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Type 'q()' to quit R. > x <- array(list(255202 + ,34 + ,131 + ,104 + ,124252 + ,135248 + ,30 + ,117 + ,111 + ,98956 + ,198520 + ,38 + ,146 + ,93 + ,98073 + ,189326 + ,34 + ,132 + ,119 + ,106816 + ,141365 + ,25 + ,80 + ,57 + ,41449 + ,65295 + ,31 + ,117 + ,80 + ,76173 + ,439387 + ,29 + ,112 + ,107 + ,177551 + ,33186 + ,18 + ,67 + ,22 + ,22807 + ,183696 + ,30 + ,116 + ,103 + ,126938 + ,186657 + ,29 + ,107 + ,72 + ,61680 + ,269127 + ,40 + ,148 + ,129 + ,72117 + ,194414 + ,50 + ,190 + ,168 + ,79738 + ,141409 + ,33 + ,109 + ,100 + ,57793 + ,306730 + ,46 + ,159 + ,143 + ,91677 + ,192691 + ,38 + ,146 + ,79 + ,64631 + ,333497 + ,52 + ,201 + ,183 + ,106385 + ,261835 + ,32 + ,124 + ,123 + ,161961 + ,263451 + ,35 + ,131 + ,81 + ,112669 + ,157448 + ,25 + ,96 + ,74 + ,114029 + ,232190 + ,42 + ,163 + ,158 + ,124550 + ,245725 + ,40 + ,151 + ,133 + ,105416 + ,388603 + ,35 + ,128 + ,128 + ,72875 + ,156540 + ,25 + ,89 + ,84 + ,81964 + ,156189 + ,46 + ,184 + ,184 + ,104880 + ,186381 + ,36 + ,136 + ,127 + 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,0 + ,0 + ,0 + ,0 + ,0 + ,203 + ,0 + ,0 + ,0 + ,0 + ,7199 + ,0 + ,0 + ,0 + ,1644 + ,46660 + ,5 + ,15 + ,13 + ,6179 + ,17547 + ,1 + ,4 + ,4 + ,3926 + ,107465 + ,38 + ,133 + ,65 + ,42087 + ,969 + ,0 + ,0 + ,0 + ,0 + ,179994 + ,28 + ,101 + ,55 + ,87656) + ,dim=c(5 + ,164) + ,dimnames=list(c('TimeRFC' + ,'RvwdCompend' + ,'SubFeedback' + ,'sublongfb' + ,'compChara') + ,1:164)) > y <- array(NA,dim=c(5,164),dimnames=list(c('TimeRFC','RvwdCompend','SubFeedback','sublongfb','compChara'),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 = '1' > 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 TimeRFC RvwdCompend SubFeedback sublongfb compChara 1 255202 34 131 104 124252 2 135248 30 117 111 98956 3 198520 38 146 93 98073 4 189326 34 132 119 106816 5 141365 25 80 57 41449 6 65295 31 117 80 76173 7 439387 29 112 107 177551 8 33186 18 67 22 22807 9 183696 30 116 103 126938 10 186657 29 107 72 61680 11 269127 40 148 129 72117 12 194414 50 190 168 79738 13 141409 33 109 100 57793 14 306730 46 159 143 91677 15 192691 38 146 79 64631 16 333497 52 201 183 106385 17 261835 32 124 123 161961 18 263451 35 131 81 112669 19 157448 25 96 74 114029 20 232190 42 163 158 124550 21 245725 40 151 133 105416 22 388603 35 128 128 72875 23 156540 25 89 84 81964 24 156189 46 184 184 104880 25 186381 36 136 127 76302 26 192167 35 134 128 96740 27 249893 38 146 118 93071 28 236812 35 130 125 78912 29 143160 28 105 89 35224 30 259667 37 142 122 90694 31 243020 40 155 151 125369 32 176062 42 154 122 80849 33 286683 44 169 162 104434 34 87485 33 125 121 65702 35 329737 38 147 144 108179 36 247082 37 139 110 63583 37 366219 41 151 141 95066 38 191653 32 124 80 62486 39 114673 17 55 46 31081 40 294371 38 147 140 94584 41 284195 33 125 103 87408 42 155568 35 128 95 68966 43 177306 32 107 100 88766 44 144595 35 130 102 57139 45 140319 45 73 45 90586 46 405267 38 138 122 109249 47 78800 26 82 66 33032 48 201970 45 173 159 96056 49 302705 44 169 153 146648 50 164733 40 145 131 80613 51 194221 33 134 113 87026 52 24188 4 12 7 5950 53 346142 41 151 147 131106 54 65029 18 67 61 32551 55 101097 14 52 41 31701 56 253745 36 131 117 91072 57 273513 49 186 184 159803 58 282220 32 120 115 143950 59 280928 37 135 132 112368 60 214872 32 123 113 82124 61 342048 43 166 149 144068 62 273924 25 90 65 162627 63 194396 42 165 94 55062 64 231162 37 143 126 95329 65 209798 33 125 112 105612 66 201345 28 110 81 62853 67 166424 31 121 116 125976 68 204441 40 151 132 79146 69 197813 32 123 104 108461 70 136421 25 92 80 99971 71 216092 42 162 145 77826 72 73566 23 88 67 22618 73 213998 42 163 159 84892 74 181728 38 133 90 92059 75 148758 34 132 120 77993 76 308343 39 147 129 104155 77 251437 32 124 118 109840 78 202388 37 140 112 238712 79 173286 34 132 123 67486 80 155529 33 122 98 68007 81 132672 25 97 78 48194 82 390163 45 175 138 134796 83 145905 26 99 99 38692 84 228012 40 106 81 93587 85 80953 8 28 27 56622 86 130805 27 101 77 15986 87 135163 32 120 118 113402 88 331003 37 143 137 97967 89 271806 50 178 103 74844 90 162828 41 155 143 136051 91 234092 37 138 85 50548 92 207158 38 141 131 112215 93 156583 28 102 81 59591 94 242395 36 140 135 59938 95 261601 32 124 116 137639 96 178489 32 124 123 143372 97 204221 33 119 119 138599 98 268066 35 129 100 174110 99 318087 58 223 221 135062 100 361799 27 102 95 175681 101 247131 45 174 153 130307 102 265849 37 141 118 139141 103 160309 32 122 50 44244 104 43287 19 71 64 43750 105 172244 22 81 34 48029 106 189021 35 131 76 95216 107 227681 36 139 112 92288 108 269329 36 137 115 94588 109 106503 23 91 69 197426 110 117891 40 157 123 151244 111 287201 40 149 143 139206 112 266805 42 155 110 106271 113 23623 1 0 0 1168 114 174954 36 139 94 71764 115 61857 11 32 30 25162 116 144889 40 149 106 45635 117 347988 34 128 91 101817 118 21054 0 0 0 855 119 224051 27 99 69 100174 120 31414 8 25 9 14116 121 277214 35 132 123 85008 122 209481 44 167 150 124254 123 156870 40 151 125 105793 124 112933 28 103 81 117129 125 38214 8 27 21 8773 126 166011 36 135 128 94747 127 316044 47 178 168 107549 128 181578 48 185 155 97392 129 358903 45 175 157 126893 130 275578 48 187 145 118850 131 368796 49 182 172 234853 132 172464 35 135 126 74783 133 94381 32 118 89 66089 134 249649 36 140 137 95684 135 382499 42 158 149 139537 136 118010 35 132 121 144253 137 365539 42 156 149 153824 138 147989 34 123 93 63995 139 231681 41 151 135 84891 140 193119 36 129 102 61263 141 189020 32 125 45 106221 142 341958 33 128 104 113587 143 219133 35 129 111 113864 144 173260 21 79 78 37238 145 274787 42 162 126 119906 146 130908 49 188 176 135096 147 204009 33 122 109 151611 148 262412 39 144 132 144645 149 1 0 0 0 0 150 14688 0 0 0 6023 151 98 0 0 0 0 152 455 0 0 0 0 153 0 0 0 0 0 154 0 0 0 0 0 155 195765 33 120 78 77457 156 330975 45 179 110 62464 157 0 0 0 0 0 158 203 0 0 0 0 159 7199 0 0 0 1644 160 46660 5 15 13 6179 161 17547 1 4 4 3926 162 107465 38 133 65 42087 163 969 0 0 0 0 164 179994 28 101 55 87656 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) RvwdCompend SubFeedback sublongfb compChara 6942.5436 1569.0423 409.2360 298.1868 0.6783 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -173966 -33146 -1284 28213 188774 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.943e+03 1.285e+04 0.540 0.590 RvwdCompend 1.569e+03 1.962e+03 0.800 0.425 SubFeedback 4.092e+02 5.938e+02 0.689 0.492 sublongfb 2.982e+02 2.976e+02 1.002 0.318 compChara 6.783e-01 1.414e-01 4.797 3.68e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 60730 on 159 degrees of freedom Multiple R-squared: 0.6358, Adjusted R-squared: 0.6267 F-statistic: 69.41 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,] 0.3511951 7.023902e-01 6.488049e-01 [2,] 0.4493145 8.986290e-01 5.506855e-01 [3,] 0.5435032 9.129937e-01 4.564968e-01 [4,] 0.7728444 4.543112e-01 2.271556e-01 [5,] 0.6881695 6.236610e-01 3.118305e-01 [6,] 0.7230816 5.538368e-01 2.769184e-01 [7,] 0.6377966 7.244069e-01 3.622034e-01 [8,] 0.5767692 8.464616e-01 4.232308e-01 [9,] 0.5988138 8.023725e-01 4.011862e-01 [10,] 0.5783738 8.432525e-01 4.216262e-01 [11,] 0.4992953 9.985906e-01 5.007047e-01 [12,] 0.4481449 8.962898e-01 5.518551e-01 [13,] 0.3903466 7.806931e-01 6.096534e-01 [14,] 0.3170964 6.341928e-01 6.829036e-01 [15,] 0.9054098 1.891803e-01 9.459017e-02 [16,] 0.8758857 2.482286e-01 1.241143e-01 [17,] 0.8970847 2.058307e-01 1.029153e-01 [18,] 0.8653648 2.692704e-01 1.346352e-01 [19,] 0.8289517 3.420967e-01 1.710483e-01 [20,] 0.8042213 3.915573e-01 1.957787e-01 [21,] 0.7774743 4.450514e-01 2.225257e-01 [22,] 0.7600559 4.798883e-01 2.399441e-01 [23,] 0.7441361 5.117279e-01 2.558639e-01 [24,] 0.7008225 5.983551e-01 2.991775e-01 [25,] 0.6989794 6.020412e-01 3.010206e-01 [26,] 0.6589222 6.821555e-01 3.410778e-01 [27,] 0.6964042 6.071916e-01 3.035958e-01 [28,] 0.7565950 4.868101e-01 2.434050e-01 [29,] 0.7644447 4.711106e-01 2.355553e-01 [30,] 0.8437027 3.125947e-01 1.562973e-01 [31,] 0.8305063 3.389874e-01 1.694937e-01 [32,] 0.8002294 3.995412e-01 1.997706e-01 [33,] 0.8096032 3.807936e-01 1.903968e-01 [34,] 0.8367040 3.265920e-01 1.632960e-01 [35,] 0.8172405 3.655190e-01 1.827595e-01 [36,] 0.8196825 3.606349e-01 1.803175e-01 [37,] 0.7935238 4.129525e-01 2.064762e-01 [38,] 0.8498947 3.002105e-01 1.501053e-01 [39,] 0.9600502 7.989961e-02 3.994980e-02 [40,] 0.9526523 9.469535e-02 4.734768e-02 [41,] 0.9520147 9.597065e-02 4.798533e-02 [42,] 0.9398374 1.203252e-01 6.016260e-02 [43,] 0.9374276 1.251448e-01 6.257239e-02 [44,] 0.9217605 1.564790e-01 7.823952e-02 [45,] 0.9024040 1.951920e-01 9.759601e-02 [46,] 0.9061582 1.876836e-01 9.384180e-02 [47,] 0.8908230 2.183541e-01 1.091770e-01 [48,] 0.8695148 2.609704e-01 1.304852e-01 [49,] 0.8519984 2.960032e-01 1.480016e-01 [50,] 0.8603027 2.793946e-01 1.396973e-01 [51,] 0.8419353 3.161293e-01 1.580647e-01 [52,] 0.8241554 3.516892e-01 1.758446e-01 [53,] 0.7943747 4.112506e-01 2.056253e-01 [54,] 0.7830214 4.339572e-01 2.169786e-01 [55,] 0.7726041 4.547918e-01 2.273959e-01 [56,] 0.7406545 5.186909e-01 2.593455e-01 [57,] 0.7018184 5.963632e-01 2.981816e-01 [58,] 0.6641706 6.716589e-01 3.358294e-01 [59,] 0.6396110 7.207780e-01 3.603890e-01 [60,] 0.6613040 6.773920e-01 3.386960e-01 [61,] 0.6214143 7.571714e-01 3.785857e-01 [62,] 0.5841376 8.317248e-01 4.158624e-01 [63,] 0.5677962 8.644077e-01 4.322038e-01 [64,] 0.5249731 9.500539e-01 4.750269e-01 [65,] 0.4947644 9.895287e-01 5.052356e-01 [66,] 0.4585018 9.170037e-01 5.414982e-01 [67,] 0.4247341 8.494682e-01 5.752659e-01 [68,] 0.4144694 8.289388e-01 5.855306e-01 [69,] 0.4281028 8.562057e-01 5.718972e-01 [70,] 0.3964871 7.929742e-01 6.035129e-01 [71,] 0.5753095 8.493811e-01 4.246905e-01 [72,] 0.5363635 9.272731e-01 4.636365e-01 [73,] 0.4998357 9.996715e-01 5.001643e-01 [74,] 0.4550929 9.101859e-01 5.449071e-01 [75,] 0.5542337 8.915327e-01 4.457663e-01 [76,] 0.5088918 9.822164e-01 4.911082e-01 [77,] 0.4724531 9.449063e-01 5.275469e-01 [78,] 0.4278205 8.556410e-01 5.721795e-01 [79,] 0.3857378 7.714756e-01 6.142622e-01 [80,] 0.4241788 8.483576e-01 5.758212e-01 [81,] 0.5021723 9.956553e-01 4.978277e-01 [82,] 0.4695157 9.390313e-01 5.304843e-01 [83,] 0.5615769 8.768463e-01 4.384231e-01 [84,] 0.5497482 9.005035e-01 4.502518e-01 [85,] 0.5155079 9.689843e-01 4.844921e-01 [86,] 0.4694264 9.388527e-01 5.305736e-01 [87,] 0.4457321 8.914642e-01 5.542679e-01 [88,] 0.4106545 8.213091e-01 5.893455e-01 [89,] 0.4102594 8.205189e-01 5.897406e-01 [90,] 0.3763062 7.526124e-01 6.236938e-01 [91,] 0.3343951 6.687902e-01 6.656049e-01 [92,] 0.3008614 6.017228e-01 6.991386e-01 [93,] 0.4603337 9.206673e-01 5.396663e-01 [94,] 0.4280619 8.561239e-01 5.719381e-01 [95,] 0.3890878 7.781757e-01 6.109122e-01 [96,] 0.3447770 6.895540e-01 6.552230e-01 [97,] 0.3581637 7.163273e-01 6.418363e-01 [98,] 0.3505545 7.011089e-01 6.494455e-01 [99,] 0.3094427 6.188853e-01 6.905573e-01 [100,] 0.2699638 5.399276e-01 7.300362e-01 [101,] 0.2596563 5.193126e-01 7.403437e-01 [102,] 0.3701147 7.402293e-01 6.298853e-01 [103,] 0.6540218 6.919565e-01 3.459782e-01 [104,] 0.6156799 7.686401e-01 3.843201e-01 [105,] 0.5808843 8.382315e-01 4.191157e-01 [106,] 0.5361679 9.276643e-01 4.638321e-01 [107,] 0.4964054 9.928109e-01 5.035946e-01 [108,] 0.4534079 9.068158e-01 5.465921e-01 [109,] 0.4248219 8.496439e-01 5.751781e-01 [110,] 0.6327670 7.344661e-01 3.672330e-01 [111,] 0.5845924 8.308152e-01 4.154076e-01 [112,] 0.5734486 8.531028e-01 4.265514e-01 [113,] 0.5220741 9.558518e-01 4.779259e-01 [114,] 0.5460080 9.079840e-01 4.539920e-01 [115,] 0.5410374 9.179252e-01 4.589626e-01 [116,] 0.5773632 8.452737e-01 4.226368e-01 [117,] 0.6100579 7.798841e-01 3.899421e-01 [118,] 0.5565467 8.869066e-01 4.434533e-01 [119,] 0.5368766 9.262468e-01 4.631234e-01 [120,] 0.5115469 9.769061e-01 4.884531e-01 [121,] 0.5929124 8.141753e-01 4.070876e-01 [122,] 0.5992352 8.015296e-01 4.007648e-01 [123,] 0.5512262 8.975475e-01 4.487738e-01 [124,] 0.4955130 9.910261e-01 5.044870e-01 [125,] 0.4641042 9.282084e-01 5.358958e-01 [126,] 0.5062393 9.875214e-01 4.937607e-01 [127,] 0.4461135 8.922270e-01 5.538865e-01 [128,] 0.6035522 7.928956e-01 3.964478e-01 [129,] 0.7905621 4.188758e-01 2.094379e-01 [130,] 0.8729688 2.540625e-01 1.270312e-01 [131,] 0.8383716 3.232568e-01 1.616284e-01 [132,] 0.7991282 4.017436e-01 2.008718e-01 [133,] 0.7656779 4.686441e-01 2.343221e-01 [134,] 0.8580393 2.839215e-01 1.419607e-01 [135,] 0.9433792 1.132417e-01 5.662083e-02 [136,] 0.9300621 1.398757e-01 6.993786e-02 [137,] 0.9947209 1.055829e-02 5.279144e-03 [138,] 0.9903266 1.934684e-02 9.673419e-03 [139,] 0.9999873 2.544579e-05 1.272289e-05 [140,] 0.9999960 7.942300e-06 3.971150e-06 [141,] 0.9999998 3.957290e-07 1.978645e-07 [142,] 0.9999989 2.180094e-06 1.090047e-06 [143,] 0.9999943 1.136546e-05 5.682728e-06 [144,] 0.9999708 5.833732e-05 2.916866e-05 [145,] 0.9998575 2.850790e-04 1.425395e-04 [146,] 0.9993541 1.291805e-03 6.459024e-04 [147,] 0.9972792 5.441549e-03 2.720775e-03 [148,] 0.9999906 1.881082e-05 9.405411e-06 [149,] 0.9999279 1.442837e-04 7.214185e-05 > postscript(file="/var/wessaorg/rcomp/tmp/12pgf1323799974.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/248d81323799974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3hz911323799974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4u3l81323799974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5xvra1323799974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 26014.33716 -66863.99336 -22045.90083 -32917.39497 17347.27995 6 7 8 9 10 -113689.23115 188774.38840 -51447.51810 -34600.59867 27118.86719 11 12 13 14 15 51474.97894 -72914.73342 -30936.57162 57720.56319 -1017.59820 16 17 18 19 20 35981.93753 7407.66121 47408.98690 -27415.46903 -38949.76578 21 22 23 24 25 3066.82908 186764.96890 -6691.97521 -124232.18809 -22326.20397 26 27 28 29 30 -28313.34763 25265.13240 30955.34467 -1115.49782 38664.61788 31 32 33 34 35 -20175.98815 -51018.86273 22401.05472 -103034.70398 86699.74409 36 37 38 39 40 49274.13094 126626.38107 17518.54861 23750.87093 61747.56462 41 42 43 44 45 84320.14205 -33778.51065 -13660.09716 -39635.39756 -41964.79812 46 47 48 49 50 171747.22985 -44579.92174 -58940.82959 12474.26936 -58050.26400 51 52 53 54 55 -12059.75123 -64.55569 80315.42898 -37842.86176 17180.11284 56 57 58 59 60 40047.80440 -49686.38021 44031.41093 45107.58947 17986.76390 61 62 63 64 65 57556.70213 61237.08940 -11346.69323 5413.85567 -5107.75651 66 67 68 69 70 38668.91001 -58711.75442 -20100.84552 -14252.13733 -39059.53398 71 72 73 74 75 -19070.63908 -40796.89958 -30541.14257 -28544.15990 -54233.82129 76 77 78 79 80 70938.86043 33852.67758 -115210.13005 -23473.80460 -28468.11015 81 82 83 84 85 -9139.58276 108419.46302 1888.89604 27298.41314 3543.49293 86 87 88 89 90 6362.27810 -83200.37423 100185.52580 32089.69275 -106796.81017 91 92 93 94 95 52989.27986 -32284.90675 -606.68705 40764.55882 25757.83865 96 97 98 99 100 -63329.98790 -32690.72280 5503.35568 -28627.34056 123263.43870 101 102 103 104 105 -35631.35175 13588.48089 8311.61669 -71281.34813 54919.45473 106 107 108 109 110 -13692.24219 11376.07488 51387.96666 -128250.90808 -155324.45125 111 112 113 114 115 19460.70797 25650.17706 14319.19543 -22062.76002 -1452.78134 116 117 118 119 120 -48352.02843 139121.44907 13531.53605 45710.09582 -10569.91482 121 122 123 124 125 67000.51573 -63847.53200 -83658.38392 -83692.19094 -4542.63499 126 127 128 129 130 -55095.83489 39469.87360 -88664.20912 76854.27688 -7055.11885 131 132 133 134 135 -92.31693 -32936.44756 -82425.52166 23176.76524 105923.87129 136 137 138 139 140 -131791.18448 80091.90759 -33774.23727 778.89371 4931.61292 141 142 143 144 145 -4751.26319 122800.83118 -5846.67779 52521.95448 16748.33190 146 147 148 149 150 -173966.35420 -39974.20471 -2122.12227 -6941.54356 3660.23951 151 152 153 154 155 -6844.54356 -6487.54356 -6942.54356 -6942.54356 12140.45195 156 157 158 159 160 105004.33628 -6942.54356 -6739.54356 -858.61855 17666.24939 161 162 163 164 3542.83699 -61458.00813 -5973.54356 11930.77409 > postscript(file="/var/wessaorg/rcomp/tmp/64u4m1323799974.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 26014.33716 NA 1 -66863.99336 26014.33716 2 -22045.90083 -66863.99336 3 -32917.39497 -22045.90083 4 17347.27995 -32917.39497 5 -113689.23115 17347.27995 6 188774.38840 -113689.23115 7 -51447.51810 188774.38840 8 -34600.59867 -51447.51810 9 27118.86719 -34600.59867 10 51474.97894 27118.86719 11 -72914.73342 51474.97894 12 -30936.57162 -72914.73342 13 57720.56319 -30936.57162 14 -1017.59820 57720.56319 15 35981.93753 -1017.59820 16 7407.66121 35981.93753 17 47408.98690 7407.66121 18 -27415.46903 47408.98690 19 -38949.76578 -27415.46903 20 3066.82908 -38949.76578 21 186764.96890 3066.82908 22 -6691.97521 186764.96890 23 -124232.18809 -6691.97521 24 -22326.20397 -124232.18809 25 -28313.34763 -22326.20397 26 25265.13240 -28313.34763 27 30955.34467 25265.13240 28 -1115.49782 30955.34467 29 38664.61788 -1115.49782 30 -20175.98815 38664.61788 31 -51018.86273 -20175.98815 32 22401.05472 -51018.86273 33 -103034.70398 22401.05472 34 86699.74409 -103034.70398 35 49274.13094 86699.74409 36 126626.38107 49274.13094 37 17518.54861 126626.38107 38 23750.87093 17518.54861 39 61747.56462 23750.87093 40 84320.14205 61747.56462 41 -33778.51065 84320.14205 42 -13660.09716 -33778.51065 43 -39635.39756 -13660.09716 44 -41964.79812 -39635.39756 45 171747.22985 -41964.79812 46 -44579.92174 171747.22985 47 -58940.82959 -44579.92174 48 12474.26936 -58940.82959 49 -58050.26400 12474.26936 50 -12059.75123 -58050.26400 51 -64.55569 -12059.75123 52 80315.42898 -64.55569 53 -37842.86176 80315.42898 54 17180.11284 -37842.86176 55 40047.80440 17180.11284 56 -49686.38021 40047.80440 57 44031.41093 -49686.38021 58 45107.58947 44031.41093 59 17986.76390 45107.58947 60 57556.70213 17986.76390 61 61237.08940 57556.70213 62 -11346.69323 61237.08940 63 5413.85567 -11346.69323 64 -5107.75651 5413.85567 65 38668.91001 -5107.75651 66 -58711.75442 38668.91001 67 -20100.84552 -58711.75442 68 -14252.13733 -20100.84552 69 -39059.53398 -14252.13733 70 -19070.63908 -39059.53398 71 -40796.89958 -19070.63908 72 -30541.14257 -40796.89958 73 -28544.15990 -30541.14257 74 -54233.82129 -28544.15990 75 70938.86043 -54233.82129 76 33852.67758 70938.86043 77 -115210.13005 33852.67758 78 -23473.80460 -115210.13005 79 -28468.11015 -23473.80460 80 -9139.58276 -28468.11015 81 108419.46302 -9139.58276 82 1888.89604 108419.46302 83 27298.41314 1888.89604 84 3543.49293 27298.41314 85 6362.27810 3543.49293 86 -83200.37423 6362.27810 87 100185.52580 -83200.37423 88 32089.69275 100185.52580 89 -106796.81017 32089.69275 90 52989.27986 -106796.81017 91 -32284.90675 52989.27986 92 -606.68705 -32284.90675 93 40764.55882 -606.68705 94 25757.83865 40764.55882 95 -63329.98790 25757.83865 96 -32690.72280 -63329.98790 97 5503.35568 -32690.72280 98 -28627.34056 5503.35568 99 123263.43870 -28627.34056 100 -35631.35175 123263.43870 101 13588.48089 -35631.35175 102 8311.61669 13588.48089 103 -71281.34813 8311.61669 104 54919.45473 -71281.34813 105 -13692.24219 54919.45473 106 11376.07488 -13692.24219 107 51387.96666 11376.07488 108 -128250.90808 51387.96666 109 -155324.45125 -128250.90808 110 19460.70797 -155324.45125 111 25650.17706 19460.70797 112 14319.19543 25650.17706 113 -22062.76002 14319.19543 114 -1452.78134 -22062.76002 115 -48352.02843 -1452.78134 116 139121.44907 -48352.02843 117 13531.53605 139121.44907 118 45710.09582 13531.53605 119 -10569.91482 45710.09582 120 67000.51573 -10569.91482 121 -63847.53200 67000.51573 122 -83658.38392 -63847.53200 123 -83692.19094 -83658.38392 124 -4542.63499 -83692.19094 125 -55095.83489 -4542.63499 126 39469.87360 -55095.83489 127 -88664.20912 39469.87360 128 76854.27688 -88664.20912 129 -7055.11885 76854.27688 130 -92.31693 -7055.11885 131 -32936.44756 -92.31693 132 -82425.52166 -32936.44756 133 23176.76524 -82425.52166 134 105923.87129 23176.76524 135 -131791.18448 105923.87129 136 80091.90759 -131791.18448 137 -33774.23727 80091.90759 138 778.89371 -33774.23727 139 4931.61292 778.89371 140 -4751.26319 4931.61292 141 122800.83118 -4751.26319 142 -5846.67779 122800.83118 143 52521.95448 -5846.67779 144 16748.33190 52521.95448 145 -173966.35420 16748.33190 146 -39974.20471 -173966.35420 147 -2122.12227 -39974.20471 148 -6941.54356 -2122.12227 149 3660.23951 -6941.54356 150 -6844.54356 3660.23951 151 -6487.54356 -6844.54356 152 -6942.54356 -6487.54356 153 -6942.54356 -6942.54356 154 12140.45195 -6942.54356 155 105004.33628 12140.45195 156 -6942.54356 105004.33628 157 -6739.54356 -6942.54356 158 -858.61855 -6739.54356 159 17666.24939 -858.61855 160 3542.83699 17666.24939 161 -61458.00813 3542.83699 162 -5973.54356 -61458.00813 163 11930.77409 -5973.54356 164 NA 11930.77409 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -66863.99336 26014.33716 [2,] -22045.90083 -66863.99336 [3,] -32917.39497 -22045.90083 [4,] 17347.27995 -32917.39497 [5,] -113689.23115 17347.27995 [6,] 188774.38840 -113689.23115 [7,] -51447.51810 188774.38840 [8,] -34600.59867 -51447.51810 [9,] 27118.86719 -34600.59867 [10,] 51474.97894 27118.86719 [11,] -72914.73342 51474.97894 [12,] -30936.57162 -72914.73342 [13,] 57720.56319 -30936.57162 [14,] -1017.59820 57720.56319 [15,] 35981.93753 -1017.59820 [16,] 7407.66121 35981.93753 [17,] 47408.98690 7407.66121 [18,] -27415.46903 47408.98690 [19,] -38949.76578 -27415.46903 [20,] 3066.82908 -38949.76578 [21,] 186764.96890 3066.82908 [22,] -6691.97521 186764.96890 [23,] -124232.18809 -6691.97521 [24,] -22326.20397 -124232.18809 [25,] -28313.34763 -22326.20397 [26,] 25265.13240 -28313.34763 [27,] 30955.34467 25265.13240 [28,] -1115.49782 30955.34467 [29,] 38664.61788 -1115.49782 [30,] -20175.98815 38664.61788 [31,] -51018.86273 -20175.98815 [32,] 22401.05472 -51018.86273 [33,] -103034.70398 22401.05472 [34,] 86699.74409 -103034.70398 [35,] 49274.13094 86699.74409 [36,] 126626.38107 49274.13094 [37,] 17518.54861 126626.38107 [38,] 23750.87093 17518.54861 [39,] 61747.56462 23750.87093 [40,] 84320.14205 61747.56462 [41,] -33778.51065 84320.14205 [42,] -13660.09716 -33778.51065 [43,] -39635.39756 -13660.09716 [44,] -41964.79812 -39635.39756 [45,] 171747.22985 -41964.79812 [46,] -44579.92174 171747.22985 [47,] -58940.82959 -44579.92174 [48,] 12474.26936 -58940.82959 [49,] -58050.26400 12474.26936 [50,] -12059.75123 -58050.26400 [51,] -64.55569 -12059.75123 [52,] 80315.42898 -64.55569 [53,] -37842.86176 80315.42898 [54,] 17180.11284 -37842.86176 [55,] 40047.80440 17180.11284 [56,] -49686.38021 40047.80440 [57,] 44031.41093 -49686.38021 [58,] 45107.58947 44031.41093 [59,] 17986.76390 45107.58947 [60,] 57556.70213 17986.76390 [61,] 61237.08940 57556.70213 [62,] -11346.69323 61237.08940 [63,] 5413.85567 -11346.69323 [64,] -5107.75651 5413.85567 [65,] 38668.91001 -5107.75651 [66,] -58711.75442 38668.91001 [67,] -20100.84552 -58711.75442 [68,] -14252.13733 -20100.84552 [69,] -39059.53398 -14252.13733 [70,] -19070.63908 -39059.53398 [71,] -40796.89958 -19070.63908 [72,] -30541.14257 -40796.89958 [73,] -28544.15990 -30541.14257 [74,] -54233.82129 -28544.15990 [75,] 70938.86043 -54233.82129 [76,] 33852.67758 70938.86043 [77,] -115210.13005 33852.67758 [78,] -23473.80460 -115210.13005 [79,] -28468.11015 -23473.80460 [80,] -9139.58276 -28468.11015 [81,] 108419.46302 -9139.58276 [82,] 1888.89604 108419.46302 [83,] 27298.41314 1888.89604 [84,] 3543.49293 27298.41314 [85,] 6362.27810 3543.49293 [86,] -83200.37423 6362.27810 [87,] 100185.52580 -83200.37423 [88,] 32089.69275 100185.52580 [89,] -106796.81017 32089.69275 [90,] 52989.27986 -106796.81017 [91,] -32284.90675 52989.27986 [92,] -606.68705 -32284.90675 [93,] 40764.55882 -606.68705 [94,] 25757.83865 40764.55882 [95,] -63329.98790 25757.83865 [96,] -32690.72280 -63329.98790 [97,] 5503.35568 -32690.72280 [98,] -28627.34056 5503.35568 [99,] 123263.43870 -28627.34056 [100,] -35631.35175 123263.43870 [101,] 13588.48089 -35631.35175 [102,] 8311.61669 13588.48089 [103,] -71281.34813 8311.61669 [104,] 54919.45473 -71281.34813 [105,] -13692.24219 54919.45473 [106,] 11376.07488 -13692.24219 [107,] 51387.96666 11376.07488 [108,] -128250.90808 51387.96666 [109,] -155324.45125 -128250.90808 [110,] 19460.70797 -155324.45125 [111,] 25650.17706 19460.70797 [112,] 14319.19543 25650.17706 [113,] -22062.76002 14319.19543 [114,] -1452.78134 -22062.76002 [115,] -48352.02843 -1452.78134 [116,] 139121.44907 -48352.02843 [117,] 13531.53605 139121.44907 [118,] 45710.09582 13531.53605 [119,] -10569.91482 45710.09582 [120,] 67000.51573 -10569.91482 [121,] -63847.53200 67000.51573 [122,] -83658.38392 -63847.53200 [123,] -83692.19094 -83658.38392 [124,] -4542.63499 -83692.19094 [125,] -55095.83489 -4542.63499 [126,] 39469.87360 -55095.83489 [127,] -88664.20912 39469.87360 [128,] 76854.27688 -88664.20912 [129,] -7055.11885 76854.27688 [130,] -92.31693 -7055.11885 [131,] -32936.44756 -92.31693 [132,] -82425.52166 -32936.44756 [133,] 23176.76524 -82425.52166 [134,] 105923.87129 23176.76524 [135,] -131791.18448 105923.87129 [136,] 80091.90759 -131791.18448 [137,] -33774.23727 80091.90759 [138,] 778.89371 -33774.23727 [139,] 4931.61292 778.89371 [140,] -4751.26319 4931.61292 [141,] 122800.83118 -4751.26319 [142,] -5846.67779 122800.83118 [143,] 52521.95448 -5846.67779 [144,] 16748.33190 52521.95448 [145,] -173966.35420 16748.33190 [146,] -39974.20471 -173966.35420 [147,] -2122.12227 -39974.20471 [148,] -6941.54356 -2122.12227 [149,] 3660.23951 -6941.54356 [150,] -6844.54356 3660.23951 [151,] -6487.54356 -6844.54356 [152,] -6942.54356 -6487.54356 [153,] -6942.54356 -6942.54356 [154,] 12140.45195 -6942.54356 [155,] 105004.33628 12140.45195 [156,] -6942.54356 105004.33628 [157,] -6739.54356 -6942.54356 [158,] -858.61855 -6739.54356 [159,] 17666.24939 -858.61855 [160,] 3542.83699 17666.24939 [161,] -61458.00813 3542.83699 [162,] -5973.54356 -61458.00813 [163,] 11930.77409 -5973.54356 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -66863.99336 26014.33716 2 -22045.90083 -66863.99336 3 -32917.39497 -22045.90083 4 17347.27995 -32917.39497 5 -113689.23115 17347.27995 6 188774.38840 -113689.23115 7 -51447.51810 188774.38840 8 -34600.59867 -51447.51810 9 27118.86719 -34600.59867 10 51474.97894 27118.86719 11 -72914.73342 51474.97894 12 -30936.57162 -72914.73342 13 57720.56319 -30936.57162 14 -1017.59820 57720.56319 15 35981.93753 -1017.59820 16 7407.66121 35981.93753 17 47408.98690 7407.66121 18 -27415.46903 47408.98690 19 -38949.76578 -27415.46903 20 3066.82908 -38949.76578 21 186764.96890 3066.82908 22 -6691.97521 186764.96890 23 -124232.18809 -6691.97521 24 -22326.20397 -124232.18809 25 -28313.34763 -22326.20397 26 25265.13240 -28313.34763 27 30955.34467 25265.13240 28 -1115.49782 30955.34467 29 38664.61788 -1115.49782 30 -20175.98815 38664.61788 31 -51018.86273 -20175.98815 32 22401.05472 -51018.86273 33 -103034.70398 22401.05472 34 86699.74409 -103034.70398 35 49274.13094 86699.74409 36 126626.38107 49274.13094 37 17518.54861 126626.38107 38 23750.87093 17518.54861 39 61747.56462 23750.87093 40 84320.14205 61747.56462 41 -33778.51065 84320.14205 42 -13660.09716 -33778.51065 43 -39635.39756 -13660.09716 44 -41964.79812 -39635.39756 45 171747.22985 -41964.79812 46 -44579.92174 171747.22985 47 -58940.82959 -44579.92174 48 12474.26936 -58940.82959 49 -58050.26400 12474.26936 50 -12059.75123 -58050.26400 51 -64.55569 -12059.75123 52 80315.42898 -64.55569 53 -37842.86176 80315.42898 54 17180.11284 -37842.86176 55 40047.80440 17180.11284 56 -49686.38021 40047.80440 57 44031.41093 -49686.38021 58 45107.58947 44031.41093 59 17986.76390 45107.58947 60 57556.70213 17986.76390 61 61237.08940 57556.70213 62 -11346.69323 61237.08940 63 5413.85567 -11346.69323 64 -5107.75651 5413.85567 65 38668.91001 -5107.75651 66 -58711.75442 38668.91001 67 -20100.84552 -58711.75442 68 -14252.13733 -20100.84552 69 -39059.53398 -14252.13733 70 -19070.63908 -39059.53398 71 -40796.89958 -19070.63908 72 -30541.14257 -40796.89958 73 -28544.15990 -30541.14257 74 -54233.82129 -28544.15990 75 70938.86043 -54233.82129 76 33852.67758 70938.86043 77 -115210.13005 33852.67758 78 -23473.80460 -115210.13005 79 -28468.11015 -23473.80460 80 -9139.58276 -28468.11015 81 108419.46302 -9139.58276 82 1888.89604 108419.46302 83 27298.41314 1888.89604 84 3543.49293 27298.41314 85 6362.27810 3543.49293 86 -83200.37423 6362.27810 87 100185.52580 -83200.37423 88 32089.69275 100185.52580 89 -106796.81017 32089.69275 90 52989.27986 -106796.81017 91 -32284.90675 52989.27986 92 -606.68705 -32284.90675 93 40764.55882 -606.68705 94 25757.83865 40764.55882 95 -63329.98790 25757.83865 96 -32690.72280 -63329.98790 97 5503.35568 -32690.72280 98 -28627.34056 5503.35568 99 123263.43870 -28627.34056 100 -35631.35175 123263.43870 101 13588.48089 -35631.35175 102 8311.61669 13588.48089 103 -71281.34813 8311.61669 104 54919.45473 -71281.34813 105 -13692.24219 54919.45473 106 11376.07488 -13692.24219 107 51387.96666 11376.07488 108 -128250.90808 51387.96666 109 -155324.45125 -128250.90808 110 19460.70797 -155324.45125 111 25650.17706 19460.70797 112 14319.19543 25650.17706 113 -22062.76002 14319.19543 114 -1452.78134 -22062.76002 115 -48352.02843 -1452.78134 116 139121.44907 -48352.02843 117 13531.53605 139121.44907 118 45710.09582 13531.53605 119 -10569.91482 45710.09582 120 67000.51573 -10569.91482 121 -63847.53200 67000.51573 122 -83658.38392 -63847.53200 123 -83692.19094 -83658.38392 124 -4542.63499 -83692.19094 125 -55095.83489 -4542.63499 126 39469.87360 -55095.83489 127 -88664.20912 39469.87360 128 76854.27688 -88664.20912 129 -7055.11885 76854.27688 130 -92.31693 -7055.11885 131 -32936.44756 -92.31693 132 -82425.52166 -32936.44756 133 23176.76524 -82425.52166 134 105923.87129 23176.76524 135 -131791.18448 105923.87129 136 80091.90759 -131791.18448 137 -33774.23727 80091.90759 138 778.89371 -33774.23727 139 4931.61292 778.89371 140 -4751.26319 4931.61292 141 122800.83118 -4751.26319 142 -5846.67779 122800.83118 143 52521.95448 -5846.67779 144 16748.33190 52521.95448 145 -173966.35420 16748.33190 146 -39974.20471 -173966.35420 147 -2122.12227 -39974.20471 148 -6941.54356 -2122.12227 149 3660.23951 -6941.54356 150 -6844.54356 3660.23951 151 -6487.54356 -6844.54356 152 -6942.54356 -6487.54356 153 -6942.54356 -6942.54356 154 12140.45195 -6942.54356 155 105004.33628 12140.45195 156 -6942.54356 105004.33628 157 -6739.54356 -6942.54356 158 -858.61855 -6739.54356 159 17666.24939 -858.61855 160 3542.83699 17666.24939 161 -61458.00813 3542.83699 162 -5973.54356 -61458.00813 163 11930.77409 -5973.54356 > 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/7kc9a1323799974.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/85bn71323799974.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/904m51323799974.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/10dzdv1323799974.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11ynws1323799974.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12qi1o1323799974.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13dsyi1323799975.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14n3571323799975.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/15gfil1323799975.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/16q5sq1323799975.tab") + } > > try(system("convert tmp/12pgf1323799974.ps tmp/12pgf1323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/248d81323799974.ps tmp/248d81323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/3hz911323799974.ps tmp/3hz911323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/4u3l81323799974.ps tmp/4u3l81323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/5xvra1323799974.ps tmp/5xvra1323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/64u4m1323799974.ps tmp/64u4m1323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/7kc9a1323799974.ps tmp/7kc9a1323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/85bn71323799974.ps tmp/85bn71323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/904m51323799974.ps tmp/904m51323799974.png",intern=TRUE)) character(0) > try(system("convert tmp/10dzdv1323799974.ps tmp/10dzdv1323799974.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.006 0.636 5.654