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Type 'q()' to quit R. > x <- array(list(4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + 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+ } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '4' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x CorrectAnalysis Weeks*t UseLimit Used Useful Outcome t 1 0 4 0 0 0 1 1 2 0 0 0 0 0 0 2 3 0 0 0 0 0 0 3 4 0 0 0 0 0 0 4 5 0 0 0 0 0 0 5 6 0 0 0 0 1 1 6 7 0 0 0 0 0 0 7 8 0 4 0 0 0 0 8 9 0 0 0 0 0 1 9 10 0 0 0 0 0 0 10 11 0 4 0 0 0 0 11 12 0 0 0 0 0 0 12 13 0 0 0 0 1 0 13 14 0 4 0 0 0 0 14 15 0 0 0 0 1 1 15 16 0 4 0 0 1 1 16 17 1 4 0 0 1 0 17 18 0 4 0 0 0 0 18 19 0 0 0 0 0 1 19 20 1 4 0 0 1 1 20 21 0 0 0 0 1 0 21 22 0 0 0 0 1 1 22 23 0 0 0 0 1 1 23 24 0 0 0 0 1 1 24 25 0 4 0 0 0 1 25 26 0 0 0 0 1 0 26 27 0 0 0 0 0 1 27 28 0 0 0 0 0 0 28 29 0 0 0 0 0 1 29 30 0 0 0 0 1 0 30 31 0 0 0 0 0 0 31 32 0 0 0 0 0 0 32 33 0 0 0 0 1 0 33 34 0 4 0 0 0 1 34 35 0 0 0 0 0 0 35 36 0 0 0 0 0 0 36 37 0 4 0 0 1 0 37 38 0 0 0 0 0 1 38 39 0 0 0 0 1 1 39 40 0 4 0 0 1 0 40 41 1 0 0 0 1 1 41 42 0 0 0 0 0 1 42 43 0 0 0 0 1 1 43 44 0 4 0 0 0 0 44 45 0 0 0 0 1 0 45 46 0 0 0 0 1 1 46 47 0 0 0 0 0 0 47 48 0 0 0 0 0 1 48 49 0 0 0 0 1 1 49 50 0 0 0 0 0 0 50 51 0 4 0 0 0 0 51 52 1 4 0 0 1 0 52 53 0 0 0 0 0 1 53 54 1 0 0 0 0 0 54 55 0 0 0 0 0 0 55 56 0 4 0 0 0 1 56 57 0 0 0 0 1 1 57 58 0 0 0 0 0 1 58 59 0 0 0 0 0 1 59 60 1 4 0 0 1 1 60 61 0 4 0 0 0 1 61 62 0 0 0 0 1 0 62 63 0 0 0 0 0 0 63 64 0 4 0 0 0 1 64 65 0 0 0 0 0 0 65 66 0 0 0 0 0 0 66 67 1 4 0 0 1 0 67 68 0 0 0 0 0 0 68 69 0 0 0 0 0 1 69 70 0 0 0 0 0 0 70 71 0 0 0 0 0 0 71 72 0 0 0 0 0 1 72 73 0 0 0 0 0 1 73 74 0 0 0 0 0 0 74 75 0 0 0 0 0 1 75 76 0 4 0 0 1 1 76 77 0 0 0 0 0 1 77 78 0 0 0 0 1 1 78 79 1 4 0 0 0 1 79 80 0 4 0 0 1 0 80 81 0 0 0 0 0 0 81 82 0 0 0 0 0 1 82 83 0 0 0 0 0 0 83 84 1 0 0 0 0 0 84 85 0 0 0 0 1 1 85 86 0 0 0 0 0 0 86 87 0 0 0 0 0 1 87 88 0 2 0 0 0 1 88 89 0 0 0 0 0 0 89 90 0 0 0 0 0 1 90 91 0 0 0 0 1 0 91 92 0 2 0 0 0 0 92 93 0 0 0 0 1 0 93 94 0 0 0 0 0 0 94 95 0 2 0 0 0 0 95 96 0 0 0 0 0 1 96 97 0 2 0 0 0 0 97 98 0 0 0 0 0 0 98 99 0 0 0 0 0 0 99 100 0 0 0 0 0 1 100 101 0 0 0 0 0 1 101 102 0 0 0 0 0 0 102 103 0 0 0 0 0 0 103 104 0 0 0 0 0 0 104 105 0 2 0 0 0 0 105 106 0 0 0 0 0 0 106 107 0 0 0 0 0 0 107 108 0 2 0 0 0 0 108 109 0 0 0 0 0 0 109 110 0 0 0 0 0 0 110 111 0 2 0 0 1 0 111 112 0 2 0 0 0 0 112 113 0 0 0 0 0 0 113 114 0 2 0 0 0 0 114 115 0 0 0 0 0 0 115 116 0 0 0 0 0 0 116 117 0 0 0 0 0 1 117 118 0 0 0 0 0 0 118 119 0 0 0 0 0 0 119 120 0 0 0 0 0 1 120 121 0 0 0 0 0 0 121 122 0 0 0 0 0 0 122 123 0 2 0 0 0 0 123 124 0 0 0 0 1 1 124 125 0 0 0 0 0 1 125 126 0 2 0 0 0 0 126 127 0 0 0 0 1 0 127 128 0 0 0 0 0 1 128 129 0 0 0 0 0 0 129 130 0 0 0 0 0 1 130 131 0 0 0 0 0 0 131 132 0 0 0 0 0 1 132 133 0 0 0 0 0 0 133 134 0 0 0 0 0 0 134 135 0 0 0 0 0 0 135 136 0 0 0 0 0 0 136 137 0 0 0 0 1 1 137 138 0 2 0 0 1 1 138 139 0 2 0 0 0 0 139 140 0 0 0 0 0 0 140 141 1 0 0 0 0 1 141 142 0 2 0 0 0 1 142 143 0 0 0 0 0 0 143 144 0 0 0 0 1 1 144 145 0 0 0 0 1 0 145 146 0 2 0 0 0 1 146 147 0 2 0 0 0 0 147 148 0 2 0 0 0 0 148 149 0 0 0 0 0 0 149 150 0 0 0 0 1 1 150 151 0 0 0 0 0 1 151 152 1 0 0 0 0 0 152 153 1 0 0 0 1 0 153 154 0 0 0 0 0 0 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `Weeks*t` UseLimit Used Useful Outcome -0.0184517 0.0414689 NA NA 0.1222127 -0.0112666 t 0.0004435 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.30512 -0.11760 -0.03277 -0.00359 0.99450 Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0184517 0.0525576 -0.351 0.72603 `Weeks*t` 0.0414689 0.0145332 2.853 0.00494 ** UseLimit NA NA NA NA Used NA NA NA NA Useful 0.1222127 0.0488134 2.504 0.01337 * Outcome -0.0112666 0.0434525 -0.259 0.79577 t 0.0004435 0.0004867 0.911 0.36364 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2593 on 149 degrees of freedom Multiple R-squared: 0.09453, Adjusted R-squared: 0.07022 F-statistic: 3.889 on 4 and 149 DF, p-value: 0.004927 > 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,] 0.000000e+00 0.000000e+00 1.000000000 [2,] 0.000000e+00 0.000000e+00 1.000000000 [3,] 0.000000e+00 0.000000e+00 1.000000000 [4,] 0.000000e+00 0.000000e+00 1.000000000 [5,] 0.000000e+00 0.000000e+00 1.000000000 [6,] 0.000000e+00 0.000000e+00 1.000000000 [7,] 0.000000e+00 0.000000e+00 1.000000000 [8,] 3.717686e-01 7.435372e-01 0.628231376 [9,] 3.042656e-01 6.085312e-01 0.695734405 [10,] 2.671251e-01 5.342501e-01 0.732874933 [11,] 6.979417e-01 6.041166e-01 0.302058282 [12,] 7.021797e-01 5.956406e-01 0.297820283 [13,] 6.582940e-01 6.834120e-01 0.341705982 [14,] 6.028135e-01 7.943730e-01 0.397186521 [15,] 5.408438e-01 9.183124e-01 0.459156181 [16,] 4.760958e-01 9.521917e-01 0.523904166 [17,] 4.260473e-01 8.520946e-01 0.573952717 [18,] 3.831960e-01 7.663920e-01 0.616803981 [19,] 3.231990e-01 6.463980e-01 0.676800994 [20,] 2.724998e-01 5.449996e-01 0.727500210 [21,] 2.349105e-01 4.698210e-01 0.765089503 [22,] 1.889052e-01 3.778103e-01 0.811094830 [23,] 1.487035e-01 2.974070e-01 0.851296491 [24,] 1.230733e-01 2.461466e-01 0.876926721 [25,] 1.016634e-01 2.033268e-01 0.898336578 [26,] 7.766423e-02 1.553285e-01 0.922335767 [27,] 5.809764e-02 1.161953e-01 0.941902360 [28,] 6.845166e-02 1.369033e-01 0.931548338 [29,] 5.214923e-02 1.042985e-01 0.947850766 [30,] 3.928911e-02 7.857823e-02 0.960710885 [31,] 3.989904e-02 7.979807e-02 0.960100963 [32,] 4.402849e-01 8.805699e-01 0.559715054 [33,] 3.855350e-01 7.710700e-01 0.614465023 [34,] 3.496260e-01 6.992520e-01 0.650374013 [35,] 3.132322e-01 6.264644e-01 0.686767782 [36,] 2.749659e-01 5.499317e-01 0.725034138 [37,] 2.415647e-01 4.831295e-01 0.758435257 [38,] 2.036490e-01 4.072980e-01 0.796351003 [39,] 1.677925e-01 3.355851e-01 0.832207455 [40,] 1.432494e-01 2.864987e-01 0.856750634 [41,] 1.167573e-01 2.335145e-01 0.883242726 [42,] 1.000129e-01 2.000259e-01 0.899987055 [43,] 3.296427e-01 6.592854e-01 0.670357323 [44,] 2.845841e-01 5.691682e-01 0.715415920 [45,] 8.313382e-01 3.373236e-01 0.168661798 [46,] 8.002279e-01 3.995442e-01 0.199772095 [47,] 7.834266e-01 4.331469e-01 0.216573448 [48,] 7.586236e-01 4.827528e-01 0.241376384 [49,] 7.185196e-01 5.629607e-01 0.281480374 [50,] 6.755616e-01 6.488768e-01 0.324438412 [51,] 8.711228e-01 2.577544e-01 0.128877190 [52,] 8.589080e-01 2.821841e-01 0.141092044 [53,] 8.449738e-01 3.100523e-01 0.155026154 [54,] 8.150914e-01 3.698173e-01 0.184908637 [55,] 7.983169e-01 4.033662e-01 0.201683112 [56,] 7.632398e-01 4.735204e-01 0.236760216 [57,] 7.250340e-01 5.499319e-01 0.274965971 [58,] 9.116143e-01 1.767714e-01 0.088385707 [59,] 8.915810e-01 2.168380e-01 0.108418996 [60,] 8.676214e-01 2.647572e-01 0.132378599 [61,] 8.411718e-01 3.176565e-01 0.158828237 [62,] 8.113900e-01 3.772201e-01 0.188610032 [63,] 7.774367e-01 4.451266e-01 0.222563312 [64,] 7.403047e-01 5.193907e-01 0.259695340 [65,] 7.013086e-01 5.973829e-01 0.298691427 [66,] 6.587857e-01 6.824286e-01 0.341214315 [67,] 6.728726e-01 6.542549e-01 0.327127447 [68,] 6.288747e-01 7.422507e-01 0.371125344 [69,] 5.963625e-01 8.072749e-01 0.403637454 [70,] 9.463340e-01 1.073320e-01 0.053666012 [71,] 9.506836e-01 9.863277e-02 0.049316387 [72,] 9.373862e-01 1.252277e-01 0.062613842 [73,] 9.212450e-01 1.575100e-01 0.078754996 [74,] 9.022992e-01 1.954017e-01 0.097700832 [75,] 9.989160e-01 2.167903e-03 0.001083952 [76,] 9.985241e-01 2.951802e-03 0.001475901 [77,] 9.978799e-01 4.240167e-03 0.002120084 [78,] 9.969686e-01 6.062808e-03 0.003031404 [79,] 9.961034e-01 7.793118e-03 0.003896559 [80,] 9.945498e-01 1.090045e-02 0.005450225 [81,] 9.924890e-01 1.502191e-02 0.007510953 [82,] 9.903322e-01 1.933555e-02 0.009667777 [83,] 9.878420e-01 2.431593e-02 0.012157965 [84,] 9.842739e-01 3.145223e-02 0.015726113 [85,] 9.788492e-01 4.230163e-02 0.021150813 [86,] 9.737885e-01 5.242295e-02 0.026211476 [87,] 9.658121e-01 6.837577e-02 0.034187884 [88,] 9.584358e-01 8.312839e-02 0.041564195 [89,] 9.462612e-01 1.074776e-01 0.053738818 [90,] 9.313276e-01 1.373448e-01 0.068672422 [91,] 9.143221e-01 1.713558e-01 0.085677886 [92,] 8.946996e-01 2.106008e-01 0.105300424 [93,] 8.700357e-01 2.599285e-01 0.129964262 [94,] 8.414400e-01 3.171201e-01 0.158560039 [95,] 8.087768e-01 3.824463e-01 0.191223155 [96,] 7.812714e-01 4.374572e-01 0.218728591 [97,] 7.414876e-01 5.170249e-01 0.258512428 [98,] 6.979320e-01 6.041359e-01 0.302067965 [99,] 6.630481e-01 6.739038e-01 0.336951915 [100,] 6.139882e-01 7.720236e-01 0.386011790 [101,] 5.626244e-01 8.747512e-01 0.437375613 [102,] 5.313627e-01 9.372747e-01 0.468637340 [103,] 4.949015e-01 9.898031e-01 0.505098463 [104,] 4.414963e-01 8.829925e-01 0.558503750 [105,] 4.101474e-01 8.202948e-01 0.589852610 [106,] 3.585692e-01 7.171383e-01 0.641430831 [107,] 3.089729e-01 6.179458e-01 0.691027102 [108,] 2.633422e-01 5.266844e-01 0.736657776 [109,] 2.198186e-01 4.396371e-01 0.780181430 [110,] 1.803116e-01 3.606232e-01 0.819688390 [111,] 1.459657e-01 2.919313e-01 0.854034344 [112,] 1.152600e-01 2.305201e-01 0.884739975 [113,] 8.913600e-02 1.782720e-01 0.910864000 [114,] 7.549560e-02 1.509912e-01 0.924504396 [115,] 5.747626e-02 1.149525e-01 0.942523738 [116,] 4.206925e-02 8.413849e-02 0.957930753 [117,] 3.575493e-02 7.150987e-02 0.964245067 [118,] 2.580551e-02 5.161102e-02 0.974194492 [119,] 1.760247e-02 3.520494e-02 0.982397530 [120,] 1.169056e-02 2.338113e-02 0.988309435 [121,] 7.473589e-03 1.494718e-02 0.992526411 [122,] 4.624383e-03 9.248765e-03 0.995375617 [123,] 2.752853e-03 5.505706e-03 0.997247147 [124,] 1.577862e-03 3.155724e-03 0.998422138 [125,] 8.711043e-04 1.742209e-03 0.999128896 [126,] 4.660324e-04 9.320648e-04 0.999533968 [127,] 2.471810e-04 4.943620e-04 0.999752819 [128,] 1.316813e-04 2.633625e-04 0.999868319 [129,] 6.709597e-05 1.341919e-04 0.999932904 [130,] 3.011978e-05 6.023956e-05 0.999969880 [131,] 1.692985e-05 3.385970e-05 0.999983070 [132,] 8.844511e-03 1.768902e-02 0.991155489 [133,] 8.282449e-03 1.656490e-02 0.991717551 [134,] 4.458528e-03 8.917055e-03 0.995541472 [135,] 2.181289e-03 4.362577e-03 0.997818711 > postscript(file="/var/wessaorg/rcomp/tmp/17d4y1355914833.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2djqx1355914833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3sqmo1355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4w0i11355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5oywe1355914834.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 = 154 Frequency = 1 1 2 3 4 5 -0.1366006449 0.0175647052 0.0171212177 0.0166777302 0.0162342427 6 7 8 9 10 -0.0951553662 0.0153472676 -0.1509716434 0.0257268783 0.0140168050 11 12 13 14 15 -0.1523021060 0.0131298299 -0.1095263647 -0.1536325686 -0.0991467540 16 17 18 19 20 -0.2654656650 0.7228242617 -0.1554065187 0.0212920030 0.7327603849 21 22 23 24 25 -0.1130742650 -0.1022511667 -0.1026946542 -0.1031381418 -0.1472443456 26 27 28 29 30 -0.1152917026 0.0177441028 0.0060340295 0.0168571277 -0.1170656527 31 32 33 34 35 0.0047035669 0.0042600793 -0.1183961153 -0.1512357334 0.0029296168 36 37 38 39 40 0.0024861292 -0.2860454889 0.0128657400 -0.1097904547 -0.2873759515 41 42 43 44 45 0.8893225702 0.0110917898 -0.1115644048 -0.1669371945 -0.1237179657 46 47 48 49 50 -0.1128948674 -0.0023922336 0.0084308647 -0.1142253300 -0.0037226962 51 52 53 54 55 -0.1700416072 0.7073021981 0.0062134270 0.9945033537 -0.0059401338 56 57 58 59 60 -0.1609924590 -0.1177732303 0.0039959894 0.0035525018 0.7150208837 61 62 63 64 65 -0.1632098967 -0.1312572537 -0.0094880341 -0.1645403593 -0.0103750091 66 67 68 69 70 -0.0108184967 0.7006498852 -0.0117054717 -0.0008823735 -0.0125924468 71 72 73 74 75 -0.0130359343 -0.0022128360 -0.0026563236 -0.0143663969 -0.0035432986 76 77 78 79 80 -0.2920749168 -0.0044302737 -0.1270864684 0.8288073278 -0.3051154527 81 82 83 84 85 -0.0174708096 -0.0066477113 -0.0183577847 0.9811987278 -0.1301908811 86 87 88 89 90 -0.0196882473 -0.0088651490 -0.0922463483 -0.0210187098 -0.0101956116 91 92 93 94 95 -0.1441183921 -0.1052868842 -0.1450053671 -0.0232361475 -0.1066173468 96 97 98 99 100 -0.0128565368 -0.1075043218 -0.0250100976 -0.0254535851 -0.0146304869 101 102 103 104 105 -0.0150739744 -0.0267840477 -0.0272275353 -0.0276710228 -0.1110522221 106 107 108 109 110 -0.0285579978 -0.0290014854 -0.1123826846 -0.0298884604 -0.0303319480 111 112 113 114 115 -0.2359258544 -0.1141566348 -0.0316624106 -0.1150436098 -0.0325493856 116 117 118 119 120 -0.0329928731 -0.0221697749 -0.0338798482 -0.0343233357 -0.0235002375 121 122 123 124 125 -0.0352103108 -0.0356537983 -0.1190349976 -0.1474868947 -0.0257176751 126 127 128 129 130 -0.1203654602 -0.1600839431 -0.0270481377 -0.0387582110 -0.0279351128 131 132 133 134 135 -0.0396451861 -0.0288220878 -0.0405321612 -0.0409756487 -0.0414191362 136 137 138 139 140 -0.0418626237 -0.1532522326 -0.2366334319 -0.1261307981 -0.0436365739 141 142 143 144 145 0.9671865244 -0.1161946749 -0.0449670364 -0.1563566453 -0.1680667187 146 147 148 149 150 -0.1179686250 -0.1296786983 -0.1301221858 -0.0476279616 -0.1590175705 151 152 153 154 -0.0372483509 0.9510415758 0.8283853811 -0.0498453993 > postscript(file="/var/wessaorg/rcomp/tmp/6sq4q1355914834.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.1366006449 NA 1 0.0175647052 -0.1366006449 2 0.0171212177 0.0175647052 3 0.0166777302 0.0171212177 4 0.0162342427 0.0166777302 5 -0.0951553662 0.0162342427 6 0.0153472676 -0.0951553662 7 -0.1509716434 0.0153472676 8 0.0257268783 -0.1509716434 9 0.0140168050 0.0257268783 10 -0.1523021060 0.0140168050 11 0.0131298299 -0.1523021060 12 -0.1095263647 0.0131298299 13 -0.1536325686 -0.1095263647 14 -0.0991467540 -0.1536325686 15 -0.2654656650 -0.0991467540 16 0.7228242617 -0.2654656650 17 -0.1554065187 0.7228242617 18 0.0212920030 -0.1554065187 19 0.7327603849 0.0212920030 20 -0.1130742650 0.7327603849 21 -0.1022511667 -0.1130742650 22 -0.1026946542 -0.1022511667 23 -0.1031381418 -0.1026946542 24 -0.1472443456 -0.1031381418 25 -0.1152917026 -0.1472443456 26 0.0177441028 -0.1152917026 27 0.0060340295 0.0177441028 28 0.0168571277 0.0060340295 29 -0.1170656527 0.0168571277 30 0.0047035669 -0.1170656527 31 0.0042600793 0.0047035669 32 -0.1183961153 0.0042600793 33 -0.1512357334 -0.1183961153 34 0.0029296168 -0.1512357334 35 0.0024861292 0.0029296168 36 -0.2860454889 0.0024861292 37 0.0128657400 -0.2860454889 38 -0.1097904547 0.0128657400 39 -0.2873759515 -0.1097904547 40 0.8893225702 -0.2873759515 41 0.0110917898 0.8893225702 42 -0.1115644048 0.0110917898 43 -0.1669371945 -0.1115644048 44 -0.1237179657 -0.1669371945 45 -0.1128948674 -0.1237179657 46 -0.0023922336 -0.1128948674 47 0.0084308647 -0.0023922336 48 -0.1142253300 0.0084308647 49 -0.0037226962 -0.1142253300 50 -0.1700416072 -0.0037226962 51 0.7073021981 -0.1700416072 52 0.0062134270 0.7073021981 53 0.9945033537 0.0062134270 54 -0.0059401338 0.9945033537 55 -0.1609924590 -0.0059401338 56 -0.1177732303 -0.1609924590 57 0.0039959894 -0.1177732303 58 0.0035525018 0.0039959894 59 0.7150208837 0.0035525018 60 -0.1632098967 0.7150208837 61 -0.1312572537 -0.1632098967 62 -0.0094880341 -0.1312572537 63 -0.1645403593 -0.0094880341 64 -0.0103750091 -0.1645403593 65 -0.0108184967 -0.0103750091 66 0.7006498852 -0.0108184967 67 -0.0117054717 0.7006498852 68 -0.0008823735 -0.0117054717 69 -0.0125924468 -0.0008823735 70 -0.0130359343 -0.0125924468 71 -0.0022128360 -0.0130359343 72 -0.0026563236 -0.0022128360 73 -0.0143663969 -0.0026563236 74 -0.0035432986 -0.0143663969 75 -0.2920749168 -0.0035432986 76 -0.0044302737 -0.2920749168 77 -0.1270864684 -0.0044302737 78 0.8288073278 -0.1270864684 79 -0.3051154527 0.8288073278 80 -0.0174708096 -0.3051154527 81 -0.0066477113 -0.0174708096 82 -0.0183577847 -0.0066477113 83 0.9811987278 -0.0183577847 84 -0.1301908811 0.9811987278 85 -0.0196882473 -0.1301908811 86 -0.0088651490 -0.0196882473 87 -0.0922463483 -0.0088651490 88 -0.0210187098 -0.0922463483 89 -0.0101956116 -0.0210187098 90 -0.1441183921 -0.0101956116 91 -0.1052868842 -0.1441183921 92 -0.1450053671 -0.1052868842 93 -0.0232361475 -0.1450053671 94 -0.1066173468 -0.0232361475 95 -0.0128565368 -0.1066173468 96 -0.1075043218 -0.0128565368 97 -0.0250100976 -0.1075043218 98 -0.0254535851 -0.0250100976 99 -0.0146304869 -0.0254535851 100 -0.0150739744 -0.0146304869 101 -0.0267840477 -0.0150739744 102 -0.0272275353 -0.0267840477 103 -0.0276710228 -0.0272275353 104 -0.1110522221 -0.0276710228 105 -0.0285579978 -0.1110522221 106 -0.0290014854 -0.0285579978 107 -0.1123826846 -0.0290014854 108 -0.0298884604 -0.1123826846 109 -0.0303319480 -0.0298884604 110 -0.2359258544 -0.0303319480 111 -0.1141566348 -0.2359258544 112 -0.0316624106 -0.1141566348 113 -0.1150436098 -0.0316624106 114 -0.0325493856 -0.1150436098 115 -0.0329928731 -0.0325493856 116 -0.0221697749 -0.0329928731 117 -0.0338798482 -0.0221697749 118 -0.0343233357 -0.0338798482 119 -0.0235002375 -0.0343233357 120 -0.0352103108 -0.0235002375 121 -0.0356537983 -0.0352103108 122 -0.1190349976 -0.0356537983 123 -0.1474868947 -0.1190349976 124 -0.0257176751 -0.1474868947 125 -0.1203654602 -0.0257176751 126 -0.1600839431 -0.1203654602 127 -0.0270481377 -0.1600839431 128 -0.0387582110 -0.0270481377 129 -0.0279351128 -0.0387582110 130 -0.0396451861 -0.0279351128 131 -0.0288220878 -0.0396451861 132 -0.0405321612 -0.0288220878 133 -0.0409756487 -0.0405321612 134 -0.0414191362 -0.0409756487 135 -0.0418626237 -0.0414191362 136 -0.1532522326 -0.0418626237 137 -0.2366334319 -0.1532522326 138 -0.1261307981 -0.2366334319 139 -0.0436365739 -0.1261307981 140 0.9671865244 -0.0436365739 141 -0.1161946749 0.9671865244 142 -0.0449670364 -0.1161946749 143 -0.1563566453 -0.0449670364 144 -0.1680667187 -0.1563566453 145 -0.1179686250 -0.1680667187 146 -0.1296786983 -0.1179686250 147 -0.1301221858 -0.1296786983 148 -0.0476279616 -0.1301221858 149 -0.1590175705 -0.0476279616 150 -0.0372483509 -0.1590175705 151 0.9510415758 -0.0372483509 152 0.8283853811 0.9510415758 153 -0.0498453993 0.8283853811 154 NA -0.0498453993 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.0175647052 -0.1366006449 [2,] 0.0171212177 0.0175647052 [3,] 0.0166777302 0.0171212177 [4,] 0.0162342427 0.0166777302 [5,] -0.0951553662 0.0162342427 [6,] 0.0153472676 -0.0951553662 [7,] -0.1509716434 0.0153472676 [8,] 0.0257268783 -0.1509716434 [9,] 0.0140168050 0.0257268783 [10,] -0.1523021060 0.0140168050 [11,] 0.0131298299 -0.1523021060 [12,] -0.1095263647 0.0131298299 [13,] -0.1536325686 -0.1095263647 [14,] -0.0991467540 -0.1536325686 [15,] -0.2654656650 -0.0991467540 [16,] 0.7228242617 -0.2654656650 [17,] -0.1554065187 0.7228242617 [18,] 0.0212920030 -0.1554065187 [19,] 0.7327603849 0.0212920030 [20,] -0.1130742650 0.7327603849 [21,] -0.1022511667 -0.1130742650 [22,] -0.1026946542 -0.1022511667 [23,] -0.1031381418 -0.1026946542 [24,] -0.1472443456 -0.1031381418 [25,] -0.1152917026 -0.1472443456 [26,] 0.0177441028 -0.1152917026 [27,] 0.0060340295 0.0177441028 [28,] 0.0168571277 0.0060340295 [29,] -0.1170656527 0.0168571277 [30,] 0.0047035669 -0.1170656527 [31,] 0.0042600793 0.0047035669 [32,] -0.1183961153 0.0042600793 [33,] -0.1512357334 -0.1183961153 [34,] 0.0029296168 -0.1512357334 [35,] 0.0024861292 0.0029296168 [36,] -0.2860454889 0.0024861292 [37,] 0.0128657400 -0.2860454889 [38,] -0.1097904547 0.0128657400 [39,] -0.2873759515 -0.1097904547 [40,] 0.8893225702 -0.2873759515 [41,] 0.0110917898 0.8893225702 [42,] -0.1115644048 0.0110917898 [43,] -0.1669371945 -0.1115644048 [44,] -0.1237179657 -0.1669371945 [45,] -0.1128948674 -0.1237179657 [46,] -0.0023922336 -0.1128948674 [47,] 0.0084308647 -0.0023922336 [48,] -0.1142253300 0.0084308647 [49,] -0.0037226962 -0.1142253300 [50,] -0.1700416072 -0.0037226962 [51,] 0.7073021981 -0.1700416072 [52,] 0.0062134270 0.7073021981 [53,] 0.9945033537 0.0062134270 [54,] -0.0059401338 0.9945033537 [55,] -0.1609924590 -0.0059401338 [56,] -0.1177732303 -0.1609924590 [57,] 0.0039959894 -0.1177732303 [58,] 0.0035525018 0.0039959894 [59,] 0.7150208837 0.0035525018 [60,] -0.1632098967 0.7150208837 [61,] -0.1312572537 -0.1632098967 [62,] -0.0094880341 -0.1312572537 [63,] -0.1645403593 -0.0094880341 [64,] -0.0103750091 -0.1645403593 [65,] -0.0108184967 -0.0103750091 [66,] 0.7006498852 -0.0108184967 [67,] -0.0117054717 0.7006498852 [68,] -0.0008823735 -0.0117054717 [69,] -0.0125924468 -0.0008823735 [70,] -0.0130359343 -0.0125924468 [71,] -0.0022128360 -0.0130359343 [72,] -0.0026563236 -0.0022128360 [73,] -0.0143663969 -0.0026563236 [74,] -0.0035432986 -0.0143663969 [75,] -0.2920749168 -0.0035432986 [76,] -0.0044302737 -0.2920749168 [77,] -0.1270864684 -0.0044302737 [78,] 0.8288073278 -0.1270864684 [79,] -0.3051154527 0.8288073278 [80,] -0.0174708096 -0.3051154527 [81,] -0.0066477113 -0.0174708096 [82,] -0.0183577847 -0.0066477113 [83,] 0.9811987278 -0.0183577847 [84,] -0.1301908811 0.9811987278 [85,] -0.0196882473 -0.1301908811 [86,] -0.0088651490 -0.0196882473 [87,] -0.0922463483 -0.0088651490 [88,] -0.0210187098 -0.0922463483 [89,] -0.0101956116 -0.0210187098 [90,] -0.1441183921 -0.0101956116 [91,] -0.1052868842 -0.1441183921 [92,] -0.1450053671 -0.1052868842 [93,] -0.0232361475 -0.1450053671 [94,] -0.1066173468 -0.0232361475 [95,] -0.0128565368 -0.1066173468 [96,] -0.1075043218 -0.0128565368 [97,] -0.0250100976 -0.1075043218 [98,] -0.0254535851 -0.0250100976 [99,] -0.0146304869 -0.0254535851 [100,] -0.0150739744 -0.0146304869 [101,] -0.0267840477 -0.0150739744 [102,] -0.0272275353 -0.0267840477 [103,] -0.0276710228 -0.0272275353 [104,] -0.1110522221 -0.0276710228 [105,] -0.0285579978 -0.1110522221 [106,] -0.0290014854 -0.0285579978 [107,] -0.1123826846 -0.0290014854 [108,] -0.0298884604 -0.1123826846 [109,] -0.0303319480 -0.0298884604 [110,] -0.2359258544 -0.0303319480 [111,] -0.1141566348 -0.2359258544 [112,] -0.0316624106 -0.1141566348 [113,] -0.1150436098 -0.0316624106 [114,] -0.0325493856 -0.1150436098 [115,] -0.0329928731 -0.0325493856 [116,] -0.0221697749 -0.0329928731 [117,] -0.0338798482 -0.0221697749 [118,] -0.0343233357 -0.0338798482 [119,] -0.0235002375 -0.0343233357 [120,] -0.0352103108 -0.0235002375 [121,] -0.0356537983 -0.0352103108 [122,] -0.1190349976 -0.0356537983 [123,] -0.1474868947 -0.1190349976 [124,] -0.0257176751 -0.1474868947 [125,] -0.1203654602 -0.0257176751 [126,] -0.1600839431 -0.1203654602 [127,] -0.0270481377 -0.1600839431 [128,] -0.0387582110 -0.0270481377 [129,] -0.0279351128 -0.0387582110 [130,] -0.0396451861 -0.0279351128 [131,] -0.0288220878 -0.0396451861 [132,] -0.0405321612 -0.0288220878 [133,] -0.0409756487 -0.0405321612 [134,] -0.0414191362 -0.0409756487 [135,] -0.0418626237 -0.0414191362 [136,] -0.1532522326 -0.0418626237 [137,] -0.2366334319 -0.1532522326 [138,] -0.1261307981 -0.2366334319 [139,] -0.0436365739 -0.1261307981 [140,] 0.9671865244 -0.0436365739 [141,] -0.1161946749 0.9671865244 [142,] -0.0449670364 -0.1161946749 [143,] -0.1563566453 -0.0449670364 [144,] -0.1680667187 -0.1563566453 [145,] -0.1179686250 -0.1680667187 [146,] -0.1296786983 -0.1179686250 [147,] -0.1301221858 -0.1296786983 [148,] -0.0476279616 -0.1301221858 [149,] -0.1590175705 -0.0476279616 [150,] -0.0372483509 -0.1590175705 [151,] 0.9510415758 -0.0372483509 [152,] 0.8283853811 0.9510415758 [153,] -0.0498453993 0.8283853811 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.0175647052 -0.1366006449 2 0.0171212177 0.0175647052 3 0.0166777302 0.0171212177 4 0.0162342427 0.0166777302 5 -0.0951553662 0.0162342427 6 0.0153472676 -0.0951553662 7 -0.1509716434 0.0153472676 8 0.0257268783 -0.1509716434 9 0.0140168050 0.0257268783 10 -0.1523021060 0.0140168050 11 0.0131298299 -0.1523021060 12 -0.1095263647 0.0131298299 13 -0.1536325686 -0.1095263647 14 -0.0991467540 -0.1536325686 15 -0.2654656650 -0.0991467540 16 0.7228242617 -0.2654656650 17 -0.1554065187 0.7228242617 18 0.0212920030 -0.1554065187 19 0.7327603849 0.0212920030 20 -0.1130742650 0.7327603849 21 -0.1022511667 -0.1130742650 22 -0.1026946542 -0.1022511667 23 -0.1031381418 -0.1026946542 24 -0.1472443456 -0.1031381418 25 -0.1152917026 -0.1472443456 26 0.0177441028 -0.1152917026 27 0.0060340295 0.0177441028 28 0.0168571277 0.0060340295 29 -0.1170656527 0.0168571277 30 0.0047035669 -0.1170656527 31 0.0042600793 0.0047035669 32 -0.1183961153 0.0042600793 33 -0.1512357334 -0.1183961153 34 0.0029296168 -0.1512357334 35 0.0024861292 0.0029296168 36 -0.2860454889 0.0024861292 37 0.0128657400 -0.2860454889 38 -0.1097904547 0.0128657400 39 -0.2873759515 -0.1097904547 40 0.8893225702 -0.2873759515 41 0.0110917898 0.8893225702 42 -0.1115644048 0.0110917898 43 -0.1669371945 -0.1115644048 44 -0.1237179657 -0.1669371945 45 -0.1128948674 -0.1237179657 46 -0.0023922336 -0.1128948674 47 0.0084308647 -0.0023922336 48 -0.1142253300 0.0084308647 49 -0.0037226962 -0.1142253300 50 -0.1700416072 -0.0037226962 51 0.7073021981 -0.1700416072 52 0.0062134270 0.7073021981 53 0.9945033537 0.0062134270 54 -0.0059401338 0.9945033537 55 -0.1609924590 -0.0059401338 56 -0.1177732303 -0.1609924590 57 0.0039959894 -0.1177732303 58 0.0035525018 0.0039959894 59 0.7150208837 0.0035525018 60 -0.1632098967 0.7150208837 61 -0.1312572537 -0.1632098967 62 -0.0094880341 -0.1312572537 63 -0.1645403593 -0.0094880341 64 -0.0103750091 -0.1645403593 65 -0.0108184967 -0.0103750091 66 0.7006498852 -0.0108184967 67 -0.0117054717 0.7006498852 68 -0.0008823735 -0.0117054717 69 -0.0125924468 -0.0008823735 70 -0.0130359343 -0.0125924468 71 -0.0022128360 -0.0130359343 72 -0.0026563236 -0.0022128360 73 -0.0143663969 -0.0026563236 74 -0.0035432986 -0.0143663969 75 -0.2920749168 -0.0035432986 76 -0.0044302737 -0.2920749168 77 -0.1270864684 -0.0044302737 78 0.8288073278 -0.1270864684 79 -0.3051154527 0.8288073278 80 -0.0174708096 -0.3051154527 81 -0.0066477113 -0.0174708096 82 -0.0183577847 -0.0066477113 83 0.9811987278 -0.0183577847 84 -0.1301908811 0.9811987278 85 -0.0196882473 -0.1301908811 86 -0.0088651490 -0.0196882473 87 -0.0922463483 -0.0088651490 88 -0.0210187098 -0.0922463483 89 -0.0101956116 -0.0210187098 90 -0.1441183921 -0.0101956116 91 -0.1052868842 -0.1441183921 92 -0.1450053671 -0.1052868842 93 -0.0232361475 -0.1450053671 94 -0.1066173468 -0.0232361475 95 -0.0128565368 -0.1066173468 96 -0.1075043218 -0.0128565368 97 -0.0250100976 -0.1075043218 98 -0.0254535851 -0.0250100976 99 -0.0146304869 -0.0254535851 100 -0.0150739744 -0.0146304869 101 -0.0267840477 -0.0150739744 102 -0.0272275353 -0.0267840477 103 -0.0276710228 -0.0272275353 104 -0.1110522221 -0.0276710228 105 -0.0285579978 -0.1110522221 106 -0.0290014854 -0.0285579978 107 -0.1123826846 -0.0290014854 108 -0.0298884604 -0.1123826846 109 -0.0303319480 -0.0298884604 110 -0.2359258544 -0.0303319480 111 -0.1141566348 -0.2359258544 112 -0.0316624106 -0.1141566348 113 -0.1150436098 -0.0316624106 114 -0.0325493856 -0.1150436098 115 -0.0329928731 -0.0325493856 116 -0.0221697749 -0.0329928731 117 -0.0338798482 -0.0221697749 118 -0.0343233357 -0.0338798482 119 -0.0235002375 -0.0343233357 120 -0.0352103108 -0.0235002375 121 -0.0356537983 -0.0352103108 122 -0.1190349976 -0.0356537983 123 -0.1474868947 -0.1190349976 124 -0.0257176751 -0.1474868947 125 -0.1203654602 -0.0257176751 126 -0.1600839431 -0.1203654602 127 -0.0270481377 -0.1600839431 128 -0.0387582110 -0.0270481377 129 -0.0279351128 -0.0387582110 130 -0.0396451861 -0.0279351128 131 -0.0288220878 -0.0396451861 132 -0.0405321612 -0.0288220878 133 -0.0409756487 -0.0405321612 134 -0.0414191362 -0.0409756487 135 -0.0418626237 -0.0414191362 136 -0.1532522326 -0.0418626237 137 -0.2366334319 -0.1532522326 138 -0.1261307981 -0.2366334319 139 -0.0436365739 -0.1261307981 140 0.9671865244 -0.0436365739 141 -0.1161946749 0.9671865244 142 -0.0449670364 -0.1161946749 143 -0.1563566453 -0.0449670364 144 -0.1680667187 -0.1563566453 145 -0.1179686250 -0.1680667187 146 -0.1296786983 -0.1179686250 147 -0.1301221858 -0.1296786983 148 -0.0476279616 -0.1301221858 149 -0.1590175705 -0.0476279616 150 -0.0372483509 -0.1590175705 151 0.9510415758 -0.0372483509 152 0.8283853811 0.9510415758 153 -0.0498453993 0.8283853811 > 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/7ul5f1355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8vy8a1355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9d4zn1355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10lbb81355914834.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } Error: subscript out of bounds Execution halted