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Type 'q()' to quit R. > x <- array(list(102.7 + ,0 + ,0 + ,103.2 + ,0 + ,0 + ,105.6 + ,0 + ,0 + ,103.9 + ,0 + ,0 + ,107.2 + ,0 + ,0 + ,100.7 + ,0 + ,0 + ,92.1 + ,0 + ,0 + ,90.3 + ,0 + ,0 + ,93.4 + ,0 + ,0 + ,98.5 + ,0 + ,0 + ,100.8 + ,0 + ,0 + ,102.3 + ,0 + ,0 + ,104.7 + ,0 + ,0 + ,101.1 + ,0 + ,0 + ,101.4 + ,0 + ,0 + ,99.5 + ,0 + ,0 + ,98.4 + ,0 + ,0 + ,96.3 + ,0 + ,0 + ,100.7 + ,0 + ,0 + ,101.2 + ,0 + ,0 + ,100.3 + ,0 + ,0 + ,97.8 + ,0 + ,0 + ,97.4 + ,0 + ,0 + ,98.6 + ,0 + ,0 + ,99.7 + ,0 + ,0 + ,99.0 + ,0 + ,0 + ,98.1 + ,0 + ,0 + ,97.0 + ,0 + ,0 + ,98.5 + ,0 + ,0 + ,103.8 + ,0 + ,0 + ,114.4 + ,0 + ,0 + ,124.5 + ,0 + ,0 + ,134.2 + ,0 + ,0 + ,131.8 + ,0 + ,0 + ,125.6 + ,0 + ,0 + ,119.9 + ,0 + ,0 + ,114.9 + ,0 + ,0 + ,115.5 + ,0 + ,0 + ,112.5 + ,0 + ,0 + ,111.4 + ,0 + ,0 + ,115.3 + ,0 + ,0 + ,110.8 + ,0 + ,0 + ,103.7 + ,0 + ,0 + ,111.1 + ,0 + ,1 + ,113.0 + ,0 + ,1 + ,111.2 + ,0 + ,1 + ,117.6 + ,0 + ,1 + ,121.7 + ,0 + ,1 + ,127.3 + ,0 + ,1 + ,129.8 + ,0 + ,1 + ,137.1 + ,0 + ,1 + ,141.4 + ,0 + ,1 + ,137.4 + ,0 + ,1 + ,130.7 + ,0 + ,1 + ,117.2 + ,0 + ,1 + ,110.8 + ,0 + ,0 + ,111.4 + ,0 + ,0 + ,108.2 + ,0 + ,0 + ,108.8 + ,0 + ,0 + ,110.2 + ,0 + ,0 + ,109.5 + ,1 + ,0 + ,109.5 + ,1 + ,0 + ,116.0 + ,1 + ,0 + ,111.2 + ,1 + ,0 + ,112.1 + ,1 + ,0 + ,114.0 + ,1 + ,0 + ,119.1 + ,1 + ,0 + ,114.1 + ,1 + ,2 + ,115.1 + ,1 + ,2 + ,115.4 + ,1 + ,2 + ,110.8 + ,1 + ,2 + ,116.0 + ,1 + ,2 + ,119.2 + ,2 + ,2 + ,126.5 + ,2 + ,2 + ,127.8 + ,2 + ,2 + ,131.3 + ,2 + ,2 + ,140.3 + ,2 + ,2 + ,137.3 + ,2 + ,2 + ,143.0 + ,2 + ,2 + ,134.5 + ,2 + ,0 + ,139.9 + ,2 + ,0 + ,159.3 + ,2 + ,0 + ,170.4 + ,2 + ,0 + ,175.0 + ,2 + ,0 + ,175.8 + ,2 + ,0 + ,180.9 + ,2 + ,0 + ,180.3 + ,2 + ,0 + ,169.6 + ,2 + ,0 + ,172.3 + ,2 + ,0 + ,184.8 + ,2 + ,0 + ,177.7 + ,2 + ,0 + ,184.6 + ,2 + ,0 + ,211.4 + ,2 + ,0 + ,215.3 + ,2 + ,0 + ,215.9 + ,2 + ,0) + ,dim=c(3 + ,95) + ,dimnames=list(c('graanprijs' + ,'ontkoppelde_bedrijfstoeslag' + ,'oogstomvang') + ,1:95)) > y <- array(NA,dim=c(3,95),dimnames=list(c('graanprijs','ontkoppelde_bedrijfstoeslag','oogstomvang'),1:95)) > 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 = '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) > 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 graanprijs ontkoppelde_bedrijfstoeslag oogstomvang t 1 102.7 0 0 1 2 103.2 0 0 2 3 105.6 0 0 3 4 103.9 0 0 4 5 107.2 0 0 5 6 100.7 0 0 6 7 92.1 0 0 7 8 90.3 0 0 8 9 93.4 0 0 9 10 98.5 0 0 10 11 100.8 0 0 11 12 102.3 0 0 12 13 104.7 0 0 13 14 101.1 0 0 14 15 101.4 0 0 15 16 99.5 0 0 16 17 98.4 0 0 17 18 96.3 0 0 18 19 100.7 0 0 19 20 101.2 0 0 20 21 100.3 0 0 21 22 97.8 0 0 22 23 97.4 0 0 23 24 98.6 0 0 24 25 99.7 0 0 25 26 99.0 0 0 26 27 98.1 0 0 27 28 97.0 0 0 28 29 98.5 0 0 29 30 103.8 0 0 30 31 114.4 0 0 31 32 124.5 0 0 32 33 134.2 0 0 33 34 131.8 0 0 34 35 125.6 0 0 35 36 119.9 0 0 36 37 114.9 0 0 37 38 115.5 0 0 38 39 112.5 0 0 39 40 111.4 0 0 40 41 115.3 0 0 41 42 110.8 0 0 42 43 103.7 0 0 43 44 111.1 0 1 44 45 113.0 0 1 45 46 111.2 0 1 46 47 117.6 0 1 47 48 121.7 0 1 48 49 127.3 0 1 49 50 129.8 0 1 50 51 137.1 0 1 51 52 141.4 0 1 52 53 137.4 0 1 53 54 130.7 0 1 54 55 117.2 0 1 55 56 110.8 0 0 56 57 111.4 0 0 57 58 108.2 0 0 58 59 108.8 0 0 59 60 110.2 0 0 60 61 109.5 1 0 61 62 109.5 1 0 62 63 116.0 1 0 63 64 111.2 1 0 64 65 112.1 1 0 65 66 114.0 1 0 66 67 119.1 1 0 67 68 114.1 1 2 68 69 115.1 1 2 69 70 115.4 1 2 70 71 110.8 1 2 71 72 116.0 1 2 72 73 119.2 2 2 73 74 126.5 2 2 74 75 127.8 2 2 75 76 131.3 2 2 76 77 140.3 2 2 77 78 137.3 2 2 78 79 143.0 2 2 79 80 134.5 2 0 80 81 139.9 2 0 81 82 159.3 2 0 82 83 170.4 2 0 83 84 175.0 2 0 84 85 175.8 2 0 85 86 180.9 2 0 86 87 180.3 2 0 87 88 169.6 2 0 88 89 172.3 2 0 89 90 184.8 2 0 90 91 177.7 2 0 91 92 184.6 2 0 92 93 211.4 2 0 93 94 215.3 2 0 94 95 215.9 2 0 95 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) ontkoppelde_bedrijfstoeslag 90.6785 11.1582 oogstomvang t -11.2396 0.6255 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -31.121 -7.869 -1.026 9.546 43.504 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 90.6785 4.0290 22.506 < 2e-16 *** ontkoppelde_bedrijfstoeslag 11.1582 3.6069 3.094 0.00263 ** oogstomvang -11.2396 2.4756 -4.540 1.72e-05 *** t 0.6255 0.1142 5.477 3.81e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 15.81 on 91 degrees of freedom Multiple R-Squared: 0.71, Adjusted R-squared: 0.7004 F-statistic: 74.27 on 3 and 91 DF, p-value: < 2.2e-16 > postscript(file="/var/www/html/rcomp/tmp/184ks1197628469.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/2rjwn1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3zlaz1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4y07v1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/57wdh1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 95 Frequency = 1 1 2 3 4 5 6 7 11.395929 11.270383 13.044837 10.719292 13.393746 6.268200 -2.957345 8 9 10 11 12 13 14 -5.382891 -2.908437 1.566018 3.240472 4.114926 5.889381 1.663835 15 16 17 18 19 20 21 1.338289 -1.187256 -2.912802 -5.638347 -1.863893 -1.989439 -3.514984 22 23 24 25 26 27 28 -6.640530 -7.666076 -7.091621 -6.617167 -7.942713 -9.468258 -11.193804 29 30 31 32 33 34 35 -10.319350 -5.644895 4.329559 13.804013 22.878468 19.852922 13.027377 36 37 38 39 40 41 42 6.701831 1.076285 1.050740 -2.574806 -4.300352 -1.025897 -6.151443 43 44 45 46 47 48 49 -13.876989 4.137077 5.411531 2.985986 8.760440 12.234894 17.209349 50 51 52 53 54 55 56 19.083803 25.758257 29.432712 24.807166 17.481620 3.356075 -14.909082 57 58 59 60 61 62 63 -14.934628 -18.760173 -18.785719 -18.011265 -30.495050 -31.120596 -25.246141 64 65 66 67 68 69 70 -30.671687 -30.397233 -29.122778 -24.648324 -7.794647 -7.420193 -7.745738 71 72 73 74 75 76 77 -12.971284 -8.396830 -16.980615 -10.306161 -9.631706 -6.757252 1.617203 78 79 80 81 82 83 84 -2.008343 3.066111 -28.538657 -23.764202 -4.989748 5.484706 9.459161 85 86 87 88 89 90 91 9.633615 14.108069 12.882524 1.556978 3.631432 15.505887 7.780341 92 93 94 95 14.054795 40.229250 43.503704 43.478158 > postscript(file="/var/www/html/rcomp/tmp/6uncu1197628469.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 = 95 Frequency = 1 lag(myerror, k = 1) myerror 0 11.395929 NA 1 11.270383 11.395929 2 13.044837 11.270383 3 10.719292 13.044837 4 13.393746 10.719292 5 6.268200 13.393746 6 -2.957345 6.268200 7 -5.382891 -2.957345 8 -2.908437 -5.382891 9 1.566018 -2.908437 10 3.240472 1.566018 11 4.114926 3.240472 12 5.889381 4.114926 13 1.663835 5.889381 14 1.338289 1.663835 15 -1.187256 1.338289 16 -2.912802 -1.187256 17 -5.638347 -2.912802 18 -1.863893 -5.638347 19 -1.989439 -1.863893 20 -3.514984 -1.989439 21 -6.640530 -3.514984 22 -7.666076 -6.640530 23 -7.091621 -7.666076 24 -6.617167 -7.091621 25 -7.942713 -6.617167 26 -9.468258 -7.942713 27 -11.193804 -9.468258 28 -10.319350 -11.193804 29 -5.644895 -10.319350 30 4.329559 -5.644895 31 13.804013 4.329559 32 22.878468 13.804013 33 19.852922 22.878468 34 13.027377 19.852922 35 6.701831 13.027377 36 1.076285 6.701831 37 1.050740 1.076285 38 -2.574806 1.050740 39 -4.300352 -2.574806 40 -1.025897 -4.300352 41 -6.151443 -1.025897 42 -13.876989 -6.151443 43 4.137077 -13.876989 44 5.411531 4.137077 45 2.985986 5.411531 46 8.760440 2.985986 47 12.234894 8.760440 48 17.209349 12.234894 49 19.083803 17.209349 50 25.758257 19.083803 51 29.432712 25.758257 52 24.807166 29.432712 53 17.481620 24.807166 54 3.356075 17.481620 55 -14.909082 3.356075 56 -14.934628 -14.909082 57 -18.760173 -14.934628 58 -18.785719 -18.760173 59 -18.011265 -18.785719 60 -30.495050 -18.011265 61 -31.120596 -30.495050 62 -25.246141 -31.120596 63 -30.671687 -25.246141 64 -30.397233 -30.671687 65 -29.122778 -30.397233 66 -24.648324 -29.122778 67 -7.794647 -24.648324 68 -7.420193 -7.794647 69 -7.745738 -7.420193 70 -12.971284 -7.745738 71 -8.396830 -12.971284 72 -16.980615 -8.396830 73 -10.306161 -16.980615 74 -9.631706 -10.306161 75 -6.757252 -9.631706 76 1.617203 -6.757252 77 -2.008343 1.617203 78 3.066111 -2.008343 79 -28.538657 3.066111 80 -23.764202 -28.538657 81 -4.989748 -23.764202 82 5.484706 -4.989748 83 9.459161 5.484706 84 9.633615 9.459161 85 14.108069 9.633615 86 12.882524 14.108069 87 1.556978 12.882524 88 3.631432 1.556978 89 15.505887 3.631432 90 7.780341 15.505887 91 14.054795 7.780341 92 40.229250 14.054795 93 43.503704 40.229250 94 43.478158 43.503704 95 NA 43.478158 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 11.270383 11.395929 [2,] 13.044837 11.270383 [3,] 10.719292 13.044837 [4,] 13.393746 10.719292 [5,] 6.268200 13.393746 [6,] -2.957345 6.268200 [7,] -5.382891 -2.957345 [8,] -2.908437 -5.382891 [9,] 1.566018 -2.908437 [10,] 3.240472 1.566018 [11,] 4.114926 3.240472 [12,] 5.889381 4.114926 [13,] 1.663835 5.889381 [14,] 1.338289 1.663835 [15,] -1.187256 1.338289 [16,] -2.912802 -1.187256 [17,] -5.638347 -2.912802 [18,] -1.863893 -5.638347 [19,] -1.989439 -1.863893 [20,] -3.514984 -1.989439 [21,] -6.640530 -3.514984 [22,] -7.666076 -6.640530 [23,] -7.091621 -7.666076 [24,] -6.617167 -7.091621 [25,] -7.942713 -6.617167 [26,] -9.468258 -7.942713 [27,] -11.193804 -9.468258 [28,] -10.319350 -11.193804 [29,] -5.644895 -10.319350 [30,] 4.329559 -5.644895 [31,] 13.804013 4.329559 [32,] 22.878468 13.804013 [33,] 19.852922 22.878468 [34,] 13.027377 19.852922 [35,] 6.701831 13.027377 [36,] 1.076285 6.701831 [37,] 1.050740 1.076285 [38,] -2.574806 1.050740 [39,] -4.300352 -2.574806 [40,] -1.025897 -4.300352 [41,] -6.151443 -1.025897 [42,] -13.876989 -6.151443 [43,] 4.137077 -13.876989 [44,] 5.411531 4.137077 [45,] 2.985986 5.411531 [46,] 8.760440 2.985986 [47,] 12.234894 8.760440 [48,] 17.209349 12.234894 [49,] 19.083803 17.209349 [50,] 25.758257 19.083803 [51,] 29.432712 25.758257 [52,] 24.807166 29.432712 [53,] 17.481620 24.807166 [54,] 3.356075 17.481620 [55,] -14.909082 3.356075 [56,] -14.934628 -14.909082 [57,] -18.760173 -14.934628 [58,] -18.785719 -18.760173 [59,] -18.011265 -18.785719 [60,] -30.495050 -18.011265 [61,] -31.120596 -30.495050 [62,] -25.246141 -31.120596 [63,] -30.671687 -25.246141 [64,] -30.397233 -30.671687 [65,] -29.122778 -30.397233 [66,] -24.648324 -29.122778 [67,] -7.794647 -24.648324 [68,] -7.420193 -7.794647 [69,] -7.745738 -7.420193 [70,] -12.971284 -7.745738 [71,] -8.396830 -12.971284 [72,] -16.980615 -8.396830 [73,] -10.306161 -16.980615 [74,] -9.631706 -10.306161 [75,] -6.757252 -9.631706 [76,] 1.617203 -6.757252 [77,] -2.008343 1.617203 [78,] 3.066111 -2.008343 [79,] -28.538657 3.066111 [80,] -23.764202 -28.538657 [81,] -4.989748 -23.764202 [82,] 5.484706 -4.989748 [83,] 9.459161 5.484706 [84,] 9.633615 9.459161 [85,] 14.108069 9.633615 [86,] 12.882524 14.108069 [87,] 1.556978 12.882524 [88,] 3.631432 1.556978 [89,] 15.505887 3.631432 [90,] 7.780341 15.505887 [91,] 14.054795 7.780341 [92,] 40.229250 14.054795 [93,] 43.503704 40.229250 [94,] 43.478158 43.503704 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 11.270383 11.395929 2 13.044837 11.270383 3 10.719292 13.044837 4 13.393746 10.719292 5 6.268200 13.393746 6 -2.957345 6.268200 7 -5.382891 -2.957345 8 -2.908437 -5.382891 9 1.566018 -2.908437 10 3.240472 1.566018 11 4.114926 3.240472 12 5.889381 4.114926 13 1.663835 5.889381 14 1.338289 1.663835 15 -1.187256 1.338289 16 -2.912802 -1.187256 17 -5.638347 -2.912802 18 -1.863893 -5.638347 19 -1.989439 -1.863893 20 -3.514984 -1.989439 21 -6.640530 -3.514984 22 -7.666076 -6.640530 23 -7.091621 -7.666076 24 -6.617167 -7.091621 25 -7.942713 -6.617167 26 -9.468258 -7.942713 27 -11.193804 -9.468258 28 -10.319350 -11.193804 29 -5.644895 -10.319350 30 4.329559 -5.644895 31 13.804013 4.329559 32 22.878468 13.804013 33 19.852922 22.878468 34 13.027377 19.852922 35 6.701831 13.027377 36 1.076285 6.701831 37 1.050740 1.076285 38 -2.574806 1.050740 39 -4.300352 -2.574806 40 -1.025897 -4.300352 41 -6.151443 -1.025897 42 -13.876989 -6.151443 43 4.137077 -13.876989 44 5.411531 4.137077 45 2.985986 5.411531 46 8.760440 2.985986 47 12.234894 8.760440 48 17.209349 12.234894 49 19.083803 17.209349 50 25.758257 19.083803 51 29.432712 25.758257 52 24.807166 29.432712 53 17.481620 24.807166 54 3.356075 17.481620 55 -14.909082 3.356075 56 -14.934628 -14.909082 57 -18.760173 -14.934628 58 -18.785719 -18.760173 59 -18.011265 -18.785719 60 -30.495050 -18.011265 61 -31.120596 -30.495050 62 -25.246141 -31.120596 63 -30.671687 -25.246141 64 -30.397233 -30.671687 65 -29.122778 -30.397233 66 -24.648324 -29.122778 67 -7.794647 -24.648324 68 -7.420193 -7.794647 69 -7.745738 -7.420193 70 -12.971284 -7.745738 71 -8.396830 -12.971284 72 -16.980615 -8.396830 73 -10.306161 -16.980615 74 -9.631706 -10.306161 75 -6.757252 -9.631706 76 1.617203 -6.757252 77 -2.008343 1.617203 78 3.066111 -2.008343 79 -28.538657 3.066111 80 -23.764202 -28.538657 81 -4.989748 -23.764202 82 5.484706 -4.989748 83 9.459161 5.484706 84 9.633615 9.459161 85 14.108069 9.633615 86 12.882524 14.108069 87 1.556978 12.882524 88 3.631432 1.556978 89 15.505887 3.631432 90 7.780341 15.505887 91 14.054795 7.780341 92 40.229250 14.054795 93 43.503704 40.229250 94 43.478158 43.503704 > 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/7sn4i1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/84cli1197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9wmf31197628469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > load(file='/var/www/html/rcomp/createtable') > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/10lwrn1197628470.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/11rxv51197628470.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/12fnne1197628470.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/138v5u1197628470.tab") > > system("convert tmp/184ks1197628469.ps tmp/184ks1197628469.png") > system("convert tmp/2rjwn1197628469.ps tmp/2rjwn1197628469.png") > system("convert tmp/3zlaz1197628469.ps tmp/3zlaz1197628469.png") > system("convert tmp/4y07v1197628469.ps tmp/4y07v1197628469.png") > system("convert tmp/57wdh1197628469.ps tmp/57wdh1197628469.png") > system("convert tmp/6uncu1197628469.ps tmp/6uncu1197628469.png") > system("convert tmp/7sn4i1197628469.ps tmp/7sn4i1197628469.png") > system("convert tmp/84cli1197628469.ps tmp/84cli1197628469.png") > system("convert tmp/9wmf31197628469.ps tmp/9wmf31197628469.png") > > > proc.time() user system elapsed 2.356 1.485 2.791