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. 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+ ,0 + ,126 + ,0 + ,63 + ,0 + ,7 + ,190 + ,0 + ,70 + ,0 + ,133 + ,0 + ,9 + ,0 + ,4 + ,220 + ,0 + ,40 + ,0 + ,88 + ,0 + ,0 + ,0 + ,3 + ,80 + ,0 + ,30 + ,0 + ,24 + ,0 + ,58 + ,0 + ,46 + ,279 + ,0 + ,414 + ,0 + ,1426 + ,0 + ,18 + ,0 + ,31 + ,72 + ,0 + ,248 + ,0 + ,279 + ,0 + ,42 + ,0 + ,21 + ,144 + ,0 + ,168 + ,0 + ,378 + ,0 + ,26 + ,0 + ,7 + ,63 + ,0 + ,49 + ,0 + ,63 + ,0 + ,38 + ,0 + ,29 + ,35 + ,0 + ,203 + ,0 + ,145 + ,0 + ,1 + ,0 + ,5 + ,44 + ,0 + ,20 + ,0 + ,55 + ,0) + ,dim=c(9 + ,269) + ,dimnames=list(c('lfm' + ,'uk' + ,'spr' + ,'hours_blogs' + ,'hours_uk' + ,'hours_spr' + ,'blogs_uk' + ,'blogs_spr' + ,'uk_spr') + ,1:269)) > y <- array(NA,dim=c(9,269),dimnames=list(c('lfm','uk','spr','hours_blogs','hours_uk','hours_spr','blogs_uk','blogs_spr','uk_spr'),1:269)) > 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 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 lfm uk spr hours_blogs hours_uk hours_spr blogs_uk blogs_spr uk_spr 1 122 1 9 8976 102 918 88 792 9 2 114 1 3 10494 99 297 106 318 3 3 140 1 2 6790 97 194 70 140 2 4 143 1 21 5740 82 1722 70 1470 21 5 122 1 10 4312 77 770 56 560 10 6 127 1 2 3250 65 130 50 100 2 7 113 1 4 3072 64 256 48 192 4 8 118 1 4 4402 62 248 71 284 4 9 161 1 4 3782 62 248 61 244 4 10 134 1 5 4092 62 310 66 330 5 11 96 1 2 4880 61 122 80 160 2 12 104 1 3 2183 59 177 37 111 3 13 135 1 3 3021 57 171 53 159 3 14 110 1 6 2184 56 336 39 234 6 15 128 1 3 2160 54 162 40 120 3 16 142 1 4 3186 54 216 59 236 4 17 117 1 3 2226 53 159 42 126 3 18 94 1 13 1716 52 676 33 429 13 19 135 1 3 1836 51 153 36 108 3 20 121 1 4 2907 51 204 57 228 4 21 103 1 8 1938 51 408 38 304 8 22 118 1 8 4900 50 400 98 784 8 23 127 1 3 2150 50 150 43 129 3 24 116 1 4 3650 50 200 73 292 4 25 129 1 2 2548 49 98 52 104 2 26 115 1 5 2597 49 245 53 265 5 27 135 1 4 2499 49 196 51 204 4 28 133 1 3 1536 48 144 32 96 3 29 113 1 3 2064 48 144 43 129 3 30 111 1 2 2491 47 94 53 106 2 31 92 1 3 2350 47 141 50 150 3 32 118 1 3 2300 46 138 50 150 3 33 134 1 3 2576 46 138 56 168 3 34 106 1 5 2385 45 225 53 265 5 35 137 1 3 2115 45 135 47 141 3 36 100 1 4 1890 45 180 42 168 4 37 102 1 3 1276 44 132 29 87 3 38 134 1 4 2322 43 172 54 216 4 39 130 1 8 1680 42 336 40 320 8 40 144 1 3 1722 42 126 41 123 3 41 120 1 4 1554 42 168 37 148 4 42 91 1 2 1050 42 84 25 50 2 43 100 1 5 1134 42 210 27 135 5 44 134 1 4 2562 42 168 61 244 4 45 161 1 7 2214 41 287 54 378 7 46 128 1 3 1435 41 123 35 105 3 47 124 1 4 2255 41 164 55 220 4 48 115 1 6 1927 41 246 47 282 6 49 123 1 7 2009 41 287 49 343 7 50 117 1 20 1558 41 820 38 760 20 51 111 1 49 2132 41 2009 52 2548 49 52 146 1 3 1400 40 120 35 105 3 53 101 1 3 2080 40 120 52 156 3 54 131 1 6 2160 40 240 54 324 6 55 122 1 6 1600 40 240 40 240 6 56 78 1 4 2080 40 160 52 208 4 57 120 1 5 1326 39 195 34 170 5 58 115 1 4 1989 39 156 51 204 4 59 142 1 4 1634 38 152 43 172 4 60 94 1 31 1520 38 1178 40 1240 31 61 114 1 3 1368 36 108 38 114 3 62 108 1 3 1188 36 108 33 99 3 63 119 1 4 945 35 140 27 108 4 64 117 1 6 1190 35 210 34 204 6 65 86 1 5 1540 35 175 44 220 5 66 138 1 3 1610 35 105 46 138 3 67 119 1 3 1700 34 102 50 150 3 68 117 1 2 1054 34 68 31 62 2 69 117 1 3 1122 34 102 33 99 3 70 76 1 3 1221 33 99 37 111 3 71 119 1 16 1584 33 528 48 768 16 72 119 1 3 1089 33 99 33 99 3 73 124 1 3 1280 32 96 40 120 3 74 116 1 3 672 32 96 21 63 3 75 118 1 2 1056 32 64 33 66 2 76 102 1 5 1271 31 155 41 205 5 77 116 1 3 1085 31 93 35 105 3 78 103 1 4 1800 30 120 60 240 4 79 117 1 5 900 30 150 30 150 5 80 108 1 2 1350 30 60 45 90 2 81 122 1 3 780 30 90 26 78 3 82 90 1 3 1189 29 87 41 123 3 83 133 1 14 1344 28 392 48 672 14 84 116 1 8 280 28 224 10 80 8 85 110 1 4 945 27 108 35 140 4 86 90 1 4 621 27 108 23 92 4 87 74 1 3 783 27 81 29 87 3 88 75 1 4 442 26 104 17 68 4 89 107 1 3 875 25 75 35 105 3 90 90 1 5 1250 25 125 50 250 5 91 96 1 3 825 25 75 33 99 3 92 115 1 3 552 24 72 23 69 3 93 91 1 2 528 24 48 22 44 2 94 77 1 4 1196 23 92 52 208 4 95 108 1 31 874 23 713 38 1178 31 96 83 1 2 736 23 46 32 64 2 97 77 1 5 644 23 115 28 140 5 98 99 1 5 989 23 115 43 215 5 99 115 1 2 704 22 44 32 64 2 100 99 1 2 770 22 44 35 70 2 101 106 1 3 550 22 66 25 75 3 102 77 1 8 308 22 176 14 112 8 103 115 1 6 340 20 120 17 102 6 104 67 1 3 342 19 57 18 54 3 105 8 1 2 228 19 38 12 24 2 106 69 1 5 459 17 85 27 135 5 107 88 1 3 476 17 51 28 84 3 108 107 1 5 192 16 80 12 60 5 109 120 1 5 336 16 80 21 105 5 110 3 1 4 45 5 20 9 36 4 111 1 1 2 44 4 8 11 22 2 112 0 1 4 9 3 12 3 12 4 113 111 0 4 17316 0 624 0 444 0 114 69 0 8 14933 0 872 0 1096 0 115 116 0 3 11648 0 312 0 336 0 116 103 0 4 7154 0 392 0 292 0 117 139 0 3 7722 0 234 0 297 0 118 135 0 3 8855 0 231 0 345 0 119 113 0 3 6935 0 219 0 285 0 120 99 0 4 4260 0 284 0 240 0 121 76 0 4 6298 0 268 0 376 0 122 110 0 5 4480 0 320 0 350 0 123 121 0 2 5394 0 124 0 174 0 124 95 0 3 6222 0 183 0 306 0 125 66 0 4 4002 0 232 0 276 0 126 111 0 7 6438 0 406 0 777 0 127 77 0 3 3080 0 168 0 165 0 128 101 0 4 6608 0 224 0 472 0 129 108 0 3 4680 0 156 0 270 0 130 135 0 4 4131 0 204 0 324 0 131 70 0 4 4488 0 204 0 352 0 132 124 0 3 3150 0 150 0 189 0 133 92 0 6 4116 0 294 0 504 0 134 104 0 4 4263 0 196 0 348 0 135 113 0 4 3744 0 192 0 312 0 136 95 0 4 4371 0 188 0 372 0 137 89 0 4 3243 0 188 0 276 0 138 83 0 3 3082 0 138 0 201 0 139 96 0 6 2745 0 270 0 366 0 140 95 0 4 5535 0 180 0 492 0 141 110 0 6 4095 0 270 0 546 0 142 106 0 6 4410 0 270 0 588 0 143 78 0 2 1672 0 88 0 76 0 144 115 0 3 3168 0 132 0 216 0 145 74 0 3 2596 0 132 0 177 0 146 93 0 2 3354 0 86 0 156 0 147 88 0 4 2494 0 172 0 232 0 148 104 0 5 4074 0 210 0 485 0 149 86 0 3 2829 0 123 0 207 0 150 104 0 7 2050 0 287 0 350 0 151 99 0 4 2640 0 160 0 264 0 152 101 0 3 2730 0 117 0 210 0 153 53 0 4 2535 0 156 0 260 0 154 96 0 4 2691 0 156 0 276 0 155 58 0 3 1911 0 117 0 147 0 156 117 0 3 2808 0 117 0 216 0 157 82 0 4 2886 0 156 0 296 0 158 57 0 5 3198 0 195 0 410 0 159 71 0 3 2318 0 114 0 183 0 160 105 0 4 2736 0 152 0 288 0 161 60 0 6 2926 0 228 0 462 0 162 77 0 5 2432 0 190 0 320 0 163 73 0 7 851 0 259 0 161 0 164 78 0 5 1443 0 185 0 195 0 165 81 0 5 3219 0 185 0 435 0 166 101 0 3 1656 0 108 0 138 0 167 118 0 5 2376 0 180 0 330 0 168 59 0 4 2052 0 144 0 228 0 169 101 0 9 1728 0 324 0 432 0 170 22 0 3 2700 0 108 0 225 0 171 77 0 3 1260 0 108 0 105 0 172 100 0 3 1855 0 105 0 159 0 173 39 0 3 2100 0 105 0 180 0 174 42 0 15 680 0 510 0 300 0 175 80 0 4 2244 0 136 0 264 0 176 48 0 6 1156 0 204 0 204 0 177 131 0 3 2720 0 102 0 240 0 178 46 0 3 2142 0 102 0 189 0 179 89 0 3 1518 0 99 0 138 0 180 51 0 5 660 0 165 0 100 0 181 108 0 3 2409 0 99 0 219 0 182 86 0 4 1881 0 132 0 228 0 183 105 0 7 2145 0 231 0 455 0 184 85 0 4 2310 0 132 0 280 0 185 103 0 5 1696 0 160 0 265 0 186 83 0 7 1920 0 224 0 420 0 187 77 0 14 1088 0 448 0 476 0 188 26 0 25 576 0 800 0 450 0 189 73 0 6 1568 0 192 0 294 0 190 42 0 4 837 0 124 0 108 0 191 71 0 3 1395 0 93 0 135 0 192 105 0 62 279 0 1922 0 558 0 193 73 0 5 690 0 150 0 115 0 194 98 0 10 1830 0 300 0 610 0 195 108 0 4 1943 0 116 0 268 0 196 57 0 5 2088 0 145 0 360 0 197 37 0 5 1682 0 145 0 290 0 198 70 0 4 1540 0 112 0 220 0 199 73 0 10 924 0 280 0 330 0 200 47 0 5 1120 0 140 0 200 0 201 73 0 3 1596 0 84 0 171 0 202 91 0 3 1708 0 84 0 183 0 203 110 0 17 2436 0 476 0 1479 0 204 78 0 4 1755 0 108 0 260 0 205 92 0 6 2295 0 162 0 510 0 206 52 0 3 2295 0 81 0 255 0 207 88 0 4 1404 0 104 0 216 0 208 100 0 8 624 0 208 0 192 0 209 33 0 3 806 0 78 0 93 0 210 42 0 4 1664 0 104 0 256 0 211 81 0 4 1750 0 100 0 280 0 212 67 0 47 50 0 1175 0 94 0 213 8 0 8 648 0 192 0 216 0 214 46 0 3 696 0 72 0 87 0 215 83 0 5 1632 0 120 0 340 0 216 87 0 3 1008 0 72 0 126 0 217 82 0 4 1872 0 96 0 312 0 218 63 0 30 312 0 720 0 390 0 219 27 0 4 1248 0 96 0 208 0 220 14 0 12 575 0 276 0 300 0 221 83 0 8 874 0 184 0 304 0 222 168 0 3 920 0 69 0 120 0 223 67 0 8 966 0 184 0 336 0 224 21 0 4 920 0 92 0 160 0 225 55 0 3 1702 0 69 0 222 0 226 54 0 4 1679 0 92 0 292 0 227 118 0 4 1232 0 88 0 224 0 228 69 0 21 66 0 462 0 63 0 229 77 0 12 189 0 252 0 108 0 230 72 0 3 1428 0 63 0 204 0 231 53 0 3 588 0 63 0 84 0 232 40 0 4 756 0 84 0 144 0 233 102 0 6 760 0 120 0 228 0 234 25 0 17 1100 0 340 0 935 0 235 31 0 3 720 0 60 0 108 0 236 77 0 18 340 0 360 0 306 0 237 38 0 5 1080 0 100 0 270 0 238 23 0 5 1083 0 95 0 285 0 239 91 0 16 570 0 304 0 480 0 240 58 0 10 760 0 190 0 400 0 241 42 0 5 666 0 90 0 185 0 242 44 0 4 828 0 72 0 184 0 243 58 0 3 576 0 54 0 96 0 244 35 0 5 612 0 90 0 170 0 245 88 0 4 396 0 72 0 88 0 246 25 0 5 1003 0 85 0 295 0 247 39 0 10 544 0 170 0 320 0 248 48 0 12 288 0 192 0 216 0 249 64 0 4 448 0 64 0 112 0 250 65 0 9 510 0 135 0 306 0 251 95 0 12 435 0 180 0 348 0 252 29 0 10 360 0 150 0 240 0 253 2 0 9 360 0 135 0 216 0 254 83 0 17 322 0 238 0 391 0 255 11 0 6 559 0 78 0 258 0 256 16 0 3 364 0 39 0 84 0 257 9 0 4 228 0 48 0 76 0 258 46 0 19 176 0 209 0 304 0 259 41 0 3 440 0 33 0 120 0 260 14 0 9 140 0 90 0 126 0 261 63 0 7 190 0 70 0 133 0 262 9 0 4 220 0 40 0 88 0 263 0 0 3 80 0 30 0 24 0 264 58 0 46 279 0 414 0 1426 0 265 18 0 31 72 0 248 0 279 0 266 42 0 21 144 0 168 0 378 0 267 26 0 7 63 0 49 0 63 0 268 38 0 29 35 0 203 0 145 0 269 1 0 5 44 0 20 0 55 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) uk spr hours_blogs hours_uk hours_spr 60.09002 26.77348 -0.73648 0.00418 0.28403 0.02172 blogs_uk blogs_spr uk_spr 0.07468 0.01442 -0.72865 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -85.566 -16.476 3.941 18.096 103.045 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 60.090020 4.219429 14.241 < 2e-16 *** uk 26.773480 9.630765 2.780 0.005833 ** spr -0.736477 0.503069 -1.464 0.144411 hours_blogs 0.004180 0.001204 3.472 0.000606 *** hours_uk 0.284033 0.250072 1.136 0.257086 hours_spr 0.021720 0.017937 1.211 0.227039 blogs_uk 0.074676 0.240888 0.310 0.756807 blogs_spr 0.014416 0.012737 1.132 0.258721 uk_spr -0.728646 0.729634 -0.999 0.318895 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 27.56 on 260 degrees of freedom Multiple R-squared: 0.4267, Adjusted R-squared: 0.409 F-statistic: 24.19 on 8 and 260 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,] 5.195834e-01 9.608332e-01 0.4804166 [2,] 4.311287e-01 8.622575e-01 0.5688713 [3,] 3.018063e-01 6.036127e-01 0.6981937 [4,] 2.191593e-01 4.383186e-01 0.7808407 [5,] 1.629367e-01 3.258734e-01 0.8370633 [6,] 9.952933e-02 1.990587e-01 0.9004707 [7,] 6.057363e-02 1.211473e-01 0.9394264 [8,] 4.785624e-02 9.571248e-02 0.9521438 [9,] 2.925537e-02 5.851075e-02 0.9707446 [10,] 2.269686e-02 4.539371e-02 0.9773031 [11,] 4.590962e-02 9.181924e-02 0.9540904 [12,] 2.851653e-02 5.703307e-02 0.9714835 [13,] 1.744947e-02 3.489894e-02 0.9825505 [14,] 1.049814e-02 2.099628e-02 0.9895019 [15,] 6.077410e-03 1.215482e-02 0.9939226 [16,] 4.517734e-03 9.035468e-03 0.9954823 [17,] 2.656653e-03 5.313306e-03 0.9973433 [18,] 1.751247e-03 3.502494e-03 0.9982488 [19,] 1.175042e-03 2.350084e-03 0.9988250 [20,] 2.131657e-03 4.263314e-03 0.9978683 [21,] 1.198552e-03 2.397104e-03 0.9988014 [22,] 9.691790e-04 1.938358e-03 0.9990308 [23,] 6.348136e-04 1.269627e-03 0.9993652 [24,] 5.506414e-04 1.101283e-03 0.9994494 [25,] 4.937863e-04 9.875726e-04 0.9995062 [26,] 3.910889e-04 7.821778e-04 0.9996089 [27,] 3.488503e-04 6.977005e-04 0.9996511 [28,] 4.188067e-04 8.376133e-04 0.9995812 [29,] 5.328676e-04 1.065735e-03 0.9994671 [30,] 3.066567e-04 6.133134e-04 0.9996933 [31,] 5.434818e-04 1.086964e-03 0.9994565 [32,] 3.739253e-04 7.478507e-04 0.9996261 [33,] 2.725177e-04 5.450354e-04 0.9997275 [34,] 1.175831e-03 2.351662e-03 0.9988242 [35,] 8.262198e-04 1.652440e-03 0.9991738 [36,] 5.123022e-04 1.024604e-03 0.9994877 [37,] 3.205524e-04 6.411047e-04 0.9996794 [38,] 1.950758e-04 3.901517e-04 0.9998049 [39,] 1.227979e-04 2.455957e-04 0.9998772 [40,] 9.327609e-05 1.865522e-04 0.9999067 [41,] 1.326850e-04 2.653701e-04 0.9998673 [42,] 1.344006e-04 2.688013e-04 0.9998656 [43,] 8.796964e-05 1.759393e-04 0.9999120 [44,] 5.336363e-05 1.067273e-04 0.9999466 [45,] 2.661763e-04 5.323527e-04 0.9997338 [46,] 1.698048e-04 3.396096e-04 0.9998302 [47,] 1.104274e-04 2.208548e-04 0.9998896 [48,] 1.168211e-04 2.336422e-04 0.9998832 [49,] 1.028439e-04 2.056878e-04 0.9998972 [50,] 6.485731e-05 1.297146e-04 0.9999351 [51,] 4.399330e-05 8.798661e-05 0.9999560 [52,] 2.708364e-05 5.416727e-05 0.9999729 [53,] 1.644896e-05 3.289791e-05 0.9999836 [54,] 3.155135e-05 6.310269e-05 0.9999684 [55,] 2.901402e-05 5.802803e-05 0.9999710 [56,] 1.756167e-05 3.512335e-05 0.9999824 [57,] 1.050943e-05 2.101885e-05 0.9999895 [58,] 6.240310e-06 1.248062e-05 0.9999938 [59,] 2.217919e-05 4.435838e-05 0.9999778 [60,] 1.496995e-05 2.993990e-05 0.9999850 [61,] 9.332840e-06 1.866568e-05 0.9999907 [62,] 6.071181e-06 1.214236e-05 0.9999939 [63,] 3.676586e-06 7.353171e-06 0.9999963 [64,] 2.179604e-06 4.359207e-06 0.9999978 [65,] 1.554905e-06 3.109811e-06 0.9999984 [66,] 9.068051e-07 1.813610e-06 0.9999991 [67,] 6.937676e-07 1.387535e-06 0.9999993 [68,] 4.178064e-07 8.356127e-07 0.9999996 [69,] 2.533532e-07 5.067064e-07 0.9999997 [70,] 1.599397e-07 3.198795e-07 0.9999998 [71,] 1.850237e-07 3.700474e-07 0.9999998 [72,] 1.582149e-07 3.164298e-07 0.9999998 [73,] 1.230575e-07 2.461150e-07 0.9999999 [74,] 6.968534e-08 1.393707e-07 0.9999999 [75,] 6.284697e-08 1.256939e-07 0.9999999 [76,] 1.704041e-07 3.408083e-07 0.9999998 [77,] 2.541352e-07 5.082704e-07 0.9999997 [78,] 1.471108e-07 2.942216e-07 0.9999999 [79,] 1.393824e-07 2.787647e-07 0.9999999 [80,] 8.892808e-08 1.778562e-07 0.9999999 [81,] 6.243700e-08 1.248740e-07 0.9999999 [82,] 4.175191e-08 8.350382e-08 1.0000000 [83,] 7.617630e-08 1.523526e-07 0.9999999 [84,] 6.054624e-08 1.210925e-07 0.9999999 [85,] 5.098241e-08 1.019648e-07 0.9999999 [86,] 5.650562e-08 1.130112e-07 0.9999999 [87,] 4.968117e-08 9.936235e-08 1.0000000 [88,] 3.755675e-08 7.511350e-08 1.0000000 [89,] 2.178561e-08 4.357121e-08 1.0000000 [90,] 1.387305e-08 2.774610e-08 1.0000000 [91,] 1.578483e-08 3.156966e-08 1.0000000 [92,] 1.593665e-08 3.187330e-08 1.0000000 [93,] 1.879290e-08 3.758580e-08 1.0000000 [94,] 3.548721e-06 7.097442e-06 0.9999965 [95,] 3.253137e-06 6.506274e-06 0.9999967 [96,] 2.177820e-06 4.355641e-06 0.9999978 [97,] 2.624060e-06 5.248120e-06 0.9999974 [98,] 3.744903e-06 7.489806e-06 0.9999963 [99,] 1.914918e-05 3.829835e-05 0.9999809 [100,] 5.284292e-05 1.056858e-04 0.9999472 [101,] 6.842674e-05 1.368535e-04 0.9999316 [102,] 8.298992e-05 1.659798e-04 0.9999170 [103,] 1.080894e-03 2.161788e-03 0.9989191 [104,] 1.054339e-02 2.108677e-02 0.9894566 [105,] 3.513536e-02 7.027072e-02 0.9648646 [106,] 2.914728e-02 5.829457e-02 0.9708527 [107,] 2.386302e-02 4.772603e-02 0.9761370 [108,] 2.493447e-02 4.986894e-02 0.9750655 [109,] 3.039840e-02 6.079681e-02 0.9696016 [110,] 5.174487e-02 1.034897e-01 0.9482551 [111,] 4.294319e-02 8.588638e-02 0.9570568 [112,] 3.698002e-02 7.396005e-02 0.9630200 [113,] 3.841297e-02 7.682594e-02 0.9615870 [114,] 5.636313e-02 1.127263e-01 0.9436369 [115,] 5.585320e-02 1.117064e-01 0.9441468 [116,] 6.079474e-02 1.215895e-01 0.9392053 [117,] 6.155531e-02 1.231106e-01 0.9384447 [118,] 5.169679e-02 1.033936e-01 0.9483032 [119,] 5.303821e-02 1.060764e-01 0.9469618 [120,] 7.056689e-02 1.411338e-01 0.9294331 [121,] 6.873602e-02 1.374720e-01 0.9312640 [122,] 6.100762e-02 1.220152e-01 0.9389924 [123,] 5.129274e-02 1.025855e-01 0.9487073 [124,] 4.335218e-02 8.670436e-02 0.9566478 [125,] 3.829004e-02 7.658009e-02 0.9617100 [126,] 3.338978e-02 6.677956e-02 0.9666102 [127,] 3.148357e-02 6.296714e-02 0.9685164 [128,] 2.572413e-02 5.144827e-02 0.9742759 [129,] 2.668302e-02 5.336603e-02 0.9733170 [130,] 2.245882e-02 4.491765e-02 0.9775412 [131,] 1.915485e-02 3.830969e-02 0.9808452 [132,] 1.907493e-02 3.814985e-02 0.9809251 [133,] 1.643863e-02 3.287726e-02 0.9835614 [134,] 1.621660e-02 3.243321e-02 0.9837834 [135,] 1.343747e-02 2.687495e-02 0.9865625 [136,] 1.100093e-02 2.200187e-02 0.9889991 [137,] 8.887008e-03 1.777402e-02 0.9911130 [138,] 7.414204e-03 1.482841e-02 0.9925858 [139,] 6.367289e-03 1.273458e-02 0.9936327 [140,] 5.038512e-03 1.007702e-02 0.9949615 [141,] 4.007447e-03 8.014894e-03 0.9959926 [142,] 6.212662e-03 1.242532e-02 0.9937873 [143,] 4.854439e-03 9.708879e-03 0.9951456 [144,] 5.551922e-03 1.110384e-02 0.9944481 [145,] 5.279893e-03 1.055979e-02 0.9947201 [146,] 4.396562e-03 8.793125e-03 0.9956034 [147,] 6.923526e-03 1.384705e-02 0.9930765 [148,] 6.324211e-03 1.264842e-02 0.9936758 [149,] 5.115127e-03 1.023025e-02 0.9948849 [150,] 6.739911e-03 1.347982e-02 0.9932601 [151,] 5.632351e-03 1.126470e-02 0.9943676 [152,] 4.527295e-03 9.054589e-03 0.9954727 [153,] 3.576175e-03 7.152350e-03 0.9964238 [154,] 3.282978e-03 6.565957e-03 0.9967170 [155,] 3.039190e-03 6.078380e-03 0.9969608 [156,] 3.212833e-03 6.425666e-03 0.9967872 [157,] 3.398513e-03 6.797025e-03 0.9966015 [158,] 3.124412e-03 6.248824e-03 0.9968756 [159,] 1.915720e-02 3.831441e-02 0.9808428 [160,] 1.622591e-02 3.245181e-02 0.9837741 [161,] 1.434970e-02 2.869940e-02 0.9856503 [162,] 2.555124e-02 5.110248e-02 0.9744488 [163,] 2.347765e-02 4.695531e-02 0.9765223 [164,] 1.946668e-02 3.893336e-02 0.9805333 [165,] 1.898922e-02 3.797845e-02 0.9810108 [166,] 2.211841e-02 4.423682e-02 0.9778816 [167,] 3.154917e-02 6.309834e-02 0.9684508 [168,] 2.714212e-02 5.428424e-02 0.9728579 [169,] 2.392295e-02 4.784590e-02 0.9760770 [170,] 2.076839e-02 4.153677e-02 0.9792316 [171,] 1.663120e-02 3.326239e-02 0.9833688 [172,] 1.465162e-02 2.930324e-02 0.9853484 [173,] 1.156015e-02 2.312030e-02 0.9884398 [174,] 1.120389e-02 2.240779e-02 0.9887961 [175,] 8.692734e-03 1.738547e-02 0.9913073 [176,] 6.955425e-03 1.391085e-02 0.9930446 [177,] 9.022103e-03 1.804421e-02 0.9909779 [178,] 7.051099e-03 1.410220e-02 0.9929489 [179,] 7.053711e-03 1.410742e-02 0.9929463 [180,] 5.599512e-03 1.119902e-02 0.9944005 [181,] 1.044442e-02 2.088883e-02 0.9895556 [182,] 8.925072e-03 1.785014e-02 0.9910749 [183,] 7.196549e-03 1.439310e-02 0.9928035 [184,] 7.500413e-03 1.500083e-02 0.9924996 [185,] 7.417583e-03 1.483517e-02 0.9925824 [186,] 1.054495e-02 2.108990e-02 0.9894551 [187,] 8.260230e-03 1.652046e-02 0.9917398 [188,] 6.476817e-03 1.295363e-02 0.9935232 [189,] 6.008700e-03 1.201740e-02 0.9939913 [190,] 4.561853e-03 9.123706e-03 0.9954381 [191,] 3.797954e-03 7.595909e-03 0.9962020 [192,] 2.818016e-03 5.636031e-03 0.9971820 [193,] 2.082285e-03 4.164570e-03 0.9979177 [194,] 1.521236e-03 3.042472e-03 0.9984788 [195,] 1.733997e-03 3.467995e-03 0.9982660 [196,] 1.428649e-03 2.857298e-03 0.9985714 [197,] 2.149235e-03 4.298469e-03 0.9978508 [198,] 2.237094e-03 4.474187e-03 0.9977629 [199,] 2.696922e-03 5.393844e-03 0.9973031 [200,] 1.944015e-03 3.888030e-03 0.9980560 [201,] 1.867199e-03 3.734398e-03 0.9981328 [202,] 4.765936e-03 9.531872e-03 0.9952341 [203,] 3.817706e-03 7.635412e-03 0.9961823 [204,] 2.801708e-03 5.603417e-03 0.9971983 [205,] 2.706025e-03 5.412051e-03 0.9972940 [206,] 1.930679e-03 3.861358e-03 0.9980693 [207,] 2.066594e-03 4.133188e-03 0.9979334 [208,] 3.030024e-03 6.060049e-03 0.9969700 [209,] 8.065477e-03 1.613095e-02 0.9919345 [210,] 6.503848e-03 1.300770e-02 0.9934962 [211,] 2.711901e-01 5.423803e-01 0.7288099 [212,] 2.344773e-01 4.689546e-01 0.7655227 [213,] 2.668161e-01 5.336322e-01 0.7331839 [214,] 2.391143e-01 4.782286e-01 0.7608857 [215,] 2.225485e-01 4.450970e-01 0.7774515 [216,] 3.856238e-01 7.712477e-01 0.6143762 [217,] 3.921043e-01 7.842087e-01 0.6078957 [218,] 3.458126e-01 6.916253e-01 0.6541874 [219,] 3.347274e-01 6.694549e-01 0.6652726 [220,] 3.034810e-01 6.069620e-01 0.6965190 [221,] 2.665448e-01 5.330897e-01 0.7334552 [222,] 4.440257e-01 8.880514e-01 0.5559743 [223,] 6.619417e-01 6.761167e-01 0.3380583 [224,] 6.211537e-01 7.576926e-01 0.3788463 [225,] 6.011825e-01 7.976349e-01 0.3988175 [226,] 5.538603e-01 8.922794e-01 0.4461397 [227,] 5.559003e-01 8.881993e-01 0.4440997 [228,] 4.932550e-01 9.865100e-01 0.5067450 [229,] 4.369842e-01 8.739683e-01 0.5630158 [230,] 3.794045e-01 7.588091e-01 0.6205955 [231,] 3.223699e-01 6.447398e-01 0.6776301 [232,] 3.077848e-01 6.155696e-01 0.6922152 [233,] 2.582438e-01 5.164876e-01 0.7417562 [234,] 4.453096e-01 8.906191e-01 0.5546904 [235,] 4.226781e-01 8.453562e-01 0.5773219 [236,] 3.994721e-01 7.989442e-01 0.6005279 [237,] 3.361604e-01 6.723208e-01 0.6638396 [238,] 3.461110e-01 6.922220e-01 0.6538890 [239,] 3.001111e-01 6.002223e-01 0.6998889 [240,] 4.525882e-01 9.051763e-01 0.5474118 [241,] 3.764153e-01 7.528306e-01 0.6235847 [242,] 5.219508e-01 9.560985e-01 0.4780492 [243,] 4.864003e-01 9.728006e-01 0.5135997 [244,] 4.705248e-01 9.410496e-01 0.5294752 [245,] 3.850154e-01 7.700308e-01 0.6149846 [246,] 2.999980e-01 5.999959e-01 0.7000020 > postscript(file="/var/wessaorg/rcomp/tmp/1d3a01358279138.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/2gesp1358279138.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3nkb21358279138.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4h0wp1358279138.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/56c7y1358279138.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 269 Frequency = 1 1 2 3 4 5 6 -56.10103939 -59.40835356 -11.32924813 -24.20385917 -19.08848689 3.01899155 7 8 9 10 11 12 -10.93627695 -13.79836851 33.11695476 3.32629403 -36.59058931 -12.55973507 13 14 15 16 17 18 13.74859125 -6.69249620 12.92860207 19.84051055 1.76603122 -19.09196969 19 20 21 22 23 24 22.80236019 1.38428855 -10.81199814 -19.13727594 13.01340043 -8.46837367 25 26 27 28 29 30 12.98623821 -2.41179301 18.62580489 23.57578649 0.07130754 -4.22403779 31 32 33 34 35 36 -22.60059843 3.95761858 18.09625051 -8.95499945 24.43397813 -11.15354976 37 38 39 40 41 42 -4.58673990 20.19420070 21.00668325 34.83204094 12.02554165 -13.66433617 43 44 45 46 47 48 -4.73159275 18.63540233 47.77587001 21.08858679 11.08377658 4.30777677 49 50 51 52 53 54 11.51082640 9.67587141 -8.88224675 39.58409640 -10.26336951 18.61985170 55 56 57 58 59 60 14.21737263 -33.41669954 14.61634201 4.46697940 34.38070086 -11.04180665 61 62 63 64 65 66 8.76086561 4.10298102 17.49126575 13.97018366 -20.17533643 31.15497598 67 68 69 70 71 72 11.65622262 14.31766768 14.07727828 -27.45909951 13.45908286 16.56442690 73 74 75 76 77 78 20.28966775 17.07198888 15.75723370 -1.03998014 14.04368292 -4.59577257 79 80 81 82 83 84 17.51795525 3.94100672 22.72925367 -12.40025566 31.29020970 24.96868831 85 86 87 88 89 90 10.39983011 -6.65758891 -24.58948674 -19.74431773 8.01674060 -11.91722003 91 92 93 94 95 96 -2.53838408 19.13133625 -5.27708881 -24.41563449 21.06222917 -14.85424800 97 98 99 100 101 102 -18.36989625 -0.01353940 17.60700018 1.02056092 10.60223129 -12.16164267 103 104 105 106 107 108 24.47885490 -25.65517171 -84.35048980 -23.09395322 -5.69622645 18.61624377 109 110 111 112 113 114 29.69342892 -81.23676912 -85.56568404 -82.55039607 -38.48767167 -82.36571234 115 116 117 118 119 120 -2.19551531 3.22482263 39.47349646 30.11016931 17.26234074 14.41857819 121 122 123 124 125 126 -18.71438153 22.86762085 34.63162274 2.72220534 -16.89240861 9.13143729 127 128 129 130 131 132 0.21582979 4.56136621 23.27395547 51.48447780 -15.41161821 46.96816010 133 134 135 136 137 138 5.47038542 19.76041802 31.53596513 10.13669006 10.23626338 6.34007588 139 140 141 142 143 144 17.71258757 3.71438426 23.47396452 17.55161914 9.38614541 37.89462674 145 146 147 148 149 150 -0.15189055 16.24468196 13.34929474 19.00787752 10.63704126 29.21593072 151 152 153 154 155 156 23.53825642 26.13798047 -21.87824629 20.23893392 -12.52996073 41.72540344 157 158 159 160 161 162 5.13540919 -22.92297908 -1.68525298 28.96469437 -19.51582104 1.68535706 163 164 165 166 167 168 6.56120601 8.73051821 0.84601930 31.86129240 42.99249987 -13.13710301 169 170 171 172 173 174 27.04921763 -52.75737164 9.99250979 29.79178913 -32.53517668 -25.28770633 175 176 177 178 179 180 6.71501045 -19.87563038 56.07309174 -25.77534540 20.63367999 -13.19219579 181 182 183 184 185 186 34.74112993 14.83840071 29.52137596 11.29531483 32.20670214 9.11860165 187 188 189 190 191 192 6.07952067 -41.94941662 2.36516713 -22.89343334 3.32144962 39.61507568 193 194 195 196 197 198 8.79194227 22.31443830 36.35007213 -16.47581278 -33.76938245 0.81367928 199 200 201 202 203 204 5.57296495 -20.01386683 4.15765860 21.51644622 20.58580686 7.42509586 205 206 207 208 209 210 15.86359812 -20.91032629 19.61365166 35.90746318 -31.28495067 -28.04993375 211 212 213 214 215 216 10.33142931 14.43928416 -56.19134375 -17.60827815 12.26181942 21.52516725 217 218 219 220 221 222 10.44696317 2.43920098 -40.44510037 -49.97571622 16.76897845 103.04470907 223 224 225 226 227 228 -0.07695272 -44.29503017 -14.69491237 -16.37099326 50.56488423 13.15723077 229 230 231 232 233 234 17.92718796 3.84035815 -9.91805604 -24.20500680 37.25832656 -48.03260162 235 236 237 238 239 240 -32.75071560 16.51458694 -28.98699921 -44.10718740 26.78797774 -7.79578858 241 242 243 244 245 246 -21.81367890 -20.82202013 -4.84540773 -28.37168580 26.36793026 -41.69971276 247 248 249 250 251 252 -24.30508844 -11.74045825 1.97830990 2.06259976 33.00267966 -31.94816407 253 254 255 256 257 258 -59.01284739 23.27779650 -52.42164927 -45.46034676 -51.23547085 -9.75478916 259 260 261 262 263 264 -21.16673605 -43.81826208 3.83324330 -51.20126446 -59.21262127 1.07167169 265 266 267 268 269 -28.96896097 -12.32435720 -31.17056479 -7.37803685 -56.81887933 > postscript(file="/var/wessaorg/rcomp/tmp/614971358279138.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 = 269 Frequency = 1 lag(myerror, k = 1) myerror 0 -56.10103939 NA 1 -59.40835356 -56.10103939 2 -11.32924813 -59.40835356 3 -24.20385917 -11.32924813 4 -19.08848689 -24.20385917 5 3.01899155 -19.08848689 6 -10.93627695 3.01899155 7 -13.79836851 -10.93627695 8 33.11695476 -13.79836851 9 3.32629403 33.11695476 10 -36.59058931 3.32629403 11 -12.55973507 -36.59058931 12 13.74859125 -12.55973507 13 -6.69249620 13.74859125 14 12.92860207 -6.69249620 15 19.84051055 12.92860207 16 1.76603122 19.84051055 17 -19.09196969 1.76603122 18 22.80236019 -19.09196969 19 1.38428855 22.80236019 20 -10.81199814 1.38428855 21 -19.13727594 -10.81199814 22 13.01340043 -19.13727594 23 -8.46837367 13.01340043 24 12.98623821 -8.46837367 25 -2.41179301 12.98623821 26 18.62580489 -2.41179301 27 23.57578649 18.62580489 28 0.07130754 23.57578649 29 -4.22403779 0.07130754 30 -22.60059843 -4.22403779 31 3.95761858 -22.60059843 32 18.09625051 3.95761858 33 -8.95499945 18.09625051 34 24.43397813 -8.95499945 35 -11.15354976 24.43397813 36 -4.58673990 -11.15354976 37 20.19420070 -4.58673990 38 21.00668325 20.19420070 39 34.83204094 21.00668325 40 12.02554165 34.83204094 41 -13.66433617 12.02554165 42 -4.73159275 -13.66433617 43 18.63540233 -4.73159275 44 47.77587001 18.63540233 45 21.08858679 47.77587001 46 11.08377658 21.08858679 47 4.30777677 11.08377658 48 11.51082640 4.30777677 49 9.67587141 11.51082640 50 -8.88224675 9.67587141 51 39.58409640 -8.88224675 52 -10.26336951 39.58409640 53 18.61985170 -10.26336951 54 14.21737263 18.61985170 55 -33.41669954 14.21737263 56 14.61634201 -33.41669954 57 4.46697940 14.61634201 58 34.38070086 4.46697940 59 -11.04180665 34.38070086 60 8.76086561 -11.04180665 61 4.10298102 8.76086561 62 17.49126575 4.10298102 63 13.97018366 17.49126575 64 -20.17533643 13.97018366 65 31.15497598 -20.17533643 66 11.65622262 31.15497598 67 14.31766768 11.65622262 68 14.07727828 14.31766768 69 -27.45909951 14.07727828 70 13.45908286 -27.45909951 71 16.56442690 13.45908286 72 20.28966775 16.56442690 73 17.07198888 20.28966775 74 15.75723370 17.07198888 75 -1.03998014 15.75723370 76 14.04368292 -1.03998014 77 -4.59577257 14.04368292 78 17.51795525 -4.59577257 79 3.94100672 17.51795525 80 22.72925367 3.94100672 81 -12.40025566 22.72925367 82 31.29020970 -12.40025566 83 24.96868831 31.29020970 84 10.39983011 24.96868831 85 -6.65758891 10.39983011 86 -24.58948674 -6.65758891 87 -19.74431773 -24.58948674 88 8.01674060 -19.74431773 89 -11.91722003 8.01674060 90 -2.53838408 -11.91722003 91 19.13133625 -2.53838408 92 -5.27708881 19.13133625 93 -24.41563449 -5.27708881 94 21.06222917 -24.41563449 95 -14.85424800 21.06222917 96 -18.36989625 -14.85424800 97 -0.01353940 -18.36989625 98 17.60700018 -0.01353940 99 1.02056092 17.60700018 100 10.60223129 1.02056092 101 -12.16164267 10.60223129 102 24.47885490 -12.16164267 103 -25.65517171 24.47885490 104 -84.35048980 -25.65517171 105 -23.09395322 -84.35048980 106 -5.69622645 -23.09395322 107 18.61624377 -5.69622645 108 29.69342892 18.61624377 109 -81.23676912 29.69342892 110 -85.56568404 -81.23676912 111 -82.55039607 -85.56568404 112 -38.48767167 -82.55039607 113 -82.36571234 -38.48767167 114 -2.19551531 -82.36571234 115 3.22482263 -2.19551531 116 39.47349646 3.22482263 117 30.11016931 39.47349646 118 17.26234074 30.11016931 119 14.41857819 17.26234074 120 -18.71438153 14.41857819 121 22.86762085 -18.71438153 122 34.63162274 22.86762085 123 2.72220534 34.63162274 124 -16.89240861 2.72220534 125 9.13143729 -16.89240861 126 0.21582979 9.13143729 127 4.56136621 0.21582979 128 23.27395547 4.56136621 129 51.48447780 23.27395547 130 -15.41161821 51.48447780 131 46.96816010 -15.41161821 132 5.47038542 46.96816010 133 19.76041802 5.47038542 134 31.53596513 19.76041802 135 10.13669006 31.53596513 136 10.23626338 10.13669006 137 6.34007588 10.23626338 138 17.71258757 6.34007588 139 3.71438426 17.71258757 140 23.47396452 3.71438426 141 17.55161914 23.47396452 142 9.38614541 17.55161914 143 37.89462674 9.38614541 144 -0.15189055 37.89462674 145 16.24468196 -0.15189055 146 13.34929474 16.24468196 147 19.00787752 13.34929474 148 10.63704126 19.00787752 149 29.21593072 10.63704126 150 23.53825642 29.21593072 151 26.13798047 23.53825642 152 -21.87824629 26.13798047 153 20.23893392 -21.87824629 154 -12.52996073 20.23893392 155 41.72540344 -12.52996073 156 5.13540919 41.72540344 157 -22.92297908 5.13540919 158 -1.68525298 -22.92297908 159 28.96469437 -1.68525298 160 -19.51582104 28.96469437 161 1.68535706 -19.51582104 162 6.56120601 1.68535706 163 8.73051821 6.56120601 164 0.84601930 8.73051821 165 31.86129240 0.84601930 166 42.99249987 31.86129240 167 -13.13710301 42.99249987 168 27.04921763 -13.13710301 169 -52.75737164 27.04921763 170 9.99250979 -52.75737164 171 29.79178913 9.99250979 172 -32.53517668 29.79178913 173 -25.28770633 -32.53517668 174 6.71501045 -25.28770633 175 -19.87563038 6.71501045 176 56.07309174 -19.87563038 177 -25.77534540 56.07309174 178 20.63367999 -25.77534540 179 -13.19219579 20.63367999 180 34.74112993 -13.19219579 181 14.83840071 34.74112993 182 29.52137596 14.83840071 183 11.29531483 29.52137596 184 32.20670214 11.29531483 185 9.11860165 32.20670214 186 6.07952067 9.11860165 187 -41.94941662 6.07952067 188 2.36516713 -41.94941662 189 -22.89343334 2.36516713 190 3.32144962 -22.89343334 191 39.61507568 3.32144962 192 8.79194227 39.61507568 193 22.31443830 8.79194227 194 36.35007213 22.31443830 195 -16.47581278 36.35007213 196 -33.76938245 -16.47581278 197 0.81367928 -33.76938245 198 5.57296495 0.81367928 199 -20.01386683 5.57296495 200 4.15765860 -20.01386683 201 21.51644622 4.15765860 202 20.58580686 21.51644622 203 7.42509586 20.58580686 204 15.86359812 7.42509586 205 -20.91032629 15.86359812 206 19.61365166 -20.91032629 207 35.90746318 19.61365166 208 -31.28495067 35.90746318 209 -28.04993375 -31.28495067 210 10.33142931 -28.04993375 211 14.43928416 10.33142931 212 -56.19134375 14.43928416 213 -17.60827815 -56.19134375 214 12.26181942 -17.60827815 215 21.52516725 12.26181942 216 10.44696317 21.52516725 217 2.43920098 10.44696317 218 -40.44510037 2.43920098 219 -49.97571622 -40.44510037 220 16.76897845 -49.97571622 221 103.04470907 16.76897845 222 -0.07695272 103.04470907 223 -44.29503017 -0.07695272 224 -14.69491237 -44.29503017 225 -16.37099326 -14.69491237 226 50.56488423 -16.37099326 227 13.15723077 50.56488423 228 17.92718796 13.15723077 229 3.84035815 17.92718796 230 -9.91805604 3.84035815 231 -24.20500680 -9.91805604 232 37.25832656 -24.20500680 233 -48.03260162 37.25832656 234 -32.75071560 -48.03260162 235 16.51458694 -32.75071560 236 -28.98699921 16.51458694 237 -44.10718740 -28.98699921 238 26.78797774 -44.10718740 239 -7.79578858 26.78797774 240 -21.81367890 -7.79578858 241 -20.82202013 -21.81367890 242 -4.84540773 -20.82202013 243 -28.37168580 -4.84540773 244 26.36793026 -28.37168580 245 -41.69971276 26.36793026 246 -24.30508844 -41.69971276 247 -11.74045825 -24.30508844 248 1.97830990 -11.74045825 249 2.06259976 1.97830990 250 33.00267966 2.06259976 251 -31.94816407 33.00267966 252 -59.01284739 -31.94816407 253 23.27779650 -59.01284739 254 -52.42164927 23.27779650 255 -45.46034676 -52.42164927 256 -51.23547085 -45.46034676 257 -9.75478916 -51.23547085 258 -21.16673605 -9.75478916 259 -43.81826208 -21.16673605 260 3.83324330 -43.81826208 261 -51.20126446 3.83324330 262 -59.21262127 -51.20126446 263 1.07167169 -59.21262127 264 -28.96896097 1.07167169 265 -12.32435720 -28.96896097 266 -31.17056479 -12.32435720 267 -7.37803685 -31.17056479 268 -56.81887933 -7.37803685 269 NA -56.81887933 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -59.40835356 -56.10103939 [2,] -11.32924813 -59.40835356 [3,] -24.20385917 -11.32924813 [4,] -19.08848689 -24.20385917 [5,] 3.01899155 -19.08848689 [6,] -10.93627695 3.01899155 [7,] -13.79836851 -10.93627695 [8,] 33.11695476 -13.79836851 [9,] 3.32629403 33.11695476 [10,] -36.59058931 3.32629403 [11,] -12.55973507 -36.59058931 [12,] 13.74859125 -12.55973507 [13,] -6.69249620 13.74859125 [14,] 12.92860207 -6.69249620 [15,] 19.84051055 12.92860207 [16,] 1.76603122 19.84051055 [17,] -19.09196969 1.76603122 [18,] 22.80236019 -19.09196969 [19,] 1.38428855 22.80236019 [20,] -10.81199814 1.38428855 [21,] -19.13727594 -10.81199814 [22,] 13.01340043 -19.13727594 [23,] -8.46837367 13.01340043 [24,] 12.98623821 -8.46837367 [25,] -2.41179301 12.98623821 [26,] 18.62580489 -2.41179301 [27,] 23.57578649 18.62580489 [28,] 0.07130754 23.57578649 [29,] -4.22403779 0.07130754 [30,] -22.60059843 -4.22403779 [31,] 3.95761858 -22.60059843 [32,] 18.09625051 3.95761858 [33,] -8.95499945 18.09625051 [34,] 24.43397813 -8.95499945 [35,] -11.15354976 24.43397813 [36,] -4.58673990 -11.15354976 [37,] 20.19420070 -4.58673990 [38,] 21.00668325 20.19420070 [39,] 34.83204094 21.00668325 [40,] 12.02554165 34.83204094 [41,] -13.66433617 12.02554165 [42,] -4.73159275 -13.66433617 [43,] 18.63540233 -4.73159275 [44,] 47.77587001 18.63540233 [45,] 21.08858679 47.77587001 [46,] 11.08377658 21.08858679 [47,] 4.30777677 11.08377658 [48,] 11.51082640 4.30777677 [49,] 9.67587141 11.51082640 [50,] -8.88224675 9.67587141 [51,] 39.58409640 -8.88224675 [52,] -10.26336951 39.58409640 [53,] 18.61985170 -10.26336951 [54,] 14.21737263 18.61985170 [55,] -33.41669954 14.21737263 [56,] 14.61634201 -33.41669954 [57,] 4.46697940 14.61634201 [58,] 34.38070086 4.46697940 [59,] -11.04180665 34.38070086 [60,] 8.76086561 -11.04180665 [61,] 4.10298102 8.76086561 [62,] 17.49126575 4.10298102 [63,] 13.97018366 17.49126575 [64,] -20.17533643 13.97018366 [65,] 31.15497598 -20.17533643 [66,] 11.65622262 31.15497598 [67,] 14.31766768 11.65622262 [68,] 14.07727828 14.31766768 [69,] -27.45909951 14.07727828 [70,] 13.45908286 -27.45909951 [71,] 16.56442690 13.45908286 [72,] 20.28966775 16.56442690 [73,] 17.07198888 20.28966775 [74,] 15.75723370 17.07198888 [75,] -1.03998014 15.75723370 [76,] 14.04368292 -1.03998014 [77,] -4.59577257 14.04368292 [78,] 17.51795525 -4.59577257 [79,] 3.94100672 17.51795525 [80,] 22.72925367 3.94100672 [81,] -12.40025566 22.72925367 [82,] 31.29020970 -12.40025566 [83,] 24.96868831 31.29020970 [84,] 10.39983011 24.96868831 [85,] -6.65758891 10.39983011 [86,] -24.58948674 -6.65758891 [87,] -19.74431773 -24.58948674 [88,] 8.01674060 -19.74431773 [89,] -11.91722003 8.01674060 [90,] -2.53838408 -11.91722003 [91,] 19.13133625 -2.53838408 [92,] -5.27708881 19.13133625 [93,] -24.41563449 -5.27708881 [94,] 21.06222917 -24.41563449 [95,] -14.85424800 21.06222917 [96,] -18.36989625 -14.85424800 [97,] -0.01353940 -18.36989625 [98,] 17.60700018 -0.01353940 [99,] 1.02056092 17.60700018 [100,] 10.60223129 1.02056092 [101,] -12.16164267 10.60223129 [102,] 24.47885490 -12.16164267 [103,] -25.65517171 24.47885490 [104,] -84.35048980 -25.65517171 [105,] -23.09395322 -84.35048980 [106,] -5.69622645 -23.09395322 [107,] 18.61624377 -5.69622645 [108,] 29.69342892 18.61624377 [109,] -81.23676912 29.69342892 [110,] -85.56568404 -81.23676912 [111,] -82.55039607 -85.56568404 [112,] -38.48767167 -82.55039607 [113,] -82.36571234 -38.48767167 [114,] -2.19551531 -82.36571234 [115,] 3.22482263 -2.19551531 [116,] 39.47349646 3.22482263 [117,] 30.11016931 39.47349646 [118,] 17.26234074 30.11016931 [119,] 14.41857819 17.26234074 [120,] -18.71438153 14.41857819 [121,] 22.86762085 -18.71438153 [122,] 34.63162274 22.86762085 [123,] 2.72220534 34.63162274 [124,] -16.89240861 2.72220534 [125,] 9.13143729 -16.89240861 [126,] 0.21582979 9.13143729 [127,] 4.56136621 0.21582979 [128,] 23.27395547 4.56136621 [129,] 51.48447780 23.27395547 [130,] -15.41161821 51.48447780 [131,] 46.96816010 -15.41161821 [132,] 5.47038542 46.96816010 [133,] 19.76041802 5.47038542 [134,] 31.53596513 19.76041802 [135,] 10.13669006 31.53596513 [136,] 10.23626338 10.13669006 [137,] 6.34007588 10.23626338 [138,] 17.71258757 6.34007588 [139,] 3.71438426 17.71258757 [140,] 23.47396452 3.71438426 [141,] 17.55161914 23.47396452 [142,] 9.38614541 17.55161914 [143,] 37.89462674 9.38614541 [144,] -0.15189055 37.89462674 [145,] 16.24468196 -0.15189055 [146,] 13.34929474 16.24468196 [147,] 19.00787752 13.34929474 [148,] 10.63704126 19.00787752 [149,] 29.21593072 10.63704126 [150,] 23.53825642 29.21593072 [151,] 26.13798047 23.53825642 [152,] -21.87824629 26.13798047 [153,] 20.23893392 -21.87824629 [154,] -12.52996073 20.23893392 [155,] 41.72540344 -12.52996073 [156,] 5.13540919 41.72540344 [157,] -22.92297908 5.13540919 [158,] -1.68525298 -22.92297908 [159,] 28.96469437 -1.68525298 [160,] -19.51582104 28.96469437 [161,] 1.68535706 -19.51582104 [162,] 6.56120601 1.68535706 [163,] 8.73051821 6.56120601 [164,] 0.84601930 8.73051821 [165,] 31.86129240 0.84601930 [166,] 42.99249987 31.86129240 [167,] -13.13710301 42.99249987 [168,] 27.04921763 -13.13710301 [169,] -52.75737164 27.04921763 [170,] 9.99250979 -52.75737164 [171,] 29.79178913 9.99250979 [172,] -32.53517668 29.79178913 [173,] -25.28770633 -32.53517668 [174,] 6.71501045 -25.28770633 [175,] -19.87563038 6.71501045 [176,] 56.07309174 -19.87563038 [177,] -25.77534540 56.07309174 [178,] 20.63367999 -25.77534540 [179,] -13.19219579 20.63367999 [180,] 34.74112993 -13.19219579 [181,] 14.83840071 34.74112993 [182,] 29.52137596 14.83840071 [183,] 11.29531483 29.52137596 [184,] 32.20670214 11.29531483 [185,] 9.11860165 32.20670214 [186,] 6.07952067 9.11860165 [187,] -41.94941662 6.07952067 [188,] 2.36516713 -41.94941662 [189,] -22.89343334 2.36516713 [190,] 3.32144962 -22.89343334 [191,] 39.61507568 3.32144962 [192,] 8.79194227 39.61507568 [193,] 22.31443830 8.79194227 [194,] 36.35007213 22.31443830 [195,] -16.47581278 36.35007213 [196,] -33.76938245 -16.47581278 [197,] 0.81367928 -33.76938245 [198,] 5.57296495 0.81367928 [199,] -20.01386683 5.57296495 [200,] 4.15765860 -20.01386683 [201,] 21.51644622 4.15765860 [202,] 20.58580686 21.51644622 [203,] 7.42509586 20.58580686 [204,] 15.86359812 7.42509586 [205,] -20.91032629 15.86359812 [206,] 19.61365166 -20.91032629 [207,] 35.90746318 19.61365166 [208,] -31.28495067 35.90746318 [209,] -28.04993375 -31.28495067 [210,] 10.33142931 -28.04993375 [211,] 14.43928416 10.33142931 [212,] -56.19134375 14.43928416 [213,] -17.60827815 -56.19134375 [214,] 12.26181942 -17.60827815 [215,] 21.52516725 12.26181942 [216,] 10.44696317 21.52516725 [217,] 2.43920098 10.44696317 [218,] -40.44510037 2.43920098 [219,] -49.97571622 -40.44510037 [220,] 16.76897845 -49.97571622 [221,] 103.04470907 16.76897845 [222,] -0.07695272 103.04470907 [223,] -44.29503017 -0.07695272 [224,] -14.69491237 -44.29503017 [225,] -16.37099326 -14.69491237 [226,] 50.56488423 -16.37099326 [227,] 13.15723077 50.56488423 [228,] 17.92718796 13.15723077 [229,] 3.84035815 17.92718796 [230,] -9.91805604 3.84035815 [231,] -24.20500680 -9.91805604 [232,] 37.25832656 -24.20500680 [233,] -48.03260162 37.25832656 [234,] -32.75071560 -48.03260162 [235,] 16.51458694 -32.75071560 [236,] -28.98699921 16.51458694 [237,] -44.10718740 -28.98699921 [238,] 26.78797774 -44.10718740 [239,] -7.79578858 26.78797774 [240,] -21.81367890 -7.79578858 [241,] -20.82202013 -21.81367890 [242,] -4.84540773 -20.82202013 [243,] -28.37168580 -4.84540773 [244,] 26.36793026 -28.37168580 [245,] -41.69971276 26.36793026 [246,] -24.30508844 -41.69971276 [247,] -11.74045825 -24.30508844 [248,] 1.97830990 -11.74045825 [249,] 2.06259976 1.97830990 [250,] 33.00267966 2.06259976 [251,] -31.94816407 33.00267966 [252,] -59.01284739 -31.94816407 [253,] 23.27779650 -59.01284739 [254,] -52.42164927 23.27779650 [255,] -45.46034676 -52.42164927 [256,] -51.23547085 -45.46034676 [257,] -9.75478916 -51.23547085 [258,] -21.16673605 -9.75478916 [259,] -43.81826208 -21.16673605 [260,] 3.83324330 -43.81826208 [261,] -51.20126446 3.83324330 [262,] -59.21262127 -51.20126446 [263,] 1.07167169 -59.21262127 [264,] -28.96896097 1.07167169 [265,] -12.32435720 -28.96896097 [266,] -31.17056479 -12.32435720 [267,] -7.37803685 -31.17056479 [268,] -56.81887933 -7.37803685 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -59.40835356 -56.10103939 2 -11.32924813 -59.40835356 3 -24.20385917 -11.32924813 4 -19.08848689 -24.20385917 5 3.01899155 -19.08848689 6 -10.93627695 3.01899155 7 -13.79836851 -10.93627695 8 33.11695476 -13.79836851 9 3.32629403 33.11695476 10 -36.59058931 3.32629403 11 -12.55973507 -36.59058931 12 13.74859125 -12.55973507 13 -6.69249620 13.74859125 14 12.92860207 -6.69249620 15 19.84051055 12.92860207 16 1.76603122 19.84051055 17 -19.09196969 1.76603122 18 22.80236019 -19.09196969 19 1.38428855 22.80236019 20 -10.81199814 1.38428855 21 -19.13727594 -10.81199814 22 13.01340043 -19.13727594 23 -8.46837367 13.01340043 24 12.98623821 -8.46837367 25 -2.41179301 12.98623821 26 18.62580489 -2.41179301 27 23.57578649 18.62580489 28 0.07130754 23.57578649 29 -4.22403779 0.07130754 30 -22.60059843 -4.22403779 31 3.95761858 -22.60059843 32 18.09625051 3.95761858 33 -8.95499945 18.09625051 34 24.43397813 -8.95499945 35 -11.15354976 24.43397813 36 -4.58673990 -11.15354976 37 20.19420070 -4.58673990 38 21.00668325 20.19420070 39 34.83204094 21.00668325 40 12.02554165 34.83204094 41 -13.66433617 12.02554165 42 -4.73159275 -13.66433617 43 18.63540233 -4.73159275 44 47.77587001 18.63540233 45 21.08858679 47.77587001 46 11.08377658 21.08858679 47 4.30777677 11.08377658 48 11.51082640 4.30777677 49 9.67587141 11.51082640 50 -8.88224675 9.67587141 51 39.58409640 -8.88224675 52 -10.26336951 39.58409640 53 18.61985170 -10.26336951 54 14.21737263 18.61985170 55 -33.41669954 14.21737263 56 14.61634201 -33.41669954 57 4.46697940 14.61634201 58 34.38070086 4.46697940 59 -11.04180665 34.38070086 60 8.76086561 -11.04180665 61 4.10298102 8.76086561 62 17.49126575 4.10298102 63 13.97018366 17.49126575 64 -20.17533643 13.97018366 65 31.15497598 -20.17533643 66 11.65622262 31.15497598 67 14.31766768 11.65622262 68 14.07727828 14.31766768 69 -27.45909951 14.07727828 70 13.45908286 -27.45909951 71 16.56442690 13.45908286 72 20.28966775 16.56442690 73 17.07198888 20.28966775 74 15.75723370 17.07198888 75 -1.03998014 15.75723370 76 14.04368292 -1.03998014 77 -4.59577257 14.04368292 78 17.51795525 -4.59577257 79 3.94100672 17.51795525 80 22.72925367 3.94100672 81 -12.40025566 22.72925367 82 31.29020970 -12.40025566 83 24.96868831 31.29020970 84 10.39983011 24.96868831 85 -6.65758891 10.39983011 86 -24.58948674 -6.65758891 87 -19.74431773 -24.58948674 88 8.01674060 -19.74431773 89 -11.91722003 8.01674060 90 -2.53838408 -11.91722003 91 19.13133625 -2.53838408 92 -5.27708881 19.13133625 93 -24.41563449 -5.27708881 94 21.06222917 -24.41563449 95 -14.85424800 21.06222917 96 -18.36989625 -14.85424800 97 -0.01353940 -18.36989625 98 17.60700018 -0.01353940 99 1.02056092 17.60700018 100 10.60223129 1.02056092 101 -12.16164267 10.60223129 102 24.47885490 -12.16164267 103 -25.65517171 24.47885490 104 -84.35048980 -25.65517171 105 -23.09395322 -84.35048980 106 -5.69622645 -23.09395322 107 18.61624377 -5.69622645 108 29.69342892 18.61624377 109 -81.23676912 29.69342892 110 -85.56568404 -81.23676912 111 -82.55039607 -85.56568404 112 -38.48767167 -82.55039607 113 -82.36571234 -38.48767167 114 -2.19551531 -82.36571234 115 3.22482263 -2.19551531 116 39.47349646 3.22482263 117 30.11016931 39.47349646 118 17.26234074 30.11016931 119 14.41857819 17.26234074 120 -18.71438153 14.41857819 121 22.86762085 -18.71438153 122 34.63162274 22.86762085 123 2.72220534 34.63162274 124 -16.89240861 2.72220534 125 9.13143729 -16.89240861 126 0.21582979 9.13143729 127 4.56136621 0.21582979 128 23.27395547 4.56136621 129 51.48447780 23.27395547 130 -15.41161821 51.48447780 131 46.96816010 -15.41161821 132 5.47038542 46.96816010 133 19.76041802 5.47038542 134 31.53596513 19.76041802 135 10.13669006 31.53596513 136 10.23626338 10.13669006 137 6.34007588 10.23626338 138 17.71258757 6.34007588 139 3.71438426 17.71258757 140 23.47396452 3.71438426 141 17.55161914 23.47396452 142 9.38614541 17.55161914 143 37.89462674 9.38614541 144 -0.15189055 37.89462674 145 16.24468196 -0.15189055 146 13.34929474 16.24468196 147 19.00787752 13.34929474 148 10.63704126 19.00787752 149 29.21593072 10.63704126 150 23.53825642 29.21593072 151 26.13798047 23.53825642 152 -21.87824629 26.13798047 153 20.23893392 -21.87824629 154 -12.52996073 20.23893392 155 41.72540344 -12.52996073 156 5.13540919 41.72540344 157 -22.92297908 5.13540919 158 -1.68525298 -22.92297908 159 28.96469437 -1.68525298 160 -19.51582104 28.96469437 161 1.68535706 -19.51582104 162 6.56120601 1.68535706 163 8.73051821 6.56120601 164 0.84601930 8.73051821 165 31.86129240 0.84601930 166 42.99249987 31.86129240 167 -13.13710301 42.99249987 168 27.04921763 -13.13710301 169 -52.75737164 27.04921763 170 9.99250979 -52.75737164 171 29.79178913 9.99250979 172 -32.53517668 29.79178913 173 -25.28770633 -32.53517668 174 6.71501045 -25.28770633 175 -19.87563038 6.71501045 176 56.07309174 -19.87563038 177 -25.77534540 56.07309174 178 20.63367999 -25.77534540 179 -13.19219579 20.63367999 180 34.74112993 -13.19219579 181 14.83840071 34.74112993 182 29.52137596 14.83840071 183 11.29531483 29.52137596 184 32.20670214 11.29531483 185 9.11860165 32.20670214 186 6.07952067 9.11860165 187 -41.94941662 6.07952067 188 2.36516713 -41.94941662 189 -22.89343334 2.36516713 190 3.32144962 -22.89343334 191 39.61507568 3.32144962 192 8.79194227 39.61507568 193 22.31443830 8.79194227 194 36.35007213 22.31443830 195 -16.47581278 36.35007213 196 -33.76938245 -16.47581278 197 0.81367928 -33.76938245 198 5.57296495 0.81367928 199 -20.01386683 5.57296495 200 4.15765860 -20.01386683 201 21.51644622 4.15765860 202 20.58580686 21.51644622 203 7.42509586 20.58580686 204 15.86359812 7.42509586 205 -20.91032629 15.86359812 206 19.61365166 -20.91032629 207 35.90746318 19.61365166 208 -31.28495067 35.90746318 209 -28.04993375 -31.28495067 210 10.33142931 -28.04993375 211 14.43928416 10.33142931 212 -56.19134375 14.43928416 213 -17.60827815 -56.19134375 214 12.26181942 -17.60827815 215 21.52516725 12.26181942 216 10.44696317 21.52516725 217 2.43920098 10.44696317 218 -40.44510037 2.43920098 219 -49.97571622 -40.44510037 220 16.76897845 -49.97571622 221 103.04470907 16.76897845 222 -0.07695272 103.04470907 223 -44.29503017 -0.07695272 224 -14.69491237 -44.29503017 225 -16.37099326 -14.69491237 226 50.56488423 -16.37099326 227 13.15723077 50.56488423 228 17.92718796 13.15723077 229 3.84035815 17.92718796 230 -9.91805604 3.84035815 231 -24.20500680 -9.91805604 232 37.25832656 -24.20500680 233 -48.03260162 37.25832656 234 -32.75071560 -48.03260162 235 16.51458694 -32.75071560 236 -28.98699921 16.51458694 237 -44.10718740 -28.98699921 238 26.78797774 -44.10718740 239 -7.79578858 26.78797774 240 -21.81367890 -7.79578858 241 -20.82202013 -21.81367890 242 -4.84540773 -20.82202013 243 -28.37168580 -4.84540773 244 26.36793026 -28.37168580 245 -41.69971276 26.36793026 246 -24.30508844 -41.69971276 247 -11.74045825 -24.30508844 248 1.97830990 -11.74045825 249 2.06259976 1.97830990 250 33.00267966 2.06259976 251 -31.94816407 33.00267966 252 -59.01284739 -31.94816407 253 23.27779650 -59.01284739 254 -52.42164927 23.27779650 255 -45.46034676 -52.42164927 256 -51.23547085 -45.46034676 257 -9.75478916 -51.23547085 258 -21.16673605 -9.75478916 259 -43.81826208 -21.16673605 260 3.83324330 -43.81826208 261 -51.20126446 3.83324330 262 -59.21262127 -51.20126446 263 1.07167169 -59.21262127 264 -28.96896097 1.07167169 265 -12.32435720 -28.96896097 266 -31.17056479 -12.32435720 267 -7.37803685 -31.17056479 268 -56.81887933 -7.37803685 > 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/78moz1358279138.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/85wot1358279138.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/9xxls1358279138.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/10sv3c1358279138.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11frnc1358279138.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12rtcf1358279138.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13p93j1358279138.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14r2vo1358279138.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/15u6va1358279138.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/16lerw1358279138.tab") + } > > try(system("convert tmp/1d3a01358279138.ps tmp/1d3a01358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/2gesp1358279138.ps tmp/2gesp1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/3nkb21358279138.ps tmp/3nkb21358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/4h0wp1358279138.ps tmp/4h0wp1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/56c7y1358279138.ps tmp/56c7y1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/614971358279138.ps tmp/614971358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/78moz1358279138.ps tmp/78moz1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/85wot1358279138.ps tmp/85wot1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/9xxls1358279138.ps tmp/9xxls1358279138.png",intern=TRUE)) character(0) > try(system("convert tmp/10sv3c1358279138.ps tmp/10sv3c1358279138.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.516 1.064 13.691