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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,72) + ,dim=c(7 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging') + ,1:264)) > y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > 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 Learning Connected Separate Software Happiness Depression Belonging t 1 13 41 38 12 14 12.0 53 1 2 16 39 32 11 18 11.0 83 2 3 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34 39 14 15 13.0 81 73 74 18 38 41 14 15 10.0 69 74 75 16 34 27 12 13 11.0 84 75 76 17 39 30 9 12 19.0 80 76 77 16 37 37 13 17 13.0 70 77 78 16 34 31 11 13 17.0 69 78 79 13 28 31 12 15 13.0 77 79 80 16 37 27 12 13 9.0 54 80 81 16 33 36 12 15 11.0 79 81 82 16 35 37 12 15 9.0 71 82 83 15 37 33 12 16 12.0 73 83 84 15 32 34 11 15 12.0 72 84 85 16 33 31 10 14 13.0 77 85 86 14 38 39 9 15 13.0 75 86 87 16 33 34 12 14 12.0 69 87 88 16 29 32 12 13 15.0 54 88 89 15 33 33 12 7 22.0 70 89 90 12 31 36 9 17 13.0 73 90 91 17 36 32 15 13 15.0 54 91 92 16 35 41 12 15 13.0 77 92 93 15 32 28 12 14 15.0 82 93 94 13 29 30 12 13 12.5 80 94 95 16 39 36 10 16 11.0 80 95 96 16 37 35 13 12 16.0 69 96 97 16 35 31 9 14 11.0 78 97 98 16 37 34 12 17 11.0 81 98 99 14 32 36 10 15 10.0 76 99 100 16 38 36 14 17 10.0 76 100 101 16 37 35 11 12 16.0 73 101 102 20 36 37 15 16 12.0 85 102 103 15 32 28 11 11 11.0 66 103 104 16 33 39 11 15 16.0 79 104 105 13 40 32 12 9 19.0 68 105 106 17 38 35 12 16 11.0 76 106 107 16 41 39 12 15 16.0 71 107 108 16 36 35 11 10 15.0 54 108 109 12 43 42 7 10 24.0 46 109 110 16 30 34 12 15 14.0 85 110 111 16 31 33 14 11 15.0 74 111 112 17 32 41 11 13 11.0 88 112 113 13 32 33 11 14 15.0 38 113 114 12 37 34 10 18 12.0 76 114 115 18 37 32 13 16 10.0 86 115 116 14 33 40 13 14 14.0 54 116 117 14 34 40 8 14 13.0 67 117 118 13 33 35 11 14 9.0 69 118 119 16 38 36 12 14 15.0 90 119 120 13 33 37 11 12 15.0 54 120 121 16 31 27 13 14 14.0 76 121 122 13 38 39 12 15 11.0 89 122 123 16 37 38 14 15 8.0 76 123 124 15 36 31 13 15 11.0 73 124 125 16 31 33 15 13 11.0 79 125 126 15 39 32 10 17 8.0 90 126 127 17 44 39 11 17 10.0 74 127 128 15 33 36 9 19 11.0 81 128 129 12 35 33 11 15 13.0 72 129 130 16 32 33 10 13 11.0 71 130 131 10 28 32 11 9 20.0 66 131 132 16 40 37 8 15 10.0 77 132 133 12 27 30 11 15 15.0 65 133 134 14 37 38 12 15 12.0 74 134 135 15 32 29 12 16 14.0 85 135 136 13 28 22 9 11 23.0 54 136 137 15 34 35 11 14 14.0 63 137 138 11 30 35 10 11 16.0 54 138 139 12 35 34 8 15 11.0 64 139 140 11 31 35 9 13 12.0 69 140 141 16 32 34 8 15 10.0 54 141 142 15 30 37 9 16 14.0 84 142 143 17 30 35 15 14 12.0 86 143 144 16 31 23 11 15 12.0 77 144 145 10 40 31 8 16 11.0 89 145 146 18 32 27 13 16 12.0 76 146 147 13 36 36 12 11 13.0 60 147 148 16 32 31 12 12 11.0 75 148 149 13 35 32 9 9 19.0 73 149 150 10 38 39 7 16 12.0 85 150 151 15 42 37 13 13 17.0 79 151 152 16 34 38 9 16 9.0 71 152 153 16 35 39 6 12 12.0 72 153 154 14 38 34 8 9 19.0 69 154 155 10 33 31 8 13 18.0 78 155 156 17 36 32 15 13 15.0 54 156 157 13 32 37 6 14 14.0 69 157 158 15 33 36 9 19 11.0 81 158 159 16 34 32 11 13 9.0 84 159 160 12 32 38 8 12 18.0 84 160 161 13 34 36 8 13 16.0 69 161 162 13 27 26 10 10 24.0 66 162 163 12 31 26 8 14 14.0 81 163 164 17 38 33 14 16 20.0 82 164 165 15 34 39 10 10 18.0 72 165 166 10 24 30 8 11 23.0 54 166 167 14 30 33 11 14 12.0 78 167 168 11 26 25 12 12 14.0 74 168 169 13 34 38 12 9 16.0 82 169 170 16 27 37 12 9 18.0 73 170 171 12 37 31 5 11 20.0 55 171 172 16 36 37 12 16 12.0 72 172 173 12 41 35 10 9 12.0 78 173 174 9 29 25 7 13 17.0 59 174 175 12 36 28 12 16 13.0 72 175 176 15 32 35 11 13 9.0 78 176 177 12 37 33 8 9 16.0 68 177 178 12 30 30 9 12 18.0 69 178 179 14 31 31 10 16 10.0 67 179 180 12 38 37 9 11 14.0 74 180 181 16 36 36 12 14 11.0 54 181 182 11 35 30 6 13 9.0 67 182 183 19 31 36 15 15 11.0 70 183 184 15 38 32 12 14 10.0 80 184 185 8 22 28 12 16 11.0 89 185 186 16 32 36 12 13 19.0 76 186 187 17 36 34 11 14 14.0 74 187 188 12 39 31 7 15 12.0 87 188 189 11 28 28 7 13 14.0 54 189 190 11 32 36 5 11 21.0 61 190 191 14 32 36 12 11 13.0 38 191 192 16 38 40 12 14 10.0 75 192 193 12 32 33 3 15 15.0 69 193 194 16 35 37 11 11 16.0 62 194 195 13 32 32 10 15 14.0 72 195 196 15 37 38 12 12 12.0 70 196 197 16 34 31 9 14 19.0 79 197 198 16 33 37 12 14 15.0 87 198 199 14 33 33 9 8 19.0 62 199 200 16 26 32 12 13 13.0 77 200 201 16 30 30 12 9 17.0 69 201 202 14 24 30 10 15 12.0 69 202 203 11 34 31 9 17 11.0 75 203 204 12 34 32 12 13 14.0 54 204 205 15 33 34 8 15 11.0 72 205 206 15 34 36 11 15 13.0 74 206 207 16 35 37 11 14 12.0 85 207 208 16 35 36 12 16 15.0 52 208 209 11 36 33 10 13 14.0 70 209 210 15 34 33 10 16 12.0 84 210 211 12 34 33 12 9 17.0 64 211 212 12 41 44 12 16 11.0 84 212 213 15 32 39 11 11 18.0 87 213 214 15 30 32 8 10 13.0 79 214 215 16 35 35 12 11 17.0 67 215 216 14 28 25 10 15 13.0 65 216 217 17 33 35 11 17 11.0 85 217 218 14 39 34 10 14 12.0 83 218 219 13 36 35 8 8 22.0 61 219 220 15 36 39 12 15 14.0 82 220 221 13 35 33 12 11 12.0 76 221 222 14 38 36 10 16 12.0 58 222 223 15 33 32 12 10 17.0 72 223 224 12 31 32 9 15 9.0 72 224 225 13 34 36 9 9 21.0 38 225 226 8 32 36 6 16 10.0 78 226 227 14 31 32 10 19 11.0 54 227 228 14 33 34 9 12 12.0 63 228 229 11 34 33 9 8 23.0 66 229 230 12 34 35 9 11 13.0 70 230 231 13 34 30 6 14 12.0 71 231 232 10 33 38 10 9 16.0 67 232 233 16 32 34 6 15 9.0 58 233 234 18 41 33 14 13 17.0 72 234 235 13 34 32 10 16 9.0 72 235 236 11 36 31 10 11 14.0 70 236 237 4 37 30 6 12 17.0 76 237 238 13 36 27 12 13 13.0 50 238 239 16 29 31 12 10 11.0 72 239 240 10 37 30 7 11 12.0 72 240 241 12 27 32 8 12 10.0 88 241 242 12 35 35 11 8 19.0 53 242 243 10 28 28 3 12 16.0 58 243 244 13 35 33 6 12 16.0 66 244 245 15 37 31 10 15 14.0 82 245 246 12 29 35 8 11 20.0 69 246 247 14 32 35 9 13 15.0 68 247 248 10 36 32 9 14 23.0 44 248 249 12 19 21 8 10 20.0 56 249 250 12 21 20 9 12 16.0 53 250 251 11 31 34 7 15 14.0 70 251 252 10 33 32 7 13 17.0 78 252 253 12 36 34 6 13 11.0 71 253 254 16 33 32 9 13 13.0 72 254 255 12 37 33 10 12 17.0 68 255 256 14 34 33 11 12 15.0 67 256 257 16 35 37 12 9 21.0 75 257 258 14 31 32 8 9 18.0 62 258 259 13 37 34 11 15 15.0 67 259 260 4 35 30 3 10 8.0 83 260 261 15 27 30 11 14 12.0 64 261 262 11 34 38 12 15 12.0 68 262 263 11 40 36 7 7 22.0 62 263 264 14 29 32 9 14 12.0 72 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software Happiness Depression 5.451533 0.032167 0.042765 0.558490 0.070233 -0.031126 Belonging t 0.007479 -0.004911 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9938 -1.0551 0.2896 1.2547 4.6512 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.451533 1.942794 2.806 0.00540 ** Connected 0.032167 0.034424 0.934 0.35097 Separate 0.042765 0.035076 1.219 0.22389 Software 0.558490 0.053726 10.395 < 2e-16 *** Happiness 0.070233 0.057809 1.215 0.22552 Depression -0.031126 0.041759 -0.745 0.45672 Belonging 0.007479 0.011869 0.630 0.52919 t -0.004911 0.001680 -2.923 0.00378 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.854 on 256 degrees of freedom Multiple R-squared: 0.4452, Adjusted R-squared: 0.43 F-statistic: 29.34 on 7 and 256 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,] 0.402475229 0.804950459 0.5975248 [2,] 0.799813020 0.400373961 0.2001870 [3,] 0.760826665 0.478346670 0.2391733 [4,] 0.673552621 0.652894758 0.3264474 [5,] 0.618114700 0.763770600 0.3818853 [6,] 0.680833201 0.638333598 0.3191668 [7,] 0.608142628 0.783714744 0.3918574 [8,] 0.857694380 0.284611239 0.1423056 [9,] 0.830980289 0.338039422 0.1690197 [10,] 0.780467389 0.439065223 0.2195326 [11,] 0.716435091 0.567129819 0.2835649 [12,] 0.677924860 0.644150281 0.3220751 [13,] 0.640080624 0.719838752 0.3599194 [14,] 0.608002819 0.783994361 0.3919972 [15,] 0.544602520 0.910794959 0.4553975 [16,] 0.495118874 0.990237748 0.5048811 [17,] 0.435695067 0.871390133 0.5643049 [18,] 0.489428728 0.978857455 0.5105713 [19,] 0.429531193 0.859062387 0.5704688 [20,] 0.439110691 0.878221383 0.5608893 [21,] 0.390777631 0.781555263 0.6092224 [22,] 0.386536251 0.773072502 0.6134637 [23,] 0.367573744 0.735147487 0.6324263 [24,] 0.311946112 0.623892223 0.6880539 [25,] 0.300790076 0.601580151 0.6992099 [26,] 0.399446956 0.798893912 0.6005530 [27,] 0.480233641 0.960467281 0.5197664 [28,] 0.456414494 0.912828988 0.5435855 [29,] 0.504036782 0.991926437 0.4959632 [30,] 0.484662564 0.969325129 0.5153374 [31,] 0.448981539 0.897963078 0.5510185 [32,] 0.419330929 0.838661858 0.5806691 [33,] 0.435488787 0.870977574 0.5645112 [34,] 0.388357019 0.776714038 0.6116430 [35,] 0.349784442 0.699568883 0.6502156 [36,] 0.574490259 0.851019482 0.4255097 [37,] 0.592513782 0.814972435 0.4074862 [38,] 0.554065144 0.891869713 0.4459349 [39,] 0.541344030 0.917311940 0.4586560 [40,] 0.514186099 0.971627801 0.4858139 [41,] 0.469970782 0.939941563 0.5300292 [42,] 0.428427599 0.856855199 0.5715724 [43,] 0.435661290 0.871322581 0.5643387 [44,] 0.406008878 0.812017756 0.5939911 [45,] 0.404277689 0.808555378 0.5957223 [46,] 0.420734697 0.841469394 0.5792653 [47,] 0.380141521 0.760283041 0.6198585 [48,] 0.365628948 0.731257895 0.6343711 [49,] 0.326508006 0.653016011 0.6734920 [50,] 0.353372037 0.706744074 0.6466280 [51,] 0.327220431 0.654440861 0.6727796 [52,] 0.293131978 0.586263956 0.7068680 [53,] 0.259117537 0.518235075 0.7408825 [54,] 0.226514477 0.453028954 0.7734855 [55,] 0.202909489 0.405818977 0.7970905 [56,] 0.193131795 0.386263589 0.8068682 [57,] 0.195831245 0.391662490 0.8041688 [58,] 0.309302991 0.618605982 0.6906970 [59,] 0.422218465 0.844436930 0.5777815 [60,] 0.390197657 0.780395315 0.6098023 [61,] 0.462670997 0.925341994 0.5373290 [62,] 0.427799440 0.855598881 0.5722006 [63,] 0.421024396 0.842048791 0.5789756 [64,] 0.399631794 0.799263587 0.6003682 [65,] 0.363591795 0.727183591 0.6364082 [66,] 0.441561639 0.883123277 0.5584384 [67,] 0.403416720 0.806833441 0.5965833 [68,] 0.382623080 0.765246159 0.6173769 [69,] 0.392051967 0.784103934 0.6079480 [70,] 0.357774231 0.715548461 0.6422258 [71,] 0.326049221 0.652098443 0.6739508 [72,] 0.294041160 0.588082321 0.7059588 [73,] 0.267059647 0.534119293 0.7329404 [74,] 0.237094036 0.474188071 0.7629060 [75,] 0.234219608 0.468439215 0.7657804 [76,] 0.205457332 0.410914664 0.7945427 [77,] 0.182360085 0.364720170 0.8176399 [78,] 0.168667247 0.337334494 0.8313328 [79,] 0.146035405 0.292070810 0.8539646 [80,] 0.141401399 0.282802797 0.8585986 [81,] 0.121453403 0.242906806 0.8785466 [82,] 0.105538335 0.211076671 0.8944617 [83,] 0.090473150 0.180946301 0.9095268 [84,] 0.094451705 0.188903409 0.9055483 [85,] 0.085145288 0.170290577 0.9148547 [86,] 0.071343760 0.142687519 0.9286562 [87,] 0.076539042 0.153078084 0.9234610 [88,] 0.063917296 0.127834591 0.9360827 [89,] 0.053256054 0.106512108 0.9467439 [90,] 0.047196464 0.094392929 0.9528035 [91,] 0.041823771 0.083647542 0.9581762 [92,] 0.049564664 0.099129328 0.9504353 [93,] 0.041815933 0.083631865 0.9581841 [94,] 0.037154625 0.074309249 0.9628454 [95,] 0.044379965 0.088759929 0.9556200 [96,] 0.039246724 0.078493448 0.9607533 [97,] 0.032101541 0.064203081 0.9678985 [98,] 0.030638962 0.061277924 0.9693610 [99,] 0.024976651 0.049953302 0.9750233 [100,] 0.020680288 0.041360576 0.9793197 [101,] 0.016536702 0.033073404 0.9834633 [102,] 0.017461097 0.034922194 0.9825389 [103,] 0.015186805 0.030373610 0.9848132 [104,] 0.020342784 0.040685568 0.9796572 [105,] 0.019554579 0.039109158 0.9804454 [106,] 0.019156583 0.038313166 0.9808434 [107,] 0.016189542 0.032379083 0.9838105 [108,] 0.016142880 0.032285760 0.9838571 [109,] 0.013111597 0.026223193 0.9868884 [110,] 0.011851286 0.023702573 0.9881487 [111,] 0.009611389 0.019222778 0.9903886 [112,] 0.014297613 0.028595226 0.9857024 [113,] 0.011918249 0.023836497 0.9880818 [114,] 0.010041723 0.020083446 0.9899583 [115,] 0.008203863 0.016407727 0.9917961 [116,] 0.006524021 0.013048043 0.9934760 [117,] 0.005924995 0.011849989 0.9940750 [118,] 0.004989580 0.009979161 0.9950104 [119,] 0.006796768 0.013593536 0.9932032 [120,] 0.007378965 0.014757929 0.9926210 [121,] 0.015038004 0.030076007 0.9849620 [122,] 0.017902001 0.035804002 0.9820980 [123,] 0.019001428 0.038002856 0.9809986 [124,] 0.017939951 0.035879902 0.9820600 [125,] 0.014328083 0.028656166 0.9856719 [126,] 0.012230943 0.024461885 0.9877691 [127,] 0.009906487 0.019812975 0.9900935 [128,] 0.012538421 0.025076842 0.9874616 [129,] 0.010761822 0.021523643 0.9892382 [130,] 0.012905296 0.025810591 0.9870947 [131,] 0.020156017 0.040312033 0.9798440 [132,] 0.018524281 0.037048563 0.9814757 [133,] 0.015020531 0.030041062 0.9849795 [134,] 0.015723601 0.031447203 0.9842764 [135,] 0.026697455 0.053394910 0.9733025 [136,] 0.032899284 0.065798568 0.9671007 [137,] 0.034710663 0.069421325 0.9652893 [138,] 0.030826816 0.061653632 0.9691732 [139,] 0.025169449 0.050338898 0.9748306 [140,] 0.034743167 0.069486334 0.9652568 [141,] 0.029564965 0.059129929 0.9704350 [142,] 0.031211900 0.062423800 0.9687881 [143,] 0.061492165 0.122984329 0.9385078 [144,] 0.059269348 0.118538697 0.9407307 [145,] 0.066851467 0.133702935 0.9331485 [146,] 0.056882837 0.113765674 0.9431172 [147,] 0.050726351 0.101452702 0.9492736 [148,] 0.044306946 0.088613892 0.9556931 [149,] 0.042896371 0.085792741 0.9571036 [150,] 0.036828929 0.073657858 0.9631711 [151,] 0.030149405 0.060298810 0.9698506 [152,] 0.024688251 0.049376502 0.9753117 [153,] 0.020455692 0.040911385 0.9795443 [154,] 0.017466334 0.034932668 0.9825337 [155,] 0.015289971 0.030579942 0.9847100 [156,] 0.016215857 0.032431714 0.9837841 [157,] 0.012951919 0.025903838 0.9870481 [158,] 0.019009794 0.038019589 0.9809902 [159,] 0.019314506 0.038629012 0.9806855 [160,] 0.017801931 0.035603863 0.9821981 [161,] 0.016327214 0.032654427 0.9836728 [162,] 0.013177650 0.026355300 0.9868223 [163,] 0.012812807 0.025625613 0.9871872 [164,] 0.014685951 0.029371902 0.9853140 [165,] 0.018892991 0.037785982 0.9811070 [166,] 0.015145406 0.030290813 0.9848546 [167,] 0.011980872 0.023961744 0.9880191 [168,] 0.010078151 0.020156301 0.9899218 [169,] 0.007837987 0.015675975 0.9921620 [170,] 0.006892284 0.013784568 0.9931077 [171,] 0.005579292 0.011158585 0.9944207 [172,] 0.004301702 0.008603405 0.9956983 [173,] 0.004542725 0.009085450 0.9954573 [174,] 0.003463408 0.006926816 0.9965366 [175,] 0.094548553 0.189097106 0.9054514 [176,] 0.083767329 0.167534657 0.9162327 [177,] 0.093731987 0.187463975 0.9062680 [178,] 0.079142243 0.158284486 0.9208578 [179,] 0.070541865 0.141083731 0.9294581 [180,] 0.059670561 0.119341123 0.9403294 [181,] 0.051688800 0.103377601 0.9483112 [182,] 0.043010977 0.086021953 0.9569890 [183,] 0.043312076 0.086624153 0.9566879 [184,] 0.041069668 0.082139337 0.9589303 [185,] 0.035702774 0.071405548 0.9642972 [186,] 0.028455014 0.056910028 0.9715450 [187,] 0.037322240 0.074644480 0.9626778 [188,] 0.030519500 0.061038999 0.9694805 [189,] 0.027405782 0.054811564 0.9725942 [190,] 0.023276539 0.046553078 0.9767235 [191,] 0.021311356 0.042622711 0.9786886 [192,] 0.017816554 0.035633109 0.9821834 [193,] 0.019846911 0.039693822 0.9801531 [194,] 0.026367508 0.052735016 0.9736325 [195,] 0.027823707 0.055647413 0.9721763 [196,] 0.021750328 0.043500656 0.9782497 [197,] 0.019386927 0.038773854 0.9806131 [198,] 0.015490372 0.030980744 0.9845096 [199,] 0.018077899 0.036155799 0.9819221 [200,] 0.014963694 0.029927388 0.9850363 [201,] 0.018395839 0.036791678 0.9816042 [202,] 0.032471761 0.064943522 0.9675282 [203,] 0.025371095 0.050742189 0.9746289 [204,] 0.031479117 0.062958234 0.9685209 [205,] 0.026761250 0.053522501 0.9732387 [206,] 0.020729367 0.041458734 0.9792706 [207,] 0.023137066 0.046274133 0.9768629 [208,] 0.019606755 0.039213511 0.9803932 [209,] 0.018294390 0.036588781 0.9817056 [210,] 0.013741904 0.027483808 0.9862581 [211,] 0.011402514 0.022805027 0.9885975 [212,] 0.008311548 0.016623096 0.9916885 [213,] 0.006416314 0.012832628 0.9935837 [214,] 0.004916912 0.009833823 0.9950831 [215,] 0.003439601 0.006879202 0.9965604 [216,] 0.007101991 0.014203982 0.9928980 [217,] 0.005651567 0.011303135 0.9943484 [218,] 0.004122618 0.008245236 0.9958774 [219,] 0.002931620 0.005863240 0.9970684 [220,] 0.002075990 0.004151981 0.9979240 [221,] 0.002268510 0.004537020 0.9977315 [222,] 0.007798519 0.015597038 0.9922015 [223,] 0.029369840 0.058739681 0.9706302 [224,] 0.044950747 0.089901494 0.9550493 [225,] 0.033091411 0.066182821 0.9669086 [226,] 0.027101151 0.054202303 0.9728988 [227,] 0.238959016 0.477918032 0.7610410 [228,] 0.192949272 0.385898544 0.8070507 [229,] 0.163289925 0.326579850 0.8367101 [230,] 0.130447621 0.260895241 0.8695524 [231,] 0.116817471 0.233634942 0.8831825 [232,] 0.177907984 0.355815968 0.8220920 [233,] 0.150453950 0.300907900 0.8495460 [234,] 0.151452823 0.302905645 0.8485472 [235,] 0.132852249 0.265704499 0.8671478 [236,] 0.116475821 0.232951642 0.8835242 [237,] 0.079256297 0.158512595 0.9207437 [238,] 0.070429088 0.140858177 0.9295709 [239,] 0.053497790 0.106995580 0.9465022 [240,] 0.130779813 0.261559626 0.8692202 [241,] 0.107472862 0.214945723 0.8925271 [242,] 0.543978390 0.912043221 0.4560216 [243,] 0.382288727 0.764577455 0.6177113 > postscript(file="/var/wessaorg/rcomp/tmp/1kw9w1352157035.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/2vuy21352157035.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3am9o1352157035.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4rkv51352157035.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5inw51352157035.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 264 Frequency = 1 1 2 3 4 5 6 -3.098538785 0.249371924 1.893678110 2.838394759 -2.401787347 -1.847013370 7 8 9 10 11 12 3.605880460 -2.103491161 -2.059658512 0.538680280 0.996010221 -0.195778066 13 14 15 16 17 18 0.454799205 0.502429577 -1.002615235 -0.496690760 0.412162340 3.577867081 19 20 21 22 23 24 2.468158708 0.401602040 0.605520846 0.786385822 2.507859621 0.839080777 25 26 27 28 29 30 0.851623609 0.778196929 1.091647210 -1.809108735 0.315176248 -0.263029559 31 32 33 34 35 36 -0.765960661 -0.849288952 -0.803962023 -0.001749614 -1.490209881 -3.030476921 37 38 39 40 41 42 -3.035534565 -1.747032491 1.445444080 1.505106075 1.279462294 -1.700951059 43 44 45 46 47 48 2.512878443 -0.153377764 -0.462355329 -4.490949562 -2.630761274 -0.146742166 49 50 51 52 53 54 0.582123384 -1.630247367 -1.025298496 -0.141940881 -3.017185579 -0.036186091 55 56 57 58 59 60 -2.343289800 1.615021855 0.145459891 0.470116771 -0.112393030 1.778939435 61 62 63 64 65 66 0.440446592 0.359318244 -0.548816853 -0.710986002 0.693678842 0.945813967 67 68 69 70 71 72 1.666822920 3.604111813 -3.899708374 0.348421327 -3.412380609 -0.924295106 73 74 75 76 77 78 1.071941325 0.859016376 0.767705125 3.508113010 -0.419099992 1.468801152 79 80 81 82 83 84 -2.216577334 0.857859743 0.341372549 0.236759169 -0.643413724 0.115767618 85 86 87 88 89 90 1.839264528 -0.155569285 0.632511822 1.127411151 0.480518610 -1.907972516 91 92 93 94 95 96 0.241505625 0.194438081 -0.053106791 -2.029851850 1.256386663 0.211757119 97 98 99 100 101 102 2.322616035 0.226291624 -0.429781644 -0.992298111 1.323375337 2.545781557 103 104 105 106 107 108 0.760339627 1.040141556 -1.842204209 1.298270035 0.298876541 1.641348531 109 110 111 112 113 114 -0.304342299 0.714319104 0.007172851 1.943590631 -1.281178077 -2.579874261 115 116 117 118 119 120 1.838527156 -1.865732844 0.771112171 -1.792915136 0.479615794 -1.429221584 121 122 123 124 125 126 0.614576732 -2.821209317 -0.854504464 -0.843764797 -0.784933914 0.341278399 127 128 129 130 131 132 1.509416094 0.951747888 -2.685865073 2.059728520 -3.723953244 2.541667846 133 134 135 136 137 138 -2.165989022 -1.544046009 -0.083656696 0.887887265 0.468722193 -2.498950057 139 140 141 142 143 144 -1.006480416 -2.339956434 3.143500085 1.355875574 0.158636042 1.875596153 145 146 147 148 149 150 -3.266751430 2.502454533 -1.945751239 1.156989895 0.172772330 -2.900457717 151 152 153 154 155 156 -0.878418291 2.175136908 4.147418985 1.603697482 -2.481628296 0.560693436 157 158 159 160 161 162 1.293314145 1.099065340 1.462603867 -0.698903764 0.306895079 0.329795885 163 164 165 166 167 168 -0.381357631 0.786901982 1.331779722 -1.619761147 -0.344192471 -3.194346992 169 170 171 172 173 174 -1.789596334 1.612807316 1.518467982 0.662211895 -1.844438422 -2.333612434 175 176 177 178 179 180 -2.907041435 0.526992426 -0.294327621 -0.650368444 0.206131805 -1.288904893 181 182 183 184 185 186 0.993126239 -0.451505878 2.298423231 -0.110981881 -6.596986586 1.301064443 187 188 189 190 191 192 2.610419292 -0.348622911 -0.412069714 0.545031937 -0.436490890 0.623572202 193 194 195 196 197 198 2.277518581 1.911359536 -0.632884513 0.001024589 3.087373988 1.008056355 199 200 201 202 203 204 1.592366903 1.539638452 1.966679997 0.704538164 -2.312960385 -2.494922264 205 206 207 208 209 210 2.322123701 0.581163430 1.467985216 1.156874554 -2.580145319 1.111446590 211 212 213 214 215 216 -2.203785389 -3.722425549 0.890920641 2.909421188 1.535256743 0.919486133 217 218 219 220 221 222 2.425128675 0.095076893 1.167894530 -0.129909691 -1.572687684 0.107851037 223 224 225 226 227 228 0.800012311 -1.055453201 0.731082508 -3.657369597 0.316720867 1.185710905 229 230 231 232 233 234 -1.197890874 -0.830389846 1.814511262 -3.218905829 4.651212489 2.226248327 235 236 237 238 239 240 -0.726660655 -2.221561835 -6.993819348 -1.179681722 1.863256037 -1.593061865 241 242 243 244 245 246 -0.162645268 -1.396015419 1.189632700 2.020251001 1.419790411 0.192867082 247 248 249 250 251 252 1.254166697 -2.383032043 1.295437316 0.477752654 -0.720829792 -1.520704011 253 254 255 256 257 258 0.726255791 3.292503160 -1.207855665 0.280291587 1.861115030 2.446320148 259 260 261 262 263 264 -1.054943238 -5.333093544 1.446898172 -3.774119819 -0.166228177 1.368916629 > postscript(file="/var/wessaorg/rcomp/tmp/66d0h1352157035.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 = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.098538785 NA 1 0.249371924 -3.098538785 2 1.893678110 0.249371924 3 2.838394759 1.893678110 4 -2.401787347 2.838394759 5 -1.847013370 -2.401787347 6 3.605880460 -1.847013370 7 -2.103491161 3.605880460 8 -2.059658512 -2.103491161 9 0.538680280 -2.059658512 10 0.996010221 0.538680280 11 -0.195778066 0.996010221 12 0.454799205 -0.195778066 13 0.502429577 0.454799205 14 -1.002615235 0.502429577 15 -0.496690760 -1.002615235 16 0.412162340 -0.496690760 17 3.577867081 0.412162340 18 2.468158708 3.577867081 19 0.401602040 2.468158708 20 0.605520846 0.401602040 21 0.786385822 0.605520846 22 2.507859621 0.786385822 23 0.839080777 2.507859621 24 0.851623609 0.839080777 25 0.778196929 0.851623609 26 1.091647210 0.778196929 27 -1.809108735 1.091647210 28 0.315176248 -1.809108735 29 -0.263029559 0.315176248 30 -0.765960661 -0.263029559 31 -0.849288952 -0.765960661 32 -0.803962023 -0.849288952 33 -0.001749614 -0.803962023 34 -1.490209881 -0.001749614 35 -3.030476921 -1.490209881 36 -3.035534565 -3.030476921 37 -1.747032491 -3.035534565 38 1.445444080 -1.747032491 39 1.505106075 1.445444080 40 1.279462294 1.505106075 41 -1.700951059 1.279462294 42 2.512878443 -1.700951059 43 -0.153377764 2.512878443 44 -0.462355329 -0.153377764 45 -4.490949562 -0.462355329 46 -2.630761274 -4.490949562 47 -0.146742166 -2.630761274 48 0.582123384 -0.146742166 49 -1.630247367 0.582123384 50 -1.025298496 -1.630247367 51 -0.141940881 -1.025298496 52 -3.017185579 -0.141940881 53 -0.036186091 -3.017185579 54 -2.343289800 -0.036186091 55 1.615021855 -2.343289800 56 0.145459891 1.615021855 57 0.470116771 0.145459891 58 -0.112393030 0.470116771 59 1.778939435 -0.112393030 60 0.440446592 1.778939435 61 0.359318244 0.440446592 62 -0.548816853 0.359318244 63 -0.710986002 -0.548816853 64 0.693678842 -0.710986002 65 0.945813967 0.693678842 66 1.666822920 0.945813967 67 3.604111813 1.666822920 68 -3.899708374 3.604111813 69 0.348421327 -3.899708374 70 -3.412380609 0.348421327 71 -0.924295106 -3.412380609 72 1.071941325 -0.924295106 73 0.859016376 1.071941325 74 0.767705125 0.859016376 75 3.508113010 0.767705125 76 -0.419099992 3.508113010 77 1.468801152 -0.419099992 78 -2.216577334 1.468801152 79 0.857859743 -2.216577334 80 0.341372549 0.857859743 81 0.236759169 0.341372549 82 -0.643413724 0.236759169 83 0.115767618 -0.643413724 84 1.839264528 0.115767618 85 -0.155569285 1.839264528 86 0.632511822 -0.155569285 87 1.127411151 0.632511822 88 0.480518610 1.127411151 89 -1.907972516 0.480518610 90 0.241505625 -1.907972516 91 0.194438081 0.241505625 92 -0.053106791 0.194438081 93 -2.029851850 -0.053106791 94 1.256386663 -2.029851850 95 0.211757119 1.256386663 96 2.322616035 0.211757119 97 0.226291624 2.322616035 98 -0.429781644 0.226291624 99 -0.992298111 -0.429781644 100 1.323375337 -0.992298111 101 2.545781557 1.323375337 102 0.760339627 2.545781557 103 1.040141556 0.760339627 104 -1.842204209 1.040141556 105 1.298270035 -1.842204209 106 0.298876541 1.298270035 107 1.641348531 0.298876541 108 -0.304342299 1.641348531 109 0.714319104 -0.304342299 110 0.007172851 0.714319104 111 1.943590631 0.007172851 112 -1.281178077 1.943590631 113 -2.579874261 -1.281178077 114 1.838527156 -2.579874261 115 -1.865732844 1.838527156 116 0.771112171 -1.865732844 117 -1.792915136 0.771112171 118 0.479615794 -1.792915136 119 -1.429221584 0.479615794 120 0.614576732 -1.429221584 121 -2.821209317 0.614576732 122 -0.854504464 -2.821209317 123 -0.843764797 -0.854504464 124 -0.784933914 -0.843764797 125 0.341278399 -0.784933914 126 1.509416094 0.341278399 127 0.951747888 1.509416094 128 -2.685865073 0.951747888 129 2.059728520 -2.685865073 130 -3.723953244 2.059728520 131 2.541667846 -3.723953244 132 -2.165989022 2.541667846 133 -1.544046009 -2.165989022 134 -0.083656696 -1.544046009 135 0.887887265 -0.083656696 136 0.468722193 0.887887265 137 -2.498950057 0.468722193 138 -1.006480416 -2.498950057 139 -2.339956434 -1.006480416 140 3.143500085 -2.339956434 141 1.355875574 3.143500085 142 0.158636042 1.355875574 143 1.875596153 0.158636042 144 -3.266751430 1.875596153 145 2.502454533 -3.266751430 146 -1.945751239 2.502454533 147 1.156989895 -1.945751239 148 0.172772330 1.156989895 149 -2.900457717 0.172772330 150 -0.878418291 -2.900457717 151 2.175136908 -0.878418291 152 4.147418985 2.175136908 153 1.603697482 4.147418985 154 -2.481628296 1.603697482 155 0.560693436 -2.481628296 156 1.293314145 0.560693436 157 1.099065340 1.293314145 158 1.462603867 1.099065340 159 -0.698903764 1.462603867 160 0.306895079 -0.698903764 161 0.329795885 0.306895079 162 -0.381357631 0.329795885 163 0.786901982 -0.381357631 164 1.331779722 0.786901982 165 -1.619761147 1.331779722 166 -0.344192471 -1.619761147 167 -3.194346992 -0.344192471 168 -1.789596334 -3.194346992 169 1.612807316 -1.789596334 170 1.518467982 1.612807316 171 0.662211895 1.518467982 172 -1.844438422 0.662211895 173 -2.333612434 -1.844438422 174 -2.907041435 -2.333612434 175 0.526992426 -2.907041435 176 -0.294327621 0.526992426 177 -0.650368444 -0.294327621 178 0.206131805 -0.650368444 179 -1.288904893 0.206131805 180 0.993126239 -1.288904893 181 -0.451505878 0.993126239 182 2.298423231 -0.451505878 183 -0.110981881 2.298423231 184 -6.596986586 -0.110981881 185 1.301064443 -6.596986586 186 2.610419292 1.301064443 187 -0.348622911 2.610419292 188 -0.412069714 -0.348622911 189 0.545031937 -0.412069714 190 -0.436490890 0.545031937 191 0.623572202 -0.436490890 192 2.277518581 0.623572202 193 1.911359536 2.277518581 194 -0.632884513 1.911359536 195 0.001024589 -0.632884513 196 3.087373988 0.001024589 197 1.008056355 3.087373988 198 1.592366903 1.008056355 199 1.539638452 1.592366903 200 1.966679997 1.539638452 201 0.704538164 1.966679997 202 -2.312960385 0.704538164 203 -2.494922264 -2.312960385 204 2.322123701 -2.494922264 205 0.581163430 2.322123701 206 1.467985216 0.581163430 207 1.156874554 1.467985216 208 -2.580145319 1.156874554 209 1.111446590 -2.580145319 210 -2.203785389 1.111446590 211 -3.722425549 -2.203785389 212 0.890920641 -3.722425549 213 2.909421188 0.890920641 214 1.535256743 2.909421188 215 0.919486133 1.535256743 216 2.425128675 0.919486133 217 0.095076893 2.425128675 218 1.167894530 0.095076893 219 -0.129909691 1.167894530 220 -1.572687684 -0.129909691 221 0.107851037 -1.572687684 222 0.800012311 0.107851037 223 -1.055453201 0.800012311 224 0.731082508 -1.055453201 225 -3.657369597 0.731082508 226 0.316720867 -3.657369597 227 1.185710905 0.316720867 228 -1.197890874 1.185710905 229 -0.830389846 -1.197890874 230 1.814511262 -0.830389846 231 -3.218905829 1.814511262 232 4.651212489 -3.218905829 233 2.226248327 4.651212489 234 -0.726660655 2.226248327 235 -2.221561835 -0.726660655 236 -6.993819348 -2.221561835 237 -1.179681722 -6.993819348 238 1.863256037 -1.179681722 239 -1.593061865 1.863256037 240 -0.162645268 -1.593061865 241 -1.396015419 -0.162645268 242 1.189632700 -1.396015419 243 2.020251001 1.189632700 244 1.419790411 2.020251001 245 0.192867082 1.419790411 246 1.254166697 0.192867082 247 -2.383032043 1.254166697 248 1.295437316 -2.383032043 249 0.477752654 1.295437316 250 -0.720829792 0.477752654 251 -1.520704011 -0.720829792 252 0.726255791 -1.520704011 253 3.292503160 0.726255791 254 -1.207855665 3.292503160 255 0.280291587 -1.207855665 256 1.861115030 0.280291587 257 2.446320148 1.861115030 258 -1.054943238 2.446320148 259 -5.333093544 -1.054943238 260 1.446898172 -5.333093544 261 -3.774119819 1.446898172 262 -0.166228177 -3.774119819 263 1.368916629 -0.166228177 264 NA 1.368916629 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.249371924 -3.098538785 [2,] 1.893678110 0.249371924 [3,] 2.838394759 1.893678110 [4,] -2.401787347 2.838394759 [5,] -1.847013370 -2.401787347 [6,] 3.605880460 -1.847013370 [7,] -2.103491161 3.605880460 [8,] -2.059658512 -2.103491161 [9,] 0.538680280 -2.059658512 [10,] 0.996010221 0.538680280 [11,] -0.195778066 0.996010221 [12,] 0.454799205 -0.195778066 [13,] 0.502429577 0.454799205 [14,] -1.002615235 0.502429577 [15,] -0.496690760 -1.002615235 [16,] 0.412162340 -0.496690760 [17,] 3.577867081 0.412162340 [18,] 2.468158708 3.577867081 [19,] 0.401602040 2.468158708 [20,] 0.605520846 0.401602040 [21,] 0.786385822 0.605520846 [22,] 2.507859621 0.786385822 [23,] 0.839080777 2.507859621 [24,] 0.851623609 0.839080777 [25,] 0.778196929 0.851623609 [26,] 1.091647210 0.778196929 [27,] -1.809108735 1.091647210 [28,] 0.315176248 -1.809108735 [29,] -0.263029559 0.315176248 [30,] -0.765960661 -0.263029559 [31,] -0.849288952 -0.765960661 [32,] -0.803962023 -0.849288952 [33,] -0.001749614 -0.803962023 [34,] -1.490209881 -0.001749614 [35,] -3.030476921 -1.490209881 [36,] -3.035534565 -3.030476921 [37,] -1.747032491 -3.035534565 [38,] 1.445444080 -1.747032491 [39,] 1.505106075 1.445444080 [40,] 1.279462294 1.505106075 [41,] -1.700951059 1.279462294 [42,] 2.512878443 -1.700951059 [43,] -0.153377764 2.512878443 [44,] -0.462355329 -0.153377764 [45,] -4.490949562 -0.462355329 [46,] -2.630761274 -4.490949562 [47,] -0.146742166 -2.630761274 [48,] 0.582123384 -0.146742166 [49,] -1.630247367 0.582123384 [50,] -1.025298496 -1.630247367 [51,] -0.141940881 -1.025298496 [52,] -3.017185579 -0.141940881 [53,] -0.036186091 -3.017185579 [54,] -2.343289800 -0.036186091 [55,] 1.615021855 -2.343289800 [56,] 0.145459891 1.615021855 [57,] 0.470116771 0.145459891 [58,] -0.112393030 0.470116771 [59,] 1.778939435 -0.112393030 [60,] 0.440446592 1.778939435 [61,] 0.359318244 0.440446592 [62,] -0.548816853 0.359318244 [63,] -0.710986002 -0.548816853 [64,] 0.693678842 -0.710986002 [65,] 0.945813967 0.693678842 [66,] 1.666822920 0.945813967 [67,] 3.604111813 1.666822920 [68,] -3.899708374 3.604111813 [69,] 0.348421327 -3.899708374 [70,] -3.412380609 0.348421327 [71,] -0.924295106 -3.412380609 [72,] 1.071941325 -0.924295106 [73,] 0.859016376 1.071941325 [74,] 0.767705125 0.859016376 [75,] 3.508113010 0.767705125 [76,] -0.419099992 3.508113010 [77,] 1.468801152 -0.419099992 [78,] -2.216577334 1.468801152 [79,] 0.857859743 -2.216577334 [80,] 0.341372549 0.857859743 [81,] 0.236759169 0.341372549 [82,] -0.643413724 0.236759169 [83,] 0.115767618 -0.643413724 [84,] 1.839264528 0.115767618 [85,] -0.155569285 1.839264528 [86,] 0.632511822 -0.155569285 [87,] 1.127411151 0.632511822 [88,] 0.480518610 1.127411151 [89,] -1.907972516 0.480518610 [90,] 0.241505625 -1.907972516 [91,] 0.194438081 0.241505625 [92,] -0.053106791 0.194438081 [93,] -2.029851850 -0.053106791 [94,] 1.256386663 -2.029851850 [95,] 0.211757119 1.256386663 [96,] 2.322616035 0.211757119 [97,] 0.226291624 2.322616035 [98,] -0.429781644 0.226291624 [99,] -0.992298111 -0.429781644 [100,] 1.323375337 -0.992298111 [101,] 2.545781557 1.323375337 [102,] 0.760339627 2.545781557 [103,] 1.040141556 0.760339627 [104,] -1.842204209 1.040141556 [105,] 1.298270035 -1.842204209 [106,] 0.298876541 1.298270035 [107,] 1.641348531 0.298876541 [108,] -0.304342299 1.641348531 [109,] 0.714319104 -0.304342299 [110,] 0.007172851 0.714319104 [111,] 1.943590631 0.007172851 [112,] -1.281178077 1.943590631 [113,] -2.579874261 -1.281178077 [114,] 1.838527156 -2.579874261 [115,] -1.865732844 1.838527156 [116,] 0.771112171 -1.865732844 [117,] -1.792915136 0.771112171 [118,] 0.479615794 -1.792915136 [119,] -1.429221584 0.479615794 [120,] 0.614576732 -1.429221584 [121,] -2.821209317 0.614576732 [122,] -0.854504464 -2.821209317 [123,] -0.843764797 -0.854504464 [124,] -0.784933914 -0.843764797 [125,] 0.341278399 -0.784933914 [126,] 1.509416094 0.341278399 [127,] 0.951747888 1.509416094 [128,] -2.685865073 0.951747888 [129,] 2.059728520 -2.685865073 [130,] -3.723953244 2.059728520 [131,] 2.541667846 -3.723953244 [132,] -2.165989022 2.541667846 [133,] -1.544046009 -2.165989022 [134,] -0.083656696 -1.544046009 [135,] 0.887887265 -0.083656696 [136,] 0.468722193 0.887887265 [137,] -2.498950057 0.468722193 [138,] -1.006480416 -2.498950057 [139,] -2.339956434 -1.006480416 [140,] 3.143500085 -2.339956434 [141,] 1.355875574 3.143500085 [142,] 0.158636042 1.355875574 [143,] 1.875596153 0.158636042 [144,] -3.266751430 1.875596153 [145,] 2.502454533 -3.266751430 [146,] -1.945751239 2.502454533 [147,] 1.156989895 -1.945751239 [148,] 0.172772330 1.156989895 [149,] -2.900457717 0.172772330 [150,] -0.878418291 -2.900457717 [151,] 2.175136908 -0.878418291 [152,] 4.147418985 2.175136908 [153,] 1.603697482 4.147418985 [154,] -2.481628296 1.603697482 [155,] 0.560693436 -2.481628296 [156,] 1.293314145 0.560693436 [157,] 1.099065340 1.293314145 [158,] 1.462603867 1.099065340 [159,] -0.698903764 1.462603867 [160,] 0.306895079 -0.698903764 [161,] 0.329795885 0.306895079 [162,] -0.381357631 0.329795885 [163,] 0.786901982 -0.381357631 [164,] 1.331779722 0.786901982 [165,] -1.619761147 1.331779722 [166,] -0.344192471 -1.619761147 [167,] -3.194346992 -0.344192471 [168,] -1.789596334 -3.194346992 [169,] 1.612807316 -1.789596334 [170,] 1.518467982 1.612807316 [171,] 0.662211895 1.518467982 [172,] -1.844438422 0.662211895 [173,] -2.333612434 -1.844438422 [174,] -2.907041435 -2.333612434 [175,] 0.526992426 -2.907041435 [176,] -0.294327621 0.526992426 [177,] -0.650368444 -0.294327621 [178,] 0.206131805 -0.650368444 [179,] -1.288904893 0.206131805 [180,] 0.993126239 -1.288904893 [181,] -0.451505878 0.993126239 [182,] 2.298423231 -0.451505878 [183,] -0.110981881 2.298423231 [184,] -6.596986586 -0.110981881 [185,] 1.301064443 -6.596986586 [186,] 2.610419292 1.301064443 [187,] -0.348622911 2.610419292 [188,] -0.412069714 -0.348622911 [189,] 0.545031937 -0.412069714 [190,] -0.436490890 0.545031937 [191,] 0.623572202 -0.436490890 [192,] 2.277518581 0.623572202 [193,] 1.911359536 2.277518581 [194,] -0.632884513 1.911359536 [195,] 0.001024589 -0.632884513 [196,] 3.087373988 0.001024589 [197,] 1.008056355 3.087373988 [198,] 1.592366903 1.008056355 [199,] 1.539638452 1.592366903 [200,] 1.966679997 1.539638452 [201,] 0.704538164 1.966679997 [202,] -2.312960385 0.704538164 [203,] -2.494922264 -2.312960385 [204,] 2.322123701 -2.494922264 [205,] 0.581163430 2.322123701 [206,] 1.467985216 0.581163430 [207,] 1.156874554 1.467985216 [208,] -2.580145319 1.156874554 [209,] 1.111446590 -2.580145319 [210,] -2.203785389 1.111446590 [211,] -3.722425549 -2.203785389 [212,] 0.890920641 -3.722425549 [213,] 2.909421188 0.890920641 [214,] 1.535256743 2.909421188 [215,] 0.919486133 1.535256743 [216,] 2.425128675 0.919486133 [217,] 0.095076893 2.425128675 [218,] 1.167894530 0.095076893 [219,] -0.129909691 1.167894530 [220,] -1.572687684 -0.129909691 [221,] 0.107851037 -1.572687684 [222,] 0.800012311 0.107851037 [223,] -1.055453201 0.800012311 [224,] 0.731082508 -1.055453201 [225,] -3.657369597 0.731082508 [226,] 0.316720867 -3.657369597 [227,] 1.185710905 0.316720867 [228,] -1.197890874 1.185710905 [229,] -0.830389846 -1.197890874 [230,] 1.814511262 -0.830389846 [231,] -3.218905829 1.814511262 [232,] 4.651212489 -3.218905829 [233,] 2.226248327 4.651212489 [234,] -0.726660655 2.226248327 [235,] -2.221561835 -0.726660655 [236,] -6.993819348 -2.221561835 [237,] -1.179681722 -6.993819348 [238,] 1.863256037 -1.179681722 [239,] -1.593061865 1.863256037 [240,] -0.162645268 -1.593061865 [241,] -1.396015419 -0.162645268 [242,] 1.189632700 -1.396015419 [243,] 2.020251001 1.189632700 [244,] 1.419790411 2.020251001 [245,] 0.192867082 1.419790411 [246,] 1.254166697 0.192867082 [247,] -2.383032043 1.254166697 [248,] 1.295437316 -2.383032043 [249,] 0.477752654 1.295437316 [250,] -0.720829792 0.477752654 [251,] -1.520704011 -0.720829792 [252,] 0.726255791 -1.520704011 [253,] 3.292503160 0.726255791 [254,] -1.207855665 3.292503160 [255,] 0.280291587 -1.207855665 [256,] 1.861115030 0.280291587 [257,] 2.446320148 1.861115030 [258,] -1.054943238 2.446320148 [259,] -5.333093544 -1.054943238 [260,] 1.446898172 -5.333093544 [261,] -3.774119819 1.446898172 [262,] -0.166228177 -3.774119819 [263,] 1.368916629 -0.166228177 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.249371924 -3.098538785 2 1.893678110 0.249371924 3 2.838394759 1.893678110 4 -2.401787347 2.838394759 5 -1.847013370 -2.401787347 6 3.605880460 -1.847013370 7 -2.103491161 3.605880460 8 -2.059658512 -2.103491161 9 0.538680280 -2.059658512 10 0.996010221 0.538680280 11 -0.195778066 0.996010221 12 0.454799205 -0.195778066 13 0.502429577 0.454799205 14 -1.002615235 0.502429577 15 -0.496690760 -1.002615235 16 0.412162340 -0.496690760 17 3.577867081 0.412162340 18 2.468158708 3.577867081 19 0.401602040 2.468158708 20 0.605520846 0.401602040 21 0.786385822 0.605520846 22 2.507859621 0.786385822 23 0.839080777 2.507859621 24 0.851623609 0.839080777 25 0.778196929 0.851623609 26 1.091647210 0.778196929 27 -1.809108735 1.091647210 28 0.315176248 -1.809108735 29 -0.263029559 0.315176248 30 -0.765960661 -0.263029559 31 -0.849288952 -0.765960661 32 -0.803962023 -0.849288952 33 -0.001749614 -0.803962023 34 -1.490209881 -0.001749614 35 -3.030476921 -1.490209881 36 -3.035534565 -3.030476921 37 -1.747032491 -3.035534565 38 1.445444080 -1.747032491 39 1.505106075 1.445444080 40 1.279462294 1.505106075 41 -1.700951059 1.279462294 42 2.512878443 -1.700951059 43 -0.153377764 2.512878443 44 -0.462355329 -0.153377764 45 -4.490949562 -0.462355329 46 -2.630761274 -4.490949562 47 -0.146742166 -2.630761274 48 0.582123384 -0.146742166 49 -1.630247367 0.582123384 50 -1.025298496 -1.630247367 51 -0.141940881 -1.025298496 52 -3.017185579 -0.141940881 53 -0.036186091 -3.017185579 54 -2.343289800 -0.036186091 55 1.615021855 -2.343289800 56 0.145459891 1.615021855 57 0.470116771 0.145459891 58 -0.112393030 0.470116771 59 1.778939435 -0.112393030 60 0.440446592 1.778939435 61 0.359318244 0.440446592 62 -0.548816853 0.359318244 63 -0.710986002 -0.548816853 64 0.693678842 -0.710986002 65 0.945813967 0.693678842 66 1.666822920 0.945813967 67 3.604111813 1.666822920 68 -3.899708374 3.604111813 69 0.348421327 -3.899708374 70 -3.412380609 0.348421327 71 -0.924295106 -3.412380609 72 1.071941325 -0.924295106 73 0.859016376 1.071941325 74 0.767705125 0.859016376 75 3.508113010 0.767705125 76 -0.419099992 3.508113010 77 1.468801152 -0.419099992 78 -2.216577334 1.468801152 79 0.857859743 -2.216577334 80 0.341372549 0.857859743 81 0.236759169 0.341372549 82 -0.643413724 0.236759169 83 0.115767618 -0.643413724 84 1.839264528 0.115767618 85 -0.155569285 1.839264528 86 0.632511822 -0.155569285 87 1.127411151 0.632511822 88 0.480518610 1.127411151 89 -1.907972516 0.480518610 90 0.241505625 -1.907972516 91 0.194438081 0.241505625 92 -0.053106791 0.194438081 93 -2.029851850 -0.053106791 94 1.256386663 -2.029851850 95 0.211757119 1.256386663 96 2.322616035 0.211757119 97 0.226291624 2.322616035 98 -0.429781644 0.226291624 99 -0.992298111 -0.429781644 100 1.323375337 -0.992298111 101 2.545781557 1.323375337 102 0.760339627 2.545781557 103 1.040141556 0.760339627 104 -1.842204209 1.040141556 105 1.298270035 -1.842204209 106 0.298876541 1.298270035 107 1.641348531 0.298876541 108 -0.304342299 1.641348531 109 0.714319104 -0.304342299 110 0.007172851 0.714319104 111 1.943590631 0.007172851 112 -1.281178077 1.943590631 113 -2.579874261 -1.281178077 114 1.838527156 -2.579874261 115 -1.865732844 1.838527156 116 0.771112171 -1.865732844 117 -1.792915136 0.771112171 118 0.479615794 -1.792915136 119 -1.429221584 0.479615794 120 0.614576732 -1.429221584 121 -2.821209317 0.614576732 122 -0.854504464 -2.821209317 123 -0.843764797 -0.854504464 124 -0.784933914 -0.843764797 125 0.341278399 -0.784933914 126 1.509416094 0.341278399 127 0.951747888 1.509416094 128 -2.685865073 0.951747888 129 2.059728520 -2.685865073 130 -3.723953244 2.059728520 131 2.541667846 -3.723953244 132 -2.165989022 2.541667846 133 -1.544046009 -2.165989022 134 -0.083656696 -1.544046009 135 0.887887265 -0.083656696 136 0.468722193 0.887887265 137 -2.498950057 0.468722193 138 -1.006480416 -2.498950057 139 -2.339956434 -1.006480416 140 3.143500085 -2.339956434 141 1.355875574 3.143500085 142 0.158636042 1.355875574 143 1.875596153 0.158636042 144 -3.266751430 1.875596153 145 2.502454533 -3.266751430 146 -1.945751239 2.502454533 147 1.156989895 -1.945751239 148 0.172772330 1.156989895 149 -2.900457717 0.172772330 150 -0.878418291 -2.900457717 151 2.175136908 -0.878418291 152 4.147418985 2.175136908 153 1.603697482 4.147418985 154 -2.481628296 1.603697482 155 0.560693436 -2.481628296 156 1.293314145 0.560693436 157 1.099065340 1.293314145 158 1.462603867 1.099065340 159 -0.698903764 1.462603867 160 0.306895079 -0.698903764 161 0.329795885 0.306895079 162 -0.381357631 0.329795885 163 0.786901982 -0.381357631 164 1.331779722 0.786901982 165 -1.619761147 1.331779722 166 -0.344192471 -1.619761147 167 -3.194346992 -0.344192471 168 -1.789596334 -3.194346992 169 1.612807316 -1.789596334 170 1.518467982 1.612807316 171 0.662211895 1.518467982 172 -1.844438422 0.662211895 173 -2.333612434 -1.844438422 174 -2.907041435 -2.333612434 175 0.526992426 -2.907041435 176 -0.294327621 0.526992426 177 -0.650368444 -0.294327621 178 0.206131805 -0.650368444 179 -1.288904893 0.206131805 180 0.993126239 -1.288904893 181 -0.451505878 0.993126239 182 2.298423231 -0.451505878 183 -0.110981881 2.298423231 184 -6.596986586 -0.110981881 185 1.301064443 -6.596986586 186 2.610419292 1.301064443 187 -0.348622911 2.610419292 188 -0.412069714 -0.348622911 189 0.545031937 -0.412069714 190 -0.436490890 0.545031937 191 0.623572202 -0.436490890 192 2.277518581 0.623572202 193 1.911359536 2.277518581 194 -0.632884513 1.911359536 195 0.001024589 -0.632884513 196 3.087373988 0.001024589 197 1.008056355 3.087373988 198 1.592366903 1.008056355 199 1.539638452 1.592366903 200 1.966679997 1.539638452 201 0.704538164 1.966679997 202 -2.312960385 0.704538164 203 -2.494922264 -2.312960385 204 2.322123701 -2.494922264 205 0.581163430 2.322123701 206 1.467985216 0.581163430 207 1.156874554 1.467985216 208 -2.580145319 1.156874554 209 1.111446590 -2.580145319 210 -2.203785389 1.111446590 211 -3.722425549 -2.203785389 212 0.890920641 -3.722425549 213 2.909421188 0.890920641 214 1.535256743 2.909421188 215 0.919486133 1.535256743 216 2.425128675 0.919486133 217 0.095076893 2.425128675 218 1.167894530 0.095076893 219 -0.129909691 1.167894530 220 -1.572687684 -0.129909691 221 0.107851037 -1.572687684 222 0.800012311 0.107851037 223 -1.055453201 0.800012311 224 0.731082508 -1.055453201 225 -3.657369597 0.731082508 226 0.316720867 -3.657369597 227 1.185710905 0.316720867 228 -1.197890874 1.185710905 229 -0.830389846 -1.197890874 230 1.814511262 -0.830389846 231 -3.218905829 1.814511262 232 4.651212489 -3.218905829 233 2.226248327 4.651212489 234 -0.726660655 2.226248327 235 -2.221561835 -0.726660655 236 -6.993819348 -2.221561835 237 -1.179681722 -6.993819348 238 1.863256037 -1.179681722 239 -1.593061865 1.863256037 240 -0.162645268 -1.593061865 241 -1.396015419 -0.162645268 242 1.189632700 -1.396015419 243 2.020251001 1.189632700 244 1.419790411 2.020251001 245 0.192867082 1.419790411 246 1.254166697 0.192867082 247 -2.383032043 1.254166697 248 1.295437316 -2.383032043 249 0.477752654 1.295437316 250 -0.720829792 0.477752654 251 -1.520704011 -0.720829792 252 0.726255791 -1.520704011 253 3.292503160 0.726255791 254 -1.207855665 3.292503160 255 0.280291587 -1.207855665 256 1.861115030 0.280291587 257 2.446320148 1.861115030 258 -1.054943238 2.446320148 259 -5.333093544 -1.054943238 260 1.446898172 -5.333093544 261 -3.774119819 1.446898172 262 -0.166228177 -3.774119819 263 1.368916629 -0.166228177 > 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/7q31x1352157035.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/8b1dh1352157035.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/9vh8j1352157035.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/10whx91352157035.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11or1q1352157035.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12pip31352157035.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13g2411352157035.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/146bgz1352157035.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/15328n1352157035.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/165whw1352157035.tab") + } > > try(system("convert tmp/1kw9w1352157035.ps tmp/1kw9w1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/2vuy21352157035.ps tmp/2vuy21352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/3am9o1352157035.ps tmp/3am9o1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/4rkv51352157035.ps tmp/4rkv51352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/5inw51352157035.ps tmp/5inw51352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/66d0h1352157035.ps tmp/66d0h1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/7q31x1352157035.ps tmp/7q31x1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/8b1dh1352157035.ps tmp/8b1dh1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/9vh8j1352157035.ps tmp/9vh8j1352157035.png",intern=TRUE)) character(0) > try(system("convert tmp/10whx91352157035.ps tmp/10whx91352157035.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.856 1.066 12.913