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(9.1 + ,4.5 + ,1.0 + ,-1.0 + ,1989.3 + ,9.0 + ,4.3 + ,1.0 + ,3.0 + ,2097.8 + ,9.0 + ,4.3 + ,1.3 + ,2.0 + ,2154.9 + ,8.9 + ,4.2 + ,1.1 + ,3.0 + ,2152.2 + ,8.8 + ,4.0 + ,0.8 + ,5.0 + ,2250.3 + ,8.7 + ,3.8 + ,0.7 + ,5.0 + ,2346.9 + ,8.5 + ,4.1 + ,0.7 + ,3.0 + ,2525.6 + ,8.3 + ,4.2 + ,0.9 + ,2.0 + ,2409.4 + ,8.1 + ,4.0 + ,1.3 + ,1.0 + ,2394.4 + ,7.9 + ,4.3 + ,1.4 + ,-4.0 + ,2401.3 + ,7.8 + ,4.7 + ,1.6 + ,1.0 + ,2354.3 + ,7.6 + ,5.0 + ,2.1 + ,1.0 + ,2450.4 + ,7.4 + ,5.1 + ,0.3 + ,6.0 + ,2504.7 + ,7.2 + ,5.4 + ,2.1 + ,3.0 + ,2661.4 + ,7.0 + ,5.4 + ,2.5 + ,2.0 + ,2880.4 + ,7.0 + ,5.4 + ,2.3 + ,2.0 + ,3064.4 + ,6.8 + ,5.5 + ,2.4 + ,2.0 + ,3141.1 + ,6.8 + ,5.8 + ,3.0 + ,-8.0 + ,3327.7 + ,6.7 + ,5.7 + ,1.7 + ,0.0 + ,3565.0 + ,6.8 + ,5.5 + ,3.5 + ,-2.0 + ,3403.1 + ,6.7 + ,5.6 + ,4.0 + ,3.0 + ,3149.9 + ,6.7 + ,5.6 + ,3.7 + ,5.0 + ,3006.8 + ,6.7 + ,5.5 + ,3.7 + ,8.0 + ,3230.7 + ,6.5 + ,5.5 + ,3.0 + ,8.0 + ,3361.1 + ,6.3 + ,5.7 + ,2.7 + ,9.0 + ,3484.7 + ,6.3 + 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,-6.0 + ,1910.4 + ,8.6 + ,3.5 + ,2.7 + ,0.0 + ,1959.7 + ,8.6 + ,3.7 + ,2.7 + ,-4.0 + ,1969.6 + ,8.6 + ,3.7 + ,2.9 + ,-2.0 + ,2061.4 + ,8.6 + ,3.5 + ,3.0 + ,-2.0 + ,2093.5 + ,8.5 + ,3.3 + ,2.2 + ,-6.0 + ,2120.9 + ,8.4 + ,3.2 + ,2.3 + ,-7.0 + ,2174.6 + ,8.4 + ,3.3 + ,2.8 + ,-6.0 + ,2196.7 + ,8.4 + ,3.1 + ,2.8 + ,-6.0 + ,2350.4 + ,8.5 + ,3.2 + ,2.8 + ,-3.0 + ,2440.3 + ,8.5 + ,3.4 + ,2.2 + ,-2.0 + ,2408.6 + ,8.6 + ,3.5 + ,2.6 + ,-5.0 + ,2472.8 + ,8.6 + ,3.3 + ,2.8 + ,-11.0 + ,2407.6 + ,8.4 + ,3.5 + ,2.5 + ,-11.0 + ,2454.6 + ,8.2 + ,3.5 + ,2.4 + ,-11.0 + ,2448.1 + ,8.0 + ,3.8 + ,2.3 + ,-10.0 + ,2497.8 + ,8.0 + ,4.0 + ,1.9 + ,-14.0 + ,2645.6 + ,8.0 + ,4.0 + ,1.7 + ,-8.0 + ,2756.8 + ,8.0 + ,4.1 + ,2.0 + ,-9.0 + ,2849.3 + ,7.9 + ,4.0 + ,2.1 + ,-5.0 + ,2921.4 + ,7.9 + ,3.8 + ,1.7 + ,-1.0 + ,2981.9 + ,7.8 + ,3.7 + ,1.8 + ,-2.0 + ,3080.6 + ,7.8 + ,3.8 + ,1.8 + ,-5.0 + ,3106.2 + ,8.0 + ,3.7 + ,1.8 + ,-4.0 + ,3119.3 + ,7.8 + ,4.0 + ,1.3 + ,-6.0 + ,3061.3 + ,7.4 + ,4.2 + ,1.3 + ,-2.0 + ,3097.3 + 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,-11.0 + ,4202.5 + ,8.1 + ,3.9 + ,-1.0 + ,-9.0 + ,4296.5 + ,8.0 + ,4.2 + ,-0.9 + ,-17.0 + ,4435.2 + ,8.1 + ,4.0 + ,0.0 + ,-22.0 + ,4105.2 + ,8.2 + ,3.8 + ,0.3 + ,-25.0 + ,4116.7 + ,8.3 + ,3.7 + ,0.8 + ,-20.0 + ,3844.5 + ,8.4 + ,3.7 + ,0.8 + ,-24.0 + ,3721.0 + ,8.4 + ,3.7 + ,1.9 + ,-24.0 + ,3674.4 + ,8.4 + ,3.7 + ,2.1 + ,-22.0 + ,3857.6 + ,8.5 + ,3.7 + ,2.5 + ,-19.0 + ,3801.1 + ,8.5 + ,3.8 + ,2.7 + ,-18.0 + ,3504.4 + ,8.6 + ,3.7 + ,2.4 + ,-17.0 + ,3032.6 + ,8.6 + ,3.5 + ,2.4 + ,-11.0 + ,3047.0 + ,8.5 + ,3.5 + ,2.9 + ,-11.0 + ,2962.3 + ,8.5 + ,3.1 + ,3.1 + ,-12.0 + ,2197.8) + ,dim=c(5 + ,142) + ,dimnames=list(c('Werkloosheid' + ,'rente' + ,'inflatie' + ,'consumer' + ,'Bel20') + ,1:142)) > y <- array(NA,dim=c(5,142),dimnames=list(c('Werkloosheid','rente','inflatie','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 = '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 Werkloosheid rente inflatie consumer Bel20 1 9.1 4.5 1.0 -1 1989.3 2 9.0 4.3 1.0 3 2097.8 3 9.0 4.3 1.3 2 2154.9 4 8.9 4.2 1.1 3 2152.2 5 8.8 4.0 0.8 5 2250.3 6 8.7 3.8 0.7 5 2346.9 7 8.5 4.1 0.7 3 2525.6 8 8.3 4.2 0.9 2 2409.4 9 8.1 4.0 1.3 1 2394.4 10 7.9 4.3 1.4 -4 2401.3 11 7.8 4.7 1.6 1 2354.3 12 7.6 5.0 2.1 1 2450.4 13 7.4 5.1 0.3 6 2504.7 14 7.2 5.4 2.1 3 2661.4 15 7.0 5.4 2.5 2 2880.4 16 7.0 5.4 2.3 2 3064.4 17 6.8 5.5 2.4 2 3141.1 18 6.8 5.8 3.0 -8 3327.7 19 6.7 5.7 1.7 0 3565.0 20 6.8 5.5 3.5 -2 3403.1 21 6.7 5.6 4.0 3 3149.9 22 6.7 5.6 3.7 5 3006.8 23 6.7 5.5 3.7 8 3230.7 24 6.5 5.5 3.0 8 3361.1 25 6.3 5.7 2.7 9 3484.7 26 6.3 5.6 2.5 11 3411.1 27 6.3 5.6 2.2 13 3288.2 28 6.5 5.4 2.9 12 3280.4 29 6.6 5.2 3.1 13 3174.0 30 6.5 5.1 3.0 15 3165.3 31 6.3 5.1 2.8 13 3092.7 32 6.3 5.0 2.5 16 3053.1 33 6.5 5.3 1.9 10 3182.0 34 7.0 5.4 1.9 14 2999.9 35 7.1 5.3 1.8 14 3249.6 36 7.3 5.1 2.0 15 3210.5 37 7.3 5.0 2.6 13 3030.3 38 7.4 5.0 2.5 8 2803.5 39 7.4 4.6 2.5 7 2767.6 40 7.3 4.8 1.6 3 2882.6 41 7.4 5.1 1.4 3 2863.4 42 7.5 5.1 0.8 4 2897.1 43 7.7 5.1 1.1 4 3012.6 44 7.7 5.4 1.3 0 3143.0 45 7.7 5.3 1.2 -4 3032.9 46 7.7 5.3 1.3 -14 3045.8 47 7.7 5.1 1.1 -18 3110.5 48 7.8 4.9 1.3 -8 3013.2 49 8.0 4.7 1.2 -1 2987.1 50 8.1 4.4 1.6 1 2995.6 51 8.1 4.6 1.7 2 2833.2 52 8.2 4.5 1.5 0 2849.0 53 8.2 4.2 0.9 1 2794.8 54 8.2 4.0 1.5 0 2845.3 55 8.1 3.9 1.4 -1 2915.0 56 8.1 4.1 1.6 -3 2892.6 57 8.2 4.1 1.7 -3 2604.4 58 8.3 3.7 1.4 -3 2641.7 59 8.3 3.8 1.8 -4 2659.8 60 8.4 4.1 1.7 -8 2638.5 61 8.5 4.1 1.4 -9 2720.3 62 8.5 4.0 1.2 -13 2745.9 63 8.4 4.3 1.0 -18 2735.7 64 8.0 4.4 1.7 -11 2811.7 65 7.9 4.2 2.4 -9 2799.4 66 8.1 4.2 2.0 -10 2555.3 67 8.5 4.0 2.1 -13 2305.0 68 8.8 4.0 2.0 -11 2215.0 69 8.8 4.3 1.8 -5 2065.8 70 8.6 4.4 2.7 -15 1940.5 71 8.3 4.4 2.3 -6 2042.0 72 8.3 4.3 1.9 -6 1995.4 73 8.3 4.1 2.0 -3 1946.8 74 8.4 4.1 2.3 -1 1765.9 75 8.4 3.9 2.8 -3 1635.3 76 8.5 3.8 2.4 -4 1833.4 77 8.6 3.7 2.3 -6 1910.4 78 8.6 3.5 2.7 0 1959.7 79 8.6 3.7 2.7 -4 1969.6 80 8.6 3.7 2.9 -2 2061.4 81 8.6 3.5 3.0 -2 2093.5 82 8.5 3.3 2.2 -6 2120.9 83 8.4 3.2 2.3 -7 2174.6 84 8.4 3.3 2.8 -6 2196.7 85 8.4 3.1 2.8 -6 2350.4 86 8.5 3.2 2.8 -3 2440.3 87 8.5 3.4 2.2 -2 2408.6 88 8.6 3.5 2.6 -5 2472.8 89 8.6 3.3 2.8 -11 2407.6 90 8.4 3.5 2.5 -11 2454.6 91 8.2 3.5 2.4 -11 2448.1 92 8.0 3.8 2.3 -10 2497.8 93 8.0 4.0 1.9 -14 2645.6 94 8.0 4.0 1.7 -8 2756.8 95 8.0 4.1 2.0 -9 2849.3 96 7.9 4.0 2.1 -5 2921.4 97 7.9 3.8 1.7 -1 2981.9 98 7.8 3.7 1.8 -2 3080.6 99 7.8 3.8 1.8 -5 3106.2 100 8.0 3.7 1.8 -4 3119.3 101 7.8 4.0 1.3 -6 3061.3 102 7.4 4.2 1.3 -2 3097.3 103 7.2 4.0 1.3 -2 3161.7 104 7.0 4.1 1.2 -2 3257.2 105 7.0 4.2 1.4 -2 3277.0 106 7.2 4.5 2.2 2 3295.3 107 7.2 4.6 2.9 1 3364.0 108 7.2 4.5 3.1 -8 3494.2 109 7.0 4.5 3.5 -1 3667.0 110 6.9 4.5 3.6 1 3813.1 111 6.8 4.4 4.4 -1 3918.0 112 6.8 4.3 4.1 2 3895.5 113 6.8 4.5 5.1 2 3801.1 114 6.9 4.1 5.8 1 3570.1 115 7.2 4.1 5.9 -1 3701.6 116 7.2 4.3 5.4 -2 3862.3 117 7.2 4.4 5.5 -2 3970.1 118 7.1 4.7 4.8 -1 4138.5 119 7.2 5.0 3.2 -8 4199.8 120 7.3 4.7 2.7 -4 4290.9 121 7.5 4.5 2.1 -6 4443.9 122 7.6 4.5 1.9 -3 4502.6 123 7.7 4.5 0.6 -3 4357.0 124 7.7 5.5 0.7 -7 4591.3 125 7.7 4.5 -0.2 -9 4697.0 126 7.8 4.4 -1.0 -11 4621.4 127 8.0 4.2 -1.7 -13 4562.8 128 8.1 3.9 -0.7 -11 4202.5 129 8.1 3.9 -1.0 -9 4296.5 130 8.0 4.2 -0.9 -17 4435.2 131 8.1 4.0 0.0 -22 4105.2 132 8.2 3.8 0.3 -25 4116.7 133 8.3 3.7 0.8 -20 3844.5 134 8.4 3.7 0.8 -24 3721.0 135 8.4 3.7 1.9 -24 3674.4 136 8.4 3.7 2.1 -22 3857.6 137 8.5 3.7 2.5 -19 3801.1 138 8.5 3.8 2.7 -18 3504.4 139 8.6 3.7 2.4 -17 3032.6 140 8.6 3.5 2.4 -11 3047.0 141 8.5 3.5 2.9 -11 2962.3 142 8.5 3.1 3.1 -12 2197.8 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) rente inflatie consumer Bel20 11.3640292 -0.4607103 -0.1630704 -0.0260913 -0.0004335 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.91975 -0.16950 -0.03403 0.19636 0.81526 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.136e+01 2.214e-01 51.330 < 2e-16 *** rente -4.607e-01 5.196e-02 -8.867 3.57e-15 *** inflatie -1.631e-01 2.196e-02 -7.425 1.08e-11 *** consumer -2.609e-02 4.013e-03 -6.502 1.37e-09 *** Bel20 -4.335e-04 4.177e-05 -10.377 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3096 on 137 degrees of freedom Multiple R-squared: 0.8172, Adjusted R-squared: 0.8119 F-statistic: 153.1 on 4 and 137 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,] 0.20629972 4.125994e-01 7.937003e-01 [2,] 0.15397083 3.079417e-01 8.460292e-01 [3,] 0.07474891 1.494978e-01 9.252511e-01 [4,] 0.15292426 3.058485e-01 8.470757e-01 [5,] 0.11169503 2.233901e-01 8.883050e-01 [6,] 0.25993349 5.198670e-01 7.400665e-01 [7,] 0.30837260 6.167452e-01 6.916274e-01 [8,] 0.40708786 8.141757e-01 5.929121e-01 [9,] 0.61171248 7.765750e-01 3.882875e-01 [10,] 0.61829190 7.634162e-01 3.817081e-01 [11,] 0.80736484 3.852703e-01 1.926352e-01 [12,] 0.84428377 3.114325e-01 1.557162e-01 [13,] 0.82179172 3.564166e-01 1.782083e-01 [14,] 0.76968970 4.606206e-01 2.303103e-01 [15,] 0.71538689 5.692262e-01 2.846131e-01 [16,] 0.65875730 6.824854e-01 3.412427e-01 [17,] 0.59799326 8.040135e-01 4.020067e-01 [18,] 0.54198292 9.160342e-01 4.580171e-01 [19,] 0.49513379 9.902676e-01 5.048662e-01 [20,] 0.46302796 9.260559e-01 5.369720e-01 [21,] 0.40644817 8.128963e-01 5.935518e-01 [22,] 0.35892560 7.178512e-01 6.410744e-01 [23,] 0.33952547 6.790509e-01 6.604745e-01 [24,] 0.49287008 9.857402e-01 5.071299e-01 [25,] 0.62562224 7.487555e-01 3.743778e-01 [26,] 0.65055549 6.988890e-01 3.494445e-01 [27,] 0.66109355 6.778129e-01 3.389064e-01 [28,] 0.76047275 4.790545e-01 2.395272e-01 [29,] 0.85362297 2.927541e-01 1.463770e-01 [30,] 0.86226661 2.754668e-01 1.377334e-01 [31,] 0.83321658 3.335668e-01 1.667834e-01 [32,] 0.80570902 3.885820e-01 1.942910e-01 [33,] 0.80316494 3.936701e-01 1.968351e-01 [34,] 0.76818068 4.636386e-01 2.318193e-01 [35,] 0.72751719 5.449656e-01 2.724828e-01 [36,] 0.73759595 5.248081e-01 2.624040e-01 [37,] 0.79302938 4.139412e-01 2.069706e-01 [38,] 0.76551642 4.689672e-01 2.344836e-01 [39,] 0.73309908 5.338018e-01 2.669009e-01 [40,] 0.74126611 5.174678e-01 2.587339e-01 [41,] 0.70184908 5.963018e-01 2.981509e-01 [42,] 0.68890767 6.221847e-01 3.110923e-01 [43,] 0.70285816 5.942837e-01 2.971418e-01 [44,] 0.72451923 5.509615e-01 2.754808e-01 [45,] 0.73793636 5.241273e-01 2.620636e-01 [46,] 0.71405457 5.718909e-01 2.859454e-01 [47,] 0.68987588 6.202482e-01 3.101241e-01 [48,] 0.66131242 6.773752e-01 3.386876e-01 [49,] 0.62200887 7.559823e-01 3.779911e-01 [50,] 0.58895647 8.220871e-01 4.110435e-01 [51,] 0.57367154 8.526569e-01 4.263285e-01 [52,] 0.53801883 9.239623e-01 4.619812e-01 [53,] 0.49802503 9.960501e-01 5.019750e-01 [54,] 0.46884368 9.376874e-01 5.311563e-01 [55,] 0.42564811 8.512962e-01 5.743519e-01 [56,] 0.39884561 7.976912e-01 6.011544e-01 [57,] 0.36870710 7.374142e-01 6.312929e-01 [58,] 0.34152608 6.830522e-01 6.584739e-01 [59,] 0.32537532 6.507506e-01 6.746247e-01 [60,] 0.29069771 5.813954e-01 7.093023e-01 [61,] 0.27318262 5.463652e-01 7.268174e-01 [62,] 0.32327860 6.465572e-01 6.767214e-01 [63,] 0.28444585 5.688917e-01 7.155542e-01 [64,] 0.25890140 5.178028e-01 7.410986e-01 [65,] 0.25276275 5.055255e-01 7.472372e-01 [66,] 0.25034287 5.006857e-01 7.496571e-01 [67,] 0.24402898 4.880580e-01 7.559710e-01 [68,] 0.23300999 4.660200e-01 7.669900e-01 [69,] 0.21895984 4.379197e-01 7.810402e-01 [70,] 0.20465362 4.093072e-01 7.953464e-01 [71,] 0.21611658 4.322332e-01 7.838834e-01 [72,] 0.22936309 4.587262e-01 7.706369e-01 [73,] 0.30178759 6.035752e-01 6.982124e-01 [74,] 0.36497393 7.299479e-01 6.350261e-01 [75,] 0.36601023 7.320205e-01 6.339898e-01 [76,] 0.37155401 7.431080e-01 6.284460e-01 [77,] 0.33986746 6.797349e-01 6.601325e-01 [78,] 0.30125760 6.025152e-01 6.987424e-01 [79,] 0.30536602 6.107320e-01 6.946340e-01 [80,] 0.35351495 7.070299e-01 6.464851e-01 [81,] 0.48926977 9.785395e-01 5.107302e-01 [82,] 0.48837797 9.767559e-01 5.116220e-01 [83,] 0.46216220 9.243244e-01 5.378378e-01 [84,] 0.44225616 8.845123e-01 5.577438e-01 [85,] 0.42761630 8.552326e-01 5.723837e-01 [86,] 0.42066244 8.413249e-01 5.793376e-01 [87,] 0.38669484 7.733897e-01 6.133052e-01 [88,] 0.34741921 6.948384e-01 6.525808e-01 [89,] 0.31868083 6.373617e-01 6.813192e-01 [90,] 0.31273815 6.254763e-01 6.872618e-01 [91,] 0.28105797 5.621159e-01 7.189420e-01 [92,] 0.24300806 4.860161e-01 7.569919e-01 [93,] 0.23611046 4.722209e-01 7.638895e-01 [94,] 0.21056876 4.211375e-01 7.894312e-01 [95,] 0.20753068 4.150614e-01 7.924693e-01 [96,] 0.29032787 5.806557e-01 7.096721e-01 [97,] 0.52442511 9.511498e-01 4.755749e-01 [98,] 0.76604571 4.679086e-01 2.339543e-01 [99,] 0.73940251 5.211950e-01 2.605975e-01 [100,] 0.71249698 5.750060e-01 2.875030e-01 [101,] 0.80228586 3.954283e-01 1.977141e-01 [102,] 0.84770255 3.045949e-01 1.522975e-01 [103,] 0.88682630 2.263474e-01 1.131737e-01 [104,] 0.93204106 1.359179e-01 6.795894e-02 [105,] 0.96289920 7.420160e-02 3.710080e-02 [106,] 0.97956487 4.087027e-02 2.043513e-02 [107,] 0.99288171 1.423658e-02 7.118288e-03 [108,] 0.99293319 1.413363e-02 7.066813e-03 [109,] 0.99314614 1.370773e-02 6.853864e-03 [110,] 0.99296404 1.407192e-02 7.035962e-03 [111,] 0.99455891 1.088219e-02 5.441093e-03 [112,] 0.99855481 2.890380e-03 1.445190e-03 [113,] 0.99972055 5.588912e-04 2.794456e-04 [114,] 0.99991548 1.690454e-04 8.452272e-05 [115,] 0.99996373 7.253293e-05 3.626646e-05 [116,] 0.99997400 5.199183e-05 2.599591e-05 [117,] 0.99995337 9.326783e-05 4.663391e-05 [118,] 0.99998264 3.471011e-05 1.735506e-05 [119,] 0.99999449 1.101652e-05 5.508259e-06 [120,] 0.99997749 4.502443e-05 2.251221e-05 [121,] 0.99990734 1.853286e-04 9.266431e-05 [122,] 0.99973647 5.270524e-04 2.635262e-04 [123,] 0.99916230 1.675397e-03 8.376986e-04 [124,] 0.99952421 9.515783e-04 4.757891e-04 [125,] 0.99892288 2.154230e-03 1.077115e-03 [126,] 0.99840363 3.192740e-03 1.596370e-03 [127,] 0.99183530 1.632939e-02 8.164696e-03 > postscript(file="/var/www/html/rcomp/tmp/1yquq1293221989.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/28zbb1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/38zbb1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/48zbb1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5j8se1293221989.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 0.808430273 0.767683793 0.815264282 0.661500086 0.515141951 0.348565181 7 8 9 10 11 12 0.312055216 0.114280900 -0.145226162 -0.318171476 -0.091189614 -0.029785706 13 14 15 16 17 18 -0.323248210 -0.101858791 -0.167793924 -0.120651189 -0.225026658 -0.169000139 19 20 21 22 23 24 -0.215472324 -0.036447456 0.011862795 -0.046904074 0.082350609 -0.175275385 25 26 27 28 29 30 -0.252387415 -0.310792731 -0.360803688 -0.168268699 -0.147825653 -0.261792306 31 32 33 34 35 36 -0.578058193 -0.611941613 -0.472245259 0.099257584 0.245114721 0.394729691 37 38 39 40 41 42 0.316208725 0.171136402 -0.054800228 -0.263938613 -0.066662065 -0.023805428 43 44 45 46 47 48 0.275180449 0.398165875 0.183698687 -0.055315275 -0.256391555 0.002817373 49 50 51 52 53 54 0.265693796 0.348575834 0.412722135 0.388703172 0.155245501 0.156744222 55 56 57 58 59 60 -0.001512885 0.061351204 0.052734790 -0.064302362 0.028751221 0.137059484 61 62 63 64 65 66 0.197504196 0.025550620 -0.103728009 -0.127925781 -0.159067571 -0.156194840 67 68 69 70 71 72 -0.018798936 0.278065061 0.475539271 0.153148381 0.066737883 -0.064760604 73 74 75 76 77 78 -0.083388026 0.039302543 -0.080096832 -0.031618679 -0.012802786 0.138200551 79 80 81 82 83 84 0.130268811 0.254857149 0.192936233 -0.222150406 -0.354728801 -0.191451799 85 86 87 88 89 90 -0.216970903 0.046342065 0.052992411 0.213846016 -0.030491202 -0.166897610 91 92 93 94 95 96 -0.386022150 -0.416481831 -0.429867475 -0.257733109 -0.148737072 -0.142883459 97 98 99 100 101 102 -0.169664241 -0.282736892 -0.303843057 -0.118144482 -0.338789883 -0.526678168 103 104 105 106 107 108 -0.790905338 -0.919745829 -0.832478184 -0.251511349 -0.087603508 -0.279445215 109 110 111 112 113 114 -0.156676123 -0.124857898 -0.147185048 -0.173656285 0.040637494 -0.055718068 115 116 117 118 119 120 0.265406566 0.319579320 0.428684487 0.451834363 0.273067049 0.297172100 121 122 123 124 125 126 0.321324780 0.492428648 0.317325157 0.791537339 0.177697952 0.016218390 127 128 129 130 131 132 -0.067656320 -0.046792432 -0.002785700 -0.096874652 -0.215751768 -0.232261688 133 134 135 136 137 138 -0.084329253 -0.142226746 0.016951455 0.181158127 0.400169552 0.376338057 139 140 141 142 0.202930266 0.273577638 0.218398712 -0.290743509 > postscript(file="/var/www/html/rcomp/tmp/6j8se1293221989.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 0.808430273 NA 1 0.767683793 0.808430273 2 0.815264282 0.767683793 3 0.661500086 0.815264282 4 0.515141951 0.661500086 5 0.348565181 0.515141951 6 0.312055216 0.348565181 7 0.114280900 0.312055216 8 -0.145226162 0.114280900 9 -0.318171476 -0.145226162 10 -0.091189614 -0.318171476 11 -0.029785706 -0.091189614 12 -0.323248210 -0.029785706 13 -0.101858791 -0.323248210 14 -0.167793924 -0.101858791 15 -0.120651189 -0.167793924 16 -0.225026658 -0.120651189 17 -0.169000139 -0.225026658 18 -0.215472324 -0.169000139 19 -0.036447456 -0.215472324 20 0.011862795 -0.036447456 21 -0.046904074 0.011862795 22 0.082350609 -0.046904074 23 -0.175275385 0.082350609 24 -0.252387415 -0.175275385 25 -0.310792731 -0.252387415 26 -0.360803688 -0.310792731 27 -0.168268699 -0.360803688 28 -0.147825653 -0.168268699 29 -0.261792306 -0.147825653 30 -0.578058193 -0.261792306 31 -0.611941613 -0.578058193 32 -0.472245259 -0.611941613 33 0.099257584 -0.472245259 34 0.245114721 0.099257584 35 0.394729691 0.245114721 36 0.316208725 0.394729691 37 0.171136402 0.316208725 38 -0.054800228 0.171136402 39 -0.263938613 -0.054800228 40 -0.066662065 -0.263938613 41 -0.023805428 -0.066662065 42 0.275180449 -0.023805428 43 0.398165875 0.275180449 44 0.183698687 0.398165875 45 -0.055315275 0.183698687 46 -0.256391555 -0.055315275 47 0.002817373 -0.256391555 48 0.265693796 0.002817373 49 0.348575834 0.265693796 50 0.412722135 0.348575834 51 0.388703172 0.412722135 52 0.155245501 0.388703172 53 0.156744222 0.155245501 54 -0.001512885 0.156744222 55 0.061351204 -0.001512885 56 0.052734790 0.061351204 57 -0.064302362 0.052734790 58 0.028751221 -0.064302362 59 0.137059484 0.028751221 60 0.197504196 0.137059484 61 0.025550620 0.197504196 62 -0.103728009 0.025550620 63 -0.127925781 -0.103728009 64 -0.159067571 -0.127925781 65 -0.156194840 -0.159067571 66 -0.018798936 -0.156194840 67 0.278065061 -0.018798936 68 0.475539271 0.278065061 69 0.153148381 0.475539271 70 0.066737883 0.153148381 71 -0.064760604 0.066737883 72 -0.083388026 -0.064760604 73 0.039302543 -0.083388026 74 -0.080096832 0.039302543 75 -0.031618679 -0.080096832 76 -0.012802786 -0.031618679 77 0.138200551 -0.012802786 78 0.130268811 0.138200551 79 0.254857149 0.130268811 80 0.192936233 0.254857149 81 -0.222150406 0.192936233 82 -0.354728801 -0.222150406 83 -0.191451799 -0.354728801 84 -0.216970903 -0.191451799 85 0.046342065 -0.216970903 86 0.052992411 0.046342065 87 0.213846016 0.052992411 88 -0.030491202 0.213846016 89 -0.166897610 -0.030491202 90 -0.386022150 -0.166897610 91 -0.416481831 -0.386022150 92 -0.429867475 -0.416481831 93 -0.257733109 -0.429867475 94 -0.148737072 -0.257733109 95 -0.142883459 -0.148737072 96 -0.169664241 -0.142883459 97 -0.282736892 -0.169664241 98 -0.303843057 -0.282736892 99 -0.118144482 -0.303843057 100 -0.338789883 -0.118144482 101 -0.526678168 -0.338789883 102 -0.790905338 -0.526678168 103 -0.919745829 -0.790905338 104 -0.832478184 -0.919745829 105 -0.251511349 -0.832478184 106 -0.087603508 -0.251511349 107 -0.279445215 -0.087603508 108 -0.156676123 -0.279445215 109 -0.124857898 -0.156676123 110 -0.147185048 -0.124857898 111 -0.173656285 -0.147185048 112 0.040637494 -0.173656285 113 -0.055718068 0.040637494 114 0.265406566 -0.055718068 115 0.319579320 0.265406566 116 0.428684487 0.319579320 117 0.451834363 0.428684487 118 0.273067049 0.451834363 119 0.297172100 0.273067049 120 0.321324780 0.297172100 121 0.492428648 0.321324780 122 0.317325157 0.492428648 123 0.791537339 0.317325157 124 0.177697952 0.791537339 125 0.016218390 0.177697952 126 -0.067656320 0.016218390 127 -0.046792432 -0.067656320 128 -0.002785700 -0.046792432 129 -0.096874652 -0.002785700 130 -0.215751768 -0.096874652 131 -0.232261688 -0.215751768 132 -0.084329253 -0.232261688 133 -0.142226746 -0.084329253 134 0.016951455 -0.142226746 135 0.181158127 0.016951455 136 0.400169552 0.181158127 137 0.376338057 0.400169552 138 0.202930266 0.376338057 139 0.273577638 0.202930266 140 0.218398712 0.273577638 141 -0.290743509 0.218398712 142 NA -0.290743509 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.767683793 0.808430273 [2,] 0.815264282 0.767683793 [3,] 0.661500086 0.815264282 [4,] 0.515141951 0.661500086 [5,] 0.348565181 0.515141951 [6,] 0.312055216 0.348565181 [7,] 0.114280900 0.312055216 [8,] -0.145226162 0.114280900 [9,] -0.318171476 -0.145226162 [10,] -0.091189614 -0.318171476 [11,] -0.029785706 -0.091189614 [12,] -0.323248210 -0.029785706 [13,] -0.101858791 -0.323248210 [14,] -0.167793924 -0.101858791 [15,] -0.120651189 -0.167793924 [16,] -0.225026658 -0.120651189 [17,] -0.169000139 -0.225026658 [18,] -0.215472324 -0.169000139 [19,] -0.036447456 -0.215472324 [20,] 0.011862795 -0.036447456 [21,] -0.046904074 0.011862795 [22,] 0.082350609 -0.046904074 [23,] -0.175275385 0.082350609 [24,] -0.252387415 -0.175275385 [25,] -0.310792731 -0.252387415 [26,] -0.360803688 -0.310792731 [27,] -0.168268699 -0.360803688 [28,] -0.147825653 -0.168268699 [29,] -0.261792306 -0.147825653 [30,] -0.578058193 -0.261792306 [31,] -0.611941613 -0.578058193 [32,] -0.472245259 -0.611941613 [33,] 0.099257584 -0.472245259 [34,] 0.245114721 0.099257584 [35,] 0.394729691 0.245114721 [36,] 0.316208725 0.394729691 [37,] 0.171136402 0.316208725 [38,] -0.054800228 0.171136402 [39,] -0.263938613 -0.054800228 [40,] -0.066662065 -0.263938613 [41,] -0.023805428 -0.066662065 [42,] 0.275180449 -0.023805428 [43,] 0.398165875 0.275180449 [44,] 0.183698687 0.398165875 [45,] -0.055315275 0.183698687 [46,] -0.256391555 -0.055315275 [47,] 0.002817373 -0.256391555 [48,] 0.265693796 0.002817373 [49,] 0.348575834 0.265693796 [50,] 0.412722135 0.348575834 [51,] 0.388703172 0.412722135 [52,] 0.155245501 0.388703172 [53,] 0.156744222 0.155245501 [54,] -0.001512885 0.156744222 [55,] 0.061351204 -0.001512885 [56,] 0.052734790 0.061351204 [57,] -0.064302362 0.052734790 [58,] 0.028751221 -0.064302362 [59,] 0.137059484 0.028751221 [60,] 0.197504196 0.137059484 [61,] 0.025550620 0.197504196 [62,] -0.103728009 0.025550620 [63,] -0.127925781 -0.103728009 [64,] -0.159067571 -0.127925781 [65,] -0.156194840 -0.159067571 [66,] -0.018798936 -0.156194840 [67,] 0.278065061 -0.018798936 [68,] 0.475539271 0.278065061 [69,] 0.153148381 0.475539271 [70,] 0.066737883 0.153148381 [71,] -0.064760604 0.066737883 [72,] -0.083388026 -0.064760604 [73,] 0.039302543 -0.083388026 [74,] -0.080096832 0.039302543 [75,] -0.031618679 -0.080096832 [76,] -0.012802786 -0.031618679 [77,] 0.138200551 -0.012802786 [78,] 0.130268811 0.138200551 [79,] 0.254857149 0.130268811 [80,] 0.192936233 0.254857149 [81,] -0.222150406 0.192936233 [82,] -0.354728801 -0.222150406 [83,] -0.191451799 -0.354728801 [84,] -0.216970903 -0.191451799 [85,] 0.046342065 -0.216970903 [86,] 0.052992411 0.046342065 [87,] 0.213846016 0.052992411 [88,] -0.030491202 0.213846016 [89,] -0.166897610 -0.030491202 [90,] -0.386022150 -0.166897610 [91,] -0.416481831 -0.386022150 [92,] -0.429867475 -0.416481831 [93,] -0.257733109 -0.429867475 [94,] -0.148737072 -0.257733109 [95,] -0.142883459 -0.148737072 [96,] -0.169664241 -0.142883459 [97,] -0.282736892 -0.169664241 [98,] -0.303843057 -0.282736892 [99,] -0.118144482 -0.303843057 [100,] -0.338789883 -0.118144482 [101,] -0.526678168 -0.338789883 [102,] -0.790905338 -0.526678168 [103,] -0.919745829 -0.790905338 [104,] -0.832478184 -0.919745829 [105,] -0.251511349 -0.832478184 [106,] -0.087603508 -0.251511349 [107,] -0.279445215 -0.087603508 [108,] -0.156676123 -0.279445215 [109,] -0.124857898 -0.156676123 [110,] -0.147185048 -0.124857898 [111,] -0.173656285 -0.147185048 [112,] 0.040637494 -0.173656285 [113,] -0.055718068 0.040637494 [114,] 0.265406566 -0.055718068 [115,] 0.319579320 0.265406566 [116,] 0.428684487 0.319579320 [117,] 0.451834363 0.428684487 [118,] 0.273067049 0.451834363 [119,] 0.297172100 0.273067049 [120,] 0.321324780 0.297172100 [121,] 0.492428648 0.321324780 [122,] 0.317325157 0.492428648 [123,] 0.791537339 0.317325157 [124,] 0.177697952 0.791537339 [125,] 0.016218390 0.177697952 [126,] -0.067656320 0.016218390 [127,] -0.046792432 -0.067656320 [128,] -0.002785700 -0.046792432 [129,] -0.096874652 -0.002785700 [130,] -0.215751768 -0.096874652 [131,] -0.232261688 -0.215751768 [132,] -0.084329253 -0.232261688 [133,] -0.142226746 -0.084329253 [134,] 0.016951455 -0.142226746 [135,] 0.181158127 0.016951455 [136,] 0.400169552 0.181158127 [137,] 0.376338057 0.400169552 [138,] 0.202930266 0.376338057 [139,] 0.273577638 0.202930266 [140,] 0.218398712 0.273577638 [141,] -0.290743509 0.218398712 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.767683793 0.808430273 2 0.815264282 0.767683793 3 0.661500086 0.815264282 4 0.515141951 0.661500086 5 0.348565181 0.515141951 6 0.312055216 0.348565181 7 0.114280900 0.312055216 8 -0.145226162 0.114280900 9 -0.318171476 -0.145226162 10 -0.091189614 -0.318171476 11 -0.029785706 -0.091189614 12 -0.323248210 -0.029785706 13 -0.101858791 -0.323248210 14 -0.167793924 -0.101858791 15 -0.120651189 -0.167793924 16 -0.225026658 -0.120651189 17 -0.169000139 -0.225026658 18 -0.215472324 -0.169000139 19 -0.036447456 -0.215472324 20 0.011862795 -0.036447456 21 -0.046904074 0.011862795 22 0.082350609 -0.046904074 23 -0.175275385 0.082350609 24 -0.252387415 -0.175275385 25 -0.310792731 -0.252387415 26 -0.360803688 -0.310792731 27 -0.168268699 -0.360803688 28 -0.147825653 -0.168268699 29 -0.261792306 -0.147825653 30 -0.578058193 -0.261792306 31 -0.611941613 -0.578058193 32 -0.472245259 -0.611941613 33 0.099257584 -0.472245259 34 0.245114721 0.099257584 35 0.394729691 0.245114721 36 0.316208725 0.394729691 37 0.171136402 0.316208725 38 -0.054800228 0.171136402 39 -0.263938613 -0.054800228 40 -0.066662065 -0.263938613 41 -0.023805428 -0.066662065 42 0.275180449 -0.023805428 43 0.398165875 0.275180449 44 0.183698687 0.398165875 45 -0.055315275 0.183698687 46 -0.256391555 -0.055315275 47 0.002817373 -0.256391555 48 0.265693796 0.002817373 49 0.348575834 0.265693796 50 0.412722135 0.348575834 51 0.388703172 0.412722135 52 0.155245501 0.388703172 53 0.156744222 0.155245501 54 -0.001512885 0.156744222 55 0.061351204 -0.001512885 56 0.052734790 0.061351204 57 -0.064302362 0.052734790 58 0.028751221 -0.064302362 59 0.137059484 0.028751221 60 0.197504196 0.137059484 61 0.025550620 0.197504196 62 -0.103728009 0.025550620 63 -0.127925781 -0.103728009 64 -0.159067571 -0.127925781 65 -0.156194840 -0.159067571 66 -0.018798936 -0.156194840 67 0.278065061 -0.018798936 68 0.475539271 0.278065061 69 0.153148381 0.475539271 70 0.066737883 0.153148381 71 -0.064760604 0.066737883 72 -0.083388026 -0.064760604 73 0.039302543 -0.083388026 74 -0.080096832 0.039302543 75 -0.031618679 -0.080096832 76 -0.012802786 -0.031618679 77 0.138200551 -0.012802786 78 0.130268811 0.138200551 79 0.254857149 0.130268811 80 0.192936233 0.254857149 81 -0.222150406 0.192936233 82 -0.354728801 -0.222150406 83 -0.191451799 -0.354728801 84 -0.216970903 -0.191451799 85 0.046342065 -0.216970903 86 0.052992411 0.046342065 87 0.213846016 0.052992411 88 -0.030491202 0.213846016 89 -0.166897610 -0.030491202 90 -0.386022150 -0.166897610 91 -0.416481831 -0.386022150 92 -0.429867475 -0.416481831 93 -0.257733109 -0.429867475 94 -0.148737072 -0.257733109 95 -0.142883459 -0.148737072 96 -0.169664241 -0.142883459 97 -0.282736892 -0.169664241 98 -0.303843057 -0.282736892 99 -0.118144482 -0.303843057 100 -0.338789883 -0.118144482 101 -0.526678168 -0.338789883 102 -0.790905338 -0.526678168 103 -0.919745829 -0.790905338 104 -0.832478184 -0.919745829 105 -0.251511349 -0.832478184 106 -0.087603508 -0.251511349 107 -0.279445215 -0.087603508 108 -0.156676123 -0.279445215 109 -0.124857898 -0.156676123 110 -0.147185048 -0.124857898 111 -0.173656285 -0.147185048 112 0.040637494 -0.173656285 113 -0.055718068 0.040637494 114 0.265406566 -0.055718068 115 0.319579320 0.265406566 116 0.428684487 0.319579320 117 0.451834363 0.428684487 118 0.273067049 0.451834363 119 0.297172100 0.273067049 120 0.321324780 0.297172100 121 0.492428648 0.321324780 122 0.317325157 0.492428648 123 0.791537339 0.317325157 124 0.177697952 0.791537339 125 0.016218390 0.177697952 126 -0.067656320 0.016218390 127 -0.046792432 -0.067656320 128 -0.002785700 -0.046792432 129 -0.096874652 -0.002785700 130 -0.215751768 -0.096874652 131 -0.232261688 -0.215751768 132 -0.084329253 -0.232261688 133 -0.142226746 -0.084329253 134 0.016951455 -0.142226746 135 0.181158127 0.016951455 136 0.400169552 0.181158127 137 0.376338057 0.400169552 138 0.202930266 0.376338057 139 0.273577638 0.202930266 140 0.218398712 0.273577638 141 -0.290743509 0.218398712 > 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/7uzrh1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8uzrh1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9mr8k1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10mr8k1293221989.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/1189pp1293221989.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/12ts6d1293221989.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/13ib371293221989.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/14bk2s1293221989.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/15e20g1293221989.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/16h3h41293221989.tab") + } > > try(system("convert tmp/1yquq1293221989.ps tmp/1yquq1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/28zbb1293221989.ps tmp/28zbb1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/38zbb1293221989.ps tmp/38zbb1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/48zbb1293221989.ps tmp/48zbb1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/5j8se1293221989.ps tmp/5j8se1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/6j8se1293221989.ps tmp/6j8se1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/7uzrh1293221989.ps tmp/7uzrh1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/8uzrh1293221989.ps tmp/8uzrh1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/9mr8k1293221989.ps tmp/9mr8k1293221989.png",intern=TRUE)) character(0) > try(system("convert tmp/10mr8k1293221989.ps tmp/10mr8k1293221989.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.688 1.715 9.370