R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(98.60,627,98.97,696,99.11,825,99.64,677,100.03,656,99.98,785,100.32,412,100.44,352,100.51,839,101.00,729,100.88,696,100.55,641,100.83,695,101.51,638,102.16,762,102.39,635,102.54,721,102.85,854,103.47,418,103.57,367,103.69,824,103.50,687,103.47,601,103.45,676,103.48,740,103.93,691,103.89,683,104.40,594,104.79,729,104.77,731,105.13,386,105.26,331,104.96,707,104.75,715,105.01,657,105.15,653,105.20,642,105.77,643,105.78,718,106.26,654,106.13,632,106.12,731,106.57,392,106.44,344,106.54,792,107.10,852,108.10,649,108.40,629,108.84,685,109.62,617,110.42,715,110.67,715,111.66,629,112.28,916,112.87,531,112.18,357,112.36,917,112.16,828,111.49,708,111.25,858,111.36,775,111.74,785,111.10,1006,111.33,789,111.25,734,111.04,906,110.97,532,111.31,387,111.02,991,111.07,841,111.36,892,111.54,782),dim=c(2,72),dimnames=list(c('CPI','Faillissementen'),1:72)) > y <- array(NA,dim=c(2,72),dimnames=list(c('CPI','Faillissementen'),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 = '2' > #'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 Faillissementen CPI M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 627 98.60 1 0 0 0 0 0 0 0 0 0 0 2 696 98.97 0 1 0 0 0 0 0 0 0 0 0 3 825 99.11 0 0 1 0 0 0 0 0 0 0 0 4 677 99.64 0 0 0 1 0 0 0 0 0 0 0 5 656 100.03 0 0 0 0 1 0 0 0 0 0 0 6 785 99.98 0 0 0 0 0 1 0 0 0 0 0 7 412 100.32 0 0 0 0 0 0 1 0 0 0 0 8 352 100.44 0 0 0 0 0 0 0 1 0 0 0 9 839 100.51 0 0 0 0 0 0 0 0 1 0 0 10 729 101.00 0 0 0 0 0 0 0 0 0 1 0 11 696 100.88 0 0 0 0 0 0 0 0 0 0 1 12 641 100.55 0 0 0 0 0 0 0 0 0 0 0 13 695 100.83 1 0 0 0 0 0 0 0 0 0 0 14 638 101.51 0 1 0 0 0 0 0 0 0 0 0 15 762 102.16 0 0 1 0 0 0 0 0 0 0 0 16 635 102.39 0 0 0 1 0 0 0 0 0 0 0 17 721 102.54 0 0 0 0 1 0 0 0 0 0 0 18 854 102.85 0 0 0 0 0 1 0 0 0 0 0 19 418 103.47 0 0 0 0 0 0 1 0 0 0 0 20 367 103.57 0 0 0 0 0 0 0 1 0 0 0 21 824 103.69 0 0 0 0 0 0 0 0 1 0 0 22 687 103.50 0 0 0 0 0 0 0 0 0 1 0 23 601 103.47 0 0 0 0 0 0 0 0 0 0 1 24 676 103.45 0 0 0 0 0 0 0 0 0 0 0 25 740 103.48 1 0 0 0 0 0 0 0 0 0 0 26 691 103.93 0 1 0 0 0 0 0 0 0 0 0 27 683 103.89 0 0 1 0 0 0 0 0 0 0 0 28 594 104.40 0 0 0 1 0 0 0 0 0 0 0 29 729 104.79 0 0 0 0 1 0 0 0 0 0 0 30 731 104.77 0 0 0 0 0 1 0 0 0 0 0 31 386 105.13 0 0 0 0 0 0 1 0 0 0 0 32 331 105.26 0 0 0 0 0 0 0 1 0 0 0 33 707 104.96 0 0 0 0 0 0 0 0 1 0 0 34 715 104.75 0 0 0 0 0 0 0 0 0 1 0 35 657 105.01 0 0 0 0 0 0 0 0 0 0 1 36 653 105.15 0 0 0 0 0 0 0 0 0 0 0 37 642 105.20 1 0 0 0 0 0 0 0 0 0 0 38 643 105.77 0 1 0 0 0 0 0 0 0 0 0 39 718 105.78 0 0 1 0 0 0 0 0 0 0 0 40 654 106.26 0 0 0 1 0 0 0 0 0 0 0 41 632 106.13 0 0 0 0 1 0 0 0 0 0 0 42 731 106.12 0 0 0 0 0 1 0 0 0 0 0 43 392 106.57 0 0 0 0 0 0 1 0 0 0 0 44 344 106.44 0 0 0 0 0 0 0 1 0 0 0 45 792 106.54 0 0 0 0 0 0 0 0 1 0 0 46 852 107.10 0 0 0 0 0 0 0 0 0 1 0 47 649 108.10 0 0 0 0 0 0 0 0 0 0 1 48 629 108.40 0 0 0 0 0 0 0 0 0 0 0 49 685 108.84 1 0 0 0 0 0 0 0 0 0 0 50 617 109.62 0 1 0 0 0 0 0 0 0 0 0 51 715 110.42 0 0 1 0 0 0 0 0 0 0 0 52 715 110.67 0 0 0 1 0 0 0 0 0 0 0 53 629 111.66 0 0 0 0 1 0 0 0 0 0 0 54 916 112.28 0 0 0 0 0 1 0 0 0 0 0 55 531 112.87 0 0 0 0 0 0 1 0 0 0 0 56 357 112.18 0 0 0 0 0 0 0 1 0 0 0 57 917 112.36 0 0 0 0 0 0 0 0 1 0 0 58 828 112.16 0 0 0 0 0 0 0 0 0 1 0 59 708 111.49 0 0 0 0 0 0 0 0 0 0 1 60 858 111.25 0 0 0 0 0 0 0 0 0 0 0 61 775 111.36 1 0 0 0 0 0 0 0 0 0 0 62 785 111.74 0 1 0 0 0 0 0 0 0 0 0 63 1006 111.10 0 0 1 0 0 0 0 0 0 0 0 64 789 111.33 0 0 0 1 0 0 0 0 0 0 0 65 734 111.25 0 0 0 0 1 0 0 0 0 0 0 66 906 111.04 0 0 0 0 0 1 0 0 0 0 0 67 532 110.97 0 0 0 0 0 0 1 0 0 0 0 68 387 111.31 0 0 0 0 0 0 0 1 0 0 0 69 991 111.02 0 0 0 0 0 0 0 0 1 0 0 70 841 111.07 0 0 0 0 0 0 0 0 0 1 0 71 892 111.36 0 0 0 0 0 0 0 0 0 0 1 72 782 111.54 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) CPI M1 M2 M3 M4 -239.726 8.866 5.277 -15.163 89.978 -20.818 M5 M6 M7 M8 M9 M10 -17.178 118.876 -259.841 -348.482 140.362 69.956 M11 -5.956 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -124.228 -47.249 1.737 43.570 170.718 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -239.726 204.543 -1.172 0.245905 CPI 8.866 1.899 4.669 1.79e-05 *** M1 5.277 39.248 0.134 0.893509 M2 -15.163 39.162 -0.387 0.700012 M3 89.978 39.142 2.299 0.025080 * M4 -20.818 39.104 -0.532 0.596468 M5 -17.178 39.083 -0.440 0.661886 M6 118.876 39.077 3.042 0.003503 ** M7 -259.841 39.064 -6.652 1.05e-08 *** M8 -348.482 39.064 -8.921 1.55e-12 *** M9 140.362 39.065 3.593 0.000667 *** M10 69.956 39.064 1.791 0.078450 . M11 -5.956 39.063 -0.152 0.879341 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 67.66 on 59 degrees of freedom Multiple R-squared: 0.8447, Adjusted R-squared: 0.8132 F-statistic: 26.75 on 12 and 59 DF, p-value: < 2.2e-16 > 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,] 0.2075397017 0.4150794033 0.7924603 [2,] 0.2263973384 0.4527946769 0.7736027 [3,] 0.2139673494 0.4279346988 0.7860327 [4,] 0.1237407238 0.2474814475 0.8762593 [5,] 0.0741583412 0.1483166823 0.9258417 [6,] 0.0415461497 0.0830922994 0.9584539 [7,] 0.0262539142 0.0525078283 0.9737461 [8,] 0.0349765733 0.0699531467 0.9650234 [9,] 0.0227825469 0.0455650938 0.9772175 [10,] 0.0366859418 0.0733718835 0.9633141 [11,] 0.0263256786 0.0526513573 0.9736743 [12,] 0.0580041784 0.1160083568 0.9419958 [13,] 0.0465339520 0.0930679039 0.9534660 [14,] 0.0620678629 0.1241357258 0.9379321 [15,] 0.0647317730 0.1294635460 0.9352682 [16,] 0.0416813743 0.0833627486 0.9583186 [17,] 0.0298058274 0.0596116548 0.9701942 [18,] 0.0551863193 0.1103726386 0.9448137 [19,] 0.0359974598 0.0719949196 0.9640025 [20,] 0.0230981349 0.0461962697 0.9769019 [21,] 0.0137112819 0.0274225638 0.9862887 [22,] 0.0082383576 0.0164767152 0.9917616 [23,] 0.0049735206 0.0099470411 0.9950265 [24,] 0.0027774836 0.0055549671 0.9972225 [25,] 0.0017191893 0.0034383785 0.9982808 [26,] 0.0014283799 0.0028567597 0.9985716 [27,] 0.0009005423 0.0018010845 0.9990995 [28,] 0.0004362960 0.0008725921 0.9995637 [29,] 0.0003493493 0.0006986986 0.9996507 [30,] 0.0001700966 0.0003401933 0.9998299 [31,] 0.0045321817 0.0090643633 0.9954678 [32,] 0.0023557894 0.0047115789 0.9976442 [33,] 0.0017011250 0.0034022500 0.9982989 [34,] 0.0008767169 0.0017534338 0.9991233 [35,] 0.0010242807 0.0020485613 0.9989757 [36,] 0.0851072852 0.1702145705 0.9148927 [37,] 0.1187542154 0.2375084308 0.8812458 [38,] 0.1382221472 0.2764442944 0.8617779 [39,] 0.1638126821 0.3276253642 0.8361873 [40,] 0.1683018269 0.3366036539 0.8316982 [41,] 0.0899578356 0.1799156711 0.9100422 > postscript(file="/var/www/html/rcomp/tmp/1r4mr1291132391.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/22d4u1291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/32d4u1291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/42d4u1291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5vm3x1291132391.ps",horizontal=F,onefile=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 -12.7538941 73.4052424 96.0234578 54.1196496 26.0220364 19.4110677 7 8 9 10 11 12 22.1138243 49.6911187 47.2264978 3.2875939 47.2635819 -10.7662554 13 14 15 16 17 18 35.4745751 -7.1147972 5.9816780 -12.2622831 68.7679815 62.9651962 19 20 21 22 23 24 0.1854287 36.9400462 4.0321175 -60.8777995 -70.6997656 -1.4781117 25 26 27 28 29 30 56.9792582 24.4291021 -88.3567742 -71.0832593 56.8191275 -77.0578259 31 32 33 34 35 36 -46.5323925 -14.0437597 -124.2279023 -43.9604961 -28.3536479 -39.5505791 37 38 39 40 41 42 -56.2705324 -39.8846274 -70.1138115 -27.5743119 -52.0615233 -89.0271383 43 44 45 46 47 48 -53.2996590 -11.5058253 -53.2364309 72.2040341 -63.7500740 -92.3655905 49 50 51 52 53 54 -45.5433451 -100.0193331 -114.2527816 -5.6740657 -104.0913733 41.3573326 55 56 57 58 59 60 29.8435498 -49.3975684 20.1625335 3.3412781 -34.8063474 111.3658612 61 62 63 64 65 66 22.1139384 49.1844133 170.7182315 62.4742704 4.5437512 42.3513677 67 68 69 70 71 72 47.6892487 -11.6840115 106.0431843 26.0053895 150.3462531 32.7946755 > postscript(file="/var/www/html/rcomp/tmp/6vm3x1291132391.ps",horizontal=F,onefile=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 -12.7538941 NA 1 73.4052424 -12.7538941 2 96.0234578 73.4052424 3 54.1196496 96.0234578 4 26.0220364 54.1196496 5 19.4110677 26.0220364 6 22.1138243 19.4110677 7 49.6911187 22.1138243 8 47.2264978 49.6911187 9 3.2875939 47.2264978 10 47.2635819 3.2875939 11 -10.7662554 47.2635819 12 35.4745751 -10.7662554 13 -7.1147972 35.4745751 14 5.9816780 -7.1147972 15 -12.2622831 5.9816780 16 68.7679815 -12.2622831 17 62.9651962 68.7679815 18 0.1854287 62.9651962 19 36.9400462 0.1854287 20 4.0321175 36.9400462 21 -60.8777995 4.0321175 22 -70.6997656 -60.8777995 23 -1.4781117 -70.6997656 24 56.9792582 -1.4781117 25 24.4291021 56.9792582 26 -88.3567742 24.4291021 27 -71.0832593 -88.3567742 28 56.8191275 -71.0832593 29 -77.0578259 56.8191275 30 -46.5323925 -77.0578259 31 -14.0437597 -46.5323925 32 -124.2279023 -14.0437597 33 -43.9604961 -124.2279023 34 -28.3536479 -43.9604961 35 -39.5505791 -28.3536479 36 -56.2705324 -39.5505791 37 -39.8846274 -56.2705324 38 -70.1138115 -39.8846274 39 -27.5743119 -70.1138115 40 -52.0615233 -27.5743119 41 -89.0271383 -52.0615233 42 -53.2996590 -89.0271383 43 -11.5058253 -53.2996590 44 -53.2364309 -11.5058253 45 72.2040341 -53.2364309 46 -63.7500740 72.2040341 47 -92.3655905 -63.7500740 48 -45.5433451 -92.3655905 49 -100.0193331 -45.5433451 50 -114.2527816 -100.0193331 51 -5.6740657 -114.2527816 52 -104.0913733 -5.6740657 53 41.3573326 -104.0913733 54 29.8435498 41.3573326 55 -49.3975684 29.8435498 56 20.1625335 -49.3975684 57 3.3412781 20.1625335 58 -34.8063474 3.3412781 59 111.3658612 -34.8063474 60 22.1139384 111.3658612 61 49.1844133 22.1139384 62 170.7182315 49.1844133 63 62.4742704 170.7182315 64 4.5437512 62.4742704 65 42.3513677 4.5437512 66 47.6892487 42.3513677 67 -11.6840115 47.6892487 68 106.0431843 -11.6840115 69 26.0053895 106.0431843 70 150.3462531 26.0053895 71 32.7946755 150.3462531 72 NA 32.7946755 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 73.4052424 -12.7538941 [2,] 96.0234578 73.4052424 [3,] 54.1196496 96.0234578 [4,] 26.0220364 54.1196496 [5,] 19.4110677 26.0220364 [6,] 22.1138243 19.4110677 [7,] 49.6911187 22.1138243 [8,] 47.2264978 49.6911187 [9,] 3.2875939 47.2264978 [10,] 47.2635819 3.2875939 [11,] -10.7662554 47.2635819 [12,] 35.4745751 -10.7662554 [13,] -7.1147972 35.4745751 [14,] 5.9816780 -7.1147972 [15,] -12.2622831 5.9816780 [16,] 68.7679815 -12.2622831 [17,] 62.9651962 68.7679815 [18,] 0.1854287 62.9651962 [19,] 36.9400462 0.1854287 [20,] 4.0321175 36.9400462 [21,] -60.8777995 4.0321175 [22,] -70.6997656 -60.8777995 [23,] -1.4781117 -70.6997656 [24,] 56.9792582 -1.4781117 [25,] 24.4291021 56.9792582 [26,] -88.3567742 24.4291021 [27,] -71.0832593 -88.3567742 [28,] 56.8191275 -71.0832593 [29,] -77.0578259 56.8191275 [30,] -46.5323925 -77.0578259 [31,] -14.0437597 -46.5323925 [32,] -124.2279023 -14.0437597 [33,] -43.9604961 -124.2279023 [34,] -28.3536479 -43.9604961 [35,] -39.5505791 -28.3536479 [36,] -56.2705324 -39.5505791 [37,] -39.8846274 -56.2705324 [38,] -70.1138115 -39.8846274 [39,] -27.5743119 -70.1138115 [40,] -52.0615233 -27.5743119 [41,] -89.0271383 -52.0615233 [42,] -53.2996590 -89.0271383 [43,] -11.5058253 -53.2996590 [44,] -53.2364309 -11.5058253 [45,] 72.2040341 -53.2364309 [46,] -63.7500740 72.2040341 [47,] -92.3655905 -63.7500740 [48,] -45.5433451 -92.3655905 [49,] -100.0193331 -45.5433451 [50,] -114.2527816 -100.0193331 [51,] -5.6740657 -114.2527816 [52,] -104.0913733 -5.6740657 [53,] 41.3573326 -104.0913733 [54,] 29.8435498 41.3573326 [55,] -49.3975684 29.8435498 [56,] 20.1625335 -49.3975684 [57,] 3.3412781 20.1625335 [58,] -34.8063474 3.3412781 [59,] 111.3658612 -34.8063474 [60,] 22.1139384 111.3658612 [61,] 49.1844133 22.1139384 [62,] 170.7182315 49.1844133 [63,] 62.4742704 170.7182315 [64,] 4.5437512 62.4742704 [65,] 42.3513677 4.5437512 [66,] 47.6892487 42.3513677 [67,] -11.6840115 47.6892487 [68,] 106.0431843 -11.6840115 [69,] 26.0053895 106.0431843 [70,] 150.3462531 26.0053895 [71,] 32.7946755 150.3462531 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 73.4052424 -12.7538941 2 96.0234578 73.4052424 3 54.1196496 96.0234578 4 26.0220364 54.1196496 5 19.4110677 26.0220364 6 22.1138243 19.4110677 7 49.6911187 22.1138243 8 47.2264978 49.6911187 9 3.2875939 47.2264978 10 47.2635819 3.2875939 11 -10.7662554 47.2635819 12 35.4745751 -10.7662554 13 -7.1147972 35.4745751 14 5.9816780 -7.1147972 15 -12.2622831 5.9816780 16 68.7679815 -12.2622831 17 62.9651962 68.7679815 18 0.1854287 62.9651962 19 36.9400462 0.1854287 20 4.0321175 36.9400462 21 -60.8777995 4.0321175 22 -70.6997656 -60.8777995 23 -1.4781117 -70.6997656 24 56.9792582 -1.4781117 25 24.4291021 56.9792582 26 -88.3567742 24.4291021 27 -71.0832593 -88.3567742 28 56.8191275 -71.0832593 29 -77.0578259 56.8191275 30 -46.5323925 -77.0578259 31 -14.0437597 -46.5323925 32 -124.2279023 -14.0437597 33 -43.9604961 -124.2279023 34 -28.3536479 -43.9604961 35 -39.5505791 -28.3536479 36 -56.2705324 -39.5505791 37 -39.8846274 -56.2705324 38 -70.1138115 -39.8846274 39 -27.5743119 -70.1138115 40 -52.0615233 -27.5743119 41 -89.0271383 -52.0615233 42 -53.2996590 -89.0271383 43 -11.5058253 -53.2996590 44 -53.2364309 -11.5058253 45 72.2040341 -53.2364309 46 -63.7500740 72.2040341 47 -92.3655905 -63.7500740 48 -45.5433451 -92.3655905 49 -100.0193331 -45.5433451 50 -114.2527816 -100.0193331 51 -5.6740657 -114.2527816 52 -104.0913733 -5.6740657 53 41.3573326 -104.0913733 54 29.8435498 41.3573326 55 -49.3975684 29.8435498 56 20.1625335 -49.3975684 57 3.3412781 20.1625335 58 -34.8063474 3.3412781 59 111.3658612 -34.8063474 60 22.1139384 111.3658612 61 49.1844133 22.1139384 62 170.7182315 49.1844133 63 62.4742704 170.7182315 64 4.5437512 62.4742704 65 42.3513677 4.5437512 66 47.6892487 42.3513677 67 -11.6840115 47.6892487 68 106.0431843 -11.6840115 69 26.0053895 106.0431843 70 150.3462531 26.0053895 71 32.7946755 150.3462531 > 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/7ne201291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8ne201291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9y5j31291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10y5j31291132391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/1115i91291132391.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/12n6zf1291132391.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/13upvr1291132391.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/14myvb1291132391.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/15qzbh1291132391.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/16mqrq1291132391.tab") + } > > try(system("convert tmp/1r4mr1291132391.ps tmp/1r4mr1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/22d4u1291132391.ps tmp/22d4u1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/32d4u1291132391.ps tmp/32d4u1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/42d4u1291132391.ps tmp/42d4u1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/5vm3x1291132391.ps tmp/5vm3x1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/6vm3x1291132391.ps tmp/6vm3x1291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/7ne201291132391.ps tmp/7ne201291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/8ne201291132391.ps tmp/8ne201291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/9y5j31291132391.ps tmp/9y5j31291132391.png",intern=TRUE)) character(0) > try(system("convert tmp/10y5j31291132391.ps tmp/10y5j31291132391.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.645 1.700 6.917