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Type 'q()' to quit R. > x <- array(list(2.057 + ,0 + ,2.058 + ,2.077 + ,2.053 + ,2.085 + ,2.076 + ,0 + ,2.057 + ,2.058 + ,2.077 + ,2.053 + ,2.07 + ,0 + ,2.076 + ,2.057 + ,2.058 + ,2.077 + ,2.062 + ,0 + ,2.07 + ,2.076 + ,2.057 + ,2.058 + ,2.073 + ,0 + ,2.062 + ,2.07 + ,2.076 + ,2.057 + ,2.061 + ,0 + ,2.073 + ,2.062 + ,2.07 + ,2.076 + ,2.094 + ,0 + ,2.061 + ,2.073 + ,2.062 + ,2.07 + ,2.067 + ,0 + ,2.094 + ,2.061 + ,2.073 + ,2.062 + ,2.086 + ,0 + ,2.067 + ,2.094 + ,2.061 + ,2.073 + ,2.276 + ,0 + ,2.086 + ,2.067 + ,2.094 + ,2.061 + ,2.326 + ,0 + ,2.276 + ,2.086 + ,2.067 + ,2.094 + ,2.349 + ,0 + ,2.326 + ,2.276 + ,2.086 + ,2.067 + ,2.52 + ,0 + ,2.349 + ,2.326 + ,2.276 + ,2.086 + ,2.628 + ,0 + ,2.52 + ,2.349 + ,2.326 + ,2.276 + ,2.577 + ,0 + ,2.628 + ,2.52 + ,2.349 + ,2.326 + ,2.698 + ,0 + ,2.577 + ,2.628 + ,2.52 + ,2.349 + ,2.814 + ,0 + ,2.698 + ,2.577 + ,2.628 + ,2.52 + ,2.968 + ,0 + ,2.814 + ,2.698 + ,2.577 + ,2.628 + ,3.041 + ,0 + ,2.968 + ,2.814 + ,2.698 + ,2.577 + ,3.278 + ,0 + ,3.041 + ,2.968 + ,2.814 + ,2.698 + ,3.328 + ,0 + ,3.278 + ,3.041 + ,2.968 + ,2.814 + ,3.5 + ,0 + ,3.328 + ,3.278 + ,3.041 + ,2.968 + ,3.563 + ,0 + ,3.5 + ,3.328 + ,3.278 + ,3.041 + ,3.569 + ,0 + ,3.563 + ,3.5 + ,3.328 + ,3.278 + ,3.69 + ,0 + ,3.569 + ,3.563 + ,3.5 + ,3.328 + ,3.819 + ,0 + ,3.69 + ,3.569 + ,3.563 + ,3.5 + ,3.79 + ,0 + ,3.819 + ,3.69 + ,3.569 + ,3.563 + ,3.956 + ,0 + ,3.79 + ,3.819 + ,3.69 + ,3.569 + ,4.063 + ,0 + ,3.956 + ,3.79 + ,3.819 + ,3.69 + ,4.047 + ,0 + ,4.063 + ,3.956 + ,3.79 + ,3.819 + ,4.029 + ,0 + ,4.047 + ,4.063 + ,3.956 + ,3.79 + ,3.941 + ,0 + ,4.029 + ,4.047 + ,4.063 + ,3.956 + ,4.022 + ,0 + ,3.941 + ,4.029 + ,4.047 + ,4.063 + ,3.879 + ,0 + ,4.022 + ,3.941 + ,4.029 + ,4.047 + ,4.022 + ,0 + ,3.879 + ,4.022 + ,3.941 + ,4.029 + ,4.028 + ,0 + ,4.022 + ,3.879 + ,4.022 + ,3.941 + ,4.091 + ,0 + ,4.028 + ,4.022 + ,3.879 + ,4.022 + ,3.987 + ,0 + ,4.091 + ,4.028 + ,4.022 + ,3.879 + ,4.01 + ,0 + ,3.987 + ,4.091 + ,4.028 + ,4.022 + ,4.007 + ,0 + ,4.01 + ,3.987 + ,4.091 + ,4.028 + ,4.191 + ,0 + ,4.007 + ,4.01 + ,3.987 + ,4.091 + ,4.299 + ,0 + ,4.191 + ,4.007 + ,4.01 + ,3.987 + ,4.273 + ,0 + ,4.299 + ,4.191 + ,4.007 + ,4.01 + ,3.82 + ,0 + ,4.273 + ,4.299 + ,4.191 + ,4.007 + ,3.15 + ,1 + ,3.82 + ,4.273 + ,4.299 + ,4.191 + ,2.486 + ,1 + ,3.15 + ,3.82 + ,4.273 + ,4.299 + ,1.812 + ,1 + ,2.486 + ,3.15 + ,3.82 + ,4.273 + ,1.257 + ,1 + ,1.812 + ,2.486 + ,3.15 + ,3.82 + ,1.062 + ,1 + ,1.257 + ,1.812 + ,2.486 + ,3.15 + ,0.842 + ,1 + ,1.062 + ,1.257 + ,1.812 + ,2.486 + ,0.782 + ,1 + ,0.842 + ,1.062 + ,1.257 + ,1.812 + ,0.698 + ,1 + ,0.782 + ,0.842 + ,1.062 + ,1.257 + ,0.358 + ,1 + ,0.698 + ,0.782 + ,0.842 + ,1.062 + ,0.347 + ,1 + ,0.358 + ,0.698 + ,0.782 + ,0.842 + ,0.363 + ,1 + ,0.347 + ,0.358 + ,0.698 + ,0.782 + ,0.359 + ,1 + ,0.363 + ,0.347 + ,0.358 + ,0.698 + ,0.355 + ,1 + ,0.359 + ,0.363 + ,0.347 + ,0.358) + ,dim=c(6 + ,57) + ,dimnames=list(c('intb' + ,'X' + ,'Yt-1' + ,'Yt-2' + ,'Yt-3' + ,'Yt-4') + ,1:57)) > y <- array(NA,dim=c(6,57),dimnames=list(c('intb','X','Yt-1','Yt-2','Yt-3','Yt-4'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal 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 intb X Yt-1 Yt-2 Yt-3 Yt-4 1 2.057 0 2.058 2.077 2.053 2.085 2 2.076 0 2.057 2.058 2.077 2.053 3 2.070 0 2.076 2.057 2.058 2.077 4 2.062 0 2.070 2.076 2.057 2.058 5 2.073 0 2.062 2.070 2.076 2.057 6 2.061 0 2.073 2.062 2.070 2.076 7 2.094 0 2.061 2.073 2.062 2.070 8 2.067 0 2.094 2.061 2.073 2.062 9 2.086 0 2.067 2.094 2.061 2.073 10 2.276 0 2.086 2.067 2.094 2.061 11 2.326 0 2.276 2.086 2.067 2.094 12 2.349 0 2.326 2.276 2.086 2.067 13 2.520 0 2.349 2.326 2.276 2.086 14 2.628 0 2.520 2.349 2.326 2.276 15 2.577 0 2.628 2.520 2.349 2.326 16 2.698 0 2.577 2.628 2.520 2.349 17 2.814 0 2.698 2.577 2.628 2.520 18 2.968 0 2.814 2.698 2.577 2.628 19 3.041 0 2.968 2.814 2.698 2.577 20 3.278 0 3.041 2.968 2.814 2.698 21 3.328 0 3.278 3.041 2.968 2.814 22 3.500 0 3.328 3.278 3.041 2.968 23 3.563 0 3.500 3.328 3.278 3.041 24 3.569 0 3.563 3.500 3.328 3.278 25 3.690 0 3.569 3.563 3.500 3.328 26 3.819 0 3.690 3.569 3.563 3.500 27 3.790 0 3.819 3.690 3.569 3.563 28 3.956 0 3.790 3.819 3.690 3.569 29 4.063 0 3.956 3.790 3.819 3.690 30 4.047 0 4.063 3.956 3.790 3.819 31 4.029 0 4.047 4.063 3.956 3.790 32 3.941 0 4.029 4.047 4.063 3.956 33 4.022 0 3.941 4.029 4.047 4.063 34 3.879 0 4.022 3.941 4.029 4.047 35 4.022 0 3.879 4.022 3.941 4.029 36 4.028 0 4.022 3.879 4.022 3.941 37 4.091 0 4.028 4.022 3.879 4.022 38 3.987 0 4.091 4.028 4.022 3.879 39 4.010 0 3.987 4.091 4.028 4.022 40 4.007 0 4.010 3.987 4.091 4.028 41 4.191 0 4.007 4.010 3.987 4.091 42 4.299 0 4.191 4.007 4.010 3.987 43 4.273 0 4.299 4.191 4.007 4.010 44 3.820 0 4.273 4.299 4.191 4.007 45 3.150 1 3.820 4.273 4.299 4.191 46 2.486 1 3.150 3.820 4.273 4.299 47 1.812 1 2.486 3.150 3.820 4.273 48 1.257 1 1.812 2.486 3.150 3.820 49 1.062 1 1.257 1.812 2.486 3.150 50 0.842 1 1.062 1.257 1.812 2.486 51 0.782 1 0.842 1.062 1.257 1.812 52 0.698 1 0.782 0.842 1.062 1.257 53 0.358 1 0.698 0.782 0.842 1.062 54 0.347 1 0.358 0.698 0.782 0.842 55 0.363 1 0.347 0.358 0.698 0.782 56 0.359 1 0.363 0.347 0.358 0.698 57 0.355 1 0.359 0.363 0.347 0.358 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X `Yt-1` `Yt-2` `Yt-3` `Yt-4` 0.2107 -0.2560 1.3942 -0.2798 -0.3449 0.1696 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.37934 -0.08599 -0.02257 0.09244 0.21546 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21066 0.06980 3.018 0.00397 ** X -0.25603 0.08016 -3.194 0.00241 ** `Yt-1` 1.39419 0.15003 9.293 1.48e-12 *** `Yt-2` -0.27978 0.24478 -1.143 0.25839 `Yt-3` -0.34488 0.24277 -1.421 0.16152 `Yt-4` 0.16957 0.13418 1.264 0.21209 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1262 on 51 degrees of freedom Multiple R-squared: 0.9905, Adjusted R-squared: 0.9896 F-statistic: 1065 on 5 and 51 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,] 0.0036601667 0.007320333 0.9963398 [2,] 0.0510923141 0.102184628 0.9489077 [3,] 0.0198462824 0.039692565 0.9801537 [4,] 0.0175675603 0.035135121 0.9824324 [5,] 0.0279969910 0.055993982 0.9720030 [6,] 0.0129068312 0.025813662 0.9870932 [7,] 0.0126125215 0.025225043 0.9873875 [8,] 0.0065098738 0.013019748 0.9934901 [9,] 0.0029184684 0.005836937 0.9970815 [10,] 0.0165622874 0.033124575 0.9834377 [11,] 0.0144926298 0.028985260 0.9855074 [12,] 0.0136885094 0.027377019 0.9863115 [13,] 0.0238407911 0.047681582 0.9761592 [14,] 0.0132665592 0.026533118 0.9867334 [15,] 0.0180411557 0.036082311 0.9819588 [16,] 0.0205701601 0.041140320 0.9794298 [17,] 0.0117667718 0.023533544 0.9882332 [18,] 0.0074157778 0.014831556 0.9925842 [19,] 0.0070972941 0.014194588 0.9929027 [20,] 0.0054273677 0.010854735 0.9945726 [21,] 0.0033948443 0.006789689 0.9966052 [22,] 0.0021061279 0.004212256 0.9978939 [23,] 0.0026926398 0.005385280 0.9973074 [24,] 0.0029752627 0.005950525 0.9970247 [25,] 0.0029174299 0.005834860 0.9970826 [26,] 0.0035727872 0.007145574 0.9964272 [27,] 0.0064827258 0.012965452 0.9935173 [28,] 0.0034572164 0.006914433 0.9965428 [29,] 0.0021382658 0.004276532 0.9978617 [30,] 0.0026021505 0.005204301 0.9973978 [31,] 0.0014557551 0.002911510 0.9985442 [32,] 0.0006933646 0.001386729 0.9993066 [33,] 0.0045486456 0.009097291 0.9954514 [34,] 0.0123871498 0.024774300 0.9876129 [35,] 0.0843416647 0.168683329 0.9156583 [36,] 0.3073556140 0.614711228 0.6926444 [37,] 0.2946413040 0.589282608 0.7053587 [38,] 0.3915469895 0.783093979 0.6084530 [39,] 0.3840121232 0.768024246 0.6159879 [40,] 0.2551337984 0.510267597 0.7448662 > postscript(file="/var/www/html/rcomp/tmp/1qifq1258619688.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2b76s1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3mtwz1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/45wqm1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/56gxf1258619688.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 -0.0873171079 -0.0585355409 -0.1019271272 -0.0933693453 -0.0661722971 6 7 8 9 10 -0.1010376012 -0.0499714022 -0.1211867715 -0.0613147855 0.1080573544 11 12 13 14 15 -0.1164299634 -0.0988507729 0.1163763937 -0.0225687393 -0.1768453406 16 17 18 19 20 0.1005479184 0.0418332388 0.0320586118 -0.0268143502 0.1709836502 21 22 23 24 25 -0.0555740425 0.1120864390 0.0186321469 -0.0380234922 0.1430776520 26 27 28 29 30 0.0976212949 -0.0859893681 0.1972459135 0.0886685798 -0.0619420095 31 32 33 34 35 0.0344679644 -0.0241594072 0.1508315363 -0.1332127945 0.2045211874 36 37 38 39 40 0.0140009344 0.0455918226 -0.0709981277 0.0924446518 0.0489912739 41 42 43 44 45 0.1970589459 0.0732560521 -0.0567719219 -0.3793412534 -0.1629695090 46 47 48 49 50 -0.0468824674 -0.1344122533 -0.0897546626 0.1850619437 -0.0382018505 51 52 53 54 55 0.0768447233 0.0418036383 -0.2406784015 0.2154563541 0.1328726064 56 57 0.0004738653 0.0603860133 > postscript(file="/var/www/html/rcomp/tmp/6h55m1258619688.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 -0.0873171079 NA 1 -0.0585355409 -0.0873171079 2 -0.1019271272 -0.0585355409 3 -0.0933693453 -0.1019271272 4 -0.0661722971 -0.0933693453 5 -0.1010376012 -0.0661722971 6 -0.0499714022 -0.1010376012 7 -0.1211867715 -0.0499714022 8 -0.0613147855 -0.1211867715 9 0.1080573544 -0.0613147855 10 -0.1164299634 0.1080573544 11 -0.0988507729 -0.1164299634 12 0.1163763937 -0.0988507729 13 -0.0225687393 0.1163763937 14 -0.1768453406 -0.0225687393 15 0.1005479184 -0.1768453406 16 0.0418332388 0.1005479184 17 0.0320586118 0.0418332388 18 -0.0268143502 0.0320586118 19 0.1709836502 -0.0268143502 20 -0.0555740425 0.1709836502 21 0.1120864390 -0.0555740425 22 0.0186321469 0.1120864390 23 -0.0380234922 0.0186321469 24 0.1430776520 -0.0380234922 25 0.0976212949 0.1430776520 26 -0.0859893681 0.0976212949 27 0.1972459135 -0.0859893681 28 0.0886685798 0.1972459135 29 -0.0619420095 0.0886685798 30 0.0344679644 -0.0619420095 31 -0.0241594072 0.0344679644 32 0.1508315363 -0.0241594072 33 -0.1332127945 0.1508315363 34 0.2045211874 -0.1332127945 35 0.0140009344 0.2045211874 36 0.0455918226 0.0140009344 37 -0.0709981277 0.0455918226 38 0.0924446518 -0.0709981277 39 0.0489912739 0.0924446518 40 0.1970589459 0.0489912739 41 0.0732560521 0.1970589459 42 -0.0567719219 0.0732560521 43 -0.3793412534 -0.0567719219 44 -0.1629695090 -0.3793412534 45 -0.0468824674 -0.1629695090 46 -0.1344122533 -0.0468824674 47 -0.0897546626 -0.1344122533 48 0.1850619437 -0.0897546626 49 -0.0382018505 0.1850619437 50 0.0768447233 -0.0382018505 51 0.0418036383 0.0768447233 52 -0.2406784015 0.0418036383 53 0.2154563541 -0.2406784015 54 0.1328726064 0.2154563541 55 0.0004738653 0.1328726064 56 0.0603860133 0.0004738653 57 NA 0.0603860133 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.0585355409 -0.0873171079 [2,] -0.1019271272 -0.0585355409 [3,] -0.0933693453 -0.1019271272 [4,] -0.0661722971 -0.0933693453 [5,] -0.1010376012 -0.0661722971 [6,] -0.0499714022 -0.1010376012 [7,] -0.1211867715 -0.0499714022 [8,] -0.0613147855 -0.1211867715 [9,] 0.1080573544 -0.0613147855 [10,] -0.1164299634 0.1080573544 [11,] -0.0988507729 -0.1164299634 [12,] 0.1163763937 -0.0988507729 [13,] -0.0225687393 0.1163763937 [14,] -0.1768453406 -0.0225687393 [15,] 0.1005479184 -0.1768453406 [16,] 0.0418332388 0.1005479184 [17,] 0.0320586118 0.0418332388 [18,] -0.0268143502 0.0320586118 [19,] 0.1709836502 -0.0268143502 [20,] -0.0555740425 0.1709836502 [21,] 0.1120864390 -0.0555740425 [22,] 0.0186321469 0.1120864390 [23,] -0.0380234922 0.0186321469 [24,] 0.1430776520 -0.0380234922 [25,] 0.0976212949 0.1430776520 [26,] -0.0859893681 0.0976212949 [27,] 0.1972459135 -0.0859893681 [28,] 0.0886685798 0.1972459135 [29,] -0.0619420095 0.0886685798 [30,] 0.0344679644 -0.0619420095 [31,] -0.0241594072 0.0344679644 [32,] 0.1508315363 -0.0241594072 [33,] -0.1332127945 0.1508315363 [34,] 0.2045211874 -0.1332127945 [35,] 0.0140009344 0.2045211874 [36,] 0.0455918226 0.0140009344 [37,] -0.0709981277 0.0455918226 [38,] 0.0924446518 -0.0709981277 [39,] 0.0489912739 0.0924446518 [40,] 0.1970589459 0.0489912739 [41,] 0.0732560521 0.1970589459 [42,] -0.0567719219 0.0732560521 [43,] -0.3793412534 -0.0567719219 [44,] -0.1629695090 -0.3793412534 [45,] -0.0468824674 -0.1629695090 [46,] -0.1344122533 -0.0468824674 [47,] -0.0897546626 -0.1344122533 [48,] 0.1850619437 -0.0897546626 [49,] -0.0382018505 0.1850619437 [50,] 0.0768447233 -0.0382018505 [51,] 0.0418036383 0.0768447233 [52,] -0.2406784015 0.0418036383 [53,] 0.2154563541 -0.2406784015 [54,] 0.1328726064 0.2154563541 [55,] 0.0004738653 0.1328726064 [56,] 0.0603860133 0.0004738653 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.0585355409 -0.0873171079 2 -0.1019271272 -0.0585355409 3 -0.0933693453 -0.1019271272 4 -0.0661722971 -0.0933693453 5 -0.1010376012 -0.0661722971 6 -0.0499714022 -0.1010376012 7 -0.1211867715 -0.0499714022 8 -0.0613147855 -0.1211867715 9 0.1080573544 -0.0613147855 10 -0.1164299634 0.1080573544 11 -0.0988507729 -0.1164299634 12 0.1163763937 -0.0988507729 13 -0.0225687393 0.1163763937 14 -0.1768453406 -0.0225687393 15 0.1005479184 -0.1768453406 16 0.0418332388 0.1005479184 17 0.0320586118 0.0418332388 18 -0.0268143502 0.0320586118 19 0.1709836502 -0.0268143502 20 -0.0555740425 0.1709836502 21 0.1120864390 -0.0555740425 22 0.0186321469 0.1120864390 23 -0.0380234922 0.0186321469 24 0.1430776520 -0.0380234922 25 0.0976212949 0.1430776520 26 -0.0859893681 0.0976212949 27 0.1972459135 -0.0859893681 28 0.0886685798 0.1972459135 29 -0.0619420095 0.0886685798 30 0.0344679644 -0.0619420095 31 -0.0241594072 0.0344679644 32 0.1508315363 -0.0241594072 33 -0.1332127945 0.1508315363 34 0.2045211874 -0.1332127945 35 0.0140009344 0.2045211874 36 0.0455918226 0.0140009344 37 -0.0709981277 0.0455918226 38 0.0924446518 -0.0709981277 39 0.0489912739 0.0924446518 40 0.1970589459 0.0489912739 41 0.0732560521 0.1970589459 42 -0.0567719219 0.0732560521 43 -0.3793412534 -0.0567719219 44 -0.1629695090 -0.3793412534 45 -0.0468824674 -0.1629695090 46 -0.1344122533 -0.0468824674 47 -0.0897546626 -0.1344122533 48 0.1850619437 -0.0897546626 49 -0.0382018505 0.1850619437 50 0.0768447233 -0.0382018505 51 0.0418036383 0.0768447233 52 -0.2406784015 0.0418036383 53 0.2154563541 -0.2406784015 54 0.1328726064 0.2154563541 55 0.0004738653 0.1328726064 56 0.0603860133 0.0004738653 > 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/7oc3g1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8glm51258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9i8vz1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10o1ma1258619688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11f5mn1258619688.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/126jpb1258619688.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/13y6gw1258619689.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/141i671258619689.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/15t3v81258619689.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/1673581258619689.tab") + } > > system("convert tmp/1qifq1258619688.ps tmp/1qifq1258619688.png") > system("convert tmp/2b76s1258619688.ps tmp/2b76s1258619688.png") > system("convert tmp/3mtwz1258619688.ps tmp/3mtwz1258619688.png") > system("convert tmp/45wqm1258619688.ps tmp/45wqm1258619688.png") > system("convert tmp/56gxf1258619688.ps tmp/56gxf1258619688.png") > system("convert tmp/6h55m1258619688.ps tmp/6h55m1258619688.png") > system("convert tmp/7oc3g1258619688.ps tmp/7oc3g1258619688.png") > system("convert tmp/8glm51258619688.ps tmp/8glm51258619688.png") > system("convert tmp/9i8vz1258619688.ps tmp/9i8vz1258619688.png") > system("convert tmp/10o1ma1258619688.ps tmp/10o1ma1258619688.png") > > > proc.time() user system elapsed 2.413 1.580 9.049