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Type 'q()' to quit R. > x <- array(list(9700,9081,9084,9743,8587,9731,9563,9998,9437,10038,9918,9252,9737,9035,9133,9487,8700,9627,8947,9283,8829,9947,9628,9318,9605,8640,9214,9567,8547,9185,9470,9123,9278,10170,9434,9655,9429,8739,9552,9687,9019,9672,9206,9069,9788,10312,10105,9863,9656,9295,9946,9701,9049,10190,9706,9765,9893,9994,10433,10073,10112,9266,9820,10097,9115,10411,9678,10408,10153,10368,10581,10597,10680,9738,9556),dim=c(1,75),dimnames=list(c('Geboortes_per_maand'),1:75)) > y <- array(NA,dim=c(1,75),dimnames=list(c('Geboortes_per_maand'),1:75)) > 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 > 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 Geboortes_per_maand M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 9700 1 0 0 0 0 0 0 0 0 0 0 1 2 9081 0 1 0 0 0 0 0 0 0 0 0 2 3 9084 0 0 1 0 0 0 0 0 0 0 0 3 4 9743 0 0 0 1 0 0 0 0 0 0 0 4 5 8587 0 0 0 0 1 0 0 0 0 0 0 5 6 9731 0 0 0 0 0 1 0 0 0 0 0 6 7 9563 0 0 0 0 0 0 1 0 0 0 0 7 8 9998 0 0 0 0 0 0 0 1 0 0 0 8 9 9437 0 0 0 0 0 0 0 0 1 0 0 9 10 10038 0 0 0 0 0 0 0 0 0 1 0 10 11 9918 0 0 0 0 0 0 0 0 0 0 1 11 12 9252 0 0 0 0 0 0 0 0 0 0 0 12 13 9737 1 0 0 0 0 0 0 0 0 0 0 13 14 9035 0 1 0 0 0 0 0 0 0 0 0 14 15 9133 0 0 1 0 0 0 0 0 0 0 0 15 16 9487 0 0 0 1 0 0 0 0 0 0 0 16 17 8700 0 0 0 0 1 0 0 0 0 0 0 17 18 9627 0 0 0 0 0 1 0 0 0 0 0 18 19 8947 0 0 0 0 0 0 1 0 0 0 0 19 20 9283 0 0 0 0 0 0 0 1 0 0 0 20 21 8829 0 0 0 0 0 0 0 0 1 0 0 21 22 9947 0 0 0 0 0 0 0 0 0 1 0 22 23 9628 0 0 0 0 0 0 0 0 0 0 1 23 24 9318 0 0 0 0 0 0 0 0 0 0 0 24 25 9605 1 0 0 0 0 0 0 0 0 0 0 25 26 8640 0 1 0 0 0 0 0 0 0 0 0 26 27 9214 0 0 1 0 0 0 0 0 0 0 0 27 28 9567 0 0 0 1 0 0 0 0 0 0 0 28 29 8547 0 0 0 0 1 0 0 0 0 0 0 29 30 9185 0 0 0 0 0 1 0 0 0 0 0 30 31 9470 0 0 0 0 0 0 1 0 0 0 0 31 32 9123 0 0 0 0 0 0 0 1 0 0 0 32 33 9278 0 0 0 0 0 0 0 0 1 0 0 33 34 10170 0 0 0 0 0 0 0 0 0 1 0 34 35 9434 0 0 0 0 0 0 0 0 0 0 1 35 36 9655 0 0 0 0 0 0 0 0 0 0 0 36 37 9429 1 0 0 0 0 0 0 0 0 0 0 37 38 8739 0 1 0 0 0 0 0 0 0 0 0 38 39 9552 0 0 1 0 0 0 0 0 0 0 0 39 40 9687 0 0 0 1 0 0 0 0 0 0 0 40 41 9019 0 0 0 0 1 0 0 0 0 0 0 41 42 9672 0 0 0 0 0 1 0 0 0 0 0 42 43 9206 0 0 0 0 0 0 1 0 0 0 0 43 44 9069 0 0 0 0 0 0 0 1 0 0 0 44 45 9788 0 0 0 0 0 0 0 0 1 0 0 45 46 10312 0 0 0 0 0 0 0 0 0 1 0 46 47 10105 0 0 0 0 0 0 0 0 0 0 1 47 48 9863 0 0 0 0 0 0 0 0 0 0 0 48 49 9656 1 0 0 0 0 0 0 0 0 0 0 49 50 9295 0 1 0 0 0 0 0 0 0 0 0 50 51 9946 0 0 1 0 0 0 0 0 0 0 0 51 52 9701 0 0 0 1 0 0 0 0 0 0 0 52 53 9049 0 0 0 0 1 0 0 0 0 0 0 53 54 10190 0 0 0 0 0 1 0 0 0 0 0 54 55 9706 0 0 0 0 0 0 1 0 0 0 0 55 56 9765 0 0 0 0 0 0 0 1 0 0 0 56 57 9893 0 0 0 0 0 0 0 0 1 0 0 57 58 9994 0 0 0 0 0 0 0 0 0 1 0 58 59 10433 0 0 0 0 0 0 0 0 0 0 1 59 60 10073 0 0 0 0 0 0 0 0 0 0 0 60 61 10112 1 0 0 0 0 0 0 0 0 0 0 61 62 9266 0 1 0 0 0 0 0 0 0 0 0 62 63 9820 0 0 1 0 0 0 0 0 0 0 0 63 64 10097 0 0 0 1 0 0 0 0 0 0 0 64 65 9115 0 0 0 0 1 0 0 0 0 0 0 65 66 10411 0 0 0 0 0 1 0 0 0 0 0 66 67 9678 0 0 0 0 0 0 1 0 0 0 0 67 68 10408 0 0 0 0 0 0 0 1 0 0 0 68 69 10153 0 0 0 0 0 0 0 0 1 0 0 69 70 10368 0 0 0 0 0 0 0 0 0 1 0 70 71 10581 0 0 0 0 0 0 0 0 0 0 1 71 72 10597 0 0 0 0 0 0 0 0 0 0 0 72 73 10680 1 0 0 0 0 0 0 0 0 0 0 73 74 9738 0 1 0 0 0 0 0 0 0 0 0 74 75 9556 0 0 1 0 0 0 0 0 0 0 0 75 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) M1 M2 M3 M4 M5 9330.587 107.621 -635.532 -287.828 8.745 -879.764 M6 M7 M8 M9 M10 M11 75.726 -309.617 -141.294 -196.970 367.186 234.510 t 11.010 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -604.73 -193.52 14.66 187.48 720.63 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9330.587 136.432 68.390 < 2e-16 *** M1 107.621 162.985 0.660 0.511500 M2 -635.532 162.917 -3.901 0.000238 *** M3 -287.828 162.864 -1.767 0.082101 . M4 8.745 169.407 0.052 0.958995 M5 -879.764 169.298 -5.197 2.40e-06 *** M6 75.726 169.203 0.448 0.656043 M7 -309.617 169.123 -1.831 0.071949 . M8 -141.294 169.058 -0.836 0.406492 M9 -196.970 169.007 -1.165 0.248298 M10 367.186 168.970 2.173 0.033604 * M11 234.510 168.949 1.388 0.170088 t 11.010 1.569 7.017 2.01e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 292.6 on 62 degrees of freedom Multiple R-squared: 0.7165, Adjusted R-squared: 0.6617 F-statistic: 13.06 on 12 and 62 DF, p-value: 8.078e-13 > 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,] 0.08579503 0.17159006 0.91420497 [2,] 0.05249914 0.10499828 0.94750086 [3,] 0.02310440 0.04620879 0.97689560 [4,] 0.23149521 0.46299042 0.76850479 [5,] 0.45579254 0.91158507 0.54420746 [6,] 0.50462485 0.99075029 0.49537515 [7,] 0.43767767 0.87535533 0.56232233 [8,] 0.34035187 0.68070374 0.65964813 [9,] 0.31085586 0.62171172 0.68914414 [10,] 0.26723634 0.53447267 0.73276366 [11,] 0.20684988 0.41369977 0.79315012 [12,] 0.23991625 0.47983251 0.76008375 [13,] 0.20563145 0.41126291 0.79436855 [14,] 0.15326896 0.30653792 0.84673104 [15,] 0.17300846 0.34601692 0.82699154 [16,] 0.26800300 0.53600600 0.73199700 [17,] 0.26141642 0.52283284 0.73858358 [18,] 0.26845892 0.53691783 0.73154108 [19,] 0.34299513 0.68599026 0.65700487 [20,] 0.35877367 0.71754735 0.64122633 [21,] 0.43190572 0.86381144 0.56809428 [22,] 0.38036295 0.76072589 0.61963705 [23,] 0.32910088 0.65820175 0.67089912 [24,] 0.45050500 0.90101001 0.54949500 [25,] 0.40319020 0.80638040 0.59680980 [26,] 0.48509413 0.97018826 0.51490587 [27,] 0.45431710 0.90863420 0.54568290 [28,] 0.38075627 0.76151254 0.61924373 [29,] 0.60612685 0.78774629 0.39387315 [30,] 0.67499677 0.65000646 0.32500323 [31,] 0.75229077 0.49541846 0.24770923 [32,] 0.72881192 0.54237616 0.27118808 [33,] 0.70413873 0.59172254 0.29586127 [34,] 0.74712683 0.50574635 0.25287317 [35,] 0.69942199 0.60115602 0.30057801 [36,] 0.91622456 0.16755088 0.08377544 [37,] 0.87268706 0.25462588 0.12731294 [38,] 0.83758669 0.32482661 0.16241331 [39,] 0.79132071 0.41735857 0.20867929 [40,] 0.78948386 0.42103228 0.21051614 [41,] 0.77592534 0.44814933 0.22407466 [42,] 0.67305864 0.65388272 0.32694136 [43,] 0.53945231 0.92109538 0.46054769 [44,] 0.41611044 0.83222088 0.58388956 > postscript(file="/var/wessaorg/rcomp/tmp/1ypr71322211226.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2rhko1322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3hna71322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/463z41322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5gaty1322211226.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 = 75 Frequency = 1 1 2 3 4 5 6 250.782609 363.925466 8.211180 359.628364 81.128364 258.628364 7 8 9 10 11 12 464.961698 720.628364 204.295031 230.128364 231.795031 -210.704969 13 14 15 16 17 18 155.664596 185.807453 -74.906832 -28.489648 62.010352 22.510352 19 20 21 22 23 24 -283.156315 -126.489648 -535.822981 7.010352 -190.322981 -276.822981 25 26 27 28 29 30 -108.453416 -341.310559 -126.024845 -80.607660 -223.107660 -551.607660 31 32 33 34 35 36 107.725673 -418.607660 -218.940994 97.892340 -516.440994 -71.940994 37 38 39 40 41 42 -416.571429 -374.428571 79.857143 -92.725673 116.774327 -196.725673 43 44 45 46 47 48 -288.392340 -604.725673 158.940994 107.774327 22.440994 3.940994 49 50 51 52 53 54 -321.689441 49.453416 341.739130 -210.843685 14.656315 189.156315 55 56 57 58 59 60 79.489648 -40.843685 131.822981 -342.343685 218.322981 81.822981 61 62 63 64 65 66 2.192547 -111.664596 83.621118 53.038302 -51.461698 278.038302 67 68 69 70 71 72 -80.628364 470.038302 259.704969 -100.461698 234.204969 473.704969 73 74 75 438.074534 228.217391 -312.496894 > postscript(file="/var/wessaorg/rcomp/tmp/66ymm1322211226.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 = 75 Frequency = 1 lag(myerror, k = 1) myerror 0 250.782609 NA 1 363.925466 250.782609 2 8.211180 363.925466 3 359.628364 8.211180 4 81.128364 359.628364 5 258.628364 81.128364 6 464.961698 258.628364 7 720.628364 464.961698 8 204.295031 720.628364 9 230.128364 204.295031 10 231.795031 230.128364 11 -210.704969 231.795031 12 155.664596 -210.704969 13 185.807453 155.664596 14 -74.906832 185.807453 15 -28.489648 -74.906832 16 62.010352 -28.489648 17 22.510352 62.010352 18 -283.156315 22.510352 19 -126.489648 -283.156315 20 -535.822981 -126.489648 21 7.010352 -535.822981 22 -190.322981 7.010352 23 -276.822981 -190.322981 24 -108.453416 -276.822981 25 -341.310559 -108.453416 26 -126.024845 -341.310559 27 -80.607660 -126.024845 28 -223.107660 -80.607660 29 -551.607660 -223.107660 30 107.725673 -551.607660 31 -418.607660 107.725673 32 -218.940994 -418.607660 33 97.892340 -218.940994 34 -516.440994 97.892340 35 -71.940994 -516.440994 36 -416.571429 -71.940994 37 -374.428571 -416.571429 38 79.857143 -374.428571 39 -92.725673 79.857143 40 116.774327 -92.725673 41 -196.725673 116.774327 42 -288.392340 -196.725673 43 -604.725673 -288.392340 44 158.940994 -604.725673 45 107.774327 158.940994 46 22.440994 107.774327 47 3.940994 22.440994 48 -321.689441 3.940994 49 49.453416 -321.689441 50 341.739130 49.453416 51 -210.843685 341.739130 52 14.656315 -210.843685 53 189.156315 14.656315 54 79.489648 189.156315 55 -40.843685 79.489648 56 131.822981 -40.843685 57 -342.343685 131.822981 58 218.322981 -342.343685 59 81.822981 218.322981 60 2.192547 81.822981 61 -111.664596 2.192547 62 83.621118 -111.664596 63 53.038302 83.621118 64 -51.461698 53.038302 65 278.038302 -51.461698 66 -80.628364 278.038302 67 470.038302 -80.628364 68 259.704969 470.038302 69 -100.461698 259.704969 70 234.204969 -100.461698 71 473.704969 234.204969 72 438.074534 473.704969 73 228.217391 438.074534 74 -312.496894 228.217391 75 NA -312.496894 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 363.925466 250.782609 [2,] 8.211180 363.925466 [3,] 359.628364 8.211180 [4,] 81.128364 359.628364 [5,] 258.628364 81.128364 [6,] 464.961698 258.628364 [7,] 720.628364 464.961698 [8,] 204.295031 720.628364 [9,] 230.128364 204.295031 [10,] 231.795031 230.128364 [11,] -210.704969 231.795031 [12,] 155.664596 -210.704969 [13,] 185.807453 155.664596 [14,] -74.906832 185.807453 [15,] -28.489648 -74.906832 [16,] 62.010352 -28.489648 [17,] 22.510352 62.010352 [18,] -283.156315 22.510352 [19,] -126.489648 -283.156315 [20,] -535.822981 -126.489648 [21,] 7.010352 -535.822981 [22,] -190.322981 7.010352 [23,] -276.822981 -190.322981 [24,] -108.453416 -276.822981 [25,] -341.310559 -108.453416 [26,] -126.024845 -341.310559 [27,] -80.607660 -126.024845 [28,] -223.107660 -80.607660 [29,] -551.607660 -223.107660 [30,] 107.725673 -551.607660 [31,] -418.607660 107.725673 [32,] -218.940994 -418.607660 [33,] 97.892340 -218.940994 [34,] -516.440994 97.892340 [35,] -71.940994 -516.440994 [36,] -416.571429 -71.940994 [37,] -374.428571 -416.571429 [38,] 79.857143 -374.428571 [39,] -92.725673 79.857143 [40,] 116.774327 -92.725673 [41,] -196.725673 116.774327 [42,] -288.392340 -196.725673 [43,] -604.725673 -288.392340 [44,] 158.940994 -604.725673 [45,] 107.774327 158.940994 [46,] 22.440994 107.774327 [47,] 3.940994 22.440994 [48,] -321.689441 3.940994 [49,] 49.453416 -321.689441 [50,] 341.739130 49.453416 [51,] -210.843685 341.739130 [52,] 14.656315 -210.843685 [53,] 189.156315 14.656315 [54,] 79.489648 189.156315 [55,] -40.843685 79.489648 [56,] 131.822981 -40.843685 [57,] -342.343685 131.822981 [58,] 218.322981 -342.343685 [59,] 81.822981 218.322981 [60,] 2.192547 81.822981 [61,] -111.664596 2.192547 [62,] 83.621118 -111.664596 [63,] 53.038302 83.621118 [64,] -51.461698 53.038302 [65,] 278.038302 -51.461698 [66,] -80.628364 278.038302 [67,] 470.038302 -80.628364 [68,] 259.704969 470.038302 [69,] -100.461698 259.704969 [70,] 234.204969 -100.461698 [71,] 473.704969 234.204969 [72,] 438.074534 473.704969 [73,] 228.217391 438.074534 [74,] -312.496894 228.217391 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 363.925466 250.782609 2 8.211180 363.925466 3 359.628364 8.211180 4 81.128364 359.628364 5 258.628364 81.128364 6 464.961698 258.628364 7 720.628364 464.961698 8 204.295031 720.628364 9 230.128364 204.295031 10 231.795031 230.128364 11 -210.704969 231.795031 12 155.664596 -210.704969 13 185.807453 155.664596 14 -74.906832 185.807453 15 -28.489648 -74.906832 16 62.010352 -28.489648 17 22.510352 62.010352 18 -283.156315 22.510352 19 -126.489648 -283.156315 20 -535.822981 -126.489648 21 7.010352 -535.822981 22 -190.322981 7.010352 23 -276.822981 -190.322981 24 -108.453416 -276.822981 25 -341.310559 -108.453416 26 -126.024845 -341.310559 27 -80.607660 -126.024845 28 -223.107660 -80.607660 29 -551.607660 -223.107660 30 107.725673 -551.607660 31 -418.607660 107.725673 32 -218.940994 -418.607660 33 97.892340 -218.940994 34 -516.440994 97.892340 35 -71.940994 -516.440994 36 -416.571429 -71.940994 37 -374.428571 -416.571429 38 79.857143 -374.428571 39 -92.725673 79.857143 40 116.774327 -92.725673 41 -196.725673 116.774327 42 -288.392340 -196.725673 43 -604.725673 -288.392340 44 158.940994 -604.725673 45 107.774327 158.940994 46 22.440994 107.774327 47 3.940994 22.440994 48 -321.689441 3.940994 49 49.453416 -321.689441 50 341.739130 49.453416 51 -210.843685 341.739130 52 14.656315 -210.843685 53 189.156315 14.656315 54 79.489648 189.156315 55 -40.843685 79.489648 56 131.822981 -40.843685 57 -342.343685 131.822981 58 218.322981 -342.343685 59 81.822981 218.322981 60 2.192547 81.822981 61 -111.664596 2.192547 62 83.621118 -111.664596 63 53.038302 83.621118 64 -51.461698 53.038302 65 278.038302 -51.461698 66 -80.628364 278.038302 67 470.038302 -80.628364 68 259.704969 470.038302 69 -100.461698 259.704969 70 234.204969 -100.461698 71 473.704969 234.204969 72 438.074534 473.704969 73 228.217391 438.074534 74 -312.496894 228.217391 > 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/wessaorg/rcomp/tmp/7hvfx1322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/89uo71322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9kffb1322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10x00e1322211226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/wessaorg/rcomp/tmp/11oamu1322211226.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/wessaorg/rcomp/tmp/12elzu1322211226.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/wessaorg/rcomp/tmp/13wts21322211226.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/wessaorg/rcomp/tmp/14rfo81322211226.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/wessaorg/rcomp/tmp/15a36j1322211226.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/wessaorg/rcomp/tmp/16yvri1322211226.tab") + } > > try(system("convert tmp/1ypr71322211226.ps tmp/1ypr71322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/2rhko1322211226.ps tmp/2rhko1322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/3hna71322211226.ps tmp/3hna71322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/463z41322211226.ps tmp/463z41322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/5gaty1322211226.ps tmp/5gaty1322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/66ymm1322211226.ps tmp/66ymm1322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/7hvfx1322211226.ps tmp/7hvfx1322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/89uo71322211226.ps tmp/89uo71322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/9kffb1322211226.ps tmp/9kffb1322211226.png",intern=TRUE)) character(0) > try(system("convert tmp/10x00e1322211226.ps tmp/10x00e1322211226.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.152 0.646 3.820