R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(9.1 + ,4.5 + ,1.0 + ,-1.0 + ,3484.7 + ,9.0 + ,4.3 + ,1.0 + ,3.0 + ,3411.1 + ,9.0 + ,4.3 + ,1.3 + ,2.0 + ,3288.2 + ,8.9 + ,4.2 + ,1.1 + ,3.0 + ,3280.4 + ,8.8 + ,4.0 + ,0.8 + ,5.0 + ,3174.0 + ,8.7 + ,3.8 + ,0.7 + ,5.0 + ,3165.3 + ,8.5 + ,4.1 + ,0.7 + ,3.0 + ,3092.7 + ,8.3 + ,4.2 + ,0.9 + ,2.0 + ,3053.1 + ,8.1 + ,4.0 + ,1.3 + ,1.0 + ,3182.0 + ,7.9 + ,4.3 + ,1.4 + ,-4.0 + ,2999.9 + ,7.8 + ,4.7 + ,1.6 + ,1.0 + ,3249.6 + ,7.6 + ,5.0 + ,2.1 + ,1.0 + ,3210.5 + ,7.4 + ,5.1 + ,0.3 + ,6.0 + ,3030.3 + ,7.2 + ,5.4 + ,2.1 + ,3.0 + ,2803.5 + ,7.0 + ,5.4 + ,2.5 + ,2.0 + ,2767.6 + ,7.0 + ,5.4 + ,2.3 + ,2.0 + ,2882.6 + ,6.8 + ,5.5 + ,2.4 + ,2.0 + ,2863.4 + ,6.8 + ,5.8 + ,3.0 + ,-8.0 + ,2897.1 + ,6.7 + ,5.7 + ,1.7 + ,0.0 + ,3012.6 + ,6.8 + ,5.5 + ,3.5 + ,-2.0 + ,3143.0 + ,6.7 + ,5.6 + ,4.0 + ,3.0 + ,3032.9 + ,6.7 + ,5.6 + ,3.7 + ,5.0 + ,3045.8 + ,6.7 + ,5.5 + ,3.7 + ,8.0 + ,3110.5 + ,6.5 + ,5.5 + ,3.0 + ,8.0 + ,3013.2 + ,6.3 + ,5.7 + ,2.7 + ,9.0 + ,2987.1 + ,6.3 + 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,-11.0 + ,2293.4 + ,8.1 + ,3.9 + ,-1.0 + ,-9.0 + ,2443.3 + ,8.0 + ,4.2 + ,-0.9 + ,-17.0 + ,2513.2 + ,8.1 + ,4.0 + ,0.0 + ,-22.0 + ,2466.9 + ,8.2 + ,3.8 + ,0.3 + ,-25.0 + ,2502.7 + ,8.3 + ,3.7 + ,0.8 + ,-20.0 + ,2539.9 + ,8.4 + ,3.7 + ,0.8 + ,-24.0 + ,2482.6 + ,8.4 + ,3.7 + ,1.9 + ,-24.0 + ,2626.2 + ,8.4 + ,3.7 + ,2.1 + ,-22.0 + ,2656.3 + ,8.5 + ,3.7 + ,2.5 + ,-19.0 + ,2446.7 + ,8.5 + ,3.8 + ,2.7 + ,-18.0 + ,2467.4 + ,8.6 + ,3.7 + ,2.4 + ,-17.0 + ,2462.3 + ,8.6 + ,3.5 + ,2.4 + ,-11.0 + ,2504.6 + ,8.5 + ,3.5 + ,2.9 + ,-11.0 + ,2579.4 + ,8.5 + ,3.1 + ,3.1 + ,-12.0 + ,2649.2) + ,dim=c(5 + ,142) + ,dimnames=list(c('werkloosheid' + ,'rente' + ,'hicp' + ,'consumer' + ,'bel20') + ,1:142)) > y <- array(NA,dim=c(5,142),dimnames=list(c('werkloosheid','rente','hicp','consumer','bel20'),1:142)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x werkloosheid rente hicp consumer bel20 t 1 9.1 4.5 1.0 -1 3484.7 1 2 9.0 4.3 1.0 3 3411.1 2 3 9.0 4.3 1.3 2 3288.2 3 4 8.9 4.2 1.1 3 3280.4 4 5 8.8 4.0 0.8 5 3174.0 5 6 8.7 3.8 0.7 5 3165.3 6 7 8.5 4.1 0.7 3 3092.7 7 8 8.3 4.2 0.9 2 3053.1 8 9 8.1 4.0 1.3 1 3182.0 9 10 7.9 4.3 1.4 -4 2999.9 10 11 7.8 4.7 1.6 1 3249.6 11 12 7.6 5.0 2.1 1 3210.5 12 13 7.4 5.1 0.3 6 3030.3 13 14 7.2 5.4 2.1 3 2803.5 14 15 7.0 5.4 2.5 2 2767.6 15 16 7.0 5.4 2.3 2 2882.6 16 17 6.8 5.5 2.4 2 2863.4 17 18 6.8 5.8 3.0 -8 2897.1 18 19 6.7 5.7 1.7 0 3012.6 19 20 6.8 5.5 3.5 -2 3143.0 20 21 6.7 5.6 4.0 3 3032.9 21 22 6.7 5.6 3.7 5 3045.8 22 23 6.7 5.5 3.7 8 3110.5 23 24 6.5 5.5 3.0 8 3013.2 24 25 6.3 5.7 2.7 9 2987.1 25 26 6.3 5.6 2.5 11 2995.6 26 27 6.3 5.6 2.2 13 2833.2 27 28 6.5 5.4 2.9 12 2849.0 28 29 6.6 5.2 3.1 13 2794.8 29 30 6.5 5.1 3.0 15 2845.3 30 31 6.3 5.1 2.8 13 2915.0 31 32 6.3 5.0 2.5 16 2892.6 32 33 6.5 5.3 1.9 10 2604.4 33 34 7.0 5.4 1.9 14 2641.7 34 35 7.1 5.3 1.8 14 2659.8 35 36 7.3 5.1 2.0 15 2638.5 36 37 7.3 5.0 2.6 13 2720.3 37 38 7.4 5.0 2.5 8 2745.9 38 39 7.4 4.6 2.5 7 2735.7 39 40 7.3 4.8 1.6 3 2811.7 40 41 7.4 5.1 1.4 3 2799.4 41 42 7.5 5.1 0.8 4 2555.3 42 43 7.7 5.1 1.1 4 2305.0 43 44 7.7 5.4 1.3 0 2215.0 44 45 7.7 5.3 1.2 -4 2065.8 45 46 7.7 5.3 1.3 -14 1940.5 46 47 7.7 5.1 1.1 -18 2042.0 47 48 7.8 4.9 1.3 -8 1995.4 48 49 8.0 4.7 1.2 -1 1946.8 49 50 8.1 4.4 1.6 1 1765.9 50 51 8.1 4.6 1.7 2 1635.3 51 52 8.2 4.5 1.5 0 1833.4 52 53 8.2 4.2 0.9 1 1910.4 53 54 8.2 4.0 1.5 0 1959.7 54 55 8.1 3.9 1.4 -1 1969.6 55 56 8.1 4.1 1.6 -3 2061.4 56 57 8.2 4.1 1.7 -3 2093.5 57 58 8.3 3.7 1.4 -3 2120.9 58 59 8.3 3.8 1.8 -4 2174.6 59 60 8.4 4.1 1.7 -8 2196.7 60 61 8.5 4.1 1.4 -9 2350.4 61 62 8.5 4.0 1.2 -13 2440.3 62 63 8.4 4.3 1.0 -18 2408.6 63 64 8.0 4.4 1.7 -11 2472.8 64 65 7.9 4.2 2.4 -9 2407.6 65 66 8.1 4.2 2.0 -10 2454.6 66 67 8.5 4.0 2.1 -13 2448.1 67 68 8.8 4.0 2.0 -11 2497.8 68 69 8.8 4.3 1.8 -5 2645.6 69 70 8.6 4.4 2.7 -15 2756.8 70 71 8.3 4.4 2.3 -6 2849.3 71 72 8.3 4.3 1.9 -6 2921.4 72 73 8.3 4.1 2.0 -3 2981.9 73 74 8.4 4.1 2.3 -1 3080.6 74 75 8.4 3.9 2.8 -3 3106.2 75 76 8.5 3.8 2.4 -4 3119.3 76 77 8.6 3.7 2.3 -6 3061.3 77 78 8.6 3.5 2.7 0 3097.3 78 79 8.6 3.7 2.7 -4 3161.7 79 80 8.6 3.7 2.9 -2 3257.2 80 81 8.6 3.5 3.0 -2 3277.0 81 82 8.5 3.3 2.2 -6 3295.3 82 83 8.4 3.2 2.3 -7 3364.0 83 84 8.4 3.3 2.8 -6 3494.2 84 85 8.4 3.1 2.8 -6 3667.0 85 86 8.5 3.2 2.8 -3 3813.1 86 87 8.5 3.4 2.2 -2 3918.0 87 88 8.6 3.5 2.6 -5 3895.5 88 89 8.6 3.3 2.8 -11 3801.1 89 90 8.4 3.5 2.5 -11 3570.1 90 91 8.2 3.5 2.4 -11 3701.6 91 92 8.0 3.8 2.3 -10 3862.3 92 93 8.0 4.0 1.9 -14 3970.1 93 94 8.0 4.0 1.7 -8 4138.5 94 95 8.0 4.1 2.0 -9 4199.8 95 96 7.9 4.0 2.1 -5 4290.9 96 97 7.9 3.8 1.7 -1 4443.9 97 98 7.8 3.7 1.8 -2 4502.6 98 99 7.8 3.8 1.8 -5 4357.0 99 100 8.0 3.7 1.8 -4 4591.3 100 101 7.8 4.0 1.3 -6 4697.0 101 102 7.4 4.2 1.3 -2 4621.4 102 103 7.2 4.0 1.3 -2 4562.8 103 104 7.0 4.1 1.2 -2 4202.5 104 105 7.0 4.2 1.4 -2 4296.5 105 106 7.2 4.5 2.2 2 4435.2 106 107 7.2 4.6 2.9 1 4105.2 107 108 7.2 4.5 3.1 -8 4116.7 108 109 7.0 4.5 3.5 -1 3844.5 109 110 6.9 4.5 3.6 1 3721.0 110 111 6.8 4.4 4.4 -1 3674.4 111 112 6.8 4.3 4.1 2 3857.6 112 113 6.8 4.5 5.1 2 3801.1 113 114 6.9 4.1 5.8 1 3504.4 114 115 7.2 4.1 5.9 -1 3032.6 115 116 7.2 4.3 5.4 -2 3047.0 116 117 7.2 4.4 5.5 -2 2962.3 117 118 7.1 4.7 4.8 -1 2197.8 118 119 7.2 5.0 3.2 -8 2014.5 119 120 7.3 4.7 2.7 -4 1862.8 120 121 7.5 4.5 2.1 -6 1905.4 121 122 7.6 4.5 1.9 -3 1811.0 122 123 7.7 4.5 0.6 -3 1670.1 123 124 7.7 5.5 0.7 -7 1864.4 124 125 7.7 4.5 -0.2 -9 2052.0 125 126 7.8 4.4 -1.0 -11 2029.6 126 127 8.0 4.2 -1.7 -13 2070.8 127 128 8.1 3.9 -0.7 -11 2293.4 128 129 8.1 3.9 -1.0 -9 2443.3 129 130 8.0 4.2 -0.9 -17 2513.2 130 131 8.1 4.0 0.0 -22 2466.9 131 132 8.2 3.8 0.3 -25 2502.7 132 133 8.3 3.7 0.8 -20 2539.9 133 134 8.4 3.7 0.8 -24 2482.6 134 135 8.4 3.7 1.9 -24 2626.2 135 136 8.4 3.7 2.1 -22 2656.3 136 137 8.5 3.7 2.5 -19 2446.7 137 138 8.5 3.8 2.7 -18 2467.4 138 139 8.6 3.7 2.4 -17 2462.3 139 140 8.6 3.5 2.4 -11 2504.6 140 141 8.5 3.5 2.9 -11 2579.4 141 142 8.5 3.1 3.1 -12 2649.2 142 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) rente hicp consumer bel20 t 13.1121249 -0.9316784 -0.0777024 -0.0276137 -0.0001959 -0.0083751 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.68289 -0.21888 -0.03415 0.20455 0.97702 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.311e+01 3.089e-01 42.447 < 2e-16 *** rente -9.317e-01 5.194e-02 -17.939 < 2e-16 *** hicp -7.770e-02 2.324e-02 -3.343 0.00107 ** consumer -2.761e-02 4.500e-03 -6.136 8.65e-09 *** bel20 -1.959e-04 3.961e-05 -4.947 2.19e-06 *** t -8.375e-03 8.908e-04 -9.401 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3058 on 136 degrees of freedom Multiple R-squared: 0.823, Adjusted R-squared: 0.8165 F-statistic: 126.5 on 5 and 136 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,] 4.483011e-03 8.966021e-03 9.955170e-01 [2,] 1.639077e-03 3.278154e-03 9.983609e-01 [3,] 6.294067e-04 1.258813e-03 9.993706e-01 [4,] 1.408611e-04 2.817221e-04 9.998591e-01 [5,] 5.715780e-05 1.143156e-04 9.999428e-01 [6,] 4.750460e-05 9.500920e-05 9.999525e-01 [7,] 1.873965e-05 3.747930e-05 9.999813e-01 [8,] 7.461770e-06 1.492354e-05 9.999925e-01 [9,] 1.622351e-06 3.244703e-06 9.999984e-01 [10,] 4.225084e-05 8.450167e-05 9.999577e-01 [11,] 3.555276e-05 7.110552e-05 9.999644e-01 [12,] 5.306661e-05 1.061332e-04 9.999469e-01 [13,] 3.152846e-05 6.305691e-05 9.999685e-01 [14,] 2.647708e-05 5.295417e-05 9.999735e-01 [15,] 1.320232e-05 2.640463e-05 9.999868e-01 [16,] 6.430783e-06 1.286157e-05 9.999936e-01 [17,] 2.682246e-06 5.364492e-06 9.999973e-01 [18,] 1.313398e-06 2.626795e-06 9.999987e-01 [19,] 3.162510e-06 6.325019e-06 9.999968e-01 [20,] 3.381361e-05 6.762722e-05 9.999662e-01 [21,] 1.810054e-04 3.620108e-04 9.998190e-01 [22,] 1.532093e-04 3.064187e-04 9.998468e-01 [23,] 1.427644e-04 2.855288e-04 9.998572e-01 [24,] 1.608975e-04 3.217950e-04 9.998391e-01 [25,] 2.127827e-02 4.255654e-02 9.787217e-01 [26,] 4.696452e-01 9.392904e-01 5.303548e-01 [27,] 8.006993e-01 3.986014e-01 1.993007e-01 [28,] 9.355164e-01 1.289672e-01 6.448359e-02 [29,] 9.703873e-01 5.922531e-02 2.961266e-02 [30,] 9.789869e-01 4.202628e-02 2.101314e-02 [31,] 9.742404e-01 5.151922e-02 2.575961e-02 [32,] 9.719263e-01 5.614733e-02 2.807367e-02 [33,] 9.648747e-01 7.025055e-02 3.512527e-02 [34,] 9.584292e-01 8.314165e-02 4.157082e-02 [35,] 9.691075e-01 6.178502e-02 3.089251e-02 [36,] 9.811275e-01 3.774491e-02 1.887246e-02 [37,] 9.768048e-01 4.639031e-02 2.319516e-02 [38,] 9.700074e-01 5.998530e-02 2.999265e-02 [39,] 9.755212e-01 4.895765e-02 2.447882e-02 [40,] 9.692285e-01 6.154291e-02 3.077145e-02 [41,] 9.611880e-01 7.762410e-02 3.881205e-02 [42,] 9.507543e-01 9.849141e-02 4.924571e-02 [43,] 9.451168e-01 1.097663e-01 5.488317e-02 [44,] 9.339227e-01 1.321545e-01 6.607726e-02 [45,] 9.205122e-01 1.589756e-01 7.948782e-02 [46,] 9.112139e-01 1.775721e-01 8.878607e-02 [47,] 9.294687e-01 1.410626e-01 7.053132e-02 [48,] 9.258091e-01 1.483819e-01 7.419094e-02 [49,] 9.141110e-01 1.717780e-01 8.588902e-02 [50,] 9.299265e-01 1.401470e-01 7.007348e-02 [51,] 9.344744e-01 1.310512e-01 6.552560e-02 [52,] 9.266865e-01 1.466271e-01 7.331355e-02 [53,] 9.142544e-01 1.714911e-01 8.574557e-02 [54,] 9.042149e-01 1.915702e-01 9.578512e-02 [55,] 8.944014e-01 2.111972e-01 1.055986e-01 [56,] 8.983837e-01 2.032325e-01 1.016163e-01 [57,] 9.353680e-01 1.292641e-01 6.463204e-02 [58,] 9.520811e-01 9.583780e-02 4.791890e-02 [59,] 9.596711e-01 8.065787e-02 4.032893e-02 [60,] 9.663362e-01 6.732760e-02 3.366380e-02 [61,] 9.903065e-01 1.938697e-02 9.693486e-03 [62,] 9.921375e-01 1.572504e-02 7.862520e-03 [63,] 9.911152e-01 1.776961e-02 8.884803e-03 [64,] 9.880026e-01 2.399488e-02 1.199744e-02 [65,] 9.836243e-01 3.275135e-02 1.637568e-02 [66,] 9.832106e-01 3.357877e-02 1.678939e-02 [67,] 9.786614e-01 4.267713e-02 2.133857e-02 [68,] 9.728520e-01 5.429609e-02 2.714805e-02 [69,] 9.653728e-01 6.925432e-02 3.462716e-02 [70,] 9.577763e-01 8.444738e-02 4.222369e-02 [71,] 9.545550e-01 9.089004e-02 4.544502e-02 [72,] 9.624253e-01 7.514946e-02 3.757473e-02 [73,] 9.626046e-01 7.479083e-02 3.739542e-02 [74,] 9.666470e-01 6.670593e-02 3.335297e-02 [75,] 9.785434e-01 4.291320e-02 2.145660e-02 [76,] 9.766544e-01 4.669129e-02 2.334564e-02 [77,] 9.804838e-01 3.903231e-02 1.951615e-02 [78,] 9.749342e-01 5.013156e-02 2.506578e-02 [79,] 9.729887e-01 5.402261e-02 2.701131e-02 [80,] 9.797481e-01 4.050378e-02 2.025189e-02 [81,] 9.761944e-01 4.761125e-02 2.380563e-02 [82,] 9.713633e-01 5.727341e-02 2.863670e-02 [83,] 9.695091e-01 6.098170e-02 3.049085e-02 [84,] 9.640618e-01 7.187638e-02 3.593819e-02 [85,] 9.567946e-01 8.641074e-02 4.320537e-02 [86,] 9.530315e-01 9.393708e-02 4.696854e-02 [87,] 9.604965e-01 7.900692e-02 3.950346e-02 [88,] 9.701963e-01 5.960737e-02 2.980369e-02 [89,] 9.797896e-01 4.042074e-02 2.021037e-02 [90,] 9.836133e-01 3.277349e-02 1.638675e-02 [91,] 9.895604e-01 2.087925e-02 1.043962e-02 [92,] 9.993596e-01 1.280832e-03 6.404158e-04 [93,] 9.999917e-01 1.656736e-05 8.283682e-06 [94,] 9.999990e-01 1.957801e-06 9.789006e-07 [95,] 9.999994e-01 1.142268e-06 5.711341e-07 [96,] 9.999993e-01 1.475883e-06 7.379415e-07 [97,] 9.999987e-01 2.681938e-06 1.340969e-06 [98,] 9.999994e-01 1.103205e-06 5.516027e-07 [99,] 1.000000e+00 5.706995e-08 2.853498e-08 [100,] 1.000000e+00 2.286024e-09 1.143012e-09 [101,] 1.000000e+00 5.378577e-10 2.689288e-10 [102,] 1.000000e+00 6.726393e-10 3.363197e-10 [103,] 1.000000e+00 2.356099e-09 1.178050e-09 [104,] 1.000000e+00 7.147059e-09 3.573529e-09 [105,] 1.000000e+00 1.630298e-08 8.151491e-09 [106,] 1.000000e+00 2.088100e-08 1.044050e-08 [107,] 1.000000e+00 3.954787e-08 1.977393e-08 [108,] 1.000000e+00 7.685353e-08 3.842677e-08 [109,] 1.000000e+00 2.756035e-08 1.378017e-08 [110,] 1.000000e+00 8.588401e-08 4.294200e-08 [111,] 9.999999e-01 2.513667e-07 1.256833e-07 [112,] 9.999998e-01 4.360989e-07 2.180494e-07 [113,] 9.999993e-01 1.474995e-06 7.374976e-07 [114,] 9.999973e-01 5.481475e-06 2.740737e-06 [115,] 9.999901e-01 1.984446e-05 9.922229e-06 [116,] 9.999834e-01 3.316267e-05 1.658134e-05 [117,] 9.999681e-01 6.384263e-05 3.192131e-05 [118,] 9.999913e-01 1.744140e-05 8.720700e-06 [119,] 9.999802e-01 3.960218e-05 1.980109e-05 [120,] 9.999301e-01 1.398892e-04 6.994459e-05 [121,] 9.998410e-01 3.179002e-04 1.589501e-04 [122,] 9.992550e-01 1.489938e-03 7.449692e-04 [123,] 9.988536e-01 2.292797e-03 1.146398e-03 [124,] 9.990022e-01 1.995587e-03 9.977934e-04 [125,] 9.939096e-01 1.218075e-02 6.090374e-03 > postscript(file="/var/www/rcomp/tmp/17pru1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/27pru1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/30z8f1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/40z8f1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/50z8f1292948297.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 = 142 Frequency = 1 1 2 3 4 5 6 0.921665492 0.739738834 0.719730653 0.545482832 0.278591437 -0.008843999 7 8 9 10 11 12 0.009582460 -0.108706764 -0.457944041 -0.536043203 -0.052462957 0.066605882 13 14 15 16 17 18 -0.068954605 0.031509457 -0.163682164 -0.148315038 -0.242763766 -0.177797371 19 20 21 22 23 24 -0.220063426 -0.187837052 -0.030946896 0.011872403 0.022597680 -0.242483345 25 26 27 28 29 30 -0.248583483 -0.292023896 -0.283551943 -0.231638697 -0.277064753 -0.404505666 31 32 33 34 35 36 -0.653241727 -0.682893093 -0.463786307 0.255519722 0.266503179 0.327523374 37 38 39 40 41 42 0.250152234 0.217704679 -0.176203776 -0.246988818 0.122939358 0.164479043 43 44 45 46 47 48 0.347122460 0.522452798 0.290201693 0.005659721 -0.278408653 -0.073822546 49 50 51 52 53 54 0.124219938 0.003955279 0.208460947 0.191715052 -0.083334177 -0.232627364 55 56 57 58 59 60 -0.450864244 -0.277853482 -0.155418606 -0.437656978 -0.322125009 -0.048141129 61 62 63 64 65 66 0.039424750 -0.153748621 -0.125689944 -0.163880541 -0.344996982 -0.186107576 67 68 69 70 71 72 -0.040412486 0.325157697 0.792137052 0.509263428 0.453204594 0.351457789 73 74 75 76 77 78 0.275962541 0.482214501 0.292893744 0.251973122 0.192818605 0.218674710 79 80 81 82 83 84 0.315549027 0.413403751 0.247092942 -0.199898662 -0.391074095 -0.197555533 85 86 87 88 89 90 -0.341658584 -0.028648537 0.167608032 0.312982434 -0.033615908 -0.107476731 91 92 93 94 95 96 -0.281106439 -0.141897649 -0.067600746 0.123911330 0.233162158 0.184444028 97 98 99 100 101 102 0.115835186 -0.077299593 -0.087125727 0.101602722 0.116113108 0.006465958 103 104 105 106 107 108 -0.382976373 -0.559798902 -0.424297582 0.263373866 0.327036440 0.011514403 109 110 111 112 113 114 -0.009067192 -0.061892403 -0.248881064 -0.238248263 0.023094691 -0.272557333 115 116 117 118 119 120 -0.104081279 0.026986102 0.119703654 0.131012229 0.165356443 0.036108221 121 122 123 124 125 126 -0.035354336 0.121825092 0.101579848 0.977019076 -0.034686443 -0.141257398 127 128 129 130 131 132 -0.220764508 -0.215348065 -0.145685717 -0.157250364 -0.312418864 -0.442895250 133 134 135 136 137 138 -0.243479607 -0.256786227 -0.134802191 -0.049761594 0.131467628 0.280220611 139 140 141 142 0.298731569 0.294741085 0.256623349 -0.106069845 > postscript(file="/var/www/rcomp/tmp/6a8701292948297.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 = 142 Frequency = 1 lag(myerror, k = 1) myerror 0 0.921665492 NA 1 0.739738834 0.921665492 2 0.719730653 0.739738834 3 0.545482832 0.719730653 4 0.278591437 0.545482832 5 -0.008843999 0.278591437 6 0.009582460 -0.008843999 7 -0.108706764 0.009582460 8 -0.457944041 -0.108706764 9 -0.536043203 -0.457944041 10 -0.052462957 -0.536043203 11 0.066605882 -0.052462957 12 -0.068954605 0.066605882 13 0.031509457 -0.068954605 14 -0.163682164 0.031509457 15 -0.148315038 -0.163682164 16 -0.242763766 -0.148315038 17 -0.177797371 -0.242763766 18 -0.220063426 -0.177797371 19 -0.187837052 -0.220063426 20 -0.030946896 -0.187837052 21 0.011872403 -0.030946896 22 0.022597680 0.011872403 23 -0.242483345 0.022597680 24 -0.248583483 -0.242483345 25 -0.292023896 -0.248583483 26 -0.283551943 -0.292023896 27 -0.231638697 -0.283551943 28 -0.277064753 -0.231638697 29 -0.404505666 -0.277064753 30 -0.653241727 -0.404505666 31 -0.682893093 -0.653241727 32 -0.463786307 -0.682893093 33 0.255519722 -0.463786307 34 0.266503179 0.255519722 35 0.327523374 0.266503179 36 0.250152234 0.327523374 37 0.217704679 0.250152234 38 -0.176203776 0.217704679 39 -0.246988818 -0.176203776 40 0.122939358 -0.246988818 41 0.164479043 0.122939358 42 0.347122460 0.164479043 43 0.522452798 0.347122460 44 0.290201693 0.522452798 45 0.005659721 0.290201693 46 -0.278408653 0.005659721 47 -0.073822546 -0.278408653 48 0.124219938 -0.073822546 49 0.003955279 0.124219938 50 0.208460947 0.003955279 51 0.191715052 0.208460947 52 -0.083334177 0.191715052 53 -0.232627364 -0.083334177 54 -0.450864244 -0.232627364 55 -0.277853482 -0.450864244 56 -0.155418606 -0.277853482 57 -0.437656978 -0.155418606 58 -0.322125009 -0.437656978 59 -0.048141129 -0.322125009 60 0.039424750 -0.048141129 61 -0.153748621 0.039424750 62 -0.125689944 -0.153748621 63 -0.163880541 -0.125689944 64 -0.344996982 -0.163880541 65 -0.186107576 -0.344996982 66 -0.040412486 -0.186107576 67 0.325157697 -0.040412486 68 0.792137052 0.325157697 69 0.509263428 0.792137052 70 0.453204594 0.509263428 71 0.351457789 0.453204594 72 0.275962541 0.351457789 73 0.482214501 0.275962541 74 0.292893744 0.482214501 75 0.251973122 0.292893744 76 0.192818605 0.251973122 77 0.218674710 0.192818605 78 0.315549027 0.218674710 79 0.413403751 0.315549027 80 0.247092942 0.413403751 81 -0.199898662 0.247092942 82 -0.391074095 -0.199898662 83 -0.197555533 -0.391074095 84 -0.341658584 -0.197555533 85 -0.028648537 -0.341658584 86 0.167608032 -0.028648537 87 0.312982434 0.167608032 88 -0.033615908 0.312982434 89 -0.107476731 -0.033615908 90 -0.281106439 -0.107476731 91 -0.141897649 -0.281106439 92 -0.067600746 -0.141897649 93 0.123911330 -0.067600746 94 0.233162158 0.123911330 95 0.184444028 0.233162158 96 0.115835186 0.184444028 97 -0.077299593 0.115835186 98 -0.087125727 -0.077299593 99 0.101602722 -0.087125727 100 0.116113108 0.101602722 101 0.006465958 0.116113108 102 -0.382976373 0.006465958 103 -0.559798902 -0.382976373 104 -0.424297582 -0.559798902 105 0.263373866 -0.424297582 106 0.327036440 0.263373866 107 0.011514403 0.327036440 108 -0.009067192 0.011514403 109 -0.061892403 -0.009067192 110 -0.248881064 -0.061892403 111 -0.238248263 -0.248881064 112 0.023094691 -0.238248263 113 -0.272557333 0.023094691 114 -0.104081279 -0.272557333 115 0.026986102 -0.104081279 116 0.119703654 0.026986102 117 0.131012229 0.119703654 118 0.165356443 0.131012229 119 0.036108221 0.165356443 120 -0.035354336 0.036108221 121 0.121825092 -0.035354336 122 0.101579848 0.121825092 123 0.977019076 0.101579848 124 -0.034686443 0.977019076 125 -0.141257398 -0.034686443 126 -0.220764508 -0.141257398 127 -0.215348065 -0.220764508 128 -0.145685717 -0.215348065 129 -0.157250364 -0.145685717 130 -0.312418864 -0.157250364 131 -0.442895250 -0.312418864 132 -0.243479607 -0.442895250 133 -0.256786227 -0.243479607 134 -0.134802191 -0.256786227 135 -0.049761594 -0.134802191 136 0.131467628 -0.049761594 137 0.280220611 0.131467628 138 0.298731569 0.280220611 139 0.294741085 0.298731569 140 0.256623349 0.294741085 141 -0.106069845 0.256623349 142 NA -0.106069845 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.739738834 0.921665492 [2,] 0.719730653 0.739738834 [3,] 0.545482832 0.719730653 [4,] 0.278591437 0.545482832 [5,] -0.008843999 0.278591437 [6,] 0.009582460 -0.008843999 [7,] -0.108706764 0.009582460 [8,] -0.457944041 -0.108706764 [9,] -0.536043203 -0.457944041 [10,] -0.052462957 -0.536043203 [11,] 0.066605882 -0.052462957 [12,] -0.068954605 0.066605882 [13,] 0.031509457 -0.068954605 [14,] -0.163682164 0.031509457 [15,] -0.148315038 -0.163682164 [16,] -0.242763766 -0.148315038 [17,] -0.177797371 -0.242763766 [18,] -0.220063426 -0.177797371 [19,] -0.187837052 -0.220063426 [20,] -0.030946896 -0.187837052 [21,] 0.011872403 -0.030946896 [22,] 0.022597680 0.011872403 [23,] -0.242483345 0.022597680 [24,] -0.248583483 -0.242483345 [25,] -0.292023896 -0.248583483 [26,] -0.283551943 -0.292023896 [27,] -0.231638697 -0.283551943 [28,] -0.277064753 -0.231638697 [29,] -0.404505666 -0.277064753 [30,] -0.653241727 -0.404505666 [31,] -0.682893093 -0.653241727 [32,] -0.463786307 -0.682893093 [33,] 0.255519722 -0.463786307 [34,] 0.266503179 0.255519722 [35,] 0.327523374 0.266503179 [36,] 0.250152234 0.327523374 [37,] 0.217704679 0.250152234 [38,] -0.176203776 0.217704679 [39,] -0.246988818 -0.176203776 [40,] 0.122939358 -0.246988818 [41,] 0.164479043 0.122939358 [42,] 0.347122460 0.164479043 [43,] 0.522452798 0.347122460 [44,] 0.290201693 0.522452798 [45,] 0.005659721 0.290201693 [46,] -0.278408653 0.005659721 [47,] -0.073822546 -0.278408653 [48,] 0.124219938 -0.073822546 [49,] 0.003955279 0.124219938 [50,] 0.208460947 0.003955279 [51,] 0.191715052 0.208460947 [52,] -0.083334177 0.191715052 [53,] -0.232627364 -0.083334177 [54,] -0.450864244 -0.232627364 [55,] -0.277853482 -0.450864244 [56,] -0.155418606 -0.277853482 [57,] -0.437656978 -0.155418606 [58,] -0.322125009 -0.437656978 [59,] -0.048141129 -0.322125009 [60,] 0.039424750 -0.048141129 [61,] -0.153748621 0.039424750 [62,] -0.125689944 -0.153748621 [63,] -0.163880541 -0.125689944 [64,] -0.344996982 -0.163880541 [65,] -0.186107576 -0.344996982 [66,] -0.040412486 -0.186107576 [67,] 0.325157697 -0.040412486 [68,] 0.792137052 0.325157697 [69,] 0.509263428 0.792137052 [70,] 0.453204594 0.509263428 [71,] 0.351457789 0.453204594 [72,] 0.275962541 0.351457789 [73,] 0.482214501 0.275962541 [74,] 0.292893744 0.482214501 [75,] 0.251973122 0.292893744 [76,] 0.192818605 0.251973122 [77,] 0.218674710 0.192818605 [78,] 0.315549027 0.218674710 [79,] 0.413403751 0.315549027 [80,] 0.247092942 0.413403751 [81,] -0.199898662 0.247092942 [82,] -0.391074095 -0.199898662 [83,] -0.197555533 -0.391074095 [84,] -0.341658584 -0.197555533 [85,] -0.028648537 -0.341658584 [86,] 0.167608032 -0.028648537 [87,] 0.312982434 0.167608032 [88,] -0.033615908 0.312982434 [89,] -0.107476731 -0.033615908 [90,] -0.281106439 -0.107476731 [91,] -0.141897649 -0.281106439 [92,] -0.067600746 -0.141897649 [93,] 0.123911330 -0.067600746 [94,] 0.233162158 0.123911330 [95,] 0.184444028 0.233162158 [96,] 0.115835186 0.184444028 [97,] -0.077299593 0.115835186 [98,] -0.087125727 -0.077299593 [99,] 0.101602722 -0.087125727 [100,] 0.116113108 0.101602722 [101,] 0.006465958 0.116113108 [102,] -0.382976373 0.006465958 [103,] -0.559798902 -0.382976373 [104,] -0.424297582 -0.559798902 [105,] 0.263373866 -0.424297582 [106,] 0.327036440 0.263373866 [107,] 0.011514403 0.327036440 [108,] -0.009067192 0.011514403 [109,] -0.061892403 -0.009067192 [110,] -0.248881064 -0.061892403 [111,] -0.238248263 -0.248881064 [112,] 0.023094691 -0.238248263 [113,] -0.272557333 0.023094691 [114,] -0.104081279 -0.272557333 [115,] 0.026986102 -0.104081279 [116,] 0.119703654 0.026986102 [117,] 0.131012229 0.119703654 [118,] 0.165356443 0.131012229 [119,] 0.036108221 0.165356443 [120,] -0.035354336 0.036108221 [121,] 0.121825092 -0.035354336 [122,] 0.101579848 0.121825092 [123,] 0.977019076 0.101579848 [124,] -0.034686443 0.977019076 [125,] -0.141257398 -0.034686443 [126,] -0.220764508 -0.141257398 [127,] -0.215348065 -0.220764508 [128,] -0.145685717 -0.215348065 [129,] -0.157250364 -0.145685717 [130,] -0.312418864 -0.157250364 [131,] -0.442895250 -0.312418864 [132,] -0.243479607 -0.442895250 [133,] -0.256786227 -0.243479607 [134,] -0.134802191 -0.256786227 [135,] -0.049761594 -0.134802191 [136,] 0.131467628 -0.049761594 [137,] 0.280220611 0.131467628 [138,] 0.298731569 0.280220611 [139,] 0.294741085 0.298731569 [140,] 0.256623349 0.294741085 [141,] -0.106069845 0.256623349 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.739738834 0.921665492 2 0.719730653 0.739738834 3 0.545482832 0.719730653 4 0.278591437 0.545482832 5 -0.008843999 0.278591437 6 0.009582460 -0.008843999 7 -0.108706764 0.009582460 8 -0.457944041 -0.108706764 9 -0.536043203 -0.457944041 10 -0.052462957 -0.536043203 11 0.066605882 -0.052462957 12 -0.068954605 0.066605882 13 0.031509457 -0.068954605 14 -0.163682164 0.031509457 15 -0.148315038 -0.163682164 16 -0.242763766 -0.148315038 17 -0.177797371 -0.242763766 18 -0.220063426 -0.177797371 19 -0.187837052 -0.220063426 20 -0.030946896 -0.187837052 21 0.011872403 -0.030946896 22 0.022597680 0.011872403 23 -0.242483345 0.022597680 24 -0.248583483 -0.242483345 25 -0.292023896 -0.248583483 26 -0.283551943 -0.292023896 27 -0.231638697 -0.283551943 28 -0.277064753 -0.231638697 29 -0.404505666 -0.277064753 30 -0.653241727 -0.404505666 31 -0.682893093 -0.653241727 32 -0.463786307 -0.682893093 33 0.255519722 -0.463786307 34 0.266503179 0.255519722 35 0.327523374 0.266503179 36 0.250152234 0.327523374 37 0.217704679 0.250152234 38 -0.176203776 0.217704679 39 -0.246988818 -0.176203776 40 0.122939358 -0.246988818 41 0.164479043 0.122939358 42 0.347122460 0.164479043 43 0.522452798 0.347122460 44 0.290201693 0.522452798 45 0.005659721 0.290201693 46 -0.278408653 0.005659721 47 -0.073822546 -0.278408653 48 0.124219938 -0.073822546 49 0.003955279 0.124219938 50 0.208460947 0.003955279 51 0.191715052 0.208460947 52 -0.083334177 0.191715052 53 -0.232627364 -0.083334177 54 -0.450864244 -0.232627364 55 -0.277853482 -0.450864244 56 -0.155418606 -0.277853482 57 -0.437656978 -0.155418606 58 -0.322125009 -0.437656978 59 -0.048141129 -0.322125009 60 0.039424750 -0.048141129 61 -0.153748621 0.039424750 62 -0.125689944 -0.153748621 63 -0.163880541 -0.125689944 64 -0.344996982 -0.163880541 65 -0.186107576 -0.344996982 66 -0.040412486 -0.186107576 67 0.325157697 -0.040412486 68 0.792137052 0.325157697 69 0.509263428 0.792137052 70 0.453204594 0.509263428 71 0.351457789 0.453204594 72 0.275962541 0.351457789 73 0.482214501 0.275962541 74 0.292893744 0.482214501 75 0.251973122 0.292893744 76 0.192818605 0.251973122 77 0.218674710 0.192818605 78 0.315549027 0.218674710 79 0.413403751 0.315549027 80 0.247092942 0.413403751 81 -0.199898662 0.247092942 82 -0.391074095 -0.199898662 83 -0.197555533 -0.391074095 84 -0.341658584 -0.197555533 85 -0.028648537 -0.341658584 86 0.167608032 -0.028648537 87 0.312982434 0.167608032 88 -0.033615908 0.312982434 89 -0.107476731 -0.033615908 90 -0.281106439 -0.107476731 91 -0.141897649 -0.281106439 92 -0.067600746 -0.141897649 93 0.123911330 -0.067600746 94 0.233162158 0.123911330 95 0.184444028 0.233162158 96 0.115835186 0.184444028 97 -0.077299593 0.115835186 98 -0.087125727 -0.077299593 99 0.101602722 -0.087125727 100 0.116113108 0.101602722 101 0.006465958 0.116113108 102 -0.382976373 0.006465958 103 -0.559798902 -0.382976373 104 -0.424297582 -0.559798902 105 0.263373866 -0.424297582 106 0.327036440 0.263373866 107 0.011514403 0.327036440 108 -0.009067192 0.011514403 109 -0.061892403 -0.009067192 110 -0.248881064 -0.061892403 111 -0.238248263 -0.248881064 112 0.023094691 -0.238248263 113 -0.272557333 0.023094691 114 -0.104081279 -0.272557333 115 0.026986102 -0.104081279 116 0.119703654 0.026986102 117 0.131012229 0.119703654 118 0.165356443 0.131012229 119 0.036108221 0.165356443 120 -0.035354336 0.036108221 121 0.121825092 -0.035354336 122 0.101579848 0.121825092 123 0.977019076 0.101579848 124 -0.034686443 0.977019076 125 -0.141257398 -0.034686443 126 -0.220764508 -0.141257398 127 -0.215348065 -0.220764508 128 -0.145685717 -0.215348065 129 -0.157250364 -0.145685717 130 -0.312418864 -0.157250364 131 -0.442895250 -0.312418864 132 -0.243479607 -0.442895250 133 -0.256786227 -0.243479607 134 -0.134802191 -0.256786227 135 -0.049761594 -0.134802191 136 0.131467628 -0.049761594 137 0.280220611 0.131467628 138 0.298731569 0.280220611 139 0.294741085 0.298731569 140 0.256623349 0.294741085 141 -0.106069845 0.256623349 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/73h6l1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/83h6l1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/93h6l1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10w8oo1292948297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/11hrmc1292948297.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/12lr301292948297.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/13zjjr1292948297.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/142khw1292948297.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/15nkf21292948297.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/166y6r1292948298.tab") + } > > try(system("convert tmp/17pru1292948297.ps tmp/17pru1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/27pru1292948297.ps tmp/27pru1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/30z8f1292948297.ps tmp/30z8f1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/40z8f1292948297.ps tmp/40z8f1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/50z8f1292948297.ps tmp/50z8f1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/6a8701292948297.ps tmp/6a8701292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/73h6l1292948297.ps tmp/73h6l1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/83h6l1292948297.ps tmp/83h6l1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/93h6l1292948297.ps tmp/93h6l1292948297.png",intern=TRUE)) character(0) > try(system("convert tmp/10w8oo1292948297.ps tmp/10w8oo1292948297.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.260 1.180 5.727