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Type 'q()' to quit R. > x <- array(list(20.005 + ,0 + ,20.155 + ,0 + ,20.005 + ,0 + ,19.975 + ,0 + ,20.155 + ,0 + ,19.510 + ,0 + ,19.480 + ,0 + ,19.630 + ,0 + ,19.940 + ,0 + ,20.300 + ,0 + ,20.415 + ,0 + ,20.395 + ,0 + ,19.970 + ,0 + ,20.030 + ,0 + ,19.960 + ,0 + ,19.930 + ,0 + ,20.020 + ,0 + ,20.220 + ,0 + ,20.570 + ,0 + ,20.495 + ,0 + ,20.565 + ,0 + ,20.155 + ,0 + ,20.485 + ,0 + ,20.375 + ,0 + ,20.505 + ,0 + ,20.760 + ,0 + ,20.760 + ,0 + ,20.450 + ,0 + ,20.720 + ,0 + ,20.420 + ,0 + ,20.350 + ,0 + ,20.380 + ,0 + ,20.280 + ,0 + ,20.410 + ,0 + ,20.620 + ,0 + ,20.875 + ,0 + ,20.430 + ,0 + ,20.310 + ,0 + ,19.020 + ,0 + ,19.300 + ,0 + ,19.340 + ,0 + ,19.320 + ,0 + ,19.240 + ,0 + ,19.060 + ,0 + ,18.410 + ,0 + ,18.350 + ,0 + ,18.550 + ,0 + ,18.560 + ,0 + ,18.750 + ,0 + ,18.820 + ,0 + ,18.550 + ,0 + ,18.910 + ,0 + ,18.810 + ,0 + ,18.690 + ,0 + ,18.930 + ,0 + ,18.620 + ,0 + ,19.170 + ,0 + ,19.000 + ,0 + ,18.570 + ,0 + ,18.570 + ,0 + ,18.470 + ,0 + ,18.390 + ,0 + ,18.190 + ,0 + ,18.300 + ,0 + ,18.310 + ,0 + ,17.920 + ,0 + ,17.980 + ,0 + ,18.220 + ,0 + ,18.180 + ,0 + ,18.300 + ,0 + ,18.455 + ,0 + ,18.210 + ,0 + ,17.975 + ,0 + ,17.960 + ,0 + ,18.380 + ,0 + ,18.720 + ,0 + ,18.650 + ,0 + ,18.885 + ,0 + ,18.915 + ,0 + ,18.860 + ,0 + ,18.870 + ,0 + ,18.835 + ,0 + ,18.295 + ,0 + ,18.450 + ,0 + ,18.240 + ,0 + ,18.450 + ,0 + ,18.440 + ,0 + ,18.280 + ,0 + ,18.200 + ,0 + ,18.200 + ,0 + ,18.380 + ,0 + ,18.320 + ,0 + ,18.160 + ,0 + ,18.140 + ,0 + ,18.100 + ,0 + ,18.180 + ,0 + ,18.330 + ,0 + ,18.370 + ,0 + ,18.550 + ,0 + ,18.600 + ,0 + ,18.740 + ,0 + ,18.380 + ,0 + ,18.330 + ,0 + ,18.290 + ,0 + ,18.810 + ,0 + ,18.960 + ,0 + ,18.950 + ,0 + ,19.090 + ,0 + ,19.050 + ,0 + ,19.070 + ,0 + ,18.930 + ,0 + ,19.000 + ,0 + ,18.920 + ,0 + ,19.060 + ,0 + ,19.330 + ,0 + ,19.420 + ,0 + ,19.720 + ,0 + ,19.690 + ,0 + ,19.570 + ,0 + ,19.500 + ,0 + ,19.680 + ,0 + ,19.120 + ,0 + ,19.050 + ,0 + ,19.080 + ,0 + ,19.030 + ,0 + ,19.000 + ,0 + ,18.860 + ,0 + ,18.800 + ,0 + ,18.810 + ,0 + ,18.890 + ,0 + ,18.370 + ,0 + ,18.405 + ,0 + ,18.350 + ,0 + ,18.360 + ,0 + ,18.445 + ,0 + ,17.520 + ,0 + ,17.760 + ,0 + ,17.980 + ,0 + ,17.380 + ,0 + ,17.280 + ,0 + ,17.650 + ,0 + ,17.875 + ,0 + ,18.010 + ,0 + ,17.935 + ,0 + ,17.925 + ,0 + ,18.225 + ,0 + ,18.130 + ,0 + ,18.270 + ,0 + ,18.265 + ,0 + ,17.920 + ,0 + ,17.900 + ,0 + ,17.985 + ,0 + ,17.985 + ,0 + ,18.240 + ,0 + ,18.110 + ,0 + ,17.630 + ,0 + ,17.160 + ,0 + ,17.290 + ,0 + ,17.630 + ,0 + ,17.590 + ,0 + ,17.730 + ,0 + ,19.280 + ,0 + ,19.010 + ,0 + ,19.300 + ,0 + ,19.510 + ,0 + ,19.530 + ,0 + ,19.480 + ,0 + ,19.265 + ,0 + ,19.700 + ,0 + ,20.125 + ,0 + ,19.985 + ,1 + ,20.085 + ,1 + ,19.545 + ,1 + ,19.875 + ,1 + ,20.050 + ,1 + ,21.735 + ,1 + ,20.865 + ,1 + ,21.820 + ,1 + ,20.550 + ,1 + ,20.160 + ,1 + ,20.080 + ,1 + ,20.050 + ,1 + ,19.260 + ,1 + ,19.280 + ,1 + ,19.100 + ,1 + ,18.955 + ,1 + ,18.265 + ,1 + ,17.760 + ,1 + ,18.760 + ,1 + ,19.280 + ,1 + ,18.920 + ,1 + ,18.950 + ,1 + ,18.490 + ,1 + ,18.470 + ,1 + ,18.570 + ,1 + ,18.900 + ,1 + ,18.485 + ,1 + ,18.635 + ,1 + ,18.865 + ,1 + ,18.860 + ,1 + ,18.400 + ,1 + ,18.515 + ,1 + ,18.950 + ,1 + ,18.830 + ,1 + ,18.840 + ,1 + ,19.200 + ,1 + ,19.340 + ,1) + ,dim=c(2 + ,207) + ,dimnames=list(c('goudprijs' + ,'dummy') + ,1:207)) > y <- array(NA,dim=c(2,207),dimnames=list(c('goudprijs','dummy'),1:207)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No 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 Attaching package: 'zoo' The following object(s) are masked from package:base : 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 goudprijs dummy 1 20.005 0 2 20.155 0 3 20.005 0 4 19.975 0 5 20.155 0 6 19.510 0 7 19.480 0 8 19.630 0 9 19.940 0 10 20.300 0 11 20.415 0 12 20.395 0 13 19.970 0 14 20.030 0 15 19.960 0 16 19.930 0 17 20.020 0 18 20.220 0 19 20.570 0 20 20.495 0 21 20.565 0 22 20.155 0 23 20.485 0 24 20.375 0 25 20.505 0 26 20.760 0 27 20.760 0 28 20.450 0 29 20.720 0 30 20.420 0 31 20.350 0 32 20.380 0 33 20.280 0 34 20.410 0 35 20.620 0 36 20.875 0 37 20.430 0 38 20.310 0 39 19.020 0 40 19.300 0 41 19.340 0 42 19.320 0 43 19.240 0 44 19.060 0 45 18.410 0 46 18.350 0 47 18.550 0 48 18.560 0 49 18.750 0 50 18.820 0 51 18.550 0 52 18.910 0 53 18.810 0 54 18.690 0 55 18.930 0 56 18.620 0 57 19.170 0 58 19.000 0 59 18.570 0 60 18.570 0 61 18.470 0 62 18.390 0 63 18.190 0 64 18.300 0 65 18.310 0 66 17.920 0 67 17.980 0 68 18.220 0 69 18.180 0 70 18.300 0 71 18.455 0 72 18.210 0 73 17.975 0 74 17.960 0 75 18.380 0 76 18.720 0 77 18.650 0 78 18.885 0 79 18.915 0 80 18.860 0 81 18.870 0 82 18.835 0 83 18.295 0 84 18.450 0 85 18.240 0 86 18.450 0 87 18.440 0 88 18.280 0 89 18.200 0 90 18.200 0 91 18.380 0 92 18.320 0 93 18.160 0 94 18.140 0 95 18.100 0 96 18.180 0 97 18.330 0 98 18.370 0 99 18.550 0 100 18.600 0 101 18.740 0 102 18.380 0 103 18.330 0 104 18.290 0 105 18.810 0 106 18.960 0 107 18.950 0 108 19.090 0 109 19.050 0 110 19.070 0 111 18.930 0 112 19.000 0 113 18.920 0 114 19.060 0 115 19.330 0 116 19.420 0 117 19.720 0 118 19.690 0 119 19.570 0 120 19.500 0 121 19.680 0 122 19.120 0 123 19.050 0 124 19.080 0 125 19.030 0 126 19.000 0 127 18.860 0 128 18.800 0 129 18.810 0 130 18.890 0 131 18.370 0 132 18.405 0 133 18.350 0 134 18.360 0 135 18.445 0 136 17.520 0 137 17.760 0 138 17.980 0 139 17.380 0 140 17.280 0 141 17.650 0 142 17.875 0 143 18.010 0 144 17.935 0 145 17.925 0 146 18.225 0 147 18.130 0 148 18.270 0 149 18.265 0 150 17.920 0 151 17.900 0 152 17.985 0 153 17.985 0 154 18.240 0 155 18.110 0 156 17.630 0 157 17.160 0 158 17.290 0 159 17.630 0 160 17.590 0 161 17.730 0 162 19.280 0 163 19.010 0 164 19.300 0 165 19.510 0 166 19.530 0 167 19.480 0 168 19.265 0 169 19.700 0 170 20.125 0 171 19.985 1 172 20.085 1 173 19.545 1 174 19.875 1 175 20.050 1 176 21.735 1 177 20.865 1 178 21.820 1 179 20.550 1 180 20.160 1 181 20.080 1 182 20.050 1 183 19.260 1 184 19.280 1 185 19.100 1 186 18.955 1 187 18.265 1 188 17.760 1 189 18.760 1 190 19.280 1 191 18.920 1 192 18.950 1 193 18.490 1 194 18.470 1 195 18.570 1 196 18.900 1 197 18.485 1 198 18.635 1 199 18.865 1 200 18.860 1 201 18.400 1 202 18.515 1 203 18.950 1 204 18.830 1 205 18.840 1 206 19.200 1 207 19.340 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) dummy 18.9461 0.3695 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.7861 -0.6536 -0.1361 0.6039 2.5043 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 18.94615 0.06831 277.353 <2e-16 *** dummy 0.36953 0.16157 2.287 0.0232 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.8907 on 205 degrees of freedom Multiple R-squared: 0.02488, Adjusted R-squared: 0.02012 F-statistic: 5.231 on 1 and 205 DF, p-value: 0.02321 > 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,] 2.117671e-03 4.235341e-03 9.978823e-01 [2,] 1.454517e-02 2.909035e-02 9.854548e-01 [3,] 1.305899e-02 2.611797e-02 9.869410e-01 [4,] 5.586165e-03 1.117233e-02 9.944138e-01 [5,] 1.768270e-03 3.536540e-03 9.982317e-01 [6,] 1.367694e-03 2.735389e-03 9.986323e-01 [7,] 1.362033e-03 2.724067e-03 9.986380e-01 [8,] 1.033276e-03 2.066553e-03 9.989667e-01 [9,] 3.795346e-04 7.590692e-04 9.996205e-01 [10,] 1.358674e-04 2.717348e-04 9.998641e-01 [11,] 4.701493e-05 9.402987e-05 9.999530e-01 [12,] 1.603194e-05 3.206387e-05 9.999840e-01 [13,] 5.297004e-06 1.059401e-05 9.999947e-01 [14,] 2.384815e-06 4.769630e-06 9.999976e-01 [15,] 4.848833e-06 9.697665e-06 9.999952e-01 [16,] 5.185553e-06 1.037111e-05 9.999948e-01 [17,] 6.677744e-06 1.335549e-05 9.999933e-01 [18,] 2.795742e-06 5.591484e-06 9.999972e-01 [19,] 2.441694e-06 4.883387e-06 9.999976e-01 [20,] 1.485587e-06 2.971174e-06 9.999985e-01 [21,] 1.329325e-06 2.658651e-06 9.999987e-01 [22,] 3.313218e-06 6.626436e-06 9.999967e-01 [23,] 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9.607539e-01 7.849220e-02 3.924610e-02 [90,] 9.603176e-01 7.936480e-02 3.968240e-02 [91,] 9.603923e-01 7.921534e-02 3.960767e-02 [92,] 9.588326e-01 8.233474e-02 4.116737e-02 [93,] 9.546614e-01 9.067715e-02 4.533857e-02 [94,] 9.494307e-01 1.011387e-01 5.056934e-02 [95,] 9.413013e-01 1.173975e-01 5.869874e-02 [96,] 9.315804e-01 1.368392e-01 6.841960e-02 [97,] 9.195219e-01 1.609561e-01 8.047806e-02 [98,] 9.106349e-01 1.787302e-01 8.936510e-02 [99,] 9.020379e-01 1.959242e-01 9.796208e-02 [100,] 8.937295e-01 2.125409e-01 1.062705e-01 [101,] 8.764354e-01 2.471292e-01 1.235646e-01 [102,] 8.576520e-01 2.846959e-01 1.423480e-01 [103,] 8.370086e-01 3.259827e-01 1.629914e-01 [104,] 8.164952e-01 3.670097e-01 1.835048e-01 [105,] 7.938707e-01 4.122585e-01 2.061293e-01 [106,] 7.702389e-01 4.595222e-01 2.297611e-01 [107,] 7.430864e-01 5.138273e-01 2.569136e-01 [108,] 7.154502e-01 5.690996e-01 2.845498e-01 [109,] 6.854290e-01 6.291420e-01 3.145710e-01 [110,] 6.569276e-01 6.861448e-01 3.430724e-01 [111,] 6.390900e-01 7.218200e-01 3.609100e-01 [112,] 6.274212e-01 7.451576e-01 3.725788e-01 [113,] 6.422938e-01 7.154123e-01 3.577062e-01 [114,] 6.565486e-01 6.869028e-01 3.434514e-01 [115,] 6.619899e-01 6.760202e-01 3.380101e-01 [116,] 6.635840e-01 6.728320e-01 3.364160e-01 [117,] 6.841208e-01 6.317584e-01 3.158792e-01 [118,] 6.651138e-01 6.697724e-01 3.348862e-01 [119,] 6.433706e-01 7.132587e-01 3.566294e-01 [120,] 6.232603e-01 7.534795e-01 3.767397e-01 [121,] 6.013938e-01 7.972124e-01 3.986062e-01 [122,] 5.787184e-01 8.425633e-01 4.212816e-01 [123,] 5.515655e-01 8.968691e-01 4.484345e-01 [124,] 5.231179e-01 9.537643e-01 4.768821e-01 [125,] 4.951482e-01 9.902964e-01 5.048518e-01 [126,] 4.700546e-01 9.401092e-01 5.299454e-01 [127,] 4.430812e-01 8.861623e-01 5.569188e-01 [128,] 4.151856e-01 8.303711e-01 5.848144e-01 [129,] 3.886543e-01 7.773085e-01 6.113457e-01 [130,] 3.620116e-01 7.240233e-01 6.379884e-01 [131,] 3.341709e-01 6.683417e-01 6.658291e-01 [132,] 3.644756e-01 7.289511e-01 6.355244e-01 [133,] 3.696273e-01 7.392546e-01 6.303727e-01 [134,] 3.571915e-01 7.143830e-01 6.428085e-01 [135,] 4.025696e-01 8.051392e-01 5.974304e-01 [136,] 4.630689e-01 9.261378e-01 5.369311e-01 [137,] 4.769303e-01 9.538607e-01 5.230697e-01 [138,] 4.693324e-01 9.386648e-01 5.306676e-01 [139,] 4.514741e-01 9.029483e-01 5.485259e-01 [140,] 4.388559e-01 8.777117e-01 5.611441e-01 [141,] 4.270258e-01 8.540516e-01 5.729742e-01 [142,] 3.971532e-01 7.943065e-01 6.028468e-01 [143,] 3.722428e-01 7.444857e-01 6.277572e-01 [144,] 3.414113e-01 6.828227e-01 6.585887e-01 [145,] 3.116036e-01 6.232071e-01 6.883964e-01 [146,] 3.013322e-01 6.026644e-01 6.986678e-01 [147,] 2.932882e-01 5.865764e-01 7.067118e-01 [148,] 2.802213e-01 5.604426e-01 7.197787e-01 [149,] 2.682537e-01 5.365074e-01 7.317463e-01 [150,] 2.435145e-01 4.870290e-01 7.564855e-01 [151,] 2.266029e-01 4.532058e-01 7.733971e-01 [152,] 2.485918e-01 4.971836e-01 7.514082e-01 [153,] 3.470066e-01 6.940132e-01 6.529934e-01 [154,] 4.531121e-01 9.062243e-01 5.468879e-01 [155,] 5.230936e-01 9.538128e-01 4.769064e-01 [156,] 6.219989e-01 7.560021e-01 3.780011e-01 [157,] 7.181389e-01 5.637222e-01 2.818611e-01 [158,] 6.783458e-01 6.433084e-01 3.216542e-01 [159,] 6.456253e-01 7.087494e-01 3.543747e-01 [160,] 6.037603e-01 7.924795e-01 3.962397e-01 [161,] 5.592207e-01 8.815585e-01 4.407793e-01 [162,] 5.137836e-01 9.724329e-01 4.862164e-01 [163,] 4.683361e-01 9.366721e-01 5.316639e-01 [164,] 4.330833e-01 8.661666e-01 5.669167e-01 [165,] 3.938933e-01 7.877867e-01 6.061067e-01 [166,] 3.598068e-01 7.196136e-01 6.401932e-01 [167,] 3.331261e-01 6.662523e-01 6.668739e-01 [168,] 3.153187e-01 6.306374e-01 6.846813e-01 [169,] 2.739431e-01 5.478861e-01 7.260569e-01 [170,] 2.462039e-01 4.924078e-01 7.537961e-01 [171,] 2.312372e-01 4.624744e-01 7.687628e-01 [172,] 5.636087e-01 8.727827e-01 4.363913e-01 [173,] 7.095529e-01 5.808942e-01 2.904471e-01 [174,] 9.845075e-01 3.098507e-02 1.549254e-02 [175,] 9.970802e-01 5.839678e-03 2.919839e-03 [176,] 9.991911e-01 1.617878e-03 8.089389e-04 [177,] 9.998487e-01 3.026616e-04 1.513308e-04 [178,] 9.999895e-01 2.106648e-05 1.053324e-05 [179,] 9.999877e-01 2.455221e-05 1.227610e-05 [180,] 9.999872e-01 2.553730e-05 1.276865e-05 [181,] 9.999799e-01 4.010567e-05 2.005283e-05 [182,] 9.999605e-01 7.901349e-05 3.950675e-05 [183,] 9.999580e-01 8.405660e-05 4.202830e-05 [184,] 9.999986e-01 2.711868e-06 1.355934e-06 [185,] 9.999959e-01 8.193739e-06 4.096870e-06 [186,] 9.999965e-01 7.086952e-06 3.543476e-06 [187,] 9.999898e-01 2.034788e-05 1.017394e-05 [188,] 9.999731e-01 5.379147e-05 2.689573e-05 [189,] 9.999448e-01 1.104455e-04 5.522275e-05 [190,] 9.998995e-01 2.009421e-04 1.004710e-04 [191,] 9.997643e-01 4.713209e-04 2.356604e-04 [192,] 9.992808e-01 1.438334e-03 7.191670e-04 [193,] 9.987417e-01 2.516643e-03 1.258321e-03 [194,] 9.968854e-01 6.229256e-03 3.114628e-03 [195,] 9.906195e-01 1.876100e-02 9.380499e-03 [196,] 9.736046e-01 5.279077e-02 2.639539e-02 [197,] 9.702083e-01 5.958331e-02 2.979166e-02 [198,] 9.679544e-01 6.409114e-02 3.204557e-02 > postscript(file="/var/www/html/rcomp/tmp/13fsz1227533800.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ezbe1227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3d3az1227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/41hzj1227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5azj91227533800.ps",horizontal=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 = 207 Frequency = 1 1 2 3 4 5 6 1.058852941 1.208852941 1.058852941 1.028852941 1.208852941 0.563852941 7 8 9 10 11 12 0.533852941 0.683852941 0.993852941 1.353852941 1.468852941 1.448852941 13 14 15 16 17 18 1.023852941 1.083852941 1.013852941 0.983852941 1.073852941 1.273852941 19 20 21 22 23 24 1.623852941 1.548852941 1.618852941 1.208852941 1.538852941 1.428852941 25 26 27 28 29 30 1.558852941 1.813852941 1.813852941 1.503852941 1.773852941 1.473852941 31 32 33 34 35 36 1.403852941 1.433852941 1.333852941 1.463852941 1.673852941 1.928852941 37 38 39 40 41 42 1.483852941 1.363852941 0.073852941 0.353852941 0.393852941 0.373852941 43 44 45 46 47 48 0.293852941 0.113852941 -0.536147059 -0.596147059 -0.396147059 -0.386147059 49 50 51 52 53 54 -0.196147059 -0.126147059 -0.396147059 -0.036147059 -0.136147059 -0.256147059 55 56 57 58 59 60 -0.016147059 -0.326147059 0.223852941 0.053852941 -0.376147059 -0.376147059 61 62 63 64 65 66 -0.476147059 -0.556147059 -0.756147059 -0.646147059 -0.636147059 -1.026147059 67 68 69 70 71 72 -0.966147059 -0.726147059 -0.766147059 -0.646147059 -0.491147059 -0.736147059 73 74 75 76 77 78 -0.971147059 -0.986147059 -0.566147059 -0.226147059 -0.296147059 -0.061147059 79 80 81 82 83 84 -0.031147059 -0.086147059 -0.076147059 -0.111147059 -0.651147059 -0.496147059 85 86 87 88 89 90 -0.706147059 -0.496147059 -0.506147059 -0.666147059 -0.746147059 -0.746147059 91 92 93 94 95 96 -0.566147059 -0.626147059 -0.786147059 -0.806147059 -0.846147059 -0.766147059 97 98 99 100 101 102 -0.616147059 -0.576147059 -0.396147059 -0.346147059 -0.206147059 -0.566147059 103 104 105 106 107 108 -0.616147059 -0.656147059 -0.136147059 0.013852941 0.003852941 0.143852941 109 110 111 112 113 114 0.103852941 0.123852941 -0.016147059 0.053852941 -0.026147059 0.113852941 115 116 117 118 119 120 0.383852941 0.473852941 0.773852941 0.743852941 0.623852941 0.553852941 121 122 123 124 125 126 0.733852941 0.173852941 0.103852941 0.133852941 0.083852941 0.053852941 127 128 129 130 131 132 -0.086147059 -0.146147059 -0.136147059 -0.056147059 -0.576147059 -0.541147059 133 134 135 136 137 138 -0.596147059 -0.586147059 -0.501147059 -1.426147059 -1.186147059 -0.966147059 139 140 141 142 143 144 -1.566147059 -1.666147059 -1.296147059 -1.071147059 -0.936147059 -1.011147059 145 146 147 148 149 150 -1.021147059 -0.721147059 -0.816147059 -0.676147059 -0.681147059 -1.026147059 151 152 153 154 155 156 -1.046147059 -0.961147059 -0.961147059 -0.706147059 -0.836147059 -1.316147059 157 158 159 160 161 162 -1.786147059 -1.656147059 -1.316147059 -1.356147059 -1.216147059 0.333852941 163 164 165 166 167 168 0.063852941 0.353852941 0.563852941 0.583852941 0.533852941 0.318852941 169 170 171 172 173 174 0.753852941 1.178852941 0.669324324 0.769324324 0.229324324 0.559324324 175 176 177 178 179 180 0.734324324 2.419324324 1.549324324 2.504324324 1.234324324 0.844324324 181 182 183 184 185 186 0.764324324 0.734324324 -0.055675676 -0.035675676 -0.215675676 -0.360675676 187 188 189 190 191 192 -1.050675676 -1.555675676 -0.555675676 -0.035675676 -0.395675676 -0.365675676 193 194 195 196 197 198 -0.825675676 -0.845675676 -0.745675676 -0.415675676 -0.830675676 -0.680675676 199 200 201 202 203 204 -0.450675676 -0.455675676 -0.915675676 -0.800675676 -0.365675676 -0.485675676 205 206 207 -0.475675676 -0.115675676 0.024324324 > postscript(file="/var/www/html/rcomp/tmp/682p21227533800.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 = 207 Frequency = 1 lag(myerror, k = 1) myerror 0 1.058852941 NA 1 1.208852941 1.058852941 2 1.058852941 1.208852941 3 1.028852941 1.058852941 4 1.208852941 1.028852941 5 0.563852941 1.208852941 6 0.533852941 0.563852941 7 0.683852941 0.533852941 8 0.993852941 0.683852941 9 1.353852941 0.993852941 10 1.468852941 1.353852941 11 1.448852941 1.468852941 12 1.023852941 1.448852941 13 1.083852941 1.023852941 14 1.013852941 1.083852941 15 0.983852941 1.013852941 16 1.073852941 0.983852941 17 1.273852941 1.073852941 18 1.623852941 1.273852941 19 1.548852941 1.623852941 20 1.618852941 1.548852941 21 1.208852941 1.618852941 22 1.538852941 1.208852941 23 1.428852941 1.538852941 24 1.558852941 1.428852941 25 1.813852941 1.558852941 26 1.813852941 1.813852941 27 1.503852941 1.813852941 28 1.773852941 1.503852941 29 1.473852941 1.773852941 30 1.403852941 1.473852941 31 1.433852941 1.403852941 32 1.333852941 1.433852941 33 1.463852941 1.333852941 34 1.673852941 1.463852941 35 1.928852941 1.673852941 36 1.483852941 1.928852941 37 1.363852941 1.483852941 38 0.073852941 1.363852941 39 0.353852941 0.073852941 40 0.393852941 0.353852941 41 0.373852941 0.393852941 42 0.293852941 0.373852941 43 0.113852941 0.293852941 44 -0.536147059 0.113852941 45 -0.596147059 -0.536147059 46 -0.396147059 -0.596147059 47 -0.386147059 -0.396147059 48 -0.196147059 -0.386147059 49 -0.126147059 -0.196147059 50 -0.396147059 -0.126147059 51 -0.036147059 -0.396147059 52 -0.136147059 -0.036147059 53 -0.256147059 -0.136147059 54 -0.016147059 -0.256147059 55 -0.326147059 -0.016147059 56 0.223852941 -0.326147059 57 0.053852941 0.223852941 58 -0.376147059 0.053852941 59 -0.376147059 -0.376147059 60 -0.476147059 -0.376147059 61 -0.556147059 -0.476147059 62 -0.756147059 -0.556147059 63 -0.646147059 -0.756147059 64 -0.636147059 -0.646147059 65 -1.026147059 -0.636147059 66 -0.966147059 -1.026147059 67 -0.726147059 -0.966147059 68 -0.766147059 -0.726147059 69 -0.646147059 -0.766147059 70 -0.491147059 -0.646147059 71 -0.736147059 -0.491147059 72 -0.971147059 -0.736147059 73 -0.986147059 -0.971147059 74 -0.566147059 -0.986147059 75 -0.226147059 -0.566147059 76 -0.296147059 -0.226147059 77 -0.061147059 -0.296147059 78 -0.031147059 -0.061147059 79 -0.086147059 -0.031147059 80 -0.076147059 -0.086147059 81 -0.111147059 -0.076147059 82 -0.651147059 -0.111147059 83 -0.496147059 -0.651147059 84 -0.706147059 -0.496147059 85 -0.496147059 -0.706147059 86 -0.506147059 -0.496147059 87 -0.666147059 -0.506147059 88 -0.746147059 -0.666147059 89 -0.746147059 -0.746147059 90 -0.566147059 -0.746147059 91 -0.626147059 -0.566147059 92 -0.786147059 -0.626147059 93 -0.806147059 -0.786147059 94 -0.846147059 -0.806147059 95 -0.766147059 -0.846147059 96 -0.616147059 -0.766147059 97 -0.576147059 -0.616147059 98 -0.396147059 -0.576147059 99 -0.346147059 -0.396147059 100 -0.206147059 -0.346147059 101 -0.566147059 -0.206147059 102 -0.616147059 -0.566147059 103 -0.656147059 -0.616147059 104 -0.136147059 -0.656147059 105 0.013852941 -0.136147059 106 0.003852941 0.013852941 107 0.143852941 0.003852941 108 0.103852941 0.143852941 109 0.123852941 0.103852941 110 -0.016147059 0.123852941 111 0.053852941 -0.016147059 112 -0.026147059 0.053852941 113 0.113852941 -0.026147059 114 0.383852941 0.113852941 115 0.473852941 0.383852941 116 0.773852941 0.473852941 117 0.743852941 0.773852941 118 0.623852941 0.743852941 119 0.553852941 0.623852941 120 0.733852941 0.553852941 121 0.173852941 0.733852941 122 0.103852941 0.173852941 123 0.133852941 0.103852941 124 0.083852941 0.133852941 125 0.053852941 0.083852941 126 -0.086147059 0.053852941 127 -0.146147059 -0.086147059 128 -0.136147059 -0.146147059 129 -0.056147059 -0.136147059 130 -0.576147059 -0.056147059 131 -0.541147059 -0.576147059 132 -0.596147059 -0.541147059 133 -0.586147059 -0.596147059 134 -0.501147059 -0.586147059 135 -1.426147059 -0.501147059 136 -1.186147059 -1.426147059 137 -0.966147059 -1.186147059 138 -1.566147059 -0.966147059 139 -1.666147059 -1.566147059 140 -1.296147059 -1.666147059 141 -1.071147059 -1.296147059 142 -0.936147059 -1.071147059 143 -1.011147059 -0.936147059 144 -1.021147059 -1.011147059 145 -0.721147059 -1.021147059 146 -0.816147059 -0.721147059 147 -0.676147059 -0.816147059 148 -0.681147059 -0.676147059 149 -1.026147059 -0.681147059 150 -1.046147059 -1.026147059 151 -0.961147059 -1.046147059 152 -0.961147059 -0.961147059 153 -0.706147059 -0.961147059 154 -0.836147059 -0.706147059 155 -1.316147059 -0.836147059 156 -1.786147059 -1.316147059 157 -1.656147059 -1.786147059 158 -1.316147059 -1.656147059 159 -1.356147059 -1.316147059 160 -1.216147059 -1.356147059 161 0.333852941 -1.216147059 162 0.063852941 0.333852941 163 0.353852941 0.063852941 164 0.563852941 0.353852941 165 0.583852941 0.563852941 166 0.533852941 0.583852941 167 0.318852941 0.533852941 168 0.753852941 0.318852941 169 1.178852941 0.753852941 170 0.669324324 1.178852941 171 0.769324324 0.669324324 172 0.229324324 0.769324324 173 0.559324324 0.229324324 174 0.734324324 0.559324324 175 2.419324324 0.734324324 176 1.549324324 2.419324324 177 2.504324324 1.549324324 178 1.234324324 2.504324324 179 0.844324324 1.234324324 180 0.764324324 0.844324324 181 0.734324324 0.764324324 182 -0.055675676 0.734324324 183 -0.035675676 -0.055675676 184 -0.215675676 -0.035675676 185 -0.360675676 -0.215675676 186 -1.050675676 -0.360675676 187 -1.555675676 -1.050675676 188 -0.555675676 -1.555675676 189 -0.035675676 -0.555675676 190 -0.395675676 -0.035675676 191 -0.365675676 -0.395675676 192 -0.825675676 -0.365675676 193 -0.845675676 -0.825675676 194 -0.745675676 -0.845675676 195 -0.415675676 -0.745675676 196 -0.830675676 -0.415675676 197 -0.680675676 -0.830675676 198 -0.450675676 -0.680675676 199 -0.455675676 -0.450675676 200 -0.915675676 -0.455675676 201 -0.800675676 -0.915675676 202 -0.365675676 -0.800675676 203 -0.485675676 -0.365675676 204 -0.475675676 -0.485675676 205 -0.115675676 -0.475675676 206 0.024324324 -0.115675676 207 NA 0.024324324 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.208852941 1.058852941 [2,] 1.058852941 1.208852941 [3,] 1.028852941 1.058852941 [4,] 1.208852941 1.028852941 [5,] 0.563852941 1.208852941 [6,] 0.533852941 0.563852941 [7,] 0.683852941 0.533852941 [8,] 0.993852941 0.683852941 [9,] 1.353852941 0.993852941 [10,] 1.468852941 1.353852941 [11,] 1.448852941 1.468852941 [12,] 1.023852941 1.448852941 [13,] 1.083852941 1.023852941 [14,] 1.013852941 1.083852941 [15,] 0.983852941 1.013852941 [16,] 1.073852941 0.983852941 [17,] 1.273852941 1.073852941 [18,] 1.623852941 1.273852941 [19,] 1.548852941 1.623852941 [20,] 1.618852941 1.548852941 [21,] 1.208852941 1.618852941 [22,] 1.538852941 1.208852941 [23,] 1.428852941 1.538852941 [24,] 1.558852941 1.428852941 [25,] 1.813852941 1.558852941 [26,] 1.813852941 1.813852941 [27,] 1.503852941 1.813852941 [28,] 1.773852941 1.503852941 [29,] 1.473852941 1.773852941 [30,] 1.403852941 1.473852941 [31,] 1.433852941 1.403852941 [32,] 1.333852941 1.433852941 [33,] 1.463852941 1.333852941 [34,] 1.673852941 1.463852941 [35,] 1.928852941 1.673852941 [36,] 1.483852941 1.928852941 [37,] 1.363852941 1.483852941 [38,] 0.073852941 1.363852941 [39,] 0.353852941 0.073852941 [40,] 0.393852941 0.353852941 [41,] 0.373852941 0.393852941 [42,] 0.293852941 0.373852941 [43,] 0.113852941 0.293852941 [44,] -0.536147059 0.113852941 [45,] -0.596147059 -0.536147059 [46,] -0.396147059 -0.596147059 [47,] -0.386147059 -0.396147059 [48,] -0.196147059 -0.386147059 [49,] -0.126147059 -0.196147059 [50,] -0.396147059 -0.126147059 [51,] -0.036147059 -0.396147059 [52,] -0.136147059 -0.036147059 [53,] -0.256147059 -0.136147059 [54,] -0.016147059 -0.256147059 [55,] -0.326147059 -0.016147059 [56,] 0.223852941 -0.326147059 [57,] 0.053852941 0.223852941 [58,] -0.376147059 0.053852941 [59,] -0.376147059 -0.376147059 [60,] -0.476147059 -0.376147059 [61,] -0.556147059 -0.476147059 [62,] -0.756147059 -0.556147059 [63,] -0.646147059 -0.756147059 [64,] -0.636147059 -0.646147059 [65,] -1.026147059 -0.636147059 [66,] -0.966147059 -1.026147059 [67,] -0.726147059 -0.966147059 [68,] -0.766147059 -0.726147059 [69,] -0.646147059 -0.766147059 [70,] -0.491147059 -0.646147059 [71,] -0.736147059 -0.491147059 [72,] -0.971147059 -0.736147059 [73,] -0.986147059 -0.971147059 [74,] -0.566147059 -0.986147059 [75,] -0.226147059 -0.566147059 [76,] -0.296147059 -0.226147059 [77,] -0.061147059 -0.296147059 [78,] -0.031147059 -0.061147059 [79,] -0.086147059 -0.031147059 [80,] -0.076147059 -0.086147059 [81,] -0.111147059 -0.076147059 [82,] -0.651147059 -0.111147059 [83,] -0.496147059 -0.651147059 [84,] -0.706147059 -0.496147059 [85,] -0.496147059 -0.706147059 [86,] -0.506147059 -0.496147059 [87,] -0.666147059 -0.506147059 [88,] -0.746147059 -0.666147059 [89,] -0.746147059 -0.746147059 [90,] -0.566147059 -0.746147059 [91,] -0.626147059 -0.566147059 [92,] -0.786147059 -0.626147059 [93,] -0.806147059 -0.786147059 [94,] -0.846147059 -0.806147059 [95,] -0.766147059 -0.846147059 [96,] -0.616147059 -0.766147059 [97,] -0.576147059 -0.616147059 [98,] -0.396147059 -0.576147059 [99,] -0.346147059 -0.396147059 [100,] -0.206147059 -0.346147059 [101,] -0.566147059 -0.206147059 [102,] -0.616147059 -0.566147059 [103,] -0.656147059 -0.616147059 [104,] -0.136147059 -0.656147059 [105,] 0.013852941 -0.136147059 [106,] 0.003852941 0.013852941 [107,] 0.143852941 0.003852941 [108,] 0.103852941 0.143852941 [109,] 0.123852941 0.103852941 [110,] -0.016147059 0.123852941 [111,] 0.053852941 -0.016147059 [112,] -0.026147059 0.053852941 [113,] 0.113852941 -0.026147059 [114,] 0.383852941 0.113852941 [115,] 0.473852941 0.383852941 [116,] 0.773852941 0.473852941 [117,] 0.743852941 0.773852941 [118,] 0.623852941 0.743852941 [119,] 0.553852941 0.623852941 [120,] 0.733852941 0.553852941 [121,] 0.173852941 0.733852941 [122,] 0.103852941 0.173852941 [123,] 0.133852941 0.103852941 [124,] 0.083852941 0.133852941 [125,] 0.053852941 0.083852941 [126,] -0.086147059 0.053852941 [127,] -0.146147059 -0.086147059 [128,] -0.136147059 -0.146147059 [129,] -0.056147059 -0.136147059 [130,] -0.576147059 -0.056147059 [131,] -0.541147059 -0.576147059 [132,] -0.596147059 -0.541147059 [133,] -0.586147059 -0.596147059 [134,] -0.501147059 -0.586147059 [135,] -1.426147059 -0.501147059 [136,] -1.186147059 -1.426147059 [137,] -0.966147059 -1.186147059 [138,] -1.566147059 -0.966147059 [139,] -1.666147059 -1.566147059 [140,] -1.296147059 -1.666147059 [141,] -1.071147059 -1.296147059 [142,] -0.936147059 -1.071147059 [143,] -1.011147059 -0.936147059 [144,] -1.021147059 -1.011147059 [145,] -0.721147059 -1.021147059 [146,] -0.816147059 -0.721147059 [147,] -0.676147059 -0.816147059 [148,] -0.681147059 -0.676147059 [149,] -1.026147059 -0.681147059 [150,] -1.046147059 -1.026147059 [151,] -0.961147059 -1.046147059 [152,] -0.961147059 -0.961147059 [153,] -0.706147059 -0.961147059 [154,] -0.836147059 -0.706147059 [155,] -1.316147059 -0.836147059 [156,] -1.786147059 -1.316147059 [157,] -1.656147059 -1.786147059 [158,] -1.316147059 -1.656147059 [159,] -1.356147059 -1.316147059 [160,] -1.216147059 -1.356147059 [161,] 0.333852941 -1.216147059 [162,] 0.063852941 0.333852941 [163,] 0.353852941 0.063852941 [164,] 0.563852941 0.353852941 [165,] 0.583852941 0.563852941 [166,] 0.533852941 0.583852941 [167,] 0.318852941 0.533852941 [168,] 0.753852941 0.318852941 [169,] 1.178852941 0.753852941 [170,] 0.669324324 1.178852941 [171,] 0.769324324 0.669324324 [172,] 0.229324324 0.769324324 [173,] 0.559324324 0.229324324 [174,] 0.734324324 0.559324324 [175,] 2.419324324 0.734324324 [176,] 1.549324324 2.419324324 [177,] 2.504324324 1.549324324 [178,] 1.234324324 2.504324324 [179,] 0.844324324 1.234324324 [180,] 0.764324324 0.844324324 [181,] 0.734324324 0.764324324 [182,] -0.055675676 0.734324324 [183,] -0.035675676 -0.055675676 [184,] -0.215675676 -0.035675676 [185,] -0.360675676 -0.215675676 [186,] -1.050675676 -0.360675676 [187,] -1.555675676 -1.050675676 [188,] -0.555675676 -1.555675676 [189,] -0.035675676 -0.555675676 [190,] -0.395675676 -0.035675676 [191,] -0.365675676 -0.395675676 [192,] -0.825675676 -0.365675676 [193,] -0.845675676 -0.825675676 [194,] -0.745675676 -0.845675676 [195,] -0.415675676 -0.745675676 [196,] -0.830675676 -0.415675676 [197,] -0.680675676 -0.830675676 [198,] -0.450675676 -0.680675676 [199,] -0.455675676 -0.450675676 [200,] -0.915675676 -0.455675676 [201,] -0.800675676 -0.915675676 [202,] -0.365675676 -0.800675676 [203,] -0.485675676 -0.365675676 [204,] -0.475675676 -0.485675676 [205,] -0.115675676 -0.475675676 [206,] 0.024324324 -0.115675676 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.208852941 1.058852941 2 1.058852941 1.208852941 3 1.028852941 1.058852941 4 1.208852941 1.028852941 5 0.563852941 1.208852941 6 0.533852941 0.563852941 7 0.683852941 0.533852941 8 0.993852941 0.683852941 9 1.353852941 0.993852941 10 1.468852941 1.353852941 11 1.448852941 1.468852941 12 1.023852941 1.448852941 13 1.083852941 1.023852941 14 1.013852941 1.083852941 15 0.983852941 1.013852941 16 1.073852941 0.983852941 17 1.273852941 1.073852941 18 1.623852941 1.273852941 19 1.548852941 1.623852941 20 1.618852941 1.548852941 21 1.208852941 1.618852941 22 1.538852941 1.208852941 23 1.428852941 1.538852941 24 1.558852941 1.428852941 25 1.813852941 1.558852941 26 1.813852941 1.813852941 27 1.503852941 1.813852941 28 1.773852941 1.503852941 29 1.473852941 1.773852941 30 1.403852941 1.473852941 31 1.433852941 1.403852941 32 1.333852941 1.433852941 33 1.463852941 1.333852941 34 1.673852941 1.463852941 35 1.928852941 1.673852941 36 1.483852941 1.928852941 37 1.363852941 1.483852941 38 0.073852941 1.363852941 39 0.353852941 0.073852941 40 0.393852941 0.353852941 41 0.373852941 0.393852941 42 0.293852941 0.373852941 43 0.113852941 0.293852941 44 -0.536147059 0.113852941 45 -0.596147059 -0.536147059 46 -0.396147059 -0.596147059 47 -0.386147059 -0.396147059 48 -0.196147059 -0.386147059 49 -0.126147059 -0.196147059 50 -0.396147059 -0.126147059 51 -0.036147059 -0.396147059 52 -0.136147059 -0.036147059 53 -0.256147059 -0.136147059 54 -0.016147059 -0.256147059 55 -0.326147059 -0.016147059 56 0.223852941 -0.326147059 57 0.053852941 0.223852941 58 -0.376147059 0.053852941 59 -0.376147059 -0.376147059 60 -0.476147059 -0.376147059 61 -0.556147059 -0.476147059 62 -0.756147059 -0.556147059 63 -0.646147059 -0.756147059 64 -0.636147059 -0.646147059 65 -1.026147059 -0.636147059 66 -0.966147059 -1.026147059 67 -0.726147059 -0.966147059 68 -0.766147059 -0.726147059 69 -0.646147059 -0.766147059 70 -0.491147059 -0.646147059 71 -0.736147059 -0.491147059 72 -0.971147059 -0.736147059 73 -0.986147059 -0.971147059 74 -0.566147059 -0.986147059 75 -0.226147059 -0.566147059 76 -0.296147059 -0.226147059 77 -0.061147059 -0.296147059 78 -0.031147059 -0.061147059 79 -0.086147059 -0.031147059 80 -0.076147059 -0.086147059 81 -0.111147059 -0.076147059 82 -0.651147059 -0.111147059 83 -0.496147059 -0.651147059 84 -0.706147059 -0.496147059 85 -0.496147059 -0.706147059 86 -0.506147059 -0.496147059 87 -0.666147059 -0.506147059 88 -0.746147059 -0.666147059 89 -0.746147059 -0.746147059 90 -0.566147059 -0.746147059 91 -0.626147059 -0.566147059 92 -0.786147059 -0.626147059 93 -0.806147059 -0.786147059 94 -0.846147059 -0.806147059 95 -0.766147059 -0.846147059 96 -0.616147059 -0.766147059 97 -0.576147059 -0.616147059 98 -0.396147059 -0.576147059 99 -0.346147059 -0.396147059 100 -0.206147059 -0.346147059 101 -0.566147059 -0.206147059 102 -0.616147059 -0.566147059 103 -0.656147059 -0.616147059 104 -0.136147059 -0.656147059 105 0.013852941 -0.136147059 106 0.003852941 0.013852941 107 0.143852941 0.003852941 108 0.103852941 0.143852941 109 0.123852941 0.103852941 110 -0.016147059 0.123852941 111 0.053852941 -0.016147059 112 -0.026147059 0.053852941 113 0.113852941 -0.026147059 114 0.383852941 0.113852941 115 0.473852941 0.383852941 116 0.773852941 0.473852941 117 0.743852941 0.773852941 118 0.623852941 0.743852941 119 0.553852941 0.623852941 120 0.733852941 0.553852941 121 0.173852941 0.733852941 122 0.103852941 0.173852941 123 0.133852941 0.103852941 124 0.083852941 0.133852941 125 0.053852941 0.083852941 126 -0.086147059 0.053852941 127 -0.146147059 -0.086147059 128 -0.136147059 -0.146147059 129 -0.056147059 -0.136147059 130 -0.576147059 -0.056147059 131 -0.541147059 -0.576147059 132 -0.596147059 -0.541147059 133 -0.586147059 -0.596147059 134 -0.501147059 -0.586147059 135 -1.426147059 -0.501147059 136 -1.186147059 -1.426147059 137 -0.966147059 -1.186147059 138 -1.566147059 -0.966147059 139 -1.666147059 -1.566147059 140 -1.296147059 -1.666147059 141 -1.071147059 -1.296147059 142 -0.936147059 -1.071147059 143 -1.011147059 -0.936147059 144 -1.021147059 -1.011147059 145 -0.721147059 -1.021147059 146 -0.816147059 -0.721147059 147 -0.676147059 -0.816147059 148 -0.681147059 -0.676147059 149 -1.026147059 -0.681147059 150 -1.046147059 -1.026147059 151 -0.961147059 -1.046147059 152 -0.961147059 -0.961147059 153 -0.706147059 -0.961147059 154 -0.836147059 -0.706147059 155 -1.316147059 -0.836147059 156 -1.786147059 -1.316147059 157 -1.656147059 -1.786147059 158 -1.316147059 -1.656147059 159 -1.356147059 -1.316147059 160 -1.216147059 -1.356147059 161 0.333852941 -1.216147059 162 0.063852941 0.333852941 163 0.353852941 0.063852941 164 0.563852941 0.353852941 165 0.583852941 0.563852941 166 0.533852941 0.583852941 167 0.318852941 0.533852941 168 0.753852941 0.318852941 169 1.178852941 0.753852941 170 0.669324324 1.178852941 171 0.769324324 0.669324324 172 0.229324324 0.769324324 173 0.559324324 0.229324324 174 0.734324324 0.559324324 175 2.419324324 0.734324324 176 1.549324324 2.419324324 177 2.504324324 1.549324324 178 1.234324324 2.504324324 179 0.844324324 1.234324324 180 0.764324324 0.844324324 181 0.734324324 0.764324324 182 -0.055675676 0.734324324 183 -0.035675676 -0.055675676 184 -0.215675676 -0.035675676 185 -0.360675676 -0.215675676 186 -1.050675676 -0.360675676 187 -1.555675676 -1.050675676 188 -0.555675676 -1.555675676 189 -0.035675676 -0.555675676 190 -0.395675676 -0.035675676 191 -0.365675676 -0.395675676 192 -0.825675676 -0.365675676 193 -0.845675676 -0.825675676 194 -0.745675676 -0.845675676 195 -0.415675676 -0.745675676 196 -0.830675676 -0.415675676 197 -0.680675676 -0.830675676 198 -0.450675676 -0.680675676 199 -0.455675676 -0.450675676 200 -0.915675676 -0.455675676 201 -0.800675676 -0.915675676 202 -0.365675676 -0.800675676 203 -0.485675676 -0.365675676 204 -0.475675676 -0.485675676 205 -0.115675676 -0.475675676 206 0.024324324 -0.115675676 > 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/7hsj51227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/85z2m1227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9mkg71227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10gjbt1227533800.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11qud81227533800.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/12ik891227533800.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/13w7hz1227533800.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/14gfmn1227533801.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/html/rcomp/tmp/158go81227533801.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/html/rcomp/tmp/16hkwh1227533801.tab") + } > > system("convert tmp/13fsz1227533800.ps tmp/13fsz1227533800.png") > system("convert tmp/2ezbe1227533800.ps tmp/2ezbe1227533800.png") > system("convert tmp/3d3az1227533800.ps tmp/3d3az1227533800.png") > system("convert tmp/41hzj1227533800.ps tmp/41hzj1227533800.png") > system("convert tmp/5azj91227533800.ps tmp/5azj91227533800.png") > system("convert tmp/682p21227533800.ps tmp/682p21227533800.png") > system("convert tmp/7hsj51227533800.ps tmp/7hsj51227533800.png") > system("convert tmp/85z2m1227533800.ps tmp/85z2m1227533800.png") > system("convert tmp/9mkg71227533800.ps tmp/9mkg71227533800.png") > system("convert tmp/10gjbt1227533800.ps tmp/10gjbt1227533800.png") > > > proc.time() user system elapsed 4.839 1.720 5.341