R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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(9.4 + ,0.5 + ,5.1 + ,-1.0 + ,2504.7 + ,9.4 + ,0.8 + ,5.0 + ,3.0 + ,2661.4 + ,9.5 + ,1.0 + ,5.0 + ,2.0 + ,2880.4 + ,9.5 + ,1.3 + ,5.1 + ,3.0 + ,3064.4 + ,9.4 + ,1.3 + ,5.0 + ,5.0 + ,3141.1 + ,9.4 + ,1.2 + ,4.9 + ,5.0 + ,3327.7 + ,9.3 + ,1.2 + ,4.8 + ,3.0 + ,3565.0 + ,9.4 + ,1.0 + ,4.5 + ,2.0 + ,3403.1 + ,9.4 + ,0.8 + ,4.3 + ,1.0 + ,3149.9 + ,9.2 + ,0.7 + ,4.3 + ,-4.0 + ,3006.8 + ,9.1 + ,0.6 + ,4.2 + ,1.0 + ,3230.7 + ,9.1 + ,0.7 + ,4.0 + ,1.0 + ,3361.1 + ,9.1 + ,1.0 + ,3.8 + ,6.0 + ,3484.7 + ,9.0 + ,1.0 + ,4.1 + ,3.0 + ,3411.1 + ,9.0 + ,1.3 + ,4.2 + ,2.0 + ,3288.2 + ,8.9 + ,1.1 + ,4.0 + ,2.0 + ,3280.4 + ,8.8 + ,0.8 + ,4.3 + ,2.0 + ,3174.0 + ,8.7 + ,0.7 + ,4.7 + ,-8.0 + ,3165.3 + ,8.5 + ,0.7 + ,5.0 + ,0.0 + ,3092.7 + ,8.3 + ,0.9 + ,5.1 + ,-2.0 + ,3053.1 + ,8.1 + ,1.3 + ,5.4 + ,3.0 + ,3182.0 + ,7.9 + ,1.4 + ,5.4 + ,5.0 + ,2999.9 + ,7.8 + ,1.6 + ,5.4 + ,8.0 + ,3249.6 + ,7.6 + ,2.1 + ,5.5 + ,8.0 + ,3210.5 + ,7.4 + ,0.3 + ,5.8 + ,9.0 + ,3030.3 + ,7.2 + 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,2579.4 + ,8.5 + ,3.1 + ,3.4 + ,-2.0 + ,2649.2) + ,dim=c(5 + ,154) + ,dimnames=list(c('werkloosheid' + ,'hicp' + ,'rente' + ,'consumer' + ,'bel20') + ,1:154)) > y <- array(NA,dim=c(5,154),dimnames=list(c('werkloosheid','hicp','rente','consumer','bel20'),1:154)) > 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 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 werkloosheid hicp rente consumer bel20 t 1 9.4 0.5 5.1 -1 2504.7 1 2 9.4 0.8 5.0 3 2661.4 2 3 9.5 1.0 5.0 2 2880.4 3 4 9.5 1.3 5.1 3 3064.4 4 5 9.4 1.3 5.0 5 3141.1 5 6 9.4 1.2 4.9 5 3327.7 6 7 9.3 1.2 4.8 3 3565.0 7 8 9.4 1.0 4.5 2 3403.1 8 9 9.4 0.8 4.3 1 3149.9 9 10 9.2 0.7 4.3 -4 3006.8 10 11 9.1 0.6 4.2 1 3230.7 11 12 9.1 0.7 4.0 1 3361.1 12 13 9.1 1.0 3.8 6 3484.7 13 14 9.0 1.0 4.1 3 3411.1 14 15 9.0 1.3 4.2 2 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51 52 7.3 1.6 5.3 0 2811.7 52 53 7.4 1.4 5.3 1 2799.4 53 54 7.5 0.8 5.1 0 2555.3 54 55 7.7 1.1 4.9 -1 2305.0 55 56 7.7 1.3 4.7 -3 2215.0 56 57 7.7 1.2 4.4 -3 2065.8 57 58 7.7 1.3 4.6 -3 1940.5 58 59 7.7 1.1 4.5 -4 2042.0 59 60 7.8 1.3 4.2 -8 1995.4 60 61 8.0 1.2 4.0 -9 1946.8 61 62 8.1 1.6 3.9 -13 1765.9 62 63 8.1 1.7 4.1 -18 1635.3 63 64 8.2 1.5 4.1 -11 1833.4 64 65 8.2 0.9 3.7 -9 1910.4 65 66 8.2 1.5 3.8 -10 1959.7 66 67 8.1 1.4 4.1 -13 1969.6 67 68 8.1 1.6 4.1 -11 2061.4 68 69 8.2 1.7 4.0 -5 2093.5 69 70 8.3 1.4 4.3 -15 2120.9 70 71 8.3 1.8 4.4 -6 2174.6 71 72 8.4 1.7 4.2 -6 2196.7 72 73 8.5 1.4 4.2 -3 2350.4 73 74 8.5 1.2 4.0 -1 2440.3 74 75 8.4 1.0 4.0 -3 2408.6 75 76 8.0 1.7 4.3 -4 2472.8 76 77 7.9 2.4 4.4 -6 2407.6 77 78 8.1 2.0 4.4 0 2454.6 78 79 8.5 2.1 4.3 -4 2448.1 79 80 8.8 2.0 4.1 -2 2497.8 80 81 8.8 1.8 4.1 -2 2645.6 81 82 8.6 2.7 3.9 -6 2756.8 82 83 8.3 2.3 3.8 -7 2849.3 83 84 8.3 1.9 3.7 -6 2921.4 84 85 8.3 2.0 3.5 -6 2981.9 85 86 8.4 2.3 3.7 -3 3080.6 86 87 8.4 2.8 3.7 -2 3106.2 87 88 8.5 2.4 3.5 -5 3119.3 88 89 8.6 2.3 3.3 -11 3061.3 89 90 8.6 2.7 3.2 -11 3097.3 90 91 8.6 2.7 3.3 -11 3161.7 91 92 8.6 2.9 3.1 -10 3257.2 92 93 8.6 3.0 3.2 -14 3277.0 93 94 8.5 2.2 3.4 -8 3295.3 94 95 8.4 2.3 3.5 -9 3364.0 95 96 8.4 2.8 3.3 -5 3494.2 96 97 8.4 2.8 3.5 -1 3667.0 97 98 8.5 2.8 3.5 -2 3813.1 98 99 8.5 2.2 3.8 -5 3918.0 99 100 8.6 2.6 4.0 -4 3895.5 100 101 8.6 2.8 4.0 -6 3801.1 101 102 8.4 2.5 4.1 -2 3570.1 102 103 8.2 2.4 4.0 -2 3701.6 103 104 8.0 2.3 3.8 -2 3862.3 104 105 8.0 1.9 3.7 -2 3970.1 105 106 8.0 1.7 3.8 2 4138.5 106 107 8.0 2.0 3.7 1 4199.8 107 108 7.9 2.1 4.0 -8 4290.9 108 109 7.9 1.7 4.2 -1 4443.9 109 110 7.8 1.8 4.0 1 4502.6 110 111 7.8 1.8 4.1 -1 4357.0 111 112 8.0 1.8 4.2 2 4591.3 112 113 7.8 1.3 4.5 2 4697.0 113 114 7.4 1.3 4.6 1 4621.4 114 115 7.2 1.3 4.5 -1 4562.8 115 116 7.0 1.2 4.5 -2 4202.5 116 117 7.0 1.4 4.5 -2 4296.5 117 118 7.2 2.2 4.4 -1 4435.2 118 119 7.2 2.9 4.3 -8 4105.2 119 120 7.2 3.1 4.5 -4 4116.7 120 121 7.0 3.5 4.1 -6 3844.5 121 122 6.9 3.6 4.1 -3 3721.0 122 123 6.8 4.4 4.3 -3 3674.4 123 124 6.8 4.1 4.4 -7 3857.6 124 125 6.8 5.1 4.7 -9 3801.1 125 126 6.9 5.8 5.0 -11 3504.4 126 127 7.2 5.9 4.7 -13 3032.6 127 128 7.2 5.4 4.5 -11 3047.0 128 129 7.2 5.5 4.5 -9 2962.3 129 130 7.1 4.8 4.5 -17 2197.8 130 131 7.2 3.2 5.5 -22 2014.5 131 132 7.3 2.7 4.5 -25 1862.8 132 133 7.5 2.1 4.4 -20 1905.4 133 134 7.6 1.9 4.2 -24 1811.0 134 135 7.7 0.6 3.9 -24 1670.1 135 136 7.7 0.7 3.9 -22 1864.4 136 137 7.7 -0.2 4.2 -19 2052.0 137 138 7.8 -1.0 4.0 -18 2029.6 138 139 8.0 -1.7 3.8 -17 2070.8 139 140 8.1 -0.7 3.7 -11 2293.4 140 141 8.1 -1.0 3.7 -11 2443.3 141 142 8.0 -0.9 3.7 -12 2513.2 142 143 8.1 0.0 3.7 -10 2466.9 143 144 8.2 0.3 3.7 -15 2502.7 144 145 8.3 0.8 3.8 -15 2539.9 145 146 8.4 0.8 3.7 -15 2482.6 146 147 8.4 1.9 3.5 -13 2626.2 147 148 8.4 2.1 3.5 -8 2656.3 148 149 8.5 2.5 3.1 -13 2446.7 149 150 8.5 2.7 3.4 -9 2467.4 150 151 8.6 2.4 3.3 -7 2462.3 151 152 8.6 2.4 2.8 -4 2504.6 152 153 8.5 2.9 3.2 -4 2579.4 153 154 8.5 3.1 3.4 -2 2649.2 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) hicp rente consumer bel20 t 12.8816811 -0.1493339 -0.9484259 -0.0146404 0.0001130 -0.0119659 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.2667 -0.2787 -0.0405 0.2760 1.3948 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.288e+01 4.710e-01 27.351 < 2e-16 *** hicp -1.493e-01 3.662e-02 -4.078 7.39e-05 *** rente -9.484e-01 8.160e-02 -11.623 < 2e-16 *** consumer -1.464e-02 7.864e-03 -1.862 0.0646 . bel20 1.130e-04 7.064e-05 1.600 0.1117 t -1.197e-02 1.441e-03 -8.304 5.84e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5045 on 148 degrees of freedom Multiple R-squared: 0.6179, Adjusted R-squared: 0.605 F-statistic: 47.87 on 5 and 148 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,] 2.591795e-03 5.183590e-03 9.974082e-01 [2,] 1.254277e-03 2.508555e-03 9.987457e-01 [3,] 1.801105e-04 3.602211e-04 9.998199e-01 [4,] 8.313087e-05 1.662617e-04 9.999169e-01 [5,] 4.153611e-05 8.307222e-05 9.999585e-01 [6,] 7.070311e-06 1.414062e-05 9.999929e-01 [7,] 1.171894e-06 2.343788e-06 9.999988e-01 [8,] 1.852192e-07 3.704384e-07 9.999998e-01 [9,] 1.043473e-07 2.086946e-07 9.999999e-01 [10,] 1.920312e-08 3.840624e-08 1.000000e+00 [11,] 3.712501e-09 7.425002e-09 1.000000e+00 [12,] 4.906457e-09 9.812914e-09 1.000000e+00 [13,] 7.845809e-09 1.569162e-08 1.000000e+00 [14,] 8.056988e-09 1.611398e-08 1.000000e+00 [15,] 6.227910e-09 1.245582e-08 1.000000e+00 [16,] 1.288039e-08 2.576078e-08 1.000000e+00 [17,] 4.382805e-09 8.765610e-09 1.000000e+00 [18,] 2.003025e-09 4.006050e-09 1.000000e+00 [19,] 1.333213e-09 2.666427e-09 1.000000e+00 [20,] 4.372148e-10 8.744295e-10 1.000000e+00 [21,] 2.480377e-10 4.960754e-10 1.000000e+00 [22,] 6.039971e-11 1.207994e-10 1.000000e+00 [23,] 4.992615e-11 9.985230e-11 1.000000e+00 [24,] 2.568609e-11 5.137218e-11 1.000000e+00 [25,] 8.229045e-12 1.645809e-11 1.000000e+00 [26,] 4.636337e-12 9.272674e-12 1.000000e+00 [27,] 1.959609e-12 3.919218e-12 1.000000e+00 [28,] 5.940964e-13 1.188193e-12 1.000000e+00 [29,] 9.990201e-13 1.998040e-12 1.000000e+00 [30,] 3.721064e-12 7.442128e-12 1.000000e+00 [31,] 3.982822e-12 7.965644e-12 1.000000e+00 [32,] 3.451623e-12 6.903245e-12 1.000000e+00 [33,] 3.347169e-11 6.694338e-11 1.000000e+00 [34,] 7.661855e-11 1.532371e-10 1.000000e+00 [35,] 1.131213e-10 2.262426e-10 1.000000e+00 [36,] 3.027007e-10 6.054013e-10 1.000000e+00 [37,] 2.177317e-09 4.354633e-09 1.000000e+00 [38,] 5.335841e-09 1.067168e-08 1.000000e+00 [39,] 5.356944e-09 1.071389e-08 1.000000e+00 [40,] 2.624193e-04 5.248386e-04 9.997376e-01 [41,] 8.719257e-02 1.743851e-01 9.128074e-01 [42,] 6.301436e-01 7.397129e-01 3.698564e-01 [43,] 9.475156e-01 1.049689e-01 5.248443e-02 [44,] 9.806863e-01 3.862730e-02 1.931365e-02 [45,] 9.925326e-01 1.493477e-02 7.467383e-03 [46,] 9.951932e-01 9.613532e-03 4.806766e-03 [47,] 9.973449e-01 5.310148e-03 2.655074e-03 [48,] 9.978078e-01 4.384340e-03 2.192170e-03 [49,] 9.980131e-01 3.973797e-03 1.986899e-03 [50,] 9.979823e-01 4.035428e-03 2.017714e-03 [51,] 9.979312e-01 4.137670e-03 2.068835e-03 [52,] 9.980854e-01 3.829150e-03 1.914575e-03 [53,] 9.981946e-01 3.610814e-03 1.805407e-03 [54,] 9.981860e-01 3.628052e-03 1.814026e-03 [55,] 9.976903e-01 4.619390e-03 2.309695e-03 [56,] 9.976844e-01 4.631280e-03 2.315640e-03 [57,] 9.982123e-01 3.575418e-03 1.787709e-03 [58,] 9.986413e-01 2.717441e-03 1.358721e-03 [59,] 9.986644e-01 2.671143e-03 1.335571e-03 [60,] 9.989597e-01 2.080569e-03 1.040284e-03 [61,] 9.994890e-01 1.022049e-03 5.110246e-04 [62,] 9.996473e-01 7.053391e-04 3.526696e-04 [63,] 9.998888e-01 2.224149e-04 1.112075e-04 [64,] 9.999498e-01 1.003348e-04 5.016742e-05 [65,] 9.999770e-01 4.603403e-05 2.301702e-05 [66,] 9.999806e-01 3.888627e-05 1.944313e-05 [67,] 9.999771e-01 4.579550e-05 2.289775e-05 [68,] 9.999803e-01 3.940655e-05 1.970327e-05 [69,] 9.999882e-01 2.357074e-05 1.178537e-05 [70,] 9.999926e-01 1.471011e-05 7.355055e-06 [71,] 9.999966e-01 6.838794e-06 3.419397e-06 [72,] 9.999986e-01 2.777320e-06 1.388660e-06 [73,] 9.999993e-01 1.329922e-06 6.649611e-07 [74,] 9.999995e-01 9.545584e-07 4.772792e-07 [75,] 9.999991e-01 1.704582e-06 8.522908e-07 [76,] 9.999986e-01 2.865620e-06 1.432810e-06 [77,] 9.999982e-01 3.559834e-06 1.779917e-06 [78,] 9.999970e-01 6.019579e-06 3.009790e-06 [79,] 9.999954e-01 9.148380e-06 4.574190e-06 [80,] 9.999924e-01 1.522786e-05 7.613931e-06 [81,] 9.999868e-01 2.635169e-05 1.317585e-05 [82,] 9.999782e-01 4.357774e-05 2.178887e-05 [83,] 9.999631e-01 7.384822e-05 3.692411e-05 [84,] 9.999426e-01 1.147380e-04 5.736898e-05 [85,] 9.999047e-01 1.905257e-04 9.526283e-05 [86,] 9.998492e-01 3.015171e-04 1.507585e-04 [87,] 9.997601e-01 4.798359e-04 2.399180e-04 [88,] 9.996936e-01 6.128115e-04 3.064058e-04 [89,] 9.995585e-01 8.830923e-04 4.415462e-04 [90,] 9.993080e-01 1.384055e-03 6.920277e-04 [91,] 9.991409e-01 1.718114e-03 8.590571e-04 [92,] 9.995769e-01 8.461528e-04 4.230764e-04 [93,] 9.998999e-01 2.001041e-04 1.000520e-04 [94,] 9.999511e-01 9.787468e-05 4.893734e-05 [95,] 9.999605e-01 7.900828e-05 3.950414e-05 [96,] 9.999471e-01 1.058284e-04 5.291418e-05 [97,] 9.999355e-01 1.289576e-04 6.447882e-05 [98,] 9.999257e-01 1.486945e-04 7.434725e-05 [99,] 9.999141e-01 1.717167e-04 8.585837e-05 [100,] 9.999424e-01 1.151851e-04 5.759256e-05 [101,] 9.999757e-01 4.864475e-05 2.432237e-05 [102,] 9.999842e-01 3.169624e-05 1.584812e-05 [103,] 9.999956e-01 8.770448e-06 4.385224e-06 [104,] 1.000000e+00 4.925699e-08 2.462850e-08 [105,] 1.000000e+00 5.425771e-11 2.712886e-11 [106,] 1.000000e+00 2.161522e-12 1.080761e-12 [107,] 1.000000e+00 1.164409e-12 5.822043e-13 [108,] 1.000000e+00 2.488500e-12 1.244250e-12 [109,] 1.000000e+00 6.911031e-12 3.455516e-12 [110,] 1.000000e+00 3.401533e-12 1.700766e-12 [111,] 1.000000e+00 1.247098e-12 6.235492e-13 [112,] 1.000000e+00 8.172591e-15 4.086295e-15 [113,] 1.000000e+00 1.078718e-14 5.393592e-15 [114,] 1.000000e+00 4.137530e-14 2.068765e-14 [115,] 1.000000e+00 2.091350e-13 1.045675e-13 [116,] 1.000000e+00 3.529283e-13 1.764642e-13 [117,] 1.000000e+00 7.410898e-14 3.705449e-14 [118,] 1.000000e+00 2.689381e-14 1.344691e-14 [119,] 1.000000e+00 6.506180e-14 3.253090e-14 [120,] 1.000000e+00 3.405531e-13 1.702766e-13 [121,] 1.000000e+00 1.174362e-12 5.871811e-13 [122,] 1.000000e+00 5.374561e-12 2.687280e-12 [123,] 1.000000e+00 1.800791e-11 9.003954e-12 [124,] 1.000000e+00 4.094970e-11 2.047485e-11 [125,] 1.000000e+00 2.407278e-10 1.203639e-10 [126,] 1.000000e+00 1.458412e-09 7.292060e-10 [127,] 1.000000e+00 9.221339e-09 4.610669e-09 [128,] 1.000000e+00 5.056253e-08 2.528126e-08 [129,] 9.999999e-01 1.533258e-07 7.666290e-08 [130,] 1.000000e+00 7.054251e-08 3.527125e-08 [131,] 9.999999e-01 2.790357e-07 1.395179e-07 [132,] 9.999989e-01 2.208463e-06 1.104232e-06 [133,] 9.999955e-01 8.956378e-06 4.478189e-06 [134,] 9.999765e-01 4.690940e-05 2.345470e-05 [135,] 9.998852e-01 2.296652e-04 1.148326e-04 [136,] 9.998728e-01 2.544885e-04 1.272443e-04 [137,] 9.992957e-01 1.408507e-03 7.042535e-04 > postscript(file="/var/www/html/rcomp/tmp/1s8n21292864413.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2s8n21292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/33hnn1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/43hnn1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/53hnn1292864413.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 6 1.144144490 1.146915682 1.249351581 1.394800723 1.232534432 1.113630563 7 8 9 10 11 12 0.874648035 0.675880709 0.482276794 0.222283872 0.072365415 -0.105161252 13 14 15 16 17 18 -0.178850540 -0.017958039 0.132903231 -0.173801051 -0.010079748 0.120902782 19 20 21 22 23 24 0.342726452 0.254597473 0.469455374 0.346220581 0.303747593 0.289643015 25 26 27 28 29 30 0.152346488 0.193189688 -0.091457006 -0.042155592 -0.198445472 -0.166250601 31 32 33 34 35 36 -0.490755920 0.108876464 -0.074729532 -0.050460485 -0.235493613 -0.692107093 37 38 39 40 41 42 -1.046114254 -1.138177879 -1.262136897 -0.721457124 -0.478654909 -0.667533243 43 44 45 46 47 48 -1.082998357 -1.266704543 -1.170321472 -1.188346225 -0.962236122 -0.287064020 49 50 51 52 53 54 -0.092262015 0.031157391 0.343444498 -0.011704667 0.086425300 -0.067940848 55 56 57 58 59 60 0.012794341 -0.154165031 -0.424794234 -0.194045462 -0.332903141 -0.528891930 61 62 63 64 65 66 -0.530691056 -0.491946227 -0.333800243 -0.171612224 -0.608040501 -0.431845077 67 68 69 70 71 72 -0.295325052 -0.234588935 -0.118318592 0.083873728 0.376108822 0.280957940 73 74 75 76 77 78 0.374670150 0.186202334 0.042604185 0.021733827 0.111165677 0.345927338 79 80 81 82 83 84 0.620157311 0.751167208 0.716558609 0.402107921 -0.065599142 -0.201719361 85 86 87 88 89 90 -0.371344323 0.007870739 0.106250076 -0.076604725 -0.250543162 -0.277755827 91 92 93 94 95 96 -0.178227283 -0.322234981 -0.261292866 -0.193335241 -0.193999779 -0.253208763 97 98 99 100 101 102 -0.012529961 0.068280001 0.219394013 0.597962505 0.621185695 0.567868514 103 104 105 106 107 108 0.255193315 -0.155625326 -0.310421563 -0.193954730 -0.253601152 -0.184235745 109 110 111 112 113 114 0.042868915 -0.197271804 -0.103284997 0.220958691 0.230836804 -0.068449017 115 116 117 118 119 120 -0.373982123 -0.550860575 -0.519653918 -0.284102173 -0.327623626 -0.038844218 121 122 123 124 125 126 -0.545025558 -0.560244287 -0.333858299 -0.351120952 0.071812753 0.577099229 127 128 129 130 131 132 0.643523700 0.418790473 0.484545292 0.261275823 1.030252307 0.092352915 133 134 135 136 137 138 0.188262225 0.032785934 -0.317982155 -0.283766330 -0.098958833 -0.278972619 139 140 141 142 143 144 -0.351242553 -0.122106428 -0.171885804 -0.267528580 0.013352474 0.092869703 145 146 147 148 149 150 0.370139945 0.393740649 0.393336437 0.504968456 0.247789476 0.630371475 151 152 153 154 0.632551943 0.209444369 0.566991953 0.819900188 > postscript(file="/var/www/html/rcomp/tmp/6w84q1292864413.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 1.144144490 NA 1 1.146915682 1.144144490 2 1.249351581 1.146915682 3 1.394800723 1.249351581 4 1.232534432 1.394800723 5 1.113630563 1.232534432 6 0.874648035 1.113630563 7 0.675880709 0.874648035 8 0.482276794 0.675880709 9 0.222283872 0.482276794 10 0.072365415 0.222283872 11 -0.105161252 0.072365415 12 -0.178850540 -0.105161252 13 -0.017958039 -0.178850540 14 0.132903231 -0.017958039 15 -0.173801051 0.132903231 16 -0.010079748 -0.173801051 17 0.120902782 -0.010079748 18 0.342726452 0.120902782 19 0.254597473 0.342726452 20 0.469455374 0.254597473 21 0.346220581 0.469455374 22 0.303747593 0.346220581 23 0.289643015 0.303747593 24 0.152346488 0.289643015 25 0.193189688 0.152346488 26 -0.091457006 0.193189688 27 -0.042155592 -0.091457006 28 -0.198445472 -0.042155592 29 -0.166250601 -0.198445472 30 -0.490755920 -0.166250601 31 0.108876464 -0.490755920 32 -0.074729532 0.108876464 33 -0.050460485 -0.074729532 34 -0.235493613 -0.050460485 35 -0.692107093 -0.235493613 36 -1.046114254 -0.692107093 37 -1.138177879 -1.046114254 38 -1.262136897 -1.138177879 39 -0.721457124 -1.262136897 40 -0.478654909 -0.721457124 41 -0.667533243 -0.478654909 42 -1.082998357 -0.667533243 43 -1.266704543 -1.082998357 44 -1.170321472 -1.266704543 45 -1.188346225 -1.170321472 46 -0.962236122 -1.188346225 47 -0.287064020 -0.962236122 48 -0.092262015 -0.287064020 49 0.031157391 -0.092262015 50 0.343444498 0.031157391 51 -0.011704667 0.343444498 52 0.086425300 -0.011704667 53 -0.067940848 0.086425300 54 0.012794341 -0.067940848 55 -0.154165031 0.012794341 56 -0.424794234 -0.154165031 57 -0.194045462 -0.424794234 58 -0.332903141 -0.194045462 59 -0.528891930 -0.332903141 60 -0.530691056 -0.528891930 61 -0.491946227 -0.530691056 62 -0.333800243 -0.491946227 63 -0.171612224 -0.333800243 64 -0.608040501 -0.171612224 65 -0.431845077 -0.608040501 66 -0.295325052 -0.431845077 67 -0.234588935 -0.295325052 68 -0.118318592 -0.234588935 69 0.083873728 -0.118318592 70 0.376108822 0.083873728 71 0.280957940 0.376108822 72 0.374670150 0.280957940 73 0.186202334 0.374670150 74 0.042604185 0.186202334 75 0.021733827 0.042604185 76 0.111165677 0.021733827 77 0.345927338 0.111165677 78 0.620157311 0.345927338 79 0.751167208 0.620157311 80 0.716558609 0.751167208 81 0.402107921 0.716558609 82 -0.065599142 0.402107921 83 -0.201719361 -0.065599142 84 -0.371344323 -0.201719361 85 0.007870739 -0.371344323 86 0.106250076 0.007870739 87 -0.076604725 0.106250076 88 -0.250543162 -0.076604725 89 -0.277755827 -0.250543162 90 -0.178227283 -0.277755827 91 -0.322234981 -0.178227283 92 -0.261292866 -0.322234981 93 -0.193335241 -0.261292866 94 -0.193999779 -0.193335241 95 -0.253208763 -0.193999779 96 -0.012529961 -0.253208763 97 0.068280001 -0.012529961 98 0.219394013 0.068280001 99 0.597962505 0.219394013 100 0.621185695 0.597962505 101 0.567868514 0.621185695 102 0.255193315 0.567868514 103 -0.155625326 0.255193315 104 -0.310421563 -0.155625326 105 -0.193954730 -0.310421563 106 -0.253601152 -0.193954730 107 -0.184235745 -0.253601152 108 0.042868915 -0.184235745 109 -0.197271804 0.042868915 110 -0.103284997 -0.197271804 111 0.220958691 -0.103284997 112 0.230836804 0.220958691 113 -0.068449017 0.230836804 114 -0.373982123 -0.068449017 115 -0.550860575 -0.373982123 116 -0.519653918 -0.550860575 117 -0.284102173 -0.519653918 118 -0.327623626 -0.284102173 119 -0.038844218 -0.327623626 120 -0.545025558 -0.038844218 121 -0.560244287 -0.545025558 122 -0.333858299 -0.560244287 123 -0.351120952 -0.333858299 124 0.071812753 -0.351120952 125 0.577099229 0.071812753 126 0.643523700 0.577099229 127 0.418790473 0.643523700 128 0.484545292 0.418790473 129 0.261275823 0.484545292 130 1.030252307 0.261275823 131 0.092352915 1.030252307 132 0.188262225 0.092352915 133 0.032785934 0.188262225 134 -0.317982155 0.032785934 135 -0.283766330 -0.317982155 136 -0.098958833 -0.283766330 137 -0.278972619 -0.098958833 138 -0.351242553 -0.278972619 139 -0.122106428 -0.351242553 140 -0.171885804 -0.122106428 141 -0.267528580 -0.171885804 142 0.013352474 -0.267528580 143 0.092869703 0.013352474 144 0.370139945 0.092869703 145 0.393740649 0.370139945 146 0.393336437 0.393740649 147 0.504968456 0.393336437 148 0.247789476 0.504968456 149 0.630371475 0.247789476 150 0.632551943 0.630371475 151 0.209444369 0.632551943 152 0.566991953 0.209444369 153 0.819900188 0.566991953 154 NA 0.819900188 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.146915682 1.144144490 [2,] 1.249351581 1.146915682 [3,] 1.394800723 1.249351581 [4,] 1.232534432 1.394800723 [5,] 1.113630563 1.232534432 [6,] 0.874648035 1.113630563 [7,] 0.675880709 0.874648035 [8,] 0.482276794 0.675880709 [9,] 0.222283872 0.482276794 [10,] 0.072365415 0.222283872 [11,] -0.105161252 0.072365415 [12,] -0.178850540 -0.105161252 [13,] -0.017958039 -0.178850540 [14,] 0.132903231 -0.017958039 [15,] -0.173801051 0.132903231 [16,] -0.010079748 -0.173801051 [17,] 0.120902782 -0.010079748 [18,] 0.342726452 0.120902782 [19,] 0.254597473 0.342726452 [20,] 0.469455374 0.254597473 [21,] 0.346220581 0.469455374 [22,] 0.303747593 0.346220581 [23,] 0.289643015 0.303747593 [24,] 0.152346488 0.289643015 [25,] 0.193189688 0.152346488 [26,] -0.091457006 0.193189688 [27,] -0.042155592 -0.091457006 [28,] -0.198445472 -0.042155592 [29,] -0.166250601 -0.198445472 [30,] -0.490755920 -0.166250601 [31,] 0.108876464 -0.490755920 [32,] -0.074729532 0.108876464 [33,] -0.050460485 -0.074729532 [34,] -0.235493613 -0.050460485 [35,] -0.692107093 -0.235493613 [36,] -1.046114254 -0.692107093 [37,] -1.138177879 -1.046114254 [38,] -1.262136897 -1.138177879 [39,] -0.721457124 -1.262136897 [40,] -0.478654909 -0.721457124 [41,] -0.667533243 -0.478654909 [42,] -1.082998357 -0.667533243 [43,] -1.266704543 -1.082998357 [44,] -1.170321472 -1.266704543 [45,] -1.188346225 -1.170321472 [46,] -0.962236122 -1.188346225 [47,] -0.287064020 -0.962236122 [48,] -0.092262015 -0.287064020 [49,] 0.031157391 -0.092262015 [50,] 0.343444498 0.031157391 [51,] -0.011704667 0.343444498 [52,] 0.086425300 -0.011704667 [53,] -0.067940848 0.086425300 [54,] 0.012794341 -0.067940848 [55,] -0.154165031 0.012794341 [56,] -0.424794234 -0.154165031 [57,] -0.194045462 -0.424794234 [58,] -0.332903141 -0.194045462 [59,] -0.528891930 -0.332903141 [60,] -0.530691056 -0.528891930 [61,] -0.491946227 -0.530691056 [62,] -0.333800243 -0.491946227 [63,] -0.171612224 -0.333800243 [64,] -0.608040501 -0.171612224 [65,] -0.431845077 -0.608040501 [66,] -0.295325052 -0.431845077 [67,] -0.234588935 -0.295325052 [68,] -0.118318592 -0.234588935 [69,] 0.083873728 -0.118318592 [70,] 0.376108822 0.083873728 [71,] 0.280957940 0.376108822 [72,] 0.374670150 0.280957940 [73,] 0.186202334 0.374670150 [74,] 0.042604185 0.186202334 [75,] 0.021733827 0.042604185 [76,] 0.111165677 0.021733827 [77,] 0.345927338 0.111165677 [78,] 0.620157311 0.345927338 [79,] 0.751167208 0.620157311 [80,] 0.716558609 0.751167208 [81,] 0.402107921 0.716558609 [82,] -0.065599142 0.402107921 [83,] -0.201719361 -0.065599142 [84,] -0.371344323 -0.201719361 [85,] 0.007870739 -0.371344323 [86,] 0.106250076 0.007870739 [87,] -0.076604725 0.106250076 [88,] -0.250543162 -0.076604725 [89,] -0.277755827 -0.250543162 [90,] -0.178227283 -0.277755827 [91,] -0.322234981 -0.178227283 [92,] -0.261292866 -0.322234981 [93,] -0.193335241 -0.261292866 [94,] -0.193999779 -0.193335241 [95,] -0.253208763 -0.193999779 [96,] -0.012529961 -0.253208763 [97,] 0.068280001 -0.012529961 [98,] 0.219394013 0.068280001 [99,] 0.597962505 0.219394013 [100,] 0.621185695 0.597962505 [101,] 0.567868514 0.621185695 [102,] 0.255193315 0.567868514 [103,] -0.155625326 0.255193315 [104,] -0.310421563 -0.155625326 [105,] -0.193954730 -0.310421563 [106,] -0.253601152 -0.193954730 [107,] -0.184235745 -0.253601152 [108,] 0.042868915 -0.184235745 [109,] -0.197271804 0.042868915 [110,] -0.103284997 -0.197271804 [111,] 0.220958691 -0.103284997 [112,] 0.230836804 0.220958691 [113,] -0.068449017 0.230836804 [114,] -0.373982123 -0.068449017 [115,] -0.550860575 -0.373982123 [116,] -0.519653918 -0.550860575 [117,] -0.284102173 -0.519653918 [118,] -0.327623626 -0.284102173 [119,] -0.038844218 -0.327623626 [120,] -0.545025558 -0.038844218 [121,] -0.560244287 -0.545025558 [122,] -0.333858299 -0.560244287 [123,] -0.351120952 -0.333858299 [124,] 0.071812753 -0.351120952 [125,] 0.577099229 0.071812753 [126,] 0.643523700 0.577099229 [127,] 0.418790473 0.643523700 [128,] 0.484545292 0.418790473 [129,] 0.261275823 0.484545292 [130,] 1.030252307 0.261275823 [131,] 0.092352915 1.030252307 [132,] 0.188262225 0.092352915 [133,] 0.032785934 0.188262225 [134,] -0.317982155 0.032785934 [135,] -0.283766330 -0.317982155 [136,] -0.098958833 -0.283766330 [137,] -0.278972619 -0.098958833 [138,] -0.351242553 -0.278972619 [139,] -0.122106428 -0.351242553 [140,] -0.171885804 -0.122106428 [141,] -0.267528580 -0.171885804 [142,] 0.013352474 -0.267528580 [143,] 0.092869703 0.013352474 [144,] 0.370139945 0.092869703 [145,] 0.393740649 0.370139945 [146,] 0.393336437 0.393740649 [147,] 0.504968456 0.393336437 [148,] 0.247789476 0.504968456 [149,] 0.630371475 0.247789476 [150,] 0.632551943 0.630371475 [151,] 0.209444369 0.632551943 [152,] 0.566991953 0.209444369 [153,] 0.819900188 0.566991953 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.146915682 1.144144490 2 1.249351581 1.146915682 3 1.394800723 1.249351581 4 1.232534432 1.394800723 5 1.113630563 1.232534432 6 0.874648035 1.113630563 7 0.675880709 0.874648035 8 0.482276794 0.675880709 9 0.222283872 0.482276794 10 0.072365415 0.222283872 11 -0.105161252 0.072365415 12 -0.178850540 -0.105161252 13 -0.017958039 -0.178850540 14 0.132903231 -0.017958039 15 -0.173801051 0.132903231 16 -0.010079748 -0.173801051 17 0.120902782 -0.010079748 18 0.342726452 0.120902782 19 0.254597473 0.342726452 20 0.469455374 0.254597473 21 0.346220581 0.469455374 22 0.303747593 0.346220581 23 0.289643015 0.303747593 24 0.152346488 0.289643015 25 0.193189688 0.152346488 26 -0.091457006 0.193189688 27 -0.042155592 -0.091457006 28 -0.198445472 -0.042155592 29 -0.166250601 -0.198445472 30 -0.490755920 -0.166250601 31 0.108876464 -0.490755920 32 -0.074729532 0.108876464 33 -0.050460485 -0.074729532 34 -0.235493613 -0.050460485 35 -0.692107093 -0.235493613 36 -1.046114254 -0.692107093 37 -1.138177879 -1.046114254 38 -1.262136897 -1.138177879 39 -0.721457124 -1.262136897 40 -0.478654909 -0.721457124 41 -0.667533243 -0.478654909 42 -1.082998357 -0.667533243 43 -1.266704543 -1.082998357 44 -1.170321472 -1.266704543 45 -1.188346225 -1.170321472 46 -0.962236122 -1.188346225 47 -0.287064020 -0.962236122 48 -0.092262015 -0.287064020 49 0.031157391 -0.092262015 50 0.343444498 0.031157391 51 -0.011704667 0.343444498 52 0.086425300 -0.011704667 53 -0.067940848 0.086425300 54 0.012794341 -0.067940848 55 -0.154165031 0.012794341 56 -0.424794234 -0.154165031 57 -0.194045462 -0.424794234 58 -0.332903141 -0.194045462 59 -0.528891930 -0.332903141 60 -0.530691056 -0.528891930 61 -0.491946227 -0.530691056 62 -0.333800243 -0.491946227 63 -0.171612224 -0.333800243 64 -0.608040501 -0.171612224 65 -0.431845077 -0.608040501 66 -0.295325052 -0.431845077 67 -0.234588935 -0.295325052 68 -0.118318592 -0.234588935 69 0.083873728 -0.118318592 70 0.376108822 0.083873728 71 0.280957940 0.376108822 72 0.374670150 0.280957940 73 0.186202334 0.374670150 74 0.042604185 0.186202334 75 0.021733827 0.042604185 76 0.111165677 0.021733827 77 0.345927338 0.111165677 78 0.620157311 0.345927338 79 0.751167208 0.620157311 80 0.716558609 0.751167208 81 0.402107921 0.716558609 82 -0.065599142 0.402107921 83 -0.201719361 -0.065599142 84 -0.371344323 -0.201719361 85 0.007870739 -0.371344323 86 0.106250076 0.007870739 87 -0.076604725 0.106250076 88 -0.250543162 -0.076604725 89 -0.277755827 -0.250543162 90 -0.178227283 -0.277755827 91 -0.322234981 -0.178227283 92 -0.261292866 -0.322234981 93 -0.193335241 -0.261292866 94 -0.193999779 -0.193335241 95 -0.253208763 -0.193999779 96 -0.012529961 -0.253208763 97 0.068280001 -0.012529961 98 0.219394013 0.068280001 99 0.597962505 0.219394013 100 0.621185695 0.597962505 101 0.567868514 0.621185695 102 0.255193315 0.567868514 103 -0.155625326 0.255193315 104 -0.310421563 -0.155625326 105 -0.193954730 -0.310421563 106 -0.253601152 -0.193954730 107 -0.184235745 -0.253601152 108 0.042868915 -0.184235745 109 -0.197271804 0.042868915 110 -0.103284997 -0.197271804 111 0.220958691 -0.103284997 112 0.230836804 0.220958691 113 -0.068449017 0.230836804 114 -0.373982123 -0.068449017 115 -0.550860575 -0.373982123 116 -0.519653918 -0.550860575 117 -0.284102173 -0.519653918 118 -0.327623626 -0.284102173 119 -0.038844218 -0.327623626 120 -0.545025558 -0.038844218 121 -0.560244287 -0.545025558 122 -0.333858299 -0.560244287 123 -0.351120952 -0.333858299 124 0.071812753 -0.351120952 125 0.577099229 0.071812753 126 0.643523700 0.577099229 127 0.418790473 0.643523700 128 0.484545292 0.418790473 129 0.261275823 0.484545292 130 1.030252307 0.261275823 131 0.092352915 1.030252307 132 0.188262225 0.092352915 133 0.032785934 0.188262225 134 -0.317982155 0.032785934 135 -0.283766330 -0.317982155 136 -0.098958833 -0.283766330 137 -0.278972619 -0.098958833 138 -0.351242553 -0.278972619 139 -0.122106428 -0.351242553 140 -0.171885804 -0.122106428 141 -0.267528580 -0.171885804 142 0.013352474 -0.267528580 143 0.092869703 0.013352474 144 0.370139945 0.092869703 145 0.393740649 0.370139945 146 0.393336437 0.393740649 147 0.504968456 0.393336437 148 0.247789476 0.504968456 149 0.630371475 0.247789476 150 0.632551943 0.630371475 151 0.209444369 0.632551943 152 0.566991953 0.209444369 153 0.819900188 0.566991953 > 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/770lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/870lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/970lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10z92e1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11l9j11292864413.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/126ah71292864413.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/132kfy1292864413.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/1462e41292864413.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/159lca1292864413.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/16c3ty1292864413.tab") + } > > try(system("convert tmp/1s8n21292864413.ps tmp/1s8n21292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/2s8n21292864413.ps tmp/2s8n21292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/33hnn1292864413.ps tmp/33hnn1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/43hnn1292864413.ps tmp/43hnn1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/53hnn1292864413.ps tmp/53hnn1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/6w84q1292864413.ps tmp/6w84q1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/770lt1292864413.ps tmp/770lt1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/870lt1292864413.ps tmp/870lt1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/970lt1292864413.ps tmp/970lt1292864413.png",intern=TRUE)) character(0) > try(system("convert tmp/10z92e1292864413.ps tmp/10z92e1292864413.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.964 1.778 9.738