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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 + ,-1 + ,111.4 + ,0 + ,-1 + ,108.2 + ,0 + ,-1 + ,108.8 + ,0 + ,-1 + ,110.2 + ,0 + ,-1 + ,109.5 + ,0 + ,-1 + ,109.5 + ,0 + ,-1 + ,116.0 + ,0 + ,-1 + ,111.2 + ,0 + ,-1 + ,112.1 + ,0 + ,-1 + ,114.0 + ,0 + ,-1 + ,119.1 + ,0 + ,-1 + ,114.1 + ,1 + ,-1 + ,115.1 + ,1 + ,-1 + ,115.4 + ,1 + ,-1 + ,110.8 + ,1 + ,0 + ,116.0 + ,1 + ,0 + ,119.2 + ,1 + ,0 + ,126.5 + ,1 + ,0 + ,127.8 + ,1 + ,0 + ,131.3 + ,1 + ,0 + ,140.3 + ,1 + ,0 + ,137.3 + ,1 + ,0 + ,143.0 + ,1 + ,0 + ,134.5 + ,1 + ,0 + ,139.9 + ,1 + ,0 + ,159.3 + ,1 + ,0 + ,170.4 + ,1 + ,0 + ,175.0 + ,1 + ,0 + ,175.8 + ,1 + ,0 + ,180.9 + ,1 + ,0 + ,180.3 + ,1 + ,0 + ,169.6 + ,1 + ,0 + ,172.3 + ,1 + ,0 + ,184.8 + ,1 + ,0 + ,177.7 + ,1 + ,0 + ,184.6 + ,1 + ,0 + ,211.4 + ,1 + ,0 + ,215.3 + ,1 + ,0 + ,215.9 + ,1 + ,0) + ,dim=c(3 + ,95) + ,dimnames=list(c('prijsindex' + ,'ontkoppelde_bedrijfstoeslag' + ,'oogstomvang') + ,1:95)) > y <- array(NA,dim=c(3,95),dimnames=list(c('prijsindex','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 prijsindex 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 -1 56 57 111.4 0 -1 57 58 108.2 0 -1 58 59 108.8 0 -1 59 60 110.2 0 -1 60 61 109.5 0 -1 61 62 109.5 0 -1 62 63 116.0 0 -1 63 64 111.2 0 -1 64 65 112.1 0 -1 65 66 114.0 0 -1 66 67 119.1 0 -1 67 68 114.1 1 -1 68 69 115.1 1 -1 69 70 115.4 1 -1 70 71 110.8 1 0 71 72 116.0 1 0 72 73 119.2 1 0 73 74 126.5 1 0 74 75 127.8 1 0 75 76 131.3 1 0 76 77 140.3 1 0 77 78 137.3 1 0 78 79 143.0 1 0 79 80 134.5 1 0 80 81 139.9 1 0 81 82 159.3 1 0 82 83 170.4 1 0 83 84 175.0 1 0 84 85 175.8 1 0 85 86 180.9 1 0 86 87 180.3 1 0 87 88 169.6 1 0 88 89 172.3 1 0 89 90 184.8 1 0 90 91 177.7 1 0 91 92 184.6 1 0 92 93 211.4 1 0 93 94 215.3 1 0 94 95 215.9 1 0 95 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) ontkoppelde_bedrijfstoeslag 87.7821 15.6852 oogstomvang t 12.9296 0.6513 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -38.908 -6.499 -0.575 8.562 50.613 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 87.78212 3.92844 22.345 < 2e-16 *** ontkoppelde_bedrijfstoeslag 15.68523 5.95775 2.633 0.00995 ** oogstomvang 12.92958 3.16664 4.083 9.54e-05 *** t 0.65127 0.09975 6.529 3.70e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 16.23 on 91 degrees of freedom Multiple R-Squared: 0.6945, Adjusted R-squared: 0.6845 F-statistic: 68.97 on 3 and 91 DF, p-value: < 2.2e-16 > postscript(file="/var/www/html/rcomp/tmp/18yjt1198060147.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/299l11198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3rt6i1198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4phhh1198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5r29z1198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 14.2666055 14.1153334 15.8640613 13.5127892 16.1615172 9.0102451 7 8 9 10 11 12 -0.2410270 -2.6922991 -0.2435712 4.2051568 5.8538847 6.7026126 13 14 15 16 17 18 8.4513405 4.2000685 3.8487964 1.2975243 -0.4537478 -3.2050199 19 20 21 22 23 24 0.5437081 0.3924360 -1.1588361 -4.3101082 -5.3613803 -4.8126523 25 26 27 28 29 30 -4.3639244 -5.7151965 -7.2664686 -9.0177407 -8.1690127 -3.5202848 31 32 33 34 35 36 6.4284431 15.8771710 24.9258989 21.8746269 15.0233548 8.6720827 37 38 39 40 41 42 3.0208106 2.9695385 -0.6817335 -2.4330056 0.8157223 -4.3355498 43 44 45 46 47 48 -12.0868218 -18.2676700 -17.0189421 -19.4702142 -13.7214863 -10.2727583 49 50 51 52 53 54 -5.3240304 -3.4753025 3.1734254 6.8221533 2.1708813 -5.1803908 55 56 57 58 59 60 -19.3316629 -0.5237828 -0.5750549 -4.4263269 -4.4775990 -3.7288711 61 62 63 64 65 66 -5.0801432 -5.7314153 0.1173127 -5.3339594 -5.0852315 -3.8365036 67 68 69 70 71 72 0.6122243 -20.7242768 -20.3755489 -20.7268210 -38.9076692 -34.3589413 73 74 75 76 77 78 -31.8102133 -25.1614854 -24.5127575 -21.6640296 -13.3153017 -16.9665737 79 80 81 82 83 84 -11.9178458 -21.0691179 -16.3203900 2.4283380 12.8770659 16.8257938 85 86 87 88 89 90 16.9745217 21.4232496 20.1719776 8.8207055 10.8694334 22.7181613 91 92 93 94 95 14.9668892 21.2156172 47.3643451 50.6130730 50.5618009 > postscript(file="/var/www/html/rcomp/tmp/6fkw01198060147.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 14.2666055 NA 1 14.1153334 14.2666055 2 15.8640613 14.1153334 3 13.5127892 15.8640613 4 16.1615172 13.5127892 5 9.0102451 16.1615172 6 -0.2410270 9.0102451 7 -2.6922991 -0.2410270 8 -0.2435712 -2.6922991 9 4.2051568 -0.2435712 10 5.8538847 4.2051568 11 6.7026126 5.8538847 12 8.4513405 6.7026126 13 4.2000685 8.4513405 14 3.8487964 4.2000685 15 1.2975243 3.8487964 16 -0.4537478 1.2975243 17 -3.2050199 -0.4537478 18 0.5437081 -3.2050199 19 0.3924360 0.5437081 20 -1.1588361 0.3924360 21 -4.3101082 -1.1588361 22 -5.3613803 -4.3101082 23 -4.8126523 -5.3613803 24 -4.3639244 -4.8126523 25 -5.7151965 -4.3639244 26 -7.2664686 -5.7151965 27 -9.0177407 -7.2664686 28 -8.1690127 -9.0177407 29 -3.5202848 -8.1690127 30 6.4284431 -3.5202848 31 15.8771710 6.4284431 32 24.9258989 15.8771710 33 21.8746269 24.9258989 34 15.0233548 21.8746269 35 8.6720827 15.0233548 36 3.0208106 8.6720827 37 2.9695385 3.0208106 38 -0.6817335 2.9695385 39 -2.4330056 -0.6817335 40 0.8157223 -2.4330056 41 -4.3355498 0.8157223 42 -12.0868218 -4.3355498 43 -18.2676700 -12.0868218 44 -17.0189421 -18.2676700 45 -19.4702142 -17.0189421 46 -13.7214863 -19.4702142 47 -10.2727583 -13.7214863 48 -5.3240304 -10.2727583 49 -3.4753025 -5.3240304 50 3.1734254 -3.4753025 51 6.8221533 3.1734254 52 2.1708813 6.8221533 53 -5.1803908 2.1708813 54 -19.3316629 -5.1803908 55 -0.5237828 -19.3316629 56 -0.5750549 -0.5237828 57 -4.4263269 -0.5750549 58 -4.4775990 -4.4263269 59 -3.7288711 -4.4775990 60 -5.0801432 -3.7288711 61 -5.7314153 -5.0801432 62 0.1173127 -5.7314153 63 -5.3339594 0.1173127 64 -5.0852315 -5.3339594 65 -3.8365036 -5.0852315 66 0.6122243 -3.8365036 67 -20.7242768 0.6122243 68 -20.3755489 -20.7242768 69 -20.7268210 -20.3755489 70 -38.9076692 -20.7268210 71 -34.3589413 -38.9076692 72 -31.8102133 -34.3589413 73 -25.1614854 -31.8102133 74 -24.5127575 -25.1614854 75 -21.6640296 -24.5127575 76 -13.3153017 -21.6640296 77 -16.9665737 -13.3153017 78 -11.9178458 -16.9665737 79 -21.0691179 -11.9178458 80 -16.3203900 -21.0691179 81 2.4283380 -16.3203900 82 12.8770659 2.4283380 83 16.8257938 12.8770659 84 16.9745217 16.8257938 85 21.4232496 16.9745217 86 20.1719776 21.4232496 87 8.8207055 20.1719776 88 10.8694334 8.8207055 89 22.7181613 10.8694334 90 14.9668892 22.7181613 91 21.2156172 14.9668892 92 47.3643451 21.2156172 93 50.6130730 47.3643451 94 50.5618009 50.6130730 95 NA 50.5618009 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 14.1153334 14.2666055 [2,] 15.8640613 14.1153334 [3,] 13.5127892 15.8640613 [4,] 16.1615172 13.5127892 [5,] 9.0102451 16.1615172 [6,] -0.2410270 9.0102451 [7,] -2.6922991 -0.2410270 [8,] -0.2435712 -2.6922991 [9,] 4.2051568 -0.2435712 [10,] 5.8538847 4.2051568 [11,] 6.7026126 5.8538847 [12,] 8.4513405 6.7026126 [13,] 4.2000685 8.4513405 [14,] 3.8487964 4.2000685 [15,] 1.2975243 3.8487964 [16,] -0.4537478 1.2975243 [17,] -3.2050199 -0.4537478 [18,] 0.5437081 -3.2050199 [19,] 0.3924360 0.5437081 [20,] -1.1588361 0.3924360 [21,] -4.3101082 -1.1588361 [22,] -5.3613803 -4.3101082 [23,] -4.8126523 -5.3613803 [24,] -4.3639244 -4.8126523 [25,] -5.7151965 -4.3639244 [26,] -7.2664686 -5.7151965 [27,] -9.0177407 -7.2664686 [28,] -8.1690127 -9.0177407 [29,] -3.5202848 -8.1690127 [30,] 6.4284431 -3.5202848 [31,] 15.8771710 6.4284431 [32,] 24.9258989 15.8771710 [33,] 21.8746269 24.9258989 [34,] 15.0233548 21.8746269 [35,] 8.6720827 15.0233548 [36,] 3.0208106 8.6720827 [37,] 2.9695385 3.0208106 [38,] -0.6817335 2.9695385 [39,] -2.4330056 -0.6817335 [40,] 0.8157223 -2.4330056 [41,] -4.3355498 0.8157223 [42,] -12.0868218 -4.3355498 [43,] -18.2676700 -12.0868218 [44,] -17.0189421 -18.2676700 [45,] -19.4702142 -17.0189421 [46,] -13.7214863 -19.4702142 [47,] -10.2727583 -13.7214863 [48,] -5.3240304 -10.2727583 [49,] -3.4753025 -5.3240304 [50,] 3.1734254 -3.4753025 [51,] 6.8221533 3.1734254 [52,] 2.1708813 6.8221533 [53,] -5.1803908 2.1708813 [54,] -19.3316629 -5.1803908 [55,] -0.5237828 -19.3316629 [56,] -0.5750549 -0.5237828 [57,] -4.4263269 -0.5750549 [58,] -4.4775990 -4.4263269 [59,] -3.7288711 -4.4775990 [60,] -5.0801432 -3.7288711 [61,] -5.7314153 -5.0801432 [62,] 0.1173127 -5.7314153 [63,] -5.3339594 0.1173127 [64,] -5.0852315 -5.3339594 [65,] -3.8365036 -5.0852315 [66,] 0.6122243 -3.8365036 [67,] -20.7242768 0.6122243 [68,] -20.3755489 -20.7242768 [69,] -20.7268210 -20.3755489 [70,] -38.9076692 -20.7268210 [71,] -34.3589413 -38.9076692 [72,] -31.8102133 -34.3589413 [73,] -25.1614854 -31.8102133 [74,] -24.5127575 -25.1614854 [75,] -21.6640296 -24.5127575 [76,] -13.3153017 -21.6640296 [77,] -16.9665737 -13.3153017 [78,] -11.9178458 -16.9665737 [79,] -21.0691179 -11.9178458 [80,] -16.3203900 -21.0691179 [81,] 2.4283380 -16.3203900 [82,] 12.8770659 2.4283380 [83,] 16.8257938 12.8770659 [84,] 16.9745217 16.8257938 [85,] 21.4232496 16.9745217 [86,] 20.1719776 21.4232496 [87,] 8.8207055 20.1719776 [88,] 10.8694334 8.8207055 [89,] 22.7181613 10.8694334 [90,] 14.9668892 22.7181613 [91,] 21.2156172 14.9668892 [92,] 47.3643451 21.2156172 [93,] 50.6130730 47.3643451 [94,] 50.5618009 50.6130730 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 14.1153334 14.2666055 2 15.8640613 14.1153334 3 13.5127892 15.8640613 4 16.1615172 13.5127892 5 9.0102451 16.1615172 6 -0.2410270 9.0102451 7 -2.6922991 -0.2410270 8 -0.2435712 -2.6922991 9 4.2051568 -0.2435712 10 5.8538847 4.2051568 11 6.7026126 5.8538847 12 8.4513405 6.7026126 13 4.2000685 8.4513405 14 3.8487964 4.2000685 15 1.2975243 3.8487964 16 -0.4537478 1.2975243 17 -3.2050199 -0.4537478 18 0.5437081 -3.2050199 19 0.3924360 0.5437081 20 -1.1588361 0.3924360 21 -4.3101082 -1.1588361 22 -5.3613803 -4.3101082 23 -4.8126523 -5.3613803 24 -4.3639244 -4.8126523 25 -5.7151965 -4.3639244 26 -7.2664686 -5.7151965 27 -9.0177407 -7.2664686 28 -8.1690127 -9.0177407 29 -3.5202848 -8.1690127 30 6.4284431 -3.5202848 31 15.8771710 6.4284431 32 24.9258989 15.8771710 33 21.8746269 24.9258989 34 15.0233548 21.8746269 35 8.6720827 15.0233548 36 3.0208106 8.6720827 37 2.9695385 3.0208106 38 -0.6817335 2.9695385 39 -2.4330056 -0.6817335 40 0.8157223 -2.4330056 41 -4.3355498 0.8157223 42 -12.0868218 -4.3355498 43 -18.2676700 -12.0868218 44 -17.0189421 -18.2676700 45 -19.4702142 -17.0189421 46 -13.7214863 -19.4702142 47 -10.2727583 -13.7214863 48 -5.3240304 -10.2727583 49 -3.4753025 -5.3240304 50 3.1734254 -3.4753025 51 6.8221533 3.1734254 52 2.1708813 6.8221533 53 -5.1803908 2.1708813 54 -19.3316629 -5.1803908 55 -0.5237828 -19.3316629 56 -0.5750549 -0.5237828 57 -4.4263269 -0.5750549 58 -4.4775990 -4.4263269 59 -3.7288711 -4.4775990 60 -5.0801432 -3.7288711 61 -5.7314153 -5.0801432 62 0.1173127 -5.7314153 63 -5.3339594 0.1173127 64 -5.0852315 -5.3339594 65 -3.8365036 -5.0852315 66 0.6122243 -3.8365036 67 -20.7242768 0.6122243 68 -20.3755489 -20.7242768 69 -20.7268210 -20.3755489 70 -38.9076692 -20.7268210 71 -34.3589413 -38.9076692 72 -31.8102133 -34.3589413 73 -25.1614854 -31.8102133 74 -24.5127575 -25.1614854 75 -21.6640296 -24.5127575 76 -13.3153017 -21.6640296 77 -16.9665737 -13.3153017 78 -11.9178458 -16.9665737 79 -21.0691179 -11.9178458 80 -16.3203900 -21.0691179 81 2.4283380 -16.3203900 82 12.8770659 2.4283380 83 16.8257938 12.8770659 84 16.9745217 16.8257938 85 21.4232496 16.9745217 86 20.1719776 21.4232496 87 8.8207055 20.1719776 88 10.8694334 8.8207055 89 22.7181613 10.8694334 90 14.9668892 22.7181613 91 21.2156172 14.9668892 92 47.3643451 21.2156172 93 50.6130730 47.3643451 94 50.5618009 50.6130730 > 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/7pg301198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8m75g1198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9nf8t1198060147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > load(file='/var/www/html/rcomp/createtable') > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/107szh1198060148.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/11moyj1198060148.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/1254as1198060148.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/13e5j11198060148.tab") > > system("convert tmp/18yjt1198060147.ps tmp/18yjt1198060147.png") > system("convert tmp/299l11198060147.ps tmp/299l11198060147.png") > system("convert tmp/3rt6i1198060147.ps tmp/3rt6i1198060147.png") > system("convert tmp/4phhh1198060147.ps tmp/4phhh1198060147.png") > system("convert tmp/5r29z1198060147.ps tmp/5r29z1198060147.png") > system("convert tmp/6fkw01198060147.ps tmp/6fkw01198060147.png") > system("convert tmp/7pg301198060147.ps tmp/7pg301198060147.png") > system("convert tmp/8m75g1198060147.ps tmp/8m75g1198060147.png") > system("convert tmp/9nf8t1198060147.ps tmp/9nf8t1198060147.png") > > > proc.time() user system elapsed 2.482 1.558 3.710