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Type 'q()' to quit R. > x <- array(list(0.7790 + ,0.0000 + ,0.7775 + ,0.7461 + ,0.7744 + ,0.0520 + ,0.7790 + ,0.7775 + ,0.7905 + ,0.3130 + ,0.7744 + ,0.7790 + ,0.7719 + ,0.3640 + ,0.7905 + ,0.7744 + ,0.7811 + ,0.3630 + ,0.7719 + ,0.7905 + ,0.7557 + ,-0.1550 + ,0.7811 + ,0.7719 + ,0.7637 + ,0.0520 + ,0.7557 + ,0.7811 + ,0.7595 + ,0.5680 + ,0.7637 + ,0.7557 + ,0.7471 + ,0.6680 + ,0.7595 + ,0.7637 + ,0.7615 + ,1.3780 + ,0.7471 + ,0.7595 + ,0.7487 + ,0.2520 + ,0.7615 + ,0.7471 + ,0.7389 + ,-0.4020 + ,0.7487 + ,0.7615 + ,0.7337 + ,-0.0500 + ,0.7389 + ,0.7487 + ,0.7510 + ,0.5550 + ,0.7337 + ,0.7389 + ,0.7382 + ,0.0500 + ,0.7510 + ,0.7337 + ,0.7159 + ,0.1500 + ,0.7382 + ,0.7510 + ,0.7542 + ,0.4500 + ,0.7159 + ,0.7382 + ,0.7636 + ,0.2990 + ,0.7542 + ,0.7159 + ,0.7433 + ,0.1990 + ,0.7636 + ,0.7542 + ,0.7658 + ,0.4960 + ,0.7433 + ,0.7636 + ,0.7627 + ,0.4440 + ,0.7658 + ,0.7433 + ,0.7480 + ,-0.3930 + ,0.7627 + ,0.7658 + ,0.7692 + ,-0.4440 + ,0.7480 + ,0.7627 + ,0.7850 + ,0.1980 + ,0.7692 + ,0.7480 + ,0.7913 + ,0.4940 + ,0.7850 + ,0.7692 + ,0.7720 + ,0.1330 + ,0.7913 + ,0.7850 + ,0.7880 + ,0.3880 + ,0.7720 + ,0.7913 + ,0.8070 + ,0.4840 + ,0.7880 + ,0.7720 + ,0.8268 + ,0.2780 + ,0.8070 + ,0.7880 + ,0.8244 + ,0.3690 + ,0.8268 + ,0.8070 + ,0.8487 + ,0.1650 + ,0.8244 + ,0.8268 + ,0.8572 + ,0.1550 + ,0.8487 + ,0.8244 + ,0.8214 + ,0.0870 + ,0.8572 + ,0.8487 + ,0.8827 + ,0.4140 + ,0.8214 + ,0.8572 + ,0.9216 + ,0.3600 + ,0.8827 + ,0.8214 + ,0.8865 + ,0.9750 + ,0.9216 + ,0.8827 + ,0.8816 + ,0.2700 + ,0.8865 + ,0.9216 + ,0.8884 + ,0.3590 + ,0.8816 + ,0.8865 + ,0.9466 + ,0.1690 + ,0.8884 + ,0.8816 + ,0.9180 + ,0.3810 + ,0.9466 + ,0.8884 + ,0.9337 + ,0.1540 + ,0.9180 + ,0.9466 + ,0.9559 + ,0.4860 + ,0.9337 + ,0.9180 + ,0.9626 + ,0.9250 + ,0.9559 + ,0.9337 + ,0.9434 + ,0.7280 + ,0.9626 + ,0.9559 + ,0.8639 + ,-0.0140 + ,0.9434 + ,0.9626 + ,0.7996 + ,0.0460 + ,0.8639 + ,0.9434 + ,0.6680 + ,-0.8190 + ,0.7996 + ,0.8639 + ,0.6572 + ,-1.6740 + ,0.6680 + ,0.7996 + ,0.6928 + ,-0.7880 + ,0.6572 + ,0.6680 + ,0.6438 + ,0.2790 + ,0.6928 + ,0.6572 + ,0.6454 + ,0.3960 + ,0.6438 + ,0.6928 + ,0.6873 + ,-0.1410 + ,0.6454 + ,0.6438 + ,0.7265 + ,-0.0190 + ,0.6873 + ,0.6454 + ,0.7912 + ,0.0990 + ,0.7265 + ,0.6873 + ,0.8114 + ,0.7420 + ,0.7912 + ,0.7265 + ,0.8281 + ,0.0050 + ,0.8114 + ,0.7912 + ,0.8393 + ,0.4480 + ,0.8281 + ,0.8114) + ,dim=c(4 + ,57) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2') + ,1:57)) > y <- array(NA,dim=c(4,57),dimnames=list(c('Y','X','Y1','Y2'),1:57)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = '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 Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 0.7790 0.000 0.7775 0.7461 1 0 0 0 0 0 0 0 0 0 0 1 2 0.7744 0.052 0.7790 0.7775 0 1 0 0 0 0 0 0 0 0 0 2 3 0.7905 0.313 0.7744 0.7790 0 0 1 0 0 0 0 0 0 0 0 3 4 0.7719 0.364 0.7905 0.7744 0 0 0 1 0 0 0 0 0 0 0 4 5 0.7811 0.363 0.7719 0.7905 0 0 0 0 1 0 0 0 0 0 0 5 6 0.7557 -0.155 0.7811 0.7719 0 0 0 0 0 1 0 0 0 0 0 6 7 0.7637 0.052 0.7557 0.7811 0 0 0 0 0 0 1 0 0 0 0 7 8 0.7595 0.568 0.7637 0.7557 0 0 0 0 0 0 0 1 0 0 0 8 9 0.7471 0.668 0.7595 0.7637 0 0 0 0 0 0 0 0 1 0 0 9 10 0.7615 1.378 0.7471 0.7595 0 0 0 0 0 0 0 0 0 1 0 10 11 0.7487 0.252 0.7615 0.7471 0 0 0 0 0 0 0 0 0 0 1 11 12 0.7389 -0.402 0.7487 0.7615 0 0 0 0 0 0 0 0 0 0 0 12 13 0.7337 -0.050 0.7389 0.7487 1 0 0 0 0 0 0 0 0 0 0 13 14 0.7510 0.555 0.7337 0.7389 0 1 0 0 0 0 0 0 0 0 0 14 15 0.7382 0.050 0.7510 0.7337 0 0 1 0 0 0 0 0 0 0 0 15 16 0.7159 0.150 0.7382 0.7510 0 0 0 1 0 0 0 0 0 0 0 16 17 0.7542 0.450 0.7159 0.7382 0 0 0 0 1 0 0 0 0 0 0 17 18 0.7636 0.299 0.7542 0.7159 0 0 0 0 0 1 0 0 0 0 0 18 19 0.7433 0.199 0.7636 0.7542 0 0 0 0 0 0 1 0 0 0 0 19 20 0.7658 0.496 0.7433 0.7636 0 0 0 0 0 0 0 1 0 0 0 20 21 0.7627 0.444 0.7658 0.7433 0 0 0 0 0 0 0 0 1 0 0 21 22 0.7480 -0.393 0.7627 0.7658 0 0 0 0 0 0 0 0 0 1 0 22 23 0.7692 -0.444 0.7480 0.7627 0 0 0 0 0 0 0 0 0 0 1 23 24 0.7850 0.198 0.7692 0.7480 0 0 0 0 0 0 0 0 0 0 0 24 25 0.7913 0.494 0.7850 0.7692 1 0 0 0 0 0 0 0 0 0 0 25 26 0.7720 0.133 0.7913 0.7850 0 1 0 0 0 0 0 0 0 0 0 26 27 0.7880 0.388 0.7720 0.7913 0 0 1 0 0 0 0 0 0 0 0 27 28 0.8070 0.484 0.7880 0.7720 0 0 0 1 0 0 0 0 0 0 0 28 29 0.8268 0.278 0.8070 0.7880 0 0 0 0 1 0 0 0 0 0 0 29 30 0.8244 0.369 0.8268 0.8070 0 0 0 0 0 1 0 0 0 0 0 30 31 0.8487 0.165 0.8244 0.8268 0 0 0 0 0 0 1 0 0 0 0 31 32 0.8572 0.155 0.8487 0.8244 0 0 0 0 0 0 0 1 0 0 0 32 33 0.8214 0.087 0.8572 0.8487 0 0 0 0 0 0 0 0 1 0 0 33 34 0.8827 0.414 0.8214 0.8572 0 0 0 0 0 0 0 0 0 1 0 34 35 0.9216 0.360 0.8827 0.8214 0 0 0 0 0 0 0 0 0 0 1 35 36 0.8865 0.975 0.9216 0.8827 0 0 0 0 0 0 0 0 0 0 0 36 37 0.8816 0.270 0.8865 0.9216 1 0 0 0 0 0 0 0 0 0 0 37 38 0.8884 0.359 0.8816 0.8865 0 1 0 0 0 0 0 0 0 0 0 38 39 0.9466 0.169 0.8884 0.8816 0 0 1 0 0 0 0 0 0 0 0 39 40 0.9180 0.381 0.9466 0.8884 0 0 0 1 0 0 0 0 0 0 0 40 41 0.9337 0.154 0.9180 0.9466 0 0 0 0 1 0 0 0 0 0 0 41 42 0.9559 0.486 0.9337 0.9180 0 0 0 0 0 1 0 0 0 0 0 42 43 0.9626 0.925 0.9559 0.9337 0 0 0 0 0 0 1 0 0 0 0 43 44 0.9434 0.728 0.9626 0.9559 0 0 0 0 0 0 0 1 0 0 0 44 45 0.8639 -0.014 0.9434 0.9626 0 0 0 0 0 0 0 0 1 0 0 45 46 0.7996 0.046 0.8639 0.9434 0 0 0 0 0 0 0 0 0 1 0 46 47 0.6680 -0.819 0.7996 0.8639 0 0 0 0 0 0 0 0 0 0 1 47 48 0.6572 -1.674 0.6680 0.7996 0 0 0 0 0 0 0 0 0 0 0 48 49 0.6928 -0.788 0.6572 0.6680 1 0 0 0 0 0 0 0 0 0 0 49 50 0.6438 0.279 0.6928 0.6572 0 1 0 0 0 0 0 0 0 0 0 50 51 0.6454 0.396 0.6438 0.6928 0 0 1 0 0 0 0 0 0 0 0 51 52 0.6873 -0.141 0.6454 0.6438 0 0 0 1 0 0 0 0 0 0 0 52 53 0.7265 -0.019 0.6873 0.6454 0 0 0 0 1 0 0 0 0 0 0 53 54 0.7912 0.099 0.7265 0.6873 0 0 0 0 0 1 0 0 0 0 0 54 55 0.8114 0.742 0.7912 0.7265 0 0 0 0 0 0 1 0 0 0 0 55 56 0.8281 0.005 0.8114 0.7912 0 0 0 0 0 0 0 1 0 0 0 56 57 0.8393 0.448 0.8281 0.8114 0 0 0 0 0 0 0 0 1 0 0 57 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 M1 M2 0.093390 0.013197 1.128980 -0.261041 0.009041 -0.012771 M3 M4 M5 M6 M7 M8 0.015713 -0.006467 0.023820 0.009559 0.005395 0.005137 M9 M10 M11 t -0.021654 0.007844 -0.014177 0.000257 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.089703 -0.013513 0.001246 0.016065 0.063684 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.0933903 0.0504272 1.852 0.0712 . X 0.0131974 0.0132894 0.993 0.3265 Y1 1.1289805 0.1810568 6.236 2e-07 *** Y2 -0.2610413 0.1692507 -1.542 0.1307 M1 0.0090410 0.0215234 0.420 0.6766 M2 -0.0127713 0.0220298 -0.580 0.5653 M3 0.0157128 0.0220695 0.712 0.4805 M4 -0.0064666 0.0221060 -0.293 0.7714 M5 0.0238196 0.0219097 1.087 0.2833 M6 0.0095592 0.0222786 0.429 0.6701 M7 0.0053951 0.0223833 0.241 0.8107 M8 0.0051366 0.0222782 0.231 0.8188 M9 -0.0216543 0.0221197 -0.979 0.3333 M10 0.0078439 0.0240088 0.327 0.7455 M11 -0.0141773 0.0226705 -0.625 0.5352 t 0.0002570 0.0002722 0.944 0.3505 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.03172 on 41 degrees of freedom Multiple R-squared: 0.8808, Adjusted R-squared: 0.8373 F-statistic: 20.21 on 15 and 41 DF, p-value: 2.757e-14 > 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,] 0.0133254149 0.0266508298 0.9866746 [2,] 0.0440524468 0.0881048936 0.9559476 [3,] 0.0363568363 0.0727136726 0.9636432 [4,] 0.0222619275 0.0445238550 0.9777381 [5,] 0.0228613644 0.0457227288 0.9771386 [6,] 0.0097697646 0.0195395292 0.9902302 [7,] 0.0047425548 0.0094851096 0.9952574 [8,] 0.0027886590 0.0055773179 0.9972113 [9,] 0.0010379448 0.0020758896 0.9989621 [10,] 0.0010498379 0.0020996759 0.9989502 [11,] 0.0003770566 0.0007541132 0.9996229 [12,] 0.0002041263 0.0004082527 0.9997959 [13,] 0.0001199849 0.0002399698 0.9998800 [14,] 0.0000500011 0.0001000022 0.9999500 [15,] 0.0002167392 0.0004334785 0.9997833 [16,] 0.0004145669 0.0008291338 0.9995854 [17,] 0.0044677781 0.0089355562 0.9955322 [18,] 0.0090866367 0.0181732733 0.9909134 [19,] 0.0039049851 0.0078099703 0.9960950 [20,] 0.0109966312 0.0219932623 0.9890034 > postscript(file="/var/www/html/rcomp/tmp/16xks1258657899.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2apc41258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/35t511258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4q4td1258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5iava1258657899.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 = 57 Frequency = 1 1 2 3 4 5 6 -0.006707704 0.016064558 0.005563882 -0.011164204 -0.007292398 -0.027094725 7 8 9 10 11 12 0.013158168 -0.013512541 0.006131721 -0.005690658 -0.001360427 0.001246263 13 14 15 16 17 18 -0.010174586 0.024008794 -0.031756341 -0.014486758 0.011145771 -0.012519178 19 20 21 22 23 24 -0.028206909 0.015746994 0.009166008 -0.014869746 0.044554323 0.009675575 25 26 27 28 29 30 -0.009532716 -0.005501289 0.001826232 0.018379854 -0.006918649 -0.013910217 31 32 33 34 35 36 0.024867285 0.005439964 -0.006181693 0.063683891 0.046508983 -0.039057249 37 38 39 40 41 42 0.005830572 0.029380757 0.052391056 -0.021016015 0.014618043 0.021249160 43 44 45 46 47 48 0.005097559 -0.013270179 -0.033018372 -0.043123487 -0.089702880 0.028135412 49 50 51 52 53 54 0.020584434 -0.063952819 -0.028024829 0.028287123 -0.011552767 0.032274961 55 56 57 -0.014916104 0.005595761 0.023902336 > postscript(file="/var/www/html/rcomp/tmp/6lmsk1258657899.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 = 57 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.006707704 NA 1 0.016064558 -0.006707704 2 0.005563882 0.016064558 3 -0.011164204 0.005563882 4 -0.007292398 -0.011164204 5 -0.027094725 -0.007292398 6 0.013158168 -0.027094725 7 -0.013512541 0.013158168 8 0.006131721 -0.013512541 9 -0.005690658 0.006131721 10 -0.001360427 -0.005690658 11 0.001246263 -0.001360427 12 -0.010174586 0.001246263 13 0.024008794 -0.010174586 14 -0.031756341 0.024008794 15 -0.014486758 -0.031756341 16 0.011145771 -0.014486758 17 -0.012519178 0.011145771 18 -0.028206909 -0.012519178 19 0.015746994 -0.028206909 20 0.009166008 0.015746994 21 -0.014869746 0.009166008 22 0.044554323 -0.014869746 23 0.009675575 0.044554323 24 -0.009532716 0.009675575 25 -0.005501289 -0.009532716 26 0.001826232 -0.005501289 27 0.018379854 0.001826232 28 -0.006918649 0.018379854 29 -0.013910217 -0.006918649 30 0.024867285 -0.013910217 31 0.005439964 0.024867285 32 -0.006181693 0.005439964 33 0.063683891 -0.006181693 34 0.046508983 0.063683891 35 -0.039057249 0.046508983 36 0.005830572 -0.039057249 37 0.029380757 0.005830572 38 0.052391056 0.029380757 39 -0.021016015 0.052391056 40 0.014618043 -0.021016015 41 0.021249160 0.014618043 42 0.005097559 0.021249160 43 -0.013270179 0.005097559 44 -0.033018372 -0.013270179 45 -0.043123487 -0.033018372 46 -0.089702880 -0.043123487 47 0.028135412 -0.089702880 48 0.020584434 0.028135412 49 -0.063952819 0.020584434 50 -0.028024829 -0.063952819 51 0.028287123 -0.028024829 52 -0.011552767 0.028287123 53 0.032274961 -0.011552767 54 -0.014916104 0.032274961 55 0.005595761 -0.014916104 56 0.023902336 0.005595761 57 NA 0.023902336 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.016064558 -0.006707704 [2,] 0.005563882 0.016064558 [3,] -0.011164204 0.005563882 [4,] -0.007292398 -0.011164204 [5,] -0.027094725 -0.007292398 [6,] 0.013158168 -0.027094725 [7,] -0.013512541 0.013158168 [8,] 0.006131721 -0.013512541 [9,] -0.005690658 0.006131721 [10,] -0.001360427 -0.005690658 [11,] 0.001246263 -0.001360427 [12,] -0.010174586 0.001246263 [13,] 0.024008794 -0.010174586 [14,] -0.031756341 0.024008794 [15,] -0.014486758 -0.031756341 [16,] 0.011145771 -0.014486758 [17,] -0.012519178 0.011145771 [18,] -0.028206909 -0.012519178 [19,] 0.015746994 -0.028206909 [20,] 0.009166008 0.015746994 [21,] -0.014869746 0.009166008 [22,] 0.044554323 -0.014869746 [23,] 0.009675575 0.044554323 [24,] -0.009532716 0.009675575 [25,] -0.005501289 -0.009532716 [26,] 0.001826232 -0.005501289 [27,] 0.018379854 0.001826232 [28,] -0.006918649 0.018379854 [29,] -0.013910217 -0.006918649 [30,] 0.024867285 -0.013910217 [31,] 0.005439964 0.024867285 [32,] -0.006181693 0.005439964 [33,] 0.063683891 -0.006181693 [34,] 0.046508983 0.063683891 [35,] -0.039057249 0.046508983 [36,] 0.005830572 -0.039057249 [37,] 0.029380757 0.005830572 [38,] 0.052391056 0.029380757 [39,] -0.021016015 0.052391056 [40,] 0.014618043 -0.021016015 [41,] 0.021249160 0.014618043 [42,] 0.005097559 0.021249160 [43,] -0.013270179 0.005097559 [44,] -0.033018372 -0.013270179 [45,] -0.043123487 -0.033018372 [46,] -0.089702880 -0.043123487 [47,] 0.028135412 -0.089702880 [48,] 0.020584434 0.028135412 [49,] -0.063952819 0.020584434 [50,] -0.028024829 -0.063952819 [51,] 0.028287123 -0.028024829 [52,] -0.011552767 0.028287123 [53,] 0.032274961 -0.011552767 [54,] -0.014916104 0.032274961 [55,] 0.005595761 -0.014916104 [56,] 0.023902336 0.005595761 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.016064558 -0.006707704 2 0.005563882 0.016064558 3 -0.011164204 0.005563882 4 -0.007292398 -0.011164204 5 -0.027094725 -0.007292398 6 0.013158168 -0.027094725 7 -0.013512541 0.013158168 8 0.006131721 -0.013512541 9 -0.005690658 0.006131721 10 -0.001360427 -0.005690658 11 0.001246263 -0.001360427 12 -0.010174586 0.001246263 13 0.024008794 -0.010174586 14 -0.031756341 0.024008794 15 -0.014486758 -0.031756341 16 0.011145771 -0.014486758 17 -0.012519178 0.011145771 18 -0.028206909 -0.012519178 19 0.015746994 -0.028206909 20 0.009166008 0.015746994 21 -0.014869746 0.009166008 22 0.044554323 -0.014869746 23 0.009675575 0.044554323 24 -0.009532716 0.009675575 25 -0.005501289 -0.009532716 26 0.001826232 -0.005501289 27 0.018379854 0.001826232 28 -0.006918649 0.018379854 29 -0.013910217 -0.006918649 30 0.024867285 -0.013910217 31 0.005439964 0.024867285 32 -0.006181693 0.005439964 33 0.063683891 -0.006181693 34 0.046508983 0.063683891 35 -0.039057249 0.046508983 36 0.005830572 -0.039057249 37 0.029380757 0.005830572 38 0.052391056 0.029380757 39 -0.021016015 0.052391056 40 0.014618043 -0.021016015 41 0.021249160 0.014618043 42 0.005097559 0.021249160 43 -0.013270179 0.005097559 44 -0.033018372 -0.013270179 45 -0.043123487 -0.033018372 46 -0.089702880 -0.043123487 47 0.028135412 -0.089702880 48 0.020584434 0.028135412 49 -0.063952819 0.020584434 50 -0.028024829 -0.063952819 51 0.028287123 -0.028024829 52 -0.011552767 0.028287123 53 0.032274961 -0.011552767 54 -0.014916104 0.032274961 55 0.005595761 -0.014916104 56 0.023902336 0.005595761 > 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/7fo611258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8126a1258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/91qqn1258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10nxb31258657899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11wqi71258657899.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/12fg8k1258657899.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/13uzyy1258657899.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/14twtl1258657899.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/15wbwd1258657899.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/16lohp1258657899.tab") + } > > system("convert tmp/16xks1258657899.ps tmp/16xks1258657899.png") > system("convert tmp/2apc41258657899.ps tmp/2apc41258657899.png") > system("convert tmp/35t511258657899.ps tmp/35t511258657899.png") > system("convert tmp/4q4td1258657899.ps tmp/4q4td1258657899.png") > system("convert tmp/5iava1258657899.ps tmp/5iava1258657899.png") > system("convert tmp/6lmsk1258657899.ps tmp/6lmsk1258657899.png") > system("convert tmp/7fo611258657899.ps tmp/7fo611258657899.png") > system("convert tmp/8126a1258657899.ps tmp/8126a1258657899.png") > system("convert tmp/91qqn1258657899.ps tmp/91qqn1258657899.png") > system("convert tmp/10nxb31258657899.ps tmp/10nxb31258657899.png") > > > proc.time() user system elapsed 2.339 1.536 2.715