46 -1.12737275 9.34004774 47 5.22263201 -1.12737275 48 8.16015359 5.22263201 49 3.36150345 8.16015359 50 5.83401570 3.36150345 51 8.06930586 5.83401570 52 -11.41808206 8.06930586 53 21.31990275 -11.41808206 54 -0.97362763 21.31990275 55 3.66080342 -0.97362763 56 3.76208527 3.66080342 57 -16.17201732 3.76208527 58 -3.64872575 -16.17201732 59 1.33933182 -3.64872575 60 -4.11891148 1.33933182 61 4.91229515 -4.11891148 62 4.91657374 4.91229515 63 -15.67857834 4.91657374 64 11.93964925 -15.67857834 65 -1.97003221 11.93964925 66 -2.18958096 -1.97003221 67 -0.20677564 -2.18958096 68 4.46700562 -0.20677564 69 11.88508362 4.46700562 70 9.92581362 11.88508362 71 -6.54688582 9.92581362 72 NA -6.54688582 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 22.63002271 26.58193394 [2,] 23.23655918 22.63002271 [3,] 5.97087210 23.23655918 [4,] 9.94950486 5.97087210 [5,] 0.53082459 9.94950486 [6,] 13.40602291 0.53082459 [7,] -2.47011605 13.40602291 [8,] -1.02597860 -2.47011605 [9,] -0.04238778 -1.02597860 [10,] 3.31951930 -0.04238778 [11,] -3.35642809 3.31951930 [12,] -6.64048032 -3.35642809 [13,] -7.22677835 -6.64048032 [14,] -14.42590582 -7.22677835 [15,] 4.83872827 -14.42590582 [16,] 2.66456145 4.83872827 [17,] -9.17770050 2.66456145 [18,] -1.26654915 -9.17770050 [19,] -0.01763559 -1.26654915 [20,] 4.91613838 -0.01763559 [21,] 18.52033363 4.91613838 [22,] -2.02833716 18.52033363 [23,] -0.52426004 -2.02833716 [24,] -11.47104011 -0.52426004 [25,] -9.78121329 -11.47104011 [26,] -18.56276988 -9.78121329 [27,] -6.65674314 -18.56276988 [28,] -2.20101760 -6.65674314 [29,] -19.46676438 -2.20101760 [30,] -1.37760779 -19.46676438 [31,] -18.91928378 -1.37760779 [32,] -18.14952469 -18.91928378 [33,] -23.53105990 -18.14952469 [34,] -6.44089726 -23.53105990 [35,] 3.86561013 -6.44089726 [36,] -12.51165563 3.86561013 [37,] -13.89582969 -12.51165563 [38,] -0.99847291 -13.89582969 [39,] 3.45641526 -0.99847291 [40,] -10.93461590 3.45641526 [41,] 8.76376975 -10.93461590 [42,] -7.59865738 8.76376975 [43,] 17.95300764 -7.59865738 [44,] 6.03027402 17.95300764 [45,] 9.34004774 6.03027402 [46,] -1.12737275 9.34004774 [47,] 5.22263201 -1.12737275 [48,] 8.16015359 5.22263201 [49,] 3.36150345 8.16015359 [50,] 5.83401570 3.36150345 [51,] 8.06930586 5.83401570 [52,] -11.41808206 8.06930586 [53,] 21.31990275 -11.41808206 [54,] -0.97362763 21.31990275 [55,] 3.66080342 -0.97362763 [56,] 3.76208527 3.66080342 [57,] -16.17201732 3.76208527 [58,] -3.64872575 -16.17201732 [59,] 1.33933182 -3.64872575 [60,] -4.11891148 1.33933182 [61,] 4.91229515 -4.11891148 [62,] 4.91657374 4.91229515 [63,] -15.67857834 4.91657374 [64,] 11.93964925 -15.67857834 [65,] -1.97003221 11.93964925 [66,] -2.18958096 -1.97003221 [67,] -0.20677564 -2.18958096 [68,] 4.46700562 -0.20677564 [69,] 11.88508362 4.46700562 [70,] 9.92581362 11.88508362 [71,] -6.54688582 9.92581362 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 22.63002271 26.58193394 2 23.23655918 22.63002271 3 5.97087210 23.23655918 4 9.94950486 5.97087210 5 0.53082459 9.94950486 6 13.40602291 0.53082459 7 -2.47011605 13.40602291 8 -1.02597860 -2.47011605 9 -0.04238778 -1.02597860 10 3.31951930 -0.04238778 11 -3.35642809 3.31951930 12 -6.64048032 -3.35642809 13 -7.22677835 -6.64048032 14 -14.42590582 -7.22677835 15 4.83872827 -14.42590582 16 2.66456145 4.83872827 17 -9.17770050 2.66456145 18 -1.26654915 -9.17770050 19 -0.01763559 -1.26654915 20 4.91613838 -0.01763559 21 18.52033363 4.91613838 22 -2.02833716 18.52033363 23 -0.52426004 -2.02833716 24 -11.47104011 -0.52426004 25 -9.78121329 -11.47104011 26 -18.56276988 -9.78121329 27 -6.65674314 -18.56276988 28 -2.20101760 -6.65674314 29 -19.46676438 -2.20101760 30 -1.37760779 -19.46676438 31 -18.91928378 -1.37760779 32 -18.14952469 -18.91928378 33 -23.53105990 -18.14952469 34 -6.44089726 -23.53105990 35 3.86561013 -6.44089726 36 -12.51165563 3.86561013 37 -13.89582969 -12.51165563 38 -0.99847291 -13.89582969 39 3.45641526 -0.99847291 40 -10.93461590 3.45641526 41 8.76376975 -10.93461590 42 -7.59865738 8.76376975 43 17.95300764 -7.59865738 44 6.03027402 17.95300764 45 9.34004774 6.03027402 46 -1.12737275 9.34004774 47 5.22263201 -1.12737275 48 8.16015359 5.22263201 49 3.36150345 8.16015359 50 5.83401570 3.36150345 51 8.06930586 5.83401570 52 -11.41808206 8.06930586 53 21.31990275 -11.41808206 54 -0.97362763 21.31990275 55 3.66080342 -0.97362763 56 3.76208527 3.66080342 57 -16.17201732 3.76208527 58 -3.64872575 -16.17201732 59 1.33933182 -3.64872575 60 -4.11891148 1.33933182 61 4.91229515 -4.11891148 62 4.91657374 4.91229515 63 -15.67857834 4.91657374 64 11.93964925 -15.67857834 65 -1.97003221 11.93964925 66 -2.18958096 -1.97003221 67 -0.20677564 -2.18958096 68 4.46700562 -0.20677564 69 11.88508362 4.46700562 70 9.92581362 11.88508362 71 -6.54688582 9.92581362 > 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() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7rpgv1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8ksyb1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9odki1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/104t1w1229762914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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="/var/www/html/rcomp/tmp/11bcps1229762914.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
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="/var/www/html/rcomp/tmp/12ruml1229762914.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="/var/www/html/rcomp/tmp/13k9f81229762914.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
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
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="/var/www/html/rcomp/tmp/14ea1o1229762914.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="/var/www/html/rcomp/tmp/15lnzc1229762914.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="/var/www/html/rcomp/tmp/1648oa1229762914.tab") + } > > system("convert tmp/1umfm1229762914.ps tmp/1umfm1229762914.png") > system("convert tmp/2iqfp1229762914.ps tmp/2iqfp1229762914.png") > system("convert tmp/3li861229762914.ps tmp/3li861229762914.png") > system("convert tmp/4v3w71229762914.ps tmp/4v3w71229762914.png") > system("convert tmp/58oi51229762914.ps tmp/58oi51229762914.png") > system("convert tmp/6ssq41229762914.ps tmp/6ssq41229762914.png") > system("convert tmp/7rpgv1229762914.ps tmp/7rpgv1229762914.png") > system("convert tmp/8ksyb1229762914.ps tmp/8ksyb1229762914.png") > system("convert tmp/9odki1229762914.ps tmp/9odki1229762914.png") > system("convert tmp/104t1w1229762914.ps tmp/104t1w1229762914.png") > > > proc.time() user system elapsed 2.478 1.588 3.912