R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-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(0 + ,255202 + ,64 + ,92 + ,34 + ,0 + ,135248 + ,59 + ,58 + ,30 + ,0 + ,207223 + ,64 + ,62 + ,42 + ,1 + ,189326 + ,95 + ,108 + ,34 + ,1 + ,141365 + ,46 + ,55 + ,25 + ,0 + ,65295 + ,27 + ,8 + ,31 + ,0 + ,439387 + ,103 + ,134 + ,29 + ,0 + ,33186 + ,19 + ,1 + ,18 + ,0 + ,183696 + ,51 + ,64 + ,30 + ,0 + ,186657 + ,38 + ,77 + ,29 + ,1 + ,276696 + ,99 + ,86 + ,42 + ,1 + ,194414 + ,98 + ,96 + ,50 + ,0 + ,141409 + ,59 + ,44 + ,33 + ,1 + ,306730 + ,68 + ,108 + ,46 + ,1 + ,192691 + ,74 + ,63 + ,38 + ,1 + ,333497 + ,164 + ,160 + ,52 + ,0 + ,261835 + ,59 + ,109 + ,32 + ,1 + ,263451 + ,130 + ,86 + ,35 + ,1 + ,157448 + ,49 + ,93 + ,25 + ,1 + ,232190 + ,73 + ,126 + ,42 + ,0 + ,245725 + ,64 + ,110 + ,40 + ,0 + ,388603 + ,92 + ,86 + ,35 + ,0 + ,156540 + ,34 + ,50 + ,25 + ,0 + ,156189 + ,47 + ,92 + ,46 + ,0 + ,189726 + ,106 + ,123 + ,39 + ,0 + ,192167 + ,106 + ,81 + ,35 + ,1 + ,249893 + ,122 + ,93 + ,38 + ,1 + ,236812 + ,76 + ,113 + ,35 + ,1 + ,143160 + ,47 + ,52 + 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+ ,0 + ,0 + ,14688 + ,10 + ,4 + ,0 + ,0 + ,98 + ,1 + ,0 + ,0 + ,0 + ,455 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,195765 + ,75 + ,56 + ,33 + ,0 + ,334258 + ,129 + ,121 + ,47 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,203 + ,4 + ,0 + ,0 + ,0 + ,7199 + ,5 + ,7 + ,0 + ,1 + ,46660 + ,20 + ,12 + ,5 + ,1 + ,17547 + ,5 + ,0 + ,1 + ,0 + ,107465 + ,38 + ,37 + ,38 + ,1 + ,969 + ,2 + ,0 + ,0 + ,1 + ,179994 + ,58 + ,47 + ,28) + ,dim=c(5 + ,164) + ,dimnames=list(c('Geslacht' + ,'Time_in_RFC' + ,'Logins' + ,'Blogged_computations' + ,'Reviewed_compendiums') + ,1:164)) > y <- array(NA,dim=c(5,164),dimnames=list(c('Geslacht','Time_in_RFC','Logins','Blogged_computations','Reviewed_compendiums'),1:164)) > 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 = '2' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Time_in_RFC Geslacht Logins Blogged_computations Reviewed_compendiums 1 255202 0 64 92 34 2 135248 0 59 58 30 3 207223 0 64 62 42 4 189326 1 95 108 34 5 141365 1 46 55 25 6 65295 0 27 8 31 7 439387 0 103 134 29 8 33186 0 19 1 18 9 183696 0 51 64 30 10 186657 0 38 77 29 11 276696 1 99 86 42 12 194414 1 98 96 50 13 141409 0 59 44 33 14 306730 1 68 108 46 15 192691 1 74 63 38 16 333497 1 164 160 52 17 261835 0 59 109 32 18 263451 1 130 86 35 19 157448 1 49 93 25 20 232190 1 73 126 42 21 245725 0 64 110 40 22 388603 0 92 86 35 23 156540 0 34 50 25 24 156189 0 47 92 46 25 189726 0 106 123 39 26 192167 0 106 81 35 27 249893 1 122 93 38 28 236812 1 76 113 35 29 143160 1 47 52 28 30 259667 0 54 113 37 31 243020 0 68 113 40 32 176062 0 67 44 42 33 286683 0 79 123 44 34 87485 1 33 38 33 35 329737 0 88 111 38 36 247082 1 51 77 37 37 378463 0 108 102 41 38 191653 1 75 74 32 39 114673 0 31 33 17 40 301596 0 167 107 39 41 284195 0 73 108 33 42 155568 1 60 66 35 43 177306 1 67 69 32 44 144595 1 51 62 35 45 140319 0 73 50 45 46 405267 1 135 91 38 47 78800 1 42 20 26 48 201970 1 69 101 45 49 302705 1 101 129 44 50 164733 1 50 93 40 51 194221 1 68 89 33 52 24188 0 24 8 4 53 346142 0 288 80 41 54 65029 0 17 21 18 55 101097 0 64 30 14 56 253745 1 51 86 36 57 273513 0 77 116 49 58 282220 1 160 106 32 59 280928 1 120 132 37 60 214872 1 74 75 32 61 342048 0 127 139 43 62 273924 0 108 121 25 63 195726 1 92 57 42 64 231162 1 80 67 37 65 209798 0 61 45 33 66 201345 1 60 88 28 67 180231 0 118 79 31 68 204441 1 129 75 40 69 197813 0 67 114 32 70 136421 1 60 127 25 71 216092 1 59 86 42 72 73566 1 32 22 23 73 213998 0 70 67 42 74 181728 1 50 77 38 75 148758 0 51 105 34 76 308343 0 71 121 39 77 251437 1 78 88 32 78 202388 0 102 78 37 79 173286 0 56 122 34 80 155529 0 58 66 33 81 132672 0 41 58 25 82 390163 1 102 134 45 83 145905 0 66 30 26 84 228012 0 88 103 40 85 80953 1 25 49 8 86 130805 0 47 26 27 87 135163 1 49 67 32 88 333790 1 168 59 37 89 271806 1 95 95 50 90 164235 1 99 156 41 91 234092 1 80 74 37 92 207158 0 69 137 38 93 156583 0 57 37 28 94 242395 0 68 111 36 95 261601 1 70 58 32 96 178489 1 35 78 32 97 204221 0 44 88 33 98 268066 1 69 152 35 99 327622 1 133 130 58 100 361799 1 101 145 27 101 247131 0 107 108 45 102 265849 1 58 138 37 103 162336 0 162 62 32 104 43287 1 14 13 19 105 172244 0 68 89 22 106 189021 0 121 86 35 107 227681 0 43 116 36 108 269329 0 81 157 36 109 106503 0 56 28 23 110 117891 1 77 83 40 111 287201 1 59 72 40 112 266805 0 78 134 42 113 23623 0 11 12 1 114 174954 1 69 120 36 115 61857 0 25 23 11 116 144889 1 43 83 40 117 347988 1 103 126 34 118 21054 0 16 4 0 119 224051 1 46 71 27 120 31414 1 19 18 8 121 278660 1 107 98 35 122 209481 0 58 68 44 123 156870 0 75 44 40 124 112933 1 46 29 28 125 38214 0 34 16 8 126 166011 0 35 61 36 127 316044 1 73 117 47 128 181578 1 56 46 48 129 358903 1 72 129 45 130 275578 1 91 139 48 131 368796 1 106 136 49 132 172464 1 31 66 35 133 94381 1 35 42 32 134 250563 1 290 75 36 135 382499 1 154 97 42 136 118010 1 42 49 35 137 365575 1 122 127 42 138 147989 1 72 55 34 139 231681 1 46 101 41 140 193119 0 77 80 36 141 189020 0 108 29 32 142 341958 0 106 95 33 143 222060 1 79 120 35 144 173260 0 63 41 21 145 274787 0 91 128 42 146 130908 1 52 142 49 147 204009 0 75 88 33 148 262412 0 94 170 39 149 1 0 0 0 0 150 14688 0 10 4 0 151 98 0 1 0 0 152 455 0 2 0 0 153 0 1 0 0 0 154 0 0 0 0 0 155 195765 1 75 56 33 156 334258 0 129 121 47 157 0 0 0 0 0 158 203 0 4 0 0 159 7199 0 5 7 0 160 46660 1 20 12 5 161 17547 1 5 0 1 162 107465 0 38 37 38 163 969 1 2 0 0 164 179994 1 58 47 28 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Geslacht Logins 5060.8 -1704.7 754.5 Blogged_computations Reviewed_compendiums 1081.2 1694.1 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -151949 -23607 -5060 19558 162597 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5060.8 10410.0 0.486 0.627533 Geslacht -1704.7 7803.2 -0.218 0.827351 Logins 754.5 109.4 6.896 1.19e-10 *** Blogged_computations 1081.2 135.7 7.966 2.97e-13 *** Reviewed_compendiums 1694.1 466.9 3.628 0.000384 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 48910 on 159 degrees of freedom Multiple R-squared: 0.7654, Adjusted R-squared: 0.7595 F-statistic: 129.7 on 4 and 159 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,] 0.7341225 5.317549e-01 2.658775e-01 [2,] 0.6235353 7.529293e-01 3.764647e-01 [3,] 0.5679412 8.641175e-01 4.320588e-01 [4,] 0.6269797 7.460405e-01 3.730203e-01 [5,] 0.5875746 8.248508e-01 4.124254e-01 [6,] 0.4978769 9.957538e-01 5.021231e-01 [7,] 0.6009612 7.980775e-01 3.990388e-01 [8,] 0.5555109 8.889782e-01 4.444891e-01 [9,] 0.6163306 7.673387e-01 3.836694e-01 [10,] 0.5807442 8.385116e-01 4.192558e-01 [11,] 0.5538397 8.923207e-01 4.461603e-01 [12,] 0.5524621 8.950759e-01 4.475379e-01 [13,] 0.5202865 9.594270e-01 4.797135e-01 [14,] 0.4668793 9.337586e-01 5.331207e-01 [15,] 0.8314083 3.371834e-01 1.685917e-01 [16,] 0.7868940 4.262120e-01 2.131060e-01 [17,] 0.8047062 3.905876e-01 1.952938e-01 [18,] 0.9640251 7.194988e-02 3.597494e-02 [19,] 0.9670459 6.590814e-02 3.295407e-02 [20,] 0.9539439 9.211216e-02 4.605608e-02 [21,] 0.9371974 1.256051e-01 6.280256e-02 [22,] 0.9168654 1.662692e-01 8.313462e-02 [23,] 0.8945778 2.108445e-01 1.054222e-01 [24,] 0.8670942 2.658115e-01 1.329058e-01 [25,] 0.8385122 3.229756e-01 1.614878e-01 [26,] 0.8019004 3.961992e-01 1.980996e-01 [27,] 0.7672081 4.655838e-01 2.327919e-01 [28,] 0.7836739 4.326521e-01 2.163261e-01 [29,] 0.8304065 3.391871e-01 1.695935e-01 [30,] 0.9147813 1.704374e-01 8.521870e-02 [31,] 0.8915402 2.169197e-01 1.084598e-01 [32,] 0.8681040 2.637920e-01 1.318960e-01 [33,] 0.8549568 2.900863e-01 1.450432e-01 [34,] 0.8401752 3.196495e-01 1.598248e-01 [35,] 0.8097953 3.804094e-01 1.902047e-01 [36,] 0.7725210 4.549580e-01 2.274790e-01 [37,] 0.7358493 5.283013e-01 2.641507e-01 [38,] 0.7172062 5.655875e-01 2.827938e-01 [39,] 0.9231476 1.537048e-01 7.685239e-02 [40,] 0.9065990 1.868020e-01 9.340101e-02 [41,] 0.8917061 2.165878e-01 1.082939e-01 [42,] 0.8677094 2.645812e-01 1.322906e-01 [43,] 0.8546173 2.907654e-01 1.453827e-01 [44,] 0.8273604 3.452792e-01 1.726396e-01 [45,] 0.8278830 3.442340e-01 1.721170e-01 [46,] 0.8311920 3.376159e-01 1.688080e-01 [47,] 0.8017520 3.964960e-01 1.982480e-01 [48,] 0.7756417 4.487166e-01 2.243583e-01 [49,] 0.7919577 4.160847e-01 2.080423e-01 [50,] 0.7567611 4.864778e-01 2.432389e-01 [51,] 0.7239209 5.521582e-01 2.760791e-01 [52,] 0.6979595 6.040810e-01 3.020405e-01 [53,] 0.6641048 6.717903e-01 3.358952e-01 [54,] 0.6295344 7.409312e-01 3.704656e-01 [55,] 0.6056032 7.887936e-01 3.943968e-01 [56,] 0.5660434 8.679133e-01 4.339566e-01 [57,] 0.5490705 9.018589e-01 4.509295e-01 [58,] 0.5616903 8.766194e-01 4.383097e-01 [59,] 0.5162425 9.675150e-01 4.837575e-01 [60,] 0.5336773 9.326455e-01 4.663227e-01 [61,] 0.5182183 9.635634e-01 4.817817e-01 [62,] 0.5210486 9.579029e-01 4.789514e-01 [63,] 0.6598466 6.803067e-01 3.401534e-01 [64,] 0.6181248 7.637504e-01 3.818752e-01 [65,] 0.5798952 8.402096e-01 4.201048e-01 [66,] 0.5377242 9.245516e-01 4.622758e-01 [67,] 0.4935262 9.870523e-01 5.064738e-01 [68,] 0.5425987 9.148026e-01 4.574013e-01 [69,] 0.5499884 9.000232e-01 4.500116e-01 [70,] 0.5351866 9.296269e-01 4.648134e-01 [71,] 0.5037062 9.925877e-01 4.962938e-01 [72,] 0.5420076 9.159848e-01 4.579924e-01 [73,] 0.5047156 9.905688e-01 4.952844e-01 [74,] 0.4612585 9.225171e-01 5.387415e-01 [75,] 0.5639935 8.720131e-01 4.360065e-01 [76,] 0.5234576 9.530847e-01 4.765424e-01 [77,] 0.4879681 9.759363e-01 5.120319e-01 [78,] 0.4438612 8.877225e-01 5.561388e-01 [79,] 0.4048993 8.097985e-01 5.951007e-01 [80,] 0.3799789 7.599577e-01 6.200211e-01 [81,] 0.4492798 8.985595e-01 5.507202e-01 [82,] 0.4061470 8.122940e-01 5.938530e-01 [83,] 0.7702118 4.595763e-01 2.297882e-01 [84,] 0.7453826 5.092348e-01 2.546174e-01 [85,] 0.7637856 4.724288e-01 2.362144e-01 [86,] 0.7368097 5.263805e-01 2.631903e-01 [87,] 0.6986761 6.026477e-01 3.013239e-01 [88,] 0.7830679 4.338643e-01 2.169321e-01 [89,] 0.7488242 5.023516e-01 2.511758e-01 [90,] 0.7152506 5.694988e-01 2.847494e-01 [91,] 0.6800231 6.399538e-01 3.199769e-01 [92,] 0.6394694 7.210612e-01 3.605306e-01 [93,] 0.7024947 5.950106e-01 2.975053e-01 [94,] 0.6730373 6.539253e-01 3.269627e-01 [95,] 0.6300575 7.398849e-01 3.699425e-01 [96,] 0.7033255 5.933490e-01 2.966745e-01 [97,] 0.6675448 6.649105e-01 3.324552e-01 [98,] 0.6277060 7.445881e-01 3.722940e-01 [99,] 0.6423195 7.153610e-01 3.576805e-01 [100,] 0.5965662 8.068676e-01 4.034338e-01 [101,] 0.5622780 8.754440e-01 4.377220e-01 [102,] 0.5156390 9.687220e-01 4.843610e-01 [103,] 0.6736341 6.527318e-01 3.263659e-01 [104,] 0.7758350 4.483301e-01 2.241650e-01 [105,] 0.7399268 5.201464e-01 2.600732e-01 [106,] 0.6979908 6.040183e-01 3.020092e-01 [107,] 0.7570389 4.859222e-01 2.429611e-01 [108,] 0.7157912 5.684177e-01 2.842088e-01 [109,] 0.7240337 5.519326e-01 2.759663e-01 [110,] 0.7638629 4.722742e-01 2.361371e-01 [111,] 0.7220000 5.560000e-01 2.780000e-01 [112,] 0.7466116 5.067769e-01 2.533884e-01 [113,] 0.7093415 5.813169e-01 2.906585e-01 [114,] 0.6792690 6.414620e-01 3.207310e-01 [115,] 0.6317756 7.364489e-01 3.682244e-01 [116,] 0.5902087 8.195827e-01 4.097913e-01 [117,] 0.5371385 9.257230e-01 4.628615e-01 [118,] 0.4932835 9.865670e-01 5.067165e-01 [119,] 0.4384704 8.769407e-01 5.615296e-01 [120,] 0.4376893 8.753787e-01 5.623107e-01 [121,] 0.3828059 7.656118e-01 6.171941e-01 [122,] 0.5079303 9.841393e-01 4.920697e-01 [123,] 0.4596485 9.192971e-01 5.403515e-01 [124,] 0.5028123 9.943754e-01 4.971877e-01 [125,] 0.4547936 9.095872e-01 5.452064e-01 [126,] 0.4195936 8.391873e-01 5.804064e-01 [127,] 0.9981170 3.765994e-03 1.882997e-03 [128,] 0.9973654 5.269245e-03 2.634623e-03 [129,] 0.9955654 8.869130e-03 4.434565e-03 [130,] 0.9951527 9.694501e-03 4.847251e-03 [131,] 0.9951308 9.738323e-03 4.869161e-03 [132,] 0.9996357 7.286135e-04 3.643067e-04 [133,] 0.9992828 1.434457e-03 7.172285e-04 [134,] 0.9999996 7.747132e-07 3.873566e-07 [135,] 1.0000000 9.392635e-09 4.696318e-09 [136,] 1.0000000 3.908893e-08 1.954446e-08 [137,] 0.9999999 1.647332e-07 8.236662e-08 [138,] 1.0000000 2.886586e-08 1.443293e-08 [139,] 1.0000000 2.330938e-09 1.165469e-09 [140,] 1.0000000 1.560346e-08 7.801730e-09 [141,] 0.9999999 1.005423e-07 5.027116e-08 [142,] 0.9999997 5.691343e-07 2.845672e-07 [143,] 0.9999983 3.419680e-06 1.709840e-06 [144,] 0.9999899 2.020267e-05 1.010133e-05 [145,] 0.9999414 1.172567e-04 5.862834e-05 [146,] 0.9996995 6.009675e-04 3.004838e-04 [147,] 0.9986575 2.684958e-03 1.342479e-03 [148,] 0.9961546 7.690828e-03 3.845414e-03 [149,] 0.9914724 1.705518e-02 8.527589e-03 > postscript(file="/var/wessaorg/rcomp/tmp/1dnba1355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2yfix1355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/322i31355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/44w2s1355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/584931355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 164 Frequency = 1 1 2 3 4 5 6 44779.2700 -27863.7177 15684.5044 -60081.5937 1480.8124 -21304.7368 7 8 9 10 11 12 162597.0536 -17785.6172 20132.9292 20540.5695 34504.7158 -71388.0014 13 14 15 16 17 18 -11647.7295 57364.8382 1007.2914 -54689.8961 40192.0059 9728.7132 19 20 21 22 23 24 -25786.6992 -33633.5297 5675.3678 161847.3506 29411.4180 -61736.3805 25 26 27 28 29 30 -94374.9453 -39745.5823 -10444.1990 -5360.2271 682.6909 29001.0634 31 32 33 34 35 36 -3291.3747 1722.2366 14483.2395 -37762.4205 73886.1486 59308.7776 37 38 39 40 41 42 112170.7895 -2514.1729 21741.8236 -11230.1641 51376.0195 -23713.9800 43 44 45 46 47 48 -5418.9245 -23571.4599 -50117.5545 137283.6668 -21916.9849 -38886.9071 49 50 51 52 53 54 9123.3149 -44667.8207 -12577.2702 -14407.3081 -32174.4912 -6058.3306 55 56 57 58 59 60 -8406.9162 57934.7567 1920.3737 -10679.9413 -18374.2983 20378.0988 61 62 63 64 65 66 18026.1675 14194.0170 -9827.8682 32320.4035 54151.0175 10134.5700 67 68 69 70 71 72 -51796.7780 -45103.7149 -35272.2425 -91875.3306 4081.0454 -16686.0528 73 74 75 76 77 78 12526.2725 -6984.8217 -65912.1973 52812.3162 39868.9914 -26651.0549 79 80 81 82 83 84 -63537.7589 -20560.4241 -8388.0316 88726.5166 14562.7766 -22577.1746 85 86 87 88 89 90 -7799.2671 16429.2717 -31818.3032 77201.5702 9348.7598 -151948.7313 91 92 93 94 95 96 27681.7481 -62469.3437 21074.4802 5022.5285 88506.1522 10177.2108 97 98 99 100 101 102 14907.4884 -10992.8927 -14902.6911 79717.3594 -31670.5514 6838.7937 103 104 105 106 107 108 -86203.2278 -16876.3458 -17623.7628 -59615.3884 3765.0521 -27588.9574 109 110 111 112 113 114 -10049.3453 -101069.1783 93715.5715 -13145.6386 -4406.3242 -71199.4325 115 116 117 118 119 120 -5570.1166 -48417.8981 73082.0835 -403.8659 63478.8133 -19292.8863 121 122 123 124 125 126 29316.5648 12593.9198 -20117.6142 -3921.3614 -23352.7127 7599.1253 127 128 129 130 131 132 51481.0609 4915.3846 85507.9464 -28047.3979 55402.5804 15062.7590 133 134 135 136 137 138 -35006.2754 -113681.1117 86916.1610 -29309.8112 61699.3299 -26759.3699 139 140 141 142 143 144 14954.2127 -17525.7143 16905.0216 98296.3219 -29944.4073 40758.2377 145 146 147 148 149 150 -8484.8268 -148229.3935 -8694.2671 -63452.9616 -5059.7881 -2242.8165 151 152 153 154 155 156 -5717.2963 -6114.8046 -3356.1124 -5060.7881 19365.9764 21412.9777 157 158 159 160 161 162 -5060.7881 -7875.8211 -9202.9848 6768.3471 8724.2388 -30648.9387 163 164 -3896.1289 34623.2828 > postscript(file="/var/wessaorg/rcomp/tmp/61paa1355163860.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 = 164 Frequency = 1 lag(myerror, k = 1) myerror 0 44779.2700 NA 1 -27863.7177 44779.2700 2 15684.5044 -27863.7177 3 -60081.5937 15684.5044 4 1480.8124 -60081.5937 5 -21304.7368 1480.8124 6 162597.0536 -21304.7368 7 -17785.6172 162597.0536 8 20132.9292 -17785.6172 9 20540.5695 20132.9292 10 34504.7158 20540.5695 11 -71388.0014 34504.7158 12 -11647.7295 -71388.0014 13 57364.8382 -11647.7295 14 1007.2914 57364.8382 15 -54689.8961 1007.2914 16 40192.0059 -54689.8961 17 9728.7132 40192.0059 18 -25786.6992 9728.7132 19 -33633.5297 -25786.6992 20 5675.3678 -33633.5297 21 161847.3506 5675.3678 22 29411.4180 161847.3506 23 -61736.3805 29411.4180 24 -94374.9453 -61736.3805 25 -39745.5823 -94374.9453 26 -10444.1990 -39745.5823 27 -5360.2271 -10444.1990 28 682.6909 -5360.2271 29 29001.0634 682.6909 30 -3291.3747 29001.0634 31 1722.2366 -3291.3747 32 14483.2395 1722.2366 33 -37762.4205 14483.2395 34 73886.1486 -37762.4205 35 59308.7776 73886.1486 36 112170.7895 59308.7776 37 -2514.1729 112170.7895 38 21741.8236 -2514.1729 39 -11230.1641 21741.8236 40 51376.0195 -11230.1641 41 -23713.9800 51376.0195 42 -5418.9245 -23713.9800 43 -23571.4599 -5418.9245 44 -50117.5545 -23571.4599 45 137283.6668 -50117.5545 46 -21916.9849 137283.6668 47 -38886.9071 -21916.9849 48 9123.3149 -38886.9071 49 -44667.8207 9123.3149 50 -12577.2702 -44667.8207 51 -14407.3081 -12577.2702 52 -32174.4912 -14407.3081 53 -6058.3306 -32174.4912 54 -8406.9162 -6058.3306 55 57934.7567 -8406.9162 56 1920.3737 57934.7567 57 -10679.9413 1920.3737 58 -18374.2983 -10679.9413 59 20378.0988 -18374.2983 60 18026.1675 20378.0988 61 14194.0170 18026.1675 62 -9827.8682 14194.0170 63 32320.4035 -9827.8682 64 54151.0175 32320.4035 65 10134.5700 54151.0175 66 -51796.7780 10134.5700 67 -45103.7149 -51796.7780 68 -35272.2425 -45103.7149 69 -91875.3306 -35272.2425 70 4081.0454 -91875.3306 71 -16686.0528 4081.0454 72 12526.2725 -16686.0528 73 -6984.8217 12526.2725 74 -65912.1973 -6984.8217 75 52812.3162 -65912.1973 76 39868.9914 52812.3162 77 -26651.0549 39868.9914 78 -63537.7589 -26651.0549 79 -20560.4241 -63537.7589 80 -8388.0316 -20560.4241 81 88726.5166 -8388.0316 82 14562.7766 88726.5166 83 -22577.1746 14562.7766 84 -7799.2671 -22577.1746 85 16429.2717 -7799.2671 86 -31818.3032 16429.2717 87 77201.5702 -31818.3032 88 9348.7598 77201.5702 89 -151948.7313 9348.7598 90 27681.7481 -151948.7313 91 -62469.3437 27681.7481 92 21074.4802 -62469.3437 93 5022.5285 21074.4802 94 88506.1522 5022.5285 95 10177.2108 88506.1522 96 14907.4884 10177.2108 97 -10992.8927 14907.4884 98 -14902.6911 -10992.8927 99 79717.3594 -14902.6911 100 -31670.5514 79717.3594 101 6838.7937 -31670.5514 102 -86203.2278 6838.7937 103 -16876.3458 -86203.2278 104 -17623.7628 -16876.3458 105 -59615.3884 -17623.7628 106 3765.0521 -59615.3884 107 -27588.9574 3765.0521 108 -10049.3453 -27588.9574 109 -101069.1783 -10049.3453 110 93715.5715 -101069.1783 111 -13145.6386 93715.5715 112 -4406.3242 -13145.6386 113 -71199.4325 -4406.3242 114 -5570.1166 -71199.4325 115 -48417.8981 -5570.1166 116 73082.0835 -48417.8981 117 -403.8659 73082.0835 118 63478.8133 -403.8659 119 -19292.8863 63478.8133 120 29316.5648 -19292.8863 121 12593.9198 29316.5648 122 -20117.6142 12593.9198 123 -3921.3614 -20117.6142 124 -23352.7127 -3921.3614 125 7599.1253 -23352.7127 126 51481.0609 7599.1253 127 4915.3846 51481.0609 128 85507.9464 4915.3846 129 -28047.3979 85507.9464 130 55402.5804 -28047.3979 131 15062.7590 55402.5804 132 -35006.2754 15062.7590 133 -113681.1117 -35006.2754 134 86916.1610 -113681.1117 135 -29309.8112 86916.1610 136 61699.3299 -29309.8112 137 -26759.3699 61699.3299 138 14954.2127 -26759.3699 139 -17525.7143 14954.2127 140 16905.0216 -17525.7143 141 98296.3219 16905.0216 142 -29944.4073 98296.3219 143 40758.2377 -29944.4073 144 -8484.8268 40758.2377 145 -148229.3935 -8484.8268 146 -8694.2671 -148229.3935 147 -63452.9616 -8694.2671 148 -5059.7881 -63452.9616 149 -2242.8165 -5059.7881 150 -5717.2963 -2242.8165 151 -6114.8046 -5717.2963 152 -3356.1124 -6114.8046 153 -5060.7881 -3356.1124 154 19365.9764 -5060.7881 155 21412.9777 19365.9764 156 -5060.7881 21412.9777 157 -7875.8211 -5060.7881 158 -9202.9848 -7875.8211 159 6768.3471 -9202.9848 160 8724.2388 6768.3471 161 -30648.9387 8724.2388 162 -3896.1289 -30648.9387 163 34623.2828 -3896.1289 164 NA 34623.2828 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -27863.7177 44779.2700 [2,] 15684.5044 -27863.7177 [3,] -60081.5937 15684.5044 [4,] 1480.8124 -60081.5937 [5,] -21304.7368 1480.8124 [6,] 162597.0536 -21304.7368 [7,] -17785.6172 162597.0536 [8,] 20132.9292 -17785.6172 [9,] 20540.5695 20132.9292 [10,] 34504.7158 20540.5695 [11,] -71388.0014 34504.7158 [12,] -11647.7295 -71388.0014 [13,] 57364.8382 -11647.7295 [14,] 1007.2914 57364.8382 [15,] -54689.8961 1007.2914 [16,] 40192.0059 -54689.8961 [17,] 9728.7132 40192.0059 [18,] -25786.6992 9728.7132 [19,] -33633.5297 -25786.6992 [20,] 5675.3678 -33633.5297 [21,] 161847.3506 5675.3678 [22,] 29411.4180 161847.3506 [23,] -61736.3805 29411.4180 [24,] -94374.9453 -61736.3805 [25,] -39745.5823 -94374.9453 [26,] -10444.1990 -39745.5823 [27,] -5360.2271 -10444.1990 [28,] 682.6909 -5360.2271 [29,] 29001.0634 682.6909 [30,] -3291.3747 29001.0634 [31,] 1722.2366 -3291.3747 [32,] 14483.2395 1722.2366 [33,] -37762.4205 14483.2395 [34,] 73886.1486 -37762.4205 [35,] 59308.7776 73886.1486 [36,] 112170.7895 59308.7776 [37,] -2514.1729 112170.7895 [38,] 21741.8236 -2514.1729 [39,] -11230.1641 21741.8236 [40,] 51376.0195 -11230.1641 [41,] -23713.9800 51376.0195 [42,] -5418.9245 -23713.9800 [43,] -23571.4599 -5418.9245 [44,] -50117.5545 -23571.4599 [45,] 137283.6668 -50117.5545 [46,] -21916.9849 137283.6668 [47,] -38886.9071 -21916.9849 [48,] 9123.3149 -38886.9071 [49,] -44667.8207 9123.3149 [50,] -12577.2702 -44667.8207 [51,] -14407.3081 -12577.2702 [52,] -32174.4912 -14407.3081 [53,] -6058.3306 -32174.4912 [54,] -8406.9162 -6058.3306 [55,] 57934.7567 -8406.9162 [56,] 1920.3737 57934.7567 [57,] -10679.9413 1920.3737 [58,] -18374.2983 -10679.9413 [59,] 20378.0988 -18374.2983 [60,] 18026.1675 20378.0988 [61,] 14194.0170 18026.1675 [62,] -9827.8682 14194.0170 [63,] 32320.4035 -9827.8682 [64,] 54151.0175 32320.4035 [65,] 10134.5700 54151.0175 [66,] -51796.7780 10134.5700 [67,] -45103.7149 -51796.7780 [68,] -35272.2425 -45103.7149 [69,] -91875.3306 -35272.2425 [70,] 4081.0454 -91875.3306 [71,] -16686.0528 4081.0454 [72,] 12526.2725 -16686.0528 [73,] -6984.8217 12526.2725 [74,] -65912.1973 -6984.8217 [75,] 52812.3162 -65912.1973 [76,] 39868.9914 52812.3162 [77,] -26651.0549 39868.9914 [78,] -63537.7589 -26651.0549 [79,] -20560.4241 -63537.7589 [80,] -8388.0316 -20560.4241 [81,] 88726.5166 -8388.0316 [82,] 14562.7766 88726.5166 [83,] -22577.1746 14562.7766 [84,] -7799.2671 -22577.1746 [85,] 16429.2717 -7799.2671 [86,] -31818.3032 16429.2717 [87,] 77201.5702 -31818.3032 [88,] 9348.7598 77201.5702 [89,] -151948.7313 9348.7598 [90,] 27681.7481 -151948.7313 [91,] -62469.3437 27681.7481 [92,] 21074.4802 -62469.3437 [93,] 5022.5285 21074.4802 [94,] 88506.1522 5022.5285 [95,] 10177.2108 88506.1522 [96,] 14907.4884 10177.2108 [97,] -10992.8927 14907.4884 [98,] -14902.6911 -10992.8927 [99,] 79717.3594 -14902.6911 [100,] -31670.5514 79717.3594 [101,] 6838.7937 -31670.5514 [102,] -86203.2278 6838.7937 [103,] -16876.3458 -86203.2278 [104,] -17623.7628 -16876.3458 [105,] -59615.3884 -17623.7628 [106,] 3765.0521 -59615.3884 [107,] -27588.9574 3765.0521 [108,] -10049.3453 -27588.9574 [109,] -101069.1783 -10049.3453 [110,] 93715.5715 -101069.1783 [111,] -13145.6386 93715.5715 [112,] -4406.3242 -13145.6386 [113,] -71199.4325 -4406.3242 [114,] -5570.1166 -71199.4325 [115,] -48417.8981 -5570.1166 [116,] 73082.0835 -48417.8981 [117,] -403.8659 73082.0835 [118,] 63478.8133 -403.8659 [119,] -19292.8863 63478.8133 [120,] 29316.5648 -19292.8863 [121,] 12593.9198 29316.5648 [122,] -20117.6142 12593.9198 [123,] -3921.3614 -20117.6142 [124,] -23352.7127 -3921.3614 [125,] 7599.1253 -23352.7127 [126,] 51481.0609 7599.1253 [127,] 4915.3846 51481.0609 [128,] 85507.9464 4915.3846 [129,] -28047.3979 85507.9464 [130,] 55402.5804 -28047.3979 [131,] 15062.7590 55402.5804 [132,] -35006.2754 15062.7590 [133,] -113681.1117 -35006.2754 [134,] 86916.1610 -113681.1117 [135,] -29309.8112 86916.1610 [136,] 61699.3299 -29309.8112 [137,] -26759.3699 61699.3299 [138,] 14954.2127 -26759.3699 [139,] -17525.7143 14954.2127 [140,] 16905.0216 -17525.7143 [141,] 98296.3219 16905.0216 [142,] -29944.4073 98296.3219 [143,] 40758.2377 -29944.4073 [144,] -8484.8268 40758.2377 [145,] -148229.3935 -8484.8268 [146,] -8694.2671 -148229.3935 [147,] -63452.9616 -8694.2671 [148,] -5059.7881 -63452.9616 [149,] -2242.8165 -5059.7881 [150,] -5717.2963 -2242.8165 [151,] -6114.8046 -5717.2963 [152,] -3356.1124 -6114.8046 [153,] -5060.7881 -3356.1124 [154,] 19365.9764 -5060.7881 [155,] 21412.9777 19365.9764 [156,] -5060.7881 21412.9777 [157,] -7875.8211 -5060.7881 [158,] -9202.9848 -7875.8211 [159,] 6768.3471 -9202.9848 [160,] 8724.2388 6768.3471 [161,] -30648.9387 8724.2388 [162,] -3896.1289 -30648.9387 [163,] 34623.2828 -3896.1289 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -27863.7177 44779.2700 2 15684.5044 -27863.7177 3 -60081.5937 15684.5044 4 1480.8124 -60081.5937 5 -21304.7368 1480.8124 6 162597.0536 -21304.7368 7 -17785.6172 162597.0536 8 20132.9292 -17785.6172 9 20540.5695 20132.9292 10 34504.7158 20540.5695 11 -71388.0014 34504.7158 12 -11647.7295 -71388.0014 13 57364.8382 -11647.7295 14 1007.2914 57364.8382 15 -54689.8961 1007.2914 16 40192.0059 -54689.8961 17 9728.7132 40192.0059 18 -25786.6992 9728.7132 19 -33633.5297 -25786.6992 20 5675.3678 -33633.5297 21 161847.3506 5675.3678 22 29411.4180 161847.3506 23 -61736.3805 29411.4180 24 -94374.9453 -61736.3805 25 -39745.5823 -94374.9453 26 -10444.1990 -39745.5823 27 -5360.2271 -10444.1990 28 682.6909 -5360.2271 29 29001.0634 682.6909 30 -3291.3747 29001.0634 31 1722.2366 -3291.3747 32 14483.2395 1722.2366 33 -37762.4205 14483.2395 34 73886.1486 -37762.4205 35 59308.7776 73886.1486 36 112170.7895 59308.7776 37 -2514.1729 112170.7895 38 21741.8236 -2514.1729 39 -11230.1641 21741.8236 40 51376.0195 -11230.1641 41 -23713.9800 51376.0195 42 -5418.9245 -23713.9800 43 -23571.4599 -5418.9245 44 -50117.5545 -23571.4599 45 137283.6668 -50117.5545 46 -21916.9849 137283.6668 47 -38886.9071 -21916.9849 48 9123.3149 -38886.9071 49 -44667.8207 9123.3149 50 -12577.2702 -44667.8207 51 -14407.3081 -12577.2702 52 -32174.4912 -14407.3081 53 -6058.3306 -32174.4912 54 -8406.9162 -6058.3306 55 57934.7567 -8406.9162 56 1920.3737 57934.7567 57 -10679.9413 1920.3737 58 -18374.2983 -10679.9413 59 20378.0988 -18374.2983 60 18026.1675 20378.0988 61 14194.0170 18026.1675 62 -9827.8682 14194.0170 63 32320.4035 -9827.8682 64 54151.0175 32320.4035 65 10134.5700 54151.0175 66 -51796.7780 10134.5700 67 -45103.7149 -51796.7780 68 -35272.2425 -45103.7149 69 -91875.3306 -35272.2425 70 4081.0454 -91875.3306 71 -16686.0528 4081.0454 72 12526.2725 -16686.0528 73 -6984.8217 12526.2725 74 -65912.1973 -6984.8217 75 52812.3162 -65912.1973 76 39868.9914 52812.3162 77 -26651.0549 39868.9914 78 -63537.7589 -26651.0549 79 -20560.4241 -63537.7589 80 -8388.0316 -20560.4241 81 88726.5166 -8388.0316 82 14562.7766 88726.5166 83 -22577.1746 14562.7766 84 -7799.2671 -22577.1746 85 16429.2717 -7799.2671 86 -31818.3032 16429.2717 87 77201.5702 -31818.3032 88 9348.7598 77201.5702 89 -151948.7313 9348.7598 90 27681.7481 -151948.7313 91 -62469.3437 27681.7481 92 21074.4802 -62469.3437 93 5022.5285 21074.4802 94 88506.1522 5022.5285 95 10177.2108 88506.1522 96 14907.4884 10177.2108 97 -10992.8927 14907.4884 98 -14902.6911 -10992.8927 99 79717.3594 -14902.6911 100 -31670.5514 79717.3594 101 6838.7937 -31670.5514 102 -86203.2278 6838.7937 103 -16876.3458 -86203.2278 104 -17623.7628 -16876.3458 105 -59615.3884 -17623.7628 106 3765.0521 -59615.3884 107 -27588.9574 3765.0521 108 -10049.3453 -27588.9574 109 -101069.1783 -10049.3453 110 93715.5715 -101069.1783 111 -13145.6386 93715.5715 112 -4406.3242 -13145.6386 113 -71199.4325 -4406.3242 114 -5570.1166 -71199.4325 115 -48417.8981 -5570.1166 116 73082.0835 -48417.8981 117 -403.8659 73082.0835 118 63478.8133 -403.8659 119 -19292.8863 63478.8133 120 29316.5648 -19292.8863 121 12593.9198 29316.5648 122 -20117.6142 12593.9198 123 -3921.3614 -20117.6142 124 -23352.7127 -3921.3614 125 7599.1253 -23352.7127 126 51481.0609 7599.1253 127 4915.3846 51481.0609 128 85507.9464 4915.3846 129 -28047.3979 85507.9464 130 55402.5804 -28047.3979 131 15062.7590 55402.5804 132 -35006.2754 15062.7590 133 -113681.1117 -35006.2754 134 86916.1610 -113681.1117 135 -29309.8112 86916.1610 136 61699.3299 -29309.8112 137 -26759.3699 61699.3299 138 14954.2127 -26759.3699 139 -17525.7143 14954.2127 140 16905.0216 -17525.7143 141 98296.3219 16905.0216 142 -29944.4073 98296.3219 143 40758.2377 -29944.4073 144 -8484.8268 40758.2377 145 -148229.3935 -8484.8268 146 -8694.2671 -148229.3935 147 -63452.9616 -8694.2671 148 -5059.7881 -63452.9616 149 -2242.8165 -5059.7881 150 -5717.2963 -2242.8165 151 -6114.8046 -5717.2963 152 -3356.1124 -6114.8046 153 -5060.7881 -3356.1124 154 19365.9764 -5060.7881 155 21412.9777 19365.9764 156 -5060.7881 21412.9777 157 -7875.8211 -5060.7881 158 -9202.9848 -7875.8211 159 6768.3471 -9202.9848 160 8724.2388 6768.3471 161 -30648.9387 8724.2388 162 -3896.1289 -30648.9387 163 34623.2828 -3896.1289 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7uu6y1355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/81l821355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/99uu91355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10kmbl1355163860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/119iwp1355163860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12ttd81355163860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13nbp21355163860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14p1wr1355163860.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/wessaorg/rcomp/tmp/154umq1355163860.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/wessaorg/rcomp/tmp/16lzwm1355163861.tab") + } > > try(system("convert tmp/1dnba1355163860.ps tmp/1dnba1355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/2yfix1355163860.ps tmp/2yfix1355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/322i31355163860.ps tmp/322i31355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/44w2s1355163860.ps tmp/44w2s1355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/584931355163860.ps tmp/584931355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/61paa1355163860.ps tmp/61paa1355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/7uu6y1355163860.ps tmp/7uu6y1355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/81l821355163860.ps tmp/81l821355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/99uu91355163860.ps tmp/99uu91355163860.png",intern=TRUE)) character(0) > try(system("convert tmp/10kmbl1355163860.ps tmp/10kmbl1355163860.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.734 1.150 8.890