R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(4 + ,0 + ,5 + ,5 + ,0 + ,4 + ,6 + ,0 + ,5 + ,6 + ,0 + ,6 + ,6 + ,0 + ,6 + ,7 + ,0 + ,6 + ,8 + ,0 + ,7 + ,7 + ,0 + ,8 + ,8 + ,0 + ,7 + ,7 + ,0 + ,8 + ,8 + ,0 + ,7 + ,8 + ,0 + ,8 + ,9 + ,0 + ,8 + ,9 + ,0 + ,9 + ,8 + ,0 + ,9 + ,9 + ,0 + ,8 + ,9 + ,0 + ,9 + ,10 + ,0 + ,9 + ,11 + ,0 + ,10 + ,12 + ,0 + ,11 + ,13 + ,0 + ,12 + ,13 + ,0 + ,13 + ,13 + ,0 + ,13 + ,14 + ,0 + ,13 + ,14 + ,0 + ,14 + ,15 + ,0 + ,14 + ,15 + ,0 + ,15 + ,16 + ,0 + ,15 + ,16 + ,0 + ,16 + ,17 + ,0 + ,16 + ,18 + ,0 + ,17 + ,19 + ,0 + ,18 + ,20 + ,0 + ,19 + ,22 + ,0 + ,20 + ,20 + ,0 + ,22 + ,22 + ,0 + ,20 + ,25 + ,0 + ,22 + ,24 + ,0 + ,25 + ,25 + ,0 + ,24 + ,28 + ,0 + ,25 + ,26 + ,0 + ,28 + ,27 + ,0 + ,26 + ,26 + ,0 + ,27 + ,25 + ,0 + ,26 + ,27 + ,0 + ,25 + ,28 + ,0 + ,27 + ,30 + ,0 + ,28 + ,31 + ,0 + ,30 + ,32 + ,0 + ,31 + ,34 + ,0 + ,32 + ,34 + ,0 + ,34 + ,33 + ,0 + ,34 + ,32 + ,0 + ,33 + ,34 + ,0 + ,32 + ,36 + ,0 + ,34 + ,37 + ,0 + ,36 + ,40 + ,0 + ,37 + ,38 + ,0 + ,40 + ,38 + ,0 + ,38 + ,36 + ,0 + ,38 + ,40 + ,0 + ,36 + ,40 + ,0 + ,40 + ,42 + ,0 + ,40 + ,44 + ,0 + ,42 + ,45 + ,0 + ,44 + ,47 + ,0 + ,45 + ,49 + ,0 + ,47 + ,47 + ,0 + ,49 + ,49 + ,0 + ,47 + ,52 + ,0 + ,49 + ,50 + ,0 + ,52 + ,50 + ,0 + ,50 + ,57 + ,0 + ,50 + ,58 + ,0 + ,57 + ,58 + ,0 + ,58 + ,58 + ,0 + ,58 + ,61 + ,0 + ,58 + ,61 + ,0 + ,61 + ,64 + ,0 + ,61 + ,68 + ,0 + ,64 + ,40 + ,0 + ,68 + ,34 + ,0 + ,40 + ,46 + ,0 + ,34 + ,36 + ,0 + ,46 + ,34 + ,0 + ,36 + ,45 + ,0 + ,34 + ,55 + ,0 + ,45 + ,50 + ,0 + ,55 + ,56 + ,0 + ,50 + ,72 + ,0 + ,56 + ,76 + ,0 + ,72 + ,78 + ,0 + ,76 + ,77 + ,0 + ,78 + ,90 + ,0 + ,77 + ,88 + ,0 + ,90 + ,97 + ,0 + ,88 + ,93 + ,0 + ,97 + ,84 + ,0 + ,93 + ,67 + ,0 + ,84 + ,72 + ,0 + ,67 + ,75 + ,0 + ,72 + ,71 + ,0 + ,75 + ,75 + ,0 + ,71 + ,90 + ,0 + ,75 + ,78 + ,0 + ,90 + ,73 + ,0 + ,78 + ,62 + ,0 + ,73 + ,65 + ,0 + ,62 + ,61 + ,0 + ,65 + ,58 + ,0 + ,61 + ,33 + ,0 + ,58 + ,39 + ,0 + ,33 + ,56 + ,0 + ,39 + ,79 + ,0 + ,56 + ,82 + ,0 + ,79 + ,79 + ,0 + ,82 + ,73 + ,0 + ,79 + ,87 + ,0 + ,73 + ,85 + ,0 + ,87 + ,83 + ,0 + ,85 + ,82 + ,0 + ,83 + ,83 + ,0 + ,82 + ,92 + ,0 + ,83 + ,95 + ,0 + ,92 + ,97 + ,0 + ,95 + ,87 + ,0 + ,97 + ,84 + ,0 + ,87 + ,84 + ,0 + ,84 + ,89 + ,0 + ,84 + ,103 + ,0 + ,89 + ,106 + ,0 + ,103 + ,109 + ,0 + ,106 + ,106 + ,0 + ,109 + ,105 + ,0 + ,106 + ,115 + ,0 + ,105 + ,120 + ,0 + ,115 + ,124 + ,0 + ,120 + ,121 + ,0 + ,124 + ,131 + ,0 + ,121 + ,139 + ,0 + ,131 + ,133 + ,0 + ,139 + ,119 + ,0 + ,133 + ,123 + ,0 + ,119 + ,120 + ,0 + ,123 + ,128 + ,0 + ,120 + ,134 + ,0 + ,128 + ,126 + ,0 + ,134 + ,115 + ,0 + ,126 + ,106 + ,0 + ,115 + ,99 + ,0 + ,106 + ,100 + ,0 + ,99 + ,99 + ,0 + ,100 + ,99 + ,0 + ,99 + ,100 + ,0 + ,99 + ,100 + ,0 + ,100 + ,108 + ,0 + ,100 + ,109 + ,0 + ,108 + ,115 + ,0 + ,109 + ,114 + ,0 + ,115 + ,108 + ,0 + ,114 + ,113 + ,0 + ,108 + ,118 + ,0 + ,113 + ,122 + ,0 + ,118 + ,118 + ,0 + ,122 + ,121 + ,0 + ,118 + ,118 + ,0 + ,121 + ,121 + ,0 + ,118 + ,121 + ,0 + ,121 + ,112 + ,0 + ,121 + ,119 + ,0 + ,112 + ,116 + ,0 + ,119 + ,110 + ,1 + ,116 + ,111 + ,1 + ,110 + ,106 + ,1 + ,111 + ,108 + ,1 + ,106) + ,dim=c(3 + ,175) + ,dimnames=list(c('CO2-uitstoot' + ,'Kyoto-protocol' + ,'Y-1') + ,1:175)) > y <- array(NA,dim=c(3,175),dimnames=list(c('CO2-uitstoot','Kyoto-protocol','Y-1'),1:175)) > 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) > 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 CO2-uitstoot Kyoto-protocol Y-1 t 1 4 0 5 1 2 5 0 4 2 3 6 0 5 3 4 6 0 6 4 5 6 0 6 5 6 7 0 6 6 7 8 0 7 7 8 7 0 8 8 9 8 0 7 9 10 7 0 8 10 11 8 0 7 11 12 8 0 8 12 13 9 0 8 13 14 9 0 9 14 15 8 0 9 15 16 9 0 8 16 17 9 0 9 17 18 10 0 9 18 19 11 0 10 19 20 12 0 11 20 21 13 0 12 21 22 13 0 13 22 23 13 0 13 23 24 14 0 13 24 25 14 0 14 25 26 15 0 14 26 27 15 0 15 27 28 16 0 15 28 29 16 0 16 29 30 17 0 16 30 31 18 0 17 31 32 19 0 18 32 33 20 0 19 33 34 22 0 20 34 35 20 0 22 35 36 22 0 20 36 37 25 0 22 37 38 24 0 25 38 39 25 0 24 39 40 28 0 25 40 41 26 0 28 41 42 27 0 26 42 43 26 0 27 43 44 25 0 26 44 45 27 0 25 45 46 28 0 27 46 47 30 0 28 47 48 31 0 30 48 49 32 0 31 49 50 34 0 32 50 51 34 0 34 51 52 33 0 34 52 53 32 0 33 53 54 34 0 32 54 55 36 0 34 55 56 37 0 36 56 57 40 0 37 57 58 38 0 40 58 59 38 0 38 59 60 36 0 38 60 61 40 0 36 61 62 40 0 40 62 63 42 0 40 63 64 44 0 42 64 65 45 0 44 65 66 47 0 45 66 67 49 0 47 67 68 47 0 49 68 69 49 0 47 69 70 52 0 49 70 71 50 0 52 71 72 50 0 50 72 73 57 0 50 73 74 58 0 57 74 75 58 0 58 75 76 58 0 58 76 77 61 0 58 77 78 61 0 61 78 79 64 0 61 79 80 68 0 64 80 81 40 0 68 81 82 34 0 40 82 83 46 0 34 83 84 36 0 46 84 85 34 0 36 85 86 45 0 34 86 87 55 0 45 87 88 50 0 55 88 89 56 0 50 89 90 72 0 56 90 91 76 0 72 91 92 78 0 76 92 93 77 0 78 93 94 90 0 77 94 95 88 0 90 95 96 97 0 88 96 97 93 0 97 97 98 84 0 93 98 99 67 0 84 99 100 72 0 67 100 101 75 0 72 101 102 71 0 75 102 103 75 0 71 103 104 90 0 75 104 105 78 0 90 105 106 73 0 78 106 107 62 0 73 107 108 65 0 62 108 109 61 0 65 109 110 58 0 61 110 111 33 0 58 111 112 39 0 33 112 113 56 0 39 113 114 79 0 56 114 115 82 0 79 115 116 79 0 82 116 117 73 0 79 117 118 87 0 73 118 119 85 0 87 119 120 83 0 85 120 121 82 0 83 121 122 83 0 82 122 123 92 0 83 123 124 95 0 92 124 125 97 0 95 125 126 87 0 97 126 127 84 0 87 127 128 84 0 84 128 129 89 0 84 129 130 103 0 89 130 131 106 0 103 131 132 109 0 106 132 133 106 0 109 133 134 105 0 106 134 135 115 0 105 135 136 120 0 115 136 137 124 0 120 137 138 121 0 124 138 139 131 0 121 139 140 139 0 131 140 141 133 0 139 141 142 119 0 133 142 143 123 0 119 143 144 120 0 123 144 145 128 0 120 145 146 134 0 128 146 147 126 0 134 147 148 115 0 126 148 149 106 0 115 149 150 99 0 106 150 151 100 0 99 151 152 99 0 100 152 153 99 0 99 153 154 100 0 99 154 155 100 0 100 155 156 108 0 100 156 157 109 0 108 157 158 115 0 109 158 159 114 0 115 159 160 108 0 114 160 161 113 0 108 161 162 118 0 113 162 163 122 0 118 163 164 118 0 122 164 165 121 0 118 165 166 118 0 121 166 167 121 0 118 167 168 121 0 121 168 169 112 0 121 169 170 119 0 112 170 171 116 0 119 171 172 110 1 116 172 173 111 1 110 173 174 106 1 111 174 175 108 1 106 175 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `Kyoto-protocol` `Y-1` t 0.04243 -5.07185 0.85168 0.11214 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -28.8872 -2.0142 0.0751 2.2454 18.4798 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.04243 0.93853 0.045 0.963994 `Kyoto-protocol` -5.07185 3.21478 -1.578 0.116490 `Y-1` 0.85168 0.04001 21.288 < 2e-16 *** t 0.11214 0.03172 3.535 0.000524 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 6.008 on 171 degrees of freedom Multiple R-squared: 0.9772, Adjusted R-squared: 0.9768 F-statistic: 2440 on 3 and 171 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,] 8.218301e-04 1.643660e-03 0.999178170 [2,] 1.588124e-04 3.176248e-04 0.999841188 [3,] 1.893818e-05 3.787636e-05 0.999981062 [4,] 1.250298e-05 2.500597e-05 0.999987497 [5,] 1.843116e-06 3.686232e-06 0.999998157 [6,] 2.613055e-07 5.226110e-07 0.999999739 [7,] 2.742778e-08 5.485555e-08 0.999999973 [8,] 2.690472e-09 5.380944e-09 0.999999997 [9,] 1.608058e-09 3.216117e-09 0.999999998 [10,] 1.948993e-10 3.897987e-10 1.000000000 [11,] 2.624462e-11 5.248925e-11 1.000000000 [12,] 2.848609e-12 5.697218e-12 1.000000000 [13,] 6.365551e-13 1.273110e-12 1.000000000 [14,] 3.020057e-13 6.040113e-13 1.000000000 [15,] 1.401879e-13 2.803758e-13 1.000000000 [16,] 1.686892e-14 3.373784e-14 1.000000000 [17,] 1.844477e-15 3.688954e-15 1.000000000 [18,] 3.441432e-16 6.882865e-16 1.000000000 [19,] 3.664806e-17 7.329611e-17 1.000000000 [20,] 6.479972e-18 1.295994e-17 1.000000000 [21,] 6.756112e-19 1.351222e-18 1.000000000 [22,] 1.141091e-19 2.282181e-19 1.000000000 [23,] 1.168731e-20 2.337462e-20 1.000000000 [24,] 1.897662e-21 3.795324e-21 1.000000000 [25,] 3.576414e-22 7.152827e-22 1.000000000 [26,] 6.973218e-23 1.394644e-22 1.000000000 [27,] 1.260840e-23 2.521679e-23 1.000000000 [28,] 1.309326e-23 2.618652e-23 1.000000000 [29,] 4.722606e-23 9.445211e-23 1.000000000 [30,] 3.004224e-23 6.008448e-23 1.000000000 [31,] 2.318575e-22 4.637150e-22 1.000000000 [32,] 5.932075e-23 1.186415e-22 1.000000000 [33,] 1.047755e-23 2.095510e-23 1.000000000 [34,] 3.492950e-23 6.985900e-23 1.000000000 [35,] 3.749629e-23 7.499258e-23 1.000000000 [36,] 6.581430e-24 1.316286e-23 1.000000000 [37,] 1.988407e-24 3.976813e-24 1.000000000 [38,] 8.031289e-25 1.606258e-24 1.000000000 [39,] 2.050701e-25 4.101402e-25 1.000000000 [40,] 3.296636e-26 6.593272e-26 1.000000000 [41,] 1.223644e-26 2.447288e-26 1.000000000 [42,] 2.235755e-27 4.471510e-27 1.000000000 [43,] 4.066161e-28 8.132323e-28 1.000000000 [44,] 1.900424e-28 3.800849e-28 1.000000000 [45,] 2.830371e-29 5.660742e-29 1.000000000 [46,] 7.972160e-30 1.594432e-29 1.000000000 [47,] 2.881991e-30 5.763981e-30 1.000000000 [48,] 7.747813e-31 1.549563e-30 1.000000000 [49,] 2.666358e-31 5.332717e-31 1.000000000 [50,] 4.621281e-32 9.242562e-32 1.000000000 [51,] 9.609146e-32 1.921829e-31 1.000000000 [52,] 6.609399e-32 1.321880e-31 1.000000000 [53,] 1.066383e-32 2.132766e-32 1.000000000 [54,] 1.489680e-32 2.979360e-32 1.000000000 [55,] 4.179645e-32 8.359290e-32 1.000000000 [56,] 6.938448e-33 1.387690e-32 1.000000000 [57,] 2.304150e-33 4.608299e-33 1.000000000 [58,] 9.562561e-34 1.912512e-33 1.000000000 [59,] 1.978486e-34 3.956973e-34 1.000000000 [60,] 9.007237e-35 1.801447e-34 1.000000000 [61,] 4.058984e-35 8.117969e-35 1.000000000 [62,] 2.129864e-35 4.259728e-35 1.000000000 [63,] 7.649524e-36 1.529905e-35 1.000000000 [64,] 9.870623e-36 1.974125e-35 1.000000000 [65,] 5.150014e-36 1.030003e-35 1.000000000 [66,] 8.755285e-37 1.751057e-36 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0.372839708 [112,] 7.123361e-01 5.753279e-01 0.287663946 [113,] 6.880873e-01 6.238254e-01 0.311912720 [114,] 6.693959e-01 6.612082e-01 0.330604077 [115,] 6.476312e-01 7.047376e-01 0.352368780 [116,] 6.136345e-01 7.727310e-01 0.386365489 [117,] 6.112342e-01 7.775315e-01 0.388765763 [118,] 5.682471e-01 8.635059e-01 0.431752948 [119,] 5.227375e-01 9.545250e-01 0.477262496 [120,] 6.645496e-01 6.709008e-01 0.335450405 [121,] 6.957032e-01 6.085936e-01 0.304296816 [122,] 7.063140e-01 5.873719e-01 0.293685962 [123,] 6.775492e-01 6.449016e-01 0.322450812 [124,] 7.320320e-01 5.359361e-01 0.267968043 [125,] 6.934925e-01 6.130150e-01 0.306507522 [126,] 6.526822e-01 6.946356e-01 0.347317788 [127,] 6.388032e-01 7.223936e-01 0.361196777 [128,] 6.156909e-01 7.686182e-01 0.384309121 [129,] 6.416896e-01 7.166207e-01 0.358310374 [130,] 6.220419e-01 7.559162e-01 0.377958107 [131,] 6.032706e-01 7.934588e-01 0.396729395 [132,] 5.527527e-01 8.944945e-01 0.447247274 [133,] 6.919014e-01 6.161972e-01 0.308098613 [134,] 8.438685e-01 3.122631e-01 0.156131541 [135,] 8.105300e-01 3.789401e-01 0.189470028 [136,] 8.635498e-01 2.729005e-01 0.136450227 [137,] 8.678168e-01 2.643665e-01 0.132183233 [138,] 8.324719e-01 3.350561e-01 0.167528059 [139,] 9.288504e-01 1.422992e-01 0.071149577 [140,] 9.945254e-01 1.094921e-02 0.005474605 [141,] 9.966853e-01 6.629380e-03 0.003314690 [142,] 9.960066e-01 7.986764e-03 0.003993382 [143,] 9.939591e-01 1.208174e-02 0.006040872 [144,] 9.937320e-01 1.253607e-02 0.006268035 [145,] 9.897084e-01 2.058321e-02 0.010291603 [146,] 9.866107e-01 2.677857e-02 0.013389286 [147,] 9.842797e-01 3.144068e-02 0.015720340 [148,] 9.831451e-01 3.370980e-02 0.016854900 [149,] 9.916727e-01 1.665450e-02 0.008327252 [150,] 9.862056e-01 2.758877e-02 0.013794386 [151,] 9.828924e-01 3.421514e-02 0.017107572 [152,] 9.716589e-01 5.668215e-02 0.028341073 [153,] 9.536051e-01 9.278985e-02 0.046394926 [154,] 9.899273e-01 2.014534e-02 0.010072668 [155,] 9.957508e-01 8.498430e-03 0.004249215 [156,] 9.971263e-01 5.747315e-03 0.002873657 [157,] 9.926753e-01 1.464942e-02 0.007324708 [158,] 9.886206e-01 2.275887e-02 0.011379435 [159,] 9.739923e-01 5.201549e-02 0.026007745 [160,] 9.607771e-01 7.844578e-02 0.039222891 [161,] 9.089368e-01 1.821264e-01 0.091063199 [162,] 8.656777e-01 2.686446e-01 0.134322290 > postscript(file="/var/www/rcomp/tmp/1mvmt1292440203.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2emlw1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3emlw1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4emlw1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/57vkh1292440203.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 = 175 Frequency = 1 1 2 3 4 5 -0.412951590 1.326585060 1.362768834 0.398952607 0.286812819 6 7 8 9 10 1.174673031 1.210856804 -0.752959422 0.986577228 -0.977238999 11 12 13 14 15 0.762297651 -0.201518575 0.686341636 -0.277474590 -1.389614378 16 17 18 19 20 0.349922272 -0.613893955 0.273966257 0.310150030 0.346333804 21 22 23 24 25 0.382517577 -0.581298649 -0.693438438 0.194421774 -0.769394452 26 27 28 29 30 0.118465759 -0.845350467 0.042509745 -0.921306482 -0.033446270 31 32 33 34 35 0.002737503 0.038921277 0.075105050 1.111288824 -2.704203841 36 37 38 39 40 0.887009247 2.071516582 -1.595652521 0.143884129 2.180067903 41 42 43 44 45 -2.487101201 0.104111888 -1.859704339 -2.120167689 0.619368961 46 47 48 49 50 -0.196123704 0.840060070 0.024567405 0.060751178 1.096934952 51 52 53 54 55 -0.718557713 -1.830697501 -2.091160851 0.648375799 0.832883134 56 57 58 59 60 0.017390469 2.053574243 -2.613594861 -1.022381772 -3.134521560 61 62 63 64 65 2.456691528 -1.062154014 0.825706198 1.010213533 0.194720868 66 67 68 69 70 1.230904642 1.415411977 -2.400080688 1.191132401 2.375639736 71 72 73 74 75 -2.291529368 -0.700316279 6.187543933 1.113669076 0.149852850 76 77 78 79 80 0.037713061 2.925573273 0.258404170 3.146264382 4.479095278 81 82 83 84 85 -27.039750263 -9.304949778 7.692969063 -12.639287985 -6.234663390 86 87 88 89 90 6.356549698 6.875969089 -6.752935083 3.393307321 14.171108903 91 92 93 94 95 4.432146101 2.913300560 0.097807895 13.837344545 0.653411059 96 97 98 99 100 11.244624147 -0.532603586 -6.238037621 -15.685089464 3.681270199 101 102 103 104 105 2.310748219 -4.356420884 2.938145081 14.419299539 -10.467986824 106 107 108 109 110 -5.360009352 -12.213766949 0.042534084 -6.624635019 -6.330069054 111 112 113 114 115 -28.887179527 -1.707408358 10.070393224 18.479753985 1.779056115 116 117 118 119 120 -3.888112988 -7.445223461 11.552695380 -2.482914544 -2.891701456 121 122 123 124 125 -2.300488367 -0.560951717 7.475232056 2.698004323 2.030835220 126 127 128 129 130 -9.784657445 -4.380032850 -1.937143324 2.950716888 12.580194908 131 132 133 134 135 3.544584984 3.877415881 -1.789753223 -0.346863696 10.392672954 136 137 138 139 140 6.763768783 6.393246803 -0.125598739 12.317290788 11.688386617 141 142 143 144 145 -1.237164678 -10.239245836 5.572084512 -0.946761030 9.496128497 146 147 148 149 150 8.570577202 -4.651621216 -8.950349498 -8.694048464 -8.141100308 151 152 153 154 155 -1.291505028 -3.255321254 -2.515784604 -1.627924393 -2.591740619 156 157 158 159 160 5.296119593 -0.629431702 4.406752071 -1.815446347 -7.075909697 161 162 163 164 165 2.922009145 3.551487165 3.180965185 -4.337880356 1.956685609 166 167 168 169 170 -3.710483494 1.732406032 -0.934763071 -10.046902859 4.506045297 171 172 173 174 175 -4.567829559 -3.053091619 2.944827223 -3.018989004 3.127253400 > postscript(file="/var/www/rcomp/tmp/67vkh1292440203.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 = 175 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.412951590 NA 1 1.326585060 -0.412951590 2 1.362768834 1.326585060 3 0.398952607 1.362768834 4 0.286812819 0.398952607 5 1.174673031 0.286812819 6 1.210856804 1.174673031 7 -0.752959422 1.210856804 8 0.986577228 -0.752959422 9 -0.977238999 0.986577228 10 0.762297651 -0.977238999 11 -0.201518575 0.762297651 12 0.686341636 -0.201518575 13 -0.277474590 0.686341636 14 -1.389614378 -0.277474590 15 0.349922272 -1.389614378 16 -0.613893955 0.349922272 17 0.273966257 -0.613893955 18 0.310150030 0.273966257 19 0.346333804 0.310150030 20 0.382517577 0.346333804 21 -0.581298649 0.382517577 22 -0.693438438 -0.581298649 23 0.194421774 -0.693438438 24 -0.769394452 0.194421774 25 0.118465759 -0.769394452 26 -0.845350467 0.118465759 27 0.042509745 -0.845350467 28 -0.921306482 0.042509745 29 -0.033446270 -0.921306482 30 0.002737503 -0.033446270 31 0.038921277 0.002737503 32 0.075105050 0.038921277 33 1.111288824 0.075105050 34 -2.704203841 1.111288824 35 0.887009247 -2.704203841 36 2.071516582 0.887009247 37 -1.595652521 2.071516582 38 0.143884129 -1.595652521 39 2.180067903 0.143884129 40 -2.487101201 2.180067903 41 0.104111888 -2.487101201 42 -1.859704339 0.104111888 43 -2.120167689 -1.859704339 44 0.619368961 -2.120167689 45 -0.196123704 0.619368961 46 0.840060070 -0.196123704 47 0.024567405 0.840060070 48 0.060751178 0.024567405 49 1.096934952 0.060751178 50 -0.718557713 1.096934952 51 -1.830697501 -0.718557713 52 -2.091160851 -1.830697501 53 0.648375799 -2.091160851 54 0.832883134 0.648375799 55 0.017390469 0.832883134 56 2.053574243 0.017390469 57 -2.613594861 2.053574243 58 -1.022381772 -2.613594861 59 -3.134521560 -1.022381772 60 2.456691528 -3.134521560 61 -1.062154014 2.456691528 62 0.825706198 -1.062154014 63 1.010213533 0.825706198 64 0.194720868 1.010213533 65 1.230904642 0.194720868 66 1.415411977 1.230904642 67 -2.400080688 1.415411977 68 1.191132401 -2.400080688 69 2.375639736 1.191132401 70 -2.291529368 2.375639736 71 -0.700316279 -2.291529368 72 6.187543933 -0.700316279 73 1.113669076 6.187543933 74 0.149852850 1.113669076 75 0.037713061 0.149852850 76 2.925573273 0.037713061 77 0.258404170 2.925573273 78 3.146264382 0.258404170 79 4.479095278 3.146264382 80 -27.039750263 4.479095278 81 -9.304949778 -27.039750263 82 7.692969063 -9.304949778 83 -12.639287985 7.692969063 84 -6.234663390 -12.639287985 85 6.356549698 -6.234663390 86 6.875969089 6.356549698 87 -6.752935083 6.875969089 88 3.393307321 -6.752935083 89 14.171108903 3.393307321 90 4.432146101 14.171108903 91 2.913300560 4.432146101 92 0.097807895 2.913300560 93 13.837344545 0.097807895 94 0.653411059 13.837344545 95 11.244624147 0.653411059 96 -0.532603586 11.244624147 97 -6.238037621 -0.532603586 98 -15.685089464 -6.238037621 99 3.681270199 -15.685089464 100 2.310748219 3.681270199 101 -4.356420884 2.310748219 102 2.938145081 -4.356420884 103 14.419299539 2.938145081 104 -10.467986824 14.419299539 105 -5.360009352 -10.467986824 106 -12.213766949 -5.360009352 107 0.042534084 -12.213766949 108 -6.624635019 0.042534084 109 -6.330069054 -6.624635019 110 -28.887179527 -6.330069054 111 -1.707408358 -28.887179527 112 10.070393224 -1.707408358 113 18.479753985 10.070393224 114 1.779056115 18.479753985 115 -3.888112988 1.779056115 116 -7.445223461 -3.888112988 117 11.552695380 -7.445223461 118 -2.482914544 11.552695380 119 -2.891701456 -2.482914544 120 -2.300488367 -2.891701456 121 -0.560951717 -2.300488367 122 7.475232056 -0.560951717 123 2.698004323 7.475232056 124 2.030835220 2.698004323 125 -9.784657445 2.030835220 126 -4.380032850 -9.784657445 127 -1.937143324 -4.380032850 128 2.950716888 -1.937143324 129 12.580194908 2.950716888 130 3.544584984 12.580194908 131 3.877415881 3.544584984 132 -1.789753223 3.877415881 133 -0.346863696 -1.789753223 134 10.392672954 -0.346863696 135 6.763768783 10.392672954 136 6.393246803 6.763768783 137 -0.125598739 6.393246803 138 12.317290788 -0.125598739 139 11.688386617 12.317290788 140 -1.237164678 11.688386617 141 -10.239245836 -1.237164678 142 5.572084512 -10.239245836 143 -0.946761030 5.572084512 144 9.496128497 -0.946761030 145 8.570577202 9.496128497 146 -4.651621216 8.570577202 147 -8.950349498 -4.651621216 148 -8.694048464 -8.950349498 149 -8.141100308 -8.694048464 150 -1.291505028 -8.141100308 151 -3.255321254 -1.291505028 152 -2.515784604 -3.255321254 153 -1.627924393 -2.515784604 154 -2.591740619 -1.627924393 155 5.296119593 -2.591740619 156 -0.629431702 5.296119593 157 4.406752071 -0.629431702 158 -1.815446347 4.406752071 159 -7.075909697 -1.815446347 160 2.922009145 -7.075909697 161 3.551487165 2.922009145 162 3.180965185 3.551487165 163 -4.337880356 3.180965185 164 1.956685609 -4.337880356 165 -3.710483494 1.956685609 166 1.732406032 -3.710483494 167 -0.934763071 1.732406032 168 -10.046902859 -0.934763071 169 4.506045297 -10.046902859 170 -4.567829559 4.506045297 171 -3.053091619 -4.567829559 172 2.944827223 -3.053091619 173 -3.018989004 2.944827223 174 3.127253400 -3.018989004 175 NA 3.127253400 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.326585060 -0.412951590 [2,] 1.362768834 1.326585060 [3,] 0.398952607 1.362768834 [4,] 0.286812819 0.398952607 [5,] 1.174673031 0.286812819 [6,] 1.210856804 1.174673031 [7,] -0.752959422 1.210856804 [8,] 0.986577228 -0.752959422 [9,] -0.977238999 0.986577228 [10,] 0.762297651 -0.977238999 [11,] -0.201518575 0.762297651 [12,] 0.686341636 -0.201518575 [13,] -0.277474590 0.686341636 [14,] -1.389614378 -0.277474590 [15,] 0.349922272 -1.389614378 [16,] -0.613893955 0.349922272 [17,] 0.273966257 -0.613893955 [18,] 0.310150030 0.273966257 [19,] 0.346333804 0.310150030 [20,] 0.382517577 0.346333804 [21,] -0.581298649 0.382517577 [22,] -0.693438438 -0.581298649 [23,] 0.194421774 -0.693438438 [24,] -0.769394452 0.194421774 [25,] 0.118465759 -0.769394452 [26,] -0.845350467 0.118465759 [27,] 0.042509745 -0.845350467 [28,] -0.921306482 0.042509745 [29,] -0.033446270 -0.921306482 [30,] 0.002737503 -0.033446270 [31,] 0.038921277 0.002737503 [32,] 0.075105050 0.038921277 [33,] 1.111288824 0.075105050 [34,] -2.704203841 1.111288824 [35,] 0.887009247 -2.704203841 [36,] 2.071516582 0.887009247 [37,] -1.595652521 2.071516582 [38,] 0.143884129 -1.595652521 [39,] 2.180067903 0.143884129 [40,] -2.487101201 2.180067903 [41,] 0.104111888 -2.487101201 [42,] -1.859704339 0.104111888 [43,] -2.120167689 -1.859704339 [44,] 0.619368961 -2.120167689 [45,] -0.196123704 0.619368961 [46,] 0.840060070 -0.196123704 [47,] 0.024567405 0.840060070 [48,] 0.060751178 0.024567405 [49,] 1.096934952 0.060751178 [50,] -0.718557713 1.096934952 [51,] -1.830697501 -0.718557713 [52,] -2.091160851 -1.830697501 [53,] 0.648375799 -2.091160851 [54,] 0.832883134 0.648375799 [55,] 0.017390469 0.832883134 [56,] 2.053574243 0.017390469 [57,] -2.613594861 2.053574243 [58,] -1.022381772 -2.613594861 [59,] -3.134521560 -1.022381772 [60,] 2.456691528 -3.134521560 [61,] -1.062154014 2.456691528 [62,] 0.825706198 -1.062154014 [63,] 1.010213533 0.825706198 [64,] 0.194720868 1.010213533 [65,] 1.230904642 0.194720868 [66,] 1.415411977 1.230904642 [67,] -2.400080688 1.415411977 [68,] 1.191132401 -2.400080688 [69,] 2.375639736 1.191132401 [70,] -2.291529368 2.375639736 [71,] -0.700316279 -2.291529368 [72,] 6.187543933 -0.700316279 [73,] 1.113669076 6.187543933 [74,] 0.149852850 1.113669076 [75,] 0.037713061 0.149852850 [76,] 2.925573273 0.037713061 [77,] 0.258404170 2.925573273 [78,] 3.146264382 0.258404170 [79,] 4.479095278 3.146264382 [80,] -27.039750263 4.479095278 [81,] -9.304949778 -27.039750263 [82,] 7.692969063 -9.304949778 [83,] -12.639287985 7.692969063 [84,] -6.234663390 -12.639287985 [85,] 6.356549698 -6.234663390 [86,] 6.875969089 6.356549698 [87,] -6.752935083 6.875969089 [88,] 3.393307321 -6.752935083 [89,] 14.171108903 3.393307321 [90,] 4.432146101 14.171108903 [91,] 2.913300560 4.432146101 [92,] 0.097807895 2.913300560 [93,] 13.837344545 0.097807895 [94,] 0.653411059 13.837344545 [95,] 11.244624147 0.653411059 [96,] -0.532603586 11.244624147 [97,] -6.238037621 -0.532603586 [98,] -15.685089464 -6.238037621 [99,] 3.681270199 -15.685089464 [100,] 2.310748219 3.681270199 [101,] -4.356420884 2.310748219 [102,] 2.938145081 -4.356420884 [103,] 14.419299539 2.938145081 [104,] -10.467986824 14.419299539 [105,] -5.360009352 -10.467986824 [106,] -12.213766949 -5.360009352 [107,] 0.042534084 -12.213766949 [108,] -6.624635019 0.042534084 [109,] -6.330069054 -6.624635019 [110,] -28.887179527 -6.330069054 [111,] -1.707408358 -28.887179527 [112,] 10.070393224 -1.707408358 [113,] 18.479753985 10.070393224 [114,] 1.779056115 18.479753985 [115,] -3.888112988 1.779056115 [116,] -7.445223461 -3.888112988 [117,] 11.552695380 -7.445223461 [118,] -2.482914544 11.552695380 [119,] -2.891701456 -2.482914544 [120,] -2.300488367 -2.891701456 [121,] -0.560951717 -2.300488367 [122,] 7.475232056 -0.560951717 [123,] 2.698004323 7.475232056 [124,] 2.030835220 2.698004323 [125,] -9.784657445 2.030835220 [126,] -4.380032850 -9.784657445 [127,] -1.937143324 -4.380032850 [128,] 2.950716888 -1.937143324 [129,] 12.580194908 2.950716888 [130,] 3.544584984 12.580194908 [131,] 3.877415881 3.544584984 [132,] -1.789753223 3.877415881 [133,] -0.346863696 -1.789753223 [134,] 10.392672954 -0.346863696 [135,] 6.763768783 10.392672954 [136,] 6.393246803 6.763768783 [137,] -0.125598739 6.393246803 [138,] 12.317290788 -0.125598739 [139,] 11.688386617 12.317290788 [140,] -1.237164678 11.688386617 [141,] -10.239245836 -1.237164678 [142,] 5.572084512 -10.239245836 [143,] -0.946761030 5.572084512 [144,] 9.496128497 -0.946761030 [145,] 8.570577202 9.496128497 [146,] -4.651621216 8.570577202 [147,] -8.950349498 -4.651621216 [148,] -8.694048464 -8.950349498 [149,] -8.141100308 -8.694048464 [150,] -1.291505028 -8.141100308 [151,] -3.255321254 -1.291505028 [152,] -2.515784604 -3.255321254 [153,] -1.627924393 -2.515784604 [154,] -2.591740619 -1.627924393 [155,] 5.296119593 -2.591740619 [156,] -0.629431702 5.296119593 [157,] 4.406752071 -0.629431702 [158,] -1.815446347 4.406752071 [159,] -7.075909697 -1.815446347 [160,] 2.922009145 -7.075909697 [161,] 3.551487165 2.922009145 [162,] 3.180965185 3.551487165 [163,] -4.337880356 3.180965185 [164,] 1.956685609 -4.337880356 [165,] -3.710483494 1.956685609 [166,] 1.732406032 -3.710483494 [167,] -0.934763071 1.732406032 [168,] -10.046902859 -0.934763071 [169,] 4.506045297 -10.046902859 [170,] -4.567829559 4.506045297 [171,] -3.053091619 -4.567829559 [172,] 2.944827223 -3.053091619 [173,] -3.018989004 2.944827223 [174,] 3.127253400 -3.018989004 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.326585060 -0.412951590 2 1.362768834 1.326585060 3 0.398952607 1.362768834 4 0.286812819 0.398952607 5 1.174673031 0.286812819 6 1.210856804 1.174673031 7 -0.752959422 1.210856804 8 0.986577228 -0.752959422 9 -0.977238999 0.986577228 10 0.762297651 -0.977238999 11 -0.201518575 0.762297651 12 0.686341636 -0.201518575 13 -0.277474590 0.686341636 14 -1.389614378 -0.277474590 15 0.349922272 -1.389614378 16 -0.613893955 0.349922272 17 0.273966257 -0.613893955 18 0.310150030 0.273966257 19 0.346333804 0.310150030 20 0.382517577 0.346333804 21 -0.581298649 0.382517577 22 -0.693438438 -0.581298649 23 0.194421774 -0.693438438 24 -0.769394452 0.194421774 25 0.118465759 -0.769394452 26 -0.845350467 0.118465759 27 0.042509745 -0.845350467 28 -0.921306482 0.042509745 29 -0.033446270 -0.921306482 30 0.002737503 -0.033446270 31 0.038921277 0.002737503 32 0.075105050 0.038921277 33 1.111288824 0.075105050 34 -2.704203841 1.111288824 35 0.887009247 -2.704203841 36 2.071516582 0.887009247 37 -1.595652521 2.071516582 38 0.143884129 -1.595652521 39 2.180067903 0.143884129 40 -2.487101201 2.180067903 41 0.104111888 -2.487101201 42 -1.859704339 0.104111888 43 -2.120167689 -1.859704339 44 0.619368961 -2.120167689 45 -0.196123704 0.619368961 46 0.840060070 -0.196123704 47 0.024567405 0.840060070 48 0.060751178 0.024567405 49 1.096934952 0.060751178 50 -0.718557713 1.096934952 51 -1.830697501 -0.718557713 52 -2.091160851 -1.830697501 53 0.648375799 -2.091160851 54 0.832883134 0.648375799 55 0.017390469 0.832883134 56 2.053574243 0.017390469 57 -2.613594861 2.053574243 58 -1.022381772 -2.613594861 59 -3.134521560 -1.022381772 60 2.456691528 -3.134521560 61 -1.062154014 2.456691528 62 0.825706198 -1.062154014 63 1.010213533 0.825706198 64 0.194720868 1.010213533 65 1.230904642 0.194720868 66 1.415411977 1.230904642 67 -2.400080688 1.415411977 68 1.191132401 -2.400080688 69 2.375639736 1.191132401 70 -2.291529368 2.375639736 71 -0.700316279 -2.291529368 72 6.187543933 -0.700316279 73 1.113669076 6.187543933 74 0.149852850 1.113669076 75 0.037713061 0.149852850 76 2.925573273 0.037713061 77 0.258404170 2.925573273 78 3.146264382 0.258404170 79 4.479095278 3.146264382 80 -27.039750263 4.479095278 81 -9.304949778 -27.039750263 82 7.692969063 -9.304949778 83 -12.639287985 7.692969063 84 -6.234663390 -12.639287985 85 6.356549698 -6.234663390 86 6.875969089 6.356549698 87 -6.752935083 6.875969089 88 3.393307321 -6.752935083 89 14.171108903 3.393307321 90 4.432146101 14.171108903 91 2.913300560 4.432146101 92 0.097807895 2.913300560 93 13.837344545 0.097807895 94 0.653411059 13.837344545 95 11.244624147 0.653411059 96 -0.532603586 11.244624147 97 -6.238037621 -0.532603586 98 -15.685089464 -6.238037621 99 3.681270199 -15.685089464 100 2.310748219 3.681270199 101 -4.356420884 2.310748219 102 2.938145081 -4.356420884 103 14.419299539 2.938145081 104 -10.467986824 14.419299539 105 -5.360009352 -10.467986824 106 -12.213766949 -5.360009352 107 0.042534084 -12.213766949 108 -6.624635019 0.042534084 109 -6.330069054 -6.624635019 110 -28.887179527 -6.330069054 111 -1.707408358 -28.887179527 112 10.070393224 -1.707408358 113 18.479753985 10.070393224 114 1.779056115 18.479753985 115 -3.888112988 1.779056115 116 -7.445223461 -3.888112988 117 11.552695380 -7.445223461 118 -2.482914544 11.552695380 119 -2.891701456 -2.482914544 120 -2.300488367 -2.891701456 121 -0.560951717 -2.300488367 122 7.475232056 -0.560951717 123 2.698004323 7.475232056 124 2.030835220 2.698004323 125 -9.784657445 2.030835220 126 -4.380032850 -9.784657445 127 -1.937143324 -4.380032850 128 2.950716888 -1.937143324 129 12.580194908 2.950716888 130 3.544584984 12.580194908 131 3.877415881 3.544584984 132 -1.789753223 3.877415881 133 -0.346863696 -1.789753223 134 10.392672954 -0.346863696 135 6.763768783 10.392672954 136 6.393246803 6.763768783 137 -0.125598739 6.393246803 138 12.317290788 -0.125598739 139 11.688386617 12.317290788 140 -1.237164678 11.688386617 141 -10.239245836 -1.237164678 142 5.572084512 -10.239245836 143 -0.946761030 5.572084512 144 9.496128497 -0.946761030 145 8.570577202 9.496128497 146 -4.651621216 8.570577202 147 -8.950349498 -4.651621216 148 -8.694048464 -8.950349498 149 -8.141100308 -8.694048464 150 -1.291505028 -8.141100308 151 -3.255321254 -1.291505028 152 -2.515784604 -3.255321254 153 -1.627924393 -2.515784604 154 -2.591740619 -1.627924393 155 5.296119593 -2.591740619 156 -0.629431702 5.296119593 157 4.406752071 -0.629431702 158 -1.815446347 4.406752071 159 -7.075909697 -1.815446347 160 2.922009145 -7.075909697 161 3.551487165 2.922009145 162 3.180965185 3.551487165 163 -4.337880356 3.180965185 164 1.956685609 -4.337880356 165 -3.710483494 1.956685609 166 1.732406032 -3.710483494 167 -0.934763071 1.732406032 168 -10.046902859 -0.934763071 169 4.506045297 -10.046902859 170 -4.567829559 4.506045297 171 -3.053091619 -4.567829559 172 2.944827223 -3.053091619 173 -3.018989004 2.944827223 174 3.127253400 -3.018989004 > 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/rcomp/tmp/70m2k1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/80m2k1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9svjn1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10svjn1292440203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11pnhw1292440203.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/rcomp/tmp/12dowp1292440203.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/rcomp/tmp/13k8tj1292440203.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/rcomp/tmp/14vha41292440203.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/rcomp/tmp/15ghra1292440203.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/rcomp/tmp/16u9611292440203.tab") + } > > try(system("convert tmp/1mvmt1292440203.ps tmp/1mvmt1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/2emlw1292440203.ps tmp/2emlw1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/3emlw1292440203.ps tmp/3emlw1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/4emlw1292440203.ps tmp/4emlw1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/57vkh1292440203.ps tmp/57vkh1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/67vkh1292440203.ps tmp/67vkh1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/70m2k1292440203.ps tmp/70m2k1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/80m2k1292440203.ps tmp/80m2k1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/9svjn1292440203.ps tmp/9svjn1292440203.png",intern=TRUE)) character(0) > try(system("convert tmp/10svjn1292440203.ps tmp/10svjn1292440203.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.570 1.750 6.336