R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(9 + ,13 + ,38 + ,14 + ,9 + ,16 + ,32 + ,18 + ,9 + ,19 + ,35 + ,11 + ,9 + ,15 + ,33 + ,12 + ,9 + ,14 + ,37 + ,16 + ,9 + ,13 + ,29 + ,18 + ,9 + ,19 + ,31 + ,14 + ,9 + ,15 + ,36 + ,14 + ,9 + ,14 + ,35 + ,15 + ,9 + ,15 + ,38 + ,15 + ,9 + ,16 + ,31 + ,17 + ,9 + ,16 + ,34 + ,19 + ,9 + ,16 + ,35 + ,10 + ,9 + ,16 + ,38 + ,16 + ,9 + ,17 + ,37 + ,18 + ,9 + ,15 + ,33 + ,14 + ,9 + ,15 + ,32 + ,14 + ,9 + ,20 + ,38 + ,17 + ,9 + ,18 + ,38 + ,14 + ,9 + ,16 + ,32 + ,16 + ,9 + ,16 + ,33 + ,18 + ,9 + ,16 + ,31 + ,11 + ,9 + ,19 + ,38 + ,14 + ,9 + ,16 + ,39 + ,12 + ,9 + ,17 + ,32 + ,17 + ,9 + ,17 + ,32 + ,9 + ,9 + ,16 + ,35 + ,16 + ,9 + ,15 + ,37 + ,14 + ,9 + ,16 + ,33 + ,15 + ,9 + ,14 + ,33 + ,11 + ,9 + ,15 + ,31 + ,16 + ,9 + ,12 + ,32 + ,13 + ,9 + ,14 + ,31 + ,17 + ,9 + ,16 + ,37 + ,15 + ,9 + ,14 + ,30 + ,14 + ,9 + ,10 + ,33 + ,16 + ,9 + ,10 + ,31 + ,9 + ,9 + ,14 + ,33 + ,15 + ,9 + ,16 + ,31 + ,17 + ,9 + ,16 + ,33 + ,13 + ,9 + ,16 + ,32 + ,15 + ,9 + ,14 + ,33 + ,16 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,12 + ,33 + ,9 + ,11 + ,12 + ,44 + ,16 + ,11 + ,15 + ,39 + ,11 + ,11 + ,15 + ,32 + ,10 + ,11 + ,16 + ,35 + ,11 + ,11 + ,14 + ,25 + ,15 + ,11 + ,17 + ,35 + ,17 + ,11 + ,14 + ,34 + ,14 + ,11 + ,13 + ,35 + ,8 + ,11 + ,15 + ,39 + ,15 + ,11 + ,13 + ,33 + ,11 + ,11 + ,14 + ,36 + ,16 + ,11 + ,15 + ,32 + ,10 + ,11 + ,12 + ,32 + ,15 + ,11 + ,13 + ,36 + ,9 + ,11 + ,8 + ,36 + ,16 + ,11 + ,14 + ,32 + ,19 + ,11 + ,14 + ,34 + ,12 + ,11 + ,11 + ,33 + ,8 + ,11 + ,12 + ,35 + ,11 + ,11 + ,13 + ,30 + ,14 + ,11 + ,10 + ,38 + ,9 + ,11 + ,16 + ,34 + ,15 + ,11 + ,18 + ,33 + ,13 + ,11 + ,13 + ,32 + ,16 + ,11 + ,11 + ,31 + ,11 + ,11 + ,4 + ,30 + ,12 + ,11 + ,13 + ,27 + ,13 + ,11 + ,16 + ,31 + ,10 + ,11 + ,10 + ,30 + ,11 + ,11 + ,12 + ,32 + ,12 + ,11 + ,12 + ,35 + ,8 + ,11 + ,10 + ,28 + ,12 + ,11 + ,13 + ,33 + ,12 + ,11 + ,15 + ,31 + ,15 + ,11 + ,12 + ,35 + ,11 + ,11 + ,14 + ,35 + ,13 + ,11 + ,10 + ,32 + ,14 + ,11 + ,12 + ,21 + ,10 + ,11 + ,12 + ,20 + ,12 + ,11 + ,11 + ,34 + ,15 + ,11 + ,10 + ,32 + ,13 + ,11 + ,12 + ,34 + ,13 + ,11 + ,16 + ,32 + ,13 + ,11 + ,12 + ,33 + ,12 + ,11 + ,14 + ,33 + ,12 + ,11 + ,16 + ,37 + ,9 + ,11 + ,14 + ,32 + ,9 + ,11 + ,13 + ,34 + ,15 + ,11 + ,4 + ,30 + ,10 + ,11 + ,15 + ,30 + ,14 + ,11 + ,11 + ,38 + ,15 + ,11 + ,11 + ,36 + ,7 + ,11 + ,14 + ,32 + ,14) + ,dim=c(4 + ,264) + ,dimnames=list(c('month' + ,'Doorzettingsvermogen' + ,'Zelfstandig' + ,'Stressbestendig') + ,1:264)) > y <- array(NA,dim=c(4,264),dimnames=list(c('month','Doorzettingsvermogen','Zelfstandig','Stressbestendig'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Doorzettingsvermogen month Zelfstandig Stressbestendig 1 13 9 38 14 2 16 9 32 18 3 19 9 35 11 4 15 9 33 12 5 14 9 37 16 6 13 9 29 18 7 19 9 31 14 8 15 9 36 14 9 14 9 35 15 10 15 9 38 15 11 16 9 31 17 12 16 9 34 19 13 16 9 35 10 14 16 9 38 16 15 17 9 37 18 16 15 9 33 14 17 15 9 32 14 18 20 9 38 17 19 18 9 38 14 20 16 9 32 16 21 16 9 33 18 22 16 9 31 11 23 19 9 38 14 24 16 9 39 12 25 17 9 32 17 26 17 9 32 9 27 16 9 35 16 28 15 9 37 14 29 16 9 33 15 30 14 9 33 11 31 15 9 31 16 32 12 9 32 13 33 14 9 31 17 34 16 9 37 15 35 14 9 30 14 36 10 9 33 16 37 10 9 31 9 38 14 9 33 15 39 16 9 31 17 40 16 9 33 13 41 16 9 32 15 42 14 9 33 16 43 20 9 32 16 44 14 9 33 12 45 14 9 28 15 46 11 9 35 11 47 14 9 39 15 48 15 9 34 15 49 16 9 38 17 50 14 9 32 13 51 16 9 38 16 52 14 9 30 14 53 12 9 33 11 54 16 9 38 12 55 9 9 32 12 56 14 9 35 15 57 16 9 34 16 58 16 9 34 15 59 15 9 36 12 60 16 9 34 12 61 12 9 28 8 62 16 9 34 13 63 16 9 35 11 64 14 9 35 14 65 16 9 31 15 66 17 9 34 9 67 18 10 37 10 68 18 10 35 11 69 12 10 27 12 70 16 10 40 15 71 10 10 37 15 72 14 10 36 14 73 18 10 38 16 74 18 10 39 15 75 16 10 41 15 76 17 10 27 13 77 16 10 30 12 78 16 10 37 17 79 13 10 31 13 80 16 10 31 15 81 16 10 27 13 82 16 10 36 15 83 15 10 37 15 84 15 10 33 16 85 16 10 34 15 86 14 10 31 14 87 16 10 39 15 88 16 10 34 14 89 15 10 32 13 90 12 10 33 7 91 17 10 36 17 92 16 10 32 13 93 15 10 41 15 94 13 10 28 14 95 16 10 30 13 96 16 10 36 16 97 16 10 35 12 98 16 10 31 14 99 14 10 34 17 100 16 10 36 15 101 16 10 36 17 102 20 10 35 12 103 15 10 37 16 104 16 10 28 11 105 13 10 39 15 106 17 10 32 9 107 16 10 35 16 108 16 10 39 15 109 12 10 35 10 110 16 10 42 10 111 16 10 34 15 112 17 10 33 11 113 13 10 41 13 114 12 10 33 14 115 18 10 34 18 116 14 10 32 16 117 14 10 40 14 118 13 10 40 14 119 16 10 35 14 120 13 10 36 14 121 16 10 37 12 122 13 10 27 14 123 16 10 39 15 124 15 10 38 15 125 16 10 31 15 126 15 10 33 13 127 17 10 32 17 128 15 10 39 17 129 12 10 36 19 130 16 10 33 15 131 10 10 33 13 132 16 10 32 9 133 12 10 37 15 134 14 10 30 15 135 15 10 38 15 136 13 10 29 16 137 15 10 22 11 138 11 10 35 14 139 12 10 35 11 140 11 10 34 15 141 16 10 35 13 142 15 10 34 15 143 17 10 37 16 144 16 10 35 14 145 10 10 23 15 146 18 10 31 16 147 13 10 27 16 148 16 10 36 11 149 13 10 31 12 150 10 10 32 9 151 15 10 39 16 152 16 10 37 13 153 16 10 38 16 154 14 10 39 12 155 10 10 31 13 156 17 10 32 13 157 13 10 37 14 158 15 10 36 19 159 16 10 32 13 160 12 10 38 12 161 13 10 36 13 162 13 11 26 10 163 12 11 26 14 164 17 11 33 16 165 15 11 39 10 166 10 11 30 11 167 14 11 33 14 168 11 11 25 12 169 13 11 38 9 170 16 11 37 9 171 12 11 31 11 172 16 11 37 16 173 12 11 35 9 174 9 11 25 13 175 12 11 28 16 176 15 11 35 13 177 12 11 33 9 178 12 11 30 12 179 14 11 31 16 180 12 11 37 11 181 16 11 36 14 182 11 11 30 13 183 19 11 36 15 184 15 11 32 14 185 8 11 28 16 186 16 11 36 13 187 17 11 34 14 188 12 11 31 15 189 11 11 28 13 190 11 11 36 11 191 14 11 36 11 192 16 11 40 14 193 12 11 33 15 194 16 11 37 11 195 13 11 32 15 196 15 11 38 12 197 16 11 31 14 198 16 11 37 14 199 14 11 33 8 200 16 11 32 13 201 16 11 30 9 202 14 11 30 15 203 11 11 31 17 204 12 11 32 13 205 15 11 34 15 206 15 11 36 15 207 16 11 37 14 208 16 11 36 16 209 11 11 33 13 210 15 11 33 16 211 12 11 33 9 212 12 11 44 16 213 15 11 39 11 214 15 11 32 10 215 16 11 35 11 216 14 11 25 15 217 17 11 35 17 218 14 11 34 14 219 13 11 35 8 220 15 11 39 15 221 13 11 33 11 222 14 11 36 16 223 15 11 32 10 224 12 11 32 15 225 13 11 36 9 226 8 11 36 16 227 14 11 32 19 228 14 11 34 12 229 11 11 33 8 230 12 11 35 11 231 13 11 30 14 232 10 11 38 9 233 16 11 34 15 234 18 11 33 13 235 13 11 32 16 236 11 11 31 11 237 4 11 30 12 238 13 11 27 13 239 16 11 31 10 240 10 11 30 11 241 12 11 32 12 242 12 11 35 8 243 10 11 28 12 244 13 11 33 12 245 15 11 31 15 246 12 11 35 11 247 14 11 35 13 248 10 11 32 14 249 12 11 21 10 250 12 11 20 12 251 11 11 34 15 252 10 11 32 13 253 12 11 34 13 254 16 11 32 13 255 12 11 33 12 256 14 11 33 12 257 16 11 37 9 258 14 11 32 9 259 13 11 34 15 260 4 11 30 10 261 15 11 30 14 262 11 11 38 15 263 11 11 36 7 264 14 11 32 14 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Zelfstandig Stressbestendig 15.5210 -0.8322 0.1572 0.1405 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.7680 -1.3003 0.1866 1.4777 5.6140 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 15.52099 2.53917 6.113 3.57e-09 *** month -0.83215 0.18009 -4.621 6.02e-06 *** Zelfstandig 0.15716 0.03741 4.201 3.66e-05 *** Stressbestendig 0.14050 0.05686 2.471 0.0141 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.225 on 260 degrees of freedom Multiple R-squared: 0.1889, Adjusted R-squared: 0.1795 F-statistic: 20.18 on 3 and 260 DF, p-value: 8.62e-12 > 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,] 0.8120228410 0.375954318 0.1879772 [2,] 0.6899118178 0.620176364 0.3100882 [3,] 0.5739349111 0.852130178 0.4260651 [4,] 0.4786860928 0.957372186 0.5213139 [5,] 0.3809111471 0.761822294 0.6190889 [6,] 0.4057454829 0.811490966 0.5942545 [7,] 0.3254052830 0.650810566 0.6745947 [8,] 0.2915018711 0.583003742 0.7084981 [9,] 0.3194990263 0.638998053 0.6805010 [10,] 0.2562346340 0.512469268 0.7437654 [11,] 0.2019936825 0.403987365 0.7980063 [12,] 0.4566727841 0.913345568 0.5433272 [13,] 0.4334325258 0.866865052 0.5665675 [14,] 0.3622065076 0.724413015 0.6377935 [15,] 0.2962839490 0.592567898 0.7037161 [16,] 0.2381216058 0.476243212 0.7618784 [17,] 0.2672332691 0.534466538 0.7327667 [18,] 0.2195611981 0.439122396 0.7804388 [19,] 0.1911546019 0.382309204 0.8088454 [20,] 0.1594459717 0.318891943 0.8405540 [21,] 0.1224872439 0.244974488 0.8775128 [22,] 0.1040339029 0.208067806 0.8959661 [23,] 0.0780805630 0.156161126 0.9219194 [24,] 0.0778544339 0.155708868 0.9221456 [25,] 0.0590977686 0.118195537 0.9409022 [26,] 0.1069365615 0.213873123 0.8930634 [27,] 0.0914430381 0.182886076 0.9085570 [28,] 0.0696966307 0.139393261 0.9303034 [29,] 0.0565122995 0.113024599 0.9434877 [30,] 0.2196731137 0.439346227 0.7803269 [31,] 0.3846997046 0.769399409 0.6153003 [32,] 0.3494957239 0.698991448 0.6505043 [33,] 0.3129952258 0.625990452 0.6870048 [34,] 0.2761751853 0.552350371 0.7238248 [35,] 0.2422161080 0.484432216 0.7577839 [36,] 0.2183100202 0.436620040 0.7816900 [37,] 0.3843669527 0.768733905 0.6156330 [38,] 0.3473684906 0.694736981 0.6526315 [39,] 0.3038760442 0.607752088 0.6961240 [40,] 0.4184297631 0.836859526 0.5815702 [41,] 0.4235688481 0.847137696 0.5764312 [42,] 0.3792869352 0.758573870 0.6207131 [43,] 0.3364099263 0.672819853 0.6635901 [44,] 0.2999801087 0.599960217 0.7000199 [45,] 0.2610026626 0.522005325 0.7389973 [46,] 0.2275156321 0.455031264 0.7724844 [47,] 0.2414813246 0.482962649 0.7585187 [48,] 0.2087985258 0.417597052 0.7912015 [49,] 0.4125517599 0.825103520 0.5874482 [50,] 0.3921848662 0.784369732 0.6078151 [51,] 0.3521223281 0.704244656 0.6478777 [52,] 0.3155681607 0.631136321 0.6844318 [53,] 0.2790050809 0.558010162 0.7209949 [54,] 0.2549415450 0.509883090 0.7450585 [55,] 0.2307250672 0.461450134 0.7692749 [56,] 0.2061008179 0.412201636 0.7938992 [57,] 0.1857482226 0.371496445 0.8142518 [58,] 0.1716754806 0.343350961 0.8283245 [59,] 0.1529655330 0.305931066 0.8470345 [60,] 0.1653715455 0.330743091 0.8346285 [61,] 0.1520907526 0.304181505 0.8479092 [62,] 0.1416045715 0.283209143 0.8583954 [63,] 0.1699205650 0.339841130 0.8300794 [64,] 0.1611968378 0.322393676 0.8388032 [65,] 0.3695175720 0.739035144 0.6304824 [66,] 0.3400076149 0.680015230 0.6599924 [67,] 0.3417542960 0.683508592 0.6582457 [68,] 0.3344161230 0.668832246 0.6655839 [69,] 0.3017141096 0.603428219 0.6982859 [70,] 0.3450221325 0.690044265 0.6549779 [71,] 0.3287874705 0.657574941 0.6712125 [72,] 0.2953961303 0.590792261 0.7046039 [73,] 0.2813418306 0.562683661 0.7186582 [74,] 0.2580726309 0.516145262 0.7419274 [75,] 0.2518471729 0.503694346 0.7481528 [76,] 0.2232741007 0.446548201 0.7767259 [77,] 0.2007860097 0.401572019 0.7992140 [78,] 0.1762327431 0.352465486 0.8237673 [79,] 0.1549112226 0.309822445 0.8450888 [80,] 0.1364652769 0.272930554 0.8635347 [81,] 0.1171667124 0.234333425 0.8828333 [82,] 0.1021191069 0.204238214 0.8978809 [83,] 0.0867528381 0.173505676 0.9132472 [84,] 0.0857731840 0.171546368 0.9142268 [85,] 0.0756951079 0.151390216 0.9243049 [86,] 0.0676185156 0.135237031 0.9323815 [87,] 0.0607292186 0.121458437 0.9392708 [88,] 0.0538620222 0.107724044 0.9461380 [89,] 0.0497775955 0.099555191 0.9502224 [90,] 0.0412897889 0.082579578 0.9587102 [91,] 0.0354430040 0.070886008 0.9645570 [92,] 0.0313303408 0.062660682 0.9686697 [93,] 0.0286108788 0.057221758 0.9713891 [94,] 0.0234608634 0.046921727 0.9765391 [95,] 0.0189798808 0.037959762 0.9810201 [96,] 0.0481819859 0.096363972 0.9518180 [97,] 0.0413044212 0.082608842 0.9586956 [98,] 0.0418601924 0.083720385 0.9581398 [99,] 0.0498184867 0.099636973 0.9501815 [100,] 0.0577824540 0.115564908 0.9422175 [101,] 0.0490589957 0.098117991 0.9509410 [102,] 0.0408622290 0.081724458 0.9591378 [103,] 0.0464097709 0.092819542 0.9535902 [104,] 0.0388714572 0.077742914 0.9611285 [105,] 0.0333059540 0.066611908 0.9666940 [106,] 0.0366446584 0.073289317 0.9633553 [107,] 0.0419291708 0.083858342 0.9580708 [108,] 0.0488808898 0.097761780 0.9511191 [109,] 0.0527307983 0.105461597 0.9472692 [110,] 0.0461434812 0.092286962 0.9538565 [111,] 0.0429457076 0.085891415 0.9570543 [112,] 0.0470365797 0.094073159 0.9529634 [113,] 0.0408839596 0.081767919 0.9591160 [114,] 0.0407541086 0.081508217 0.9592459 [115,] 0.0353714857 0.070742971 0.9646285 [116,] 0.0311557279 0.062311456 0.9688443 [117,] 0.0256741320 0.051348264 0.9743259 [118,] 0.0210823483 0.042164697 0.9789177 [119,] 0.0190361853 0.038072371 0.9809638 [120,] 0.0157224777 0.031444955 0.9842775 [121,] 0.0157443698 0.031488740 0.9842556 [122,] 0.0130048076 0.026009615 0.9869952 [123,] 0.0196527942 0.039305588 0.9803472 [124,] 0.0172837649 0.034567530 0.9827162 [125,] 0.0342336622 0.068467324 0.9657663 [126,] 0.0352835792 0.070567158 0.9647164 [127,] 0.0440093708 0.088018742 0.9559906 [128,] 0.0371586035 0.074317207 0.9628414 [129,] 0.0306882502 0.061376500 0.9693117 [130,] 0.0272842526 0.054568505 0.9727157 [131,] 0.0309519396 0.061903879 0.9690481 [132,] 0.0451783670 0.090356734 0.9548216 [133,] 0.0468332262 0.093666452 0.9531668 [134,] 0.0663661616 0.132732323 0.9336338 [135,] 0.0597282090 0.119456418 0.9402718 [136,] 0.0501716870 0.100343374 0.9498283 [137,] 0.0465334063 0.093066813 0.9534666 [138,] 0.0413836675 0.082767335 0.9586163 [139,] 0.0507437360 0.101487472 0.9492563 [140,] 0.0702547744 0.140509549 0.9297452 [141,] 0.0612863800 0.122572760 0.9387136 [142,] 0.0578886258 0.115777252 0.9421114 [143,] 0.0503821712 0.100764342 0.9496178 [144,] 0.0646010713 0.129202143 0.9353989 [145,] 0.0545753623 0.109150725 0.9454246 [146,] 0.0487707826 0.097541565 0.9512292 [147,] 0.0416381225 0.083276245 0.9583619 [148,] 0.0353517369 0.070703474 0.9646483 [149,] 0.0502939888 0.100587978 0.9497060 [150,] 0.0614869364 0.122973873 0.9385131 [151,] 0.0570618290 0.114123658 0.9429382 [152,] 0.0477855835 0.095571167 0.9522144 [153,] 0.0537161129 0.107432226 0.9462839 [154,] 0.0530389905 0.106077981 0.9469610 [155,] 0.0467755402 0.093551080 0.9532245 [156,] 0.0414022327 0.082804465 0.9585978 [157,] 0.0355285861 0.071057172 0.9644714 [158,] 0.0406823229 0.081364646 0.9593177 [159,] 0.0341111903 0.068222381 0.9658888 [160,] 0.0390350852 0.078070170 0.9609649 [161,] 0.0322383063 0.064476613 0.9677617 [162,] 0.0281712712 0.056342542 0.9718287 [163,] 0.0235074024 0.047014805 0.9764926 [164,] 0.0238575452 0.047715090 0.9761425 [165,] 0.0200858390 0.040171678 0.9799142 [166,] 0.0173243208 0.034648642 0.9826757 [167,] 0.0149136374 0.029827275 0.9850864 [168,] 0.0185959002 0.037191800 0.9814041 [169,] 0.0156347755 0.031269551 0.9843652 [170,] 0.0132087652 0.026417530 0.9867912 [171,] 0.0107998137 0.021599627 0.9892002 [172,] 0.0087244403 0.017448881 0.9912756 [173,] 0.0068466734 0.013693347 0.9931533 [174,] 0.0062203335 0.012440667 0.9937797 [175,] 0.0057441547 0.011488309 0.9942558 [176,] 0.0053103542 0.010620708 0.9946896 [177,] 0.0126144981 0.025228996 0.9873855 [178,] 0.0111902800 0.022380560 0.9888097 [179,] 0.0268620873 0.053724175 0.9731379 [180,] 0.0261758424 0.052351685 0.9738242 [181,] 0.0332616859 0.066523372 0.9667383 [182,] 0.0289329493 0.057865899 0.9710671 [183,] 0.0259742355 0.051948471 0.9740258 [184,] 0.0272228123 0.054445625 0.9727772 [185,] 0.0219291915 0.043858383 0.9780708 [186,] 0.0192595460 0.038519092 0.9807405 [187,] 0.0170654214 0.034130843 0.9829346 [188,] 0.0173892731 0.034778546 0.9826107 [189,] 0.0137455289 0.027491058 0.9862545 [190,] 0.0114256291 0.022851258 0.9885744 [191,] 0.0127785494 0.025557099 0.9872215 [192,] 0.0121487635 0.024297527 0.9878512 [193,] 0.0103456254 0.020691251 0.9896544 [194,] 0.0118174406 0.023634881 0.9881826 [195,] 0.0178472427 0.035694485 0.9821528 [196,] 0.0143434469 0.028686894 0.9856566 [197,] 0.0154649223 0.030929845 0.9845351 [198,] 0.0126932636 0.025386527 0.9873067 [199,] 0.0105615789 0.021123158 0.9894384 [200,] 0.0085361057 0.017072211 0.9914639 [201,] 0.0082843402 0.016568680 0.9917157 [202,] 0.0078015804 0.015603161 0.9921984 [203,] 0.0075005493 0.015001099 0.9924995 [204,] 0.0062569639 0.012513928 0.9937430 [205,] 0.0047492166 0.009498433 0.9952508 [206,] 0.0056386183 0.011277237 0.9943614 [207,] 0.0046939431 0.009387886 0.9953061 [208,] 0.0049078319 0.009815664 0.9950922 [209,] 0.0061090038 0.012218008 0.9938910 [210,] 0.0050912889 0.010182578 0.9949087 [211,] 0.0065075192 0.013015038 0.9934925 [212,] 0.0049839562 0.009967912 0.9950160 [213,] 0.0037376360 0.007475272 0.9962624 [214,] 0.0029838815 0.005967763 0.9970161 [215,] 0.0021584510 0.004316902 0.9978415 [216,] 0.0015560891 0.003112178 0.9984439 [217,] 0.0017459277 0.003491855 0.9982541 [218,] 0.0012920061 0.002584012 0.9987080 [219,] 0.0009302197 0.001860439 0.9990698 [220,] 0.0051676288 0.010335258 0.9948324 [221,] 0.0036145101 0.007229020 0.9963855 [222,] 0.0027770734 0.005554147 0.9972229 [223,] 0.0020238359 0.004047672 0.9979762 [224,] 0.0014203285 0.002840657 0.9985797 [225,] 0.0009400360 0.001880072 0.9990600 [226,] 0.0010193690 0.002038738 0.9989806 [227,] 0.0010847517 0.002169503 0.9989152 [228,] 0.0047367710 0.009473542 0.9952632 [229,] 0.0032273742 0.006454748 0.9967726 [230,] 0.0023307219 0.004661444 0.9976693 [231,] 0.0820979418 0.164195884 0.9179021 [232,] 0.0640218751 0.128043750 0.9359781 [233,] 0.1034971703 0.206994341 0.8965028 [234,] 0.0957090000 0.191418000 0.9042910 [235,] 0.0722792468 0.144558494 0.9277208 [236,] 0.0529938371 0.105987674 0.9470062 [237,] 0.0483025216 0.096605043 0.9516975 [238,] 0.0342283116 0.068456623 0.9657717 [239,] 0.0322782400 0.064556480 0.9677218 [240,] 0.0219556544 0.043911309 0.9780443 [241,] 0.0161435851 0.032287170 0.9838564 [242,] 0.0157810476 0.031562095 0.9842190 [243,] 0.0099539814 0.019907963 0.9900460 [244,] 0.0066256561 0.013251312 0.9933743 [245,] 0.0053518269 0.010703654 0.9946482 [246,] 0.0048852984 0.009770597 0.9951147 [247,] 0.0027870662 0.005574132 0.9972129 [248,] 0.0036876436 0.007375287 0.9963124 [249,] 0.0016994959 0.003398992 0.9983005 [250,] 0.0008932529 0.001786506 0.9991067 [251,] 0.0016608284 0.003321657 0.9983392 > postscript(file="/var/wessaorg/rcomp/tmp/1pych1351951242.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/2islx1351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3gpj01351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4srev1351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5srwk1351951243.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -2.970582396 0.410367813 3.922387460 0.096203846 -2.094422628 -2.118159183 7 8 9 10 11 12 4.129521282 -0.656267059 -1.639608341 -1.111081346 0.708024431 -0.044446474 13 14 15 16 17 18 1.062886410 -0.251580297 0.624579471 -0.184794054 -0.027636386 3.607920753 19 20 21 22 23 24 2.029417604 0.691365713 0.253210144 1.551018133 3.029417604 0.153257836 25 26 27 28 29 30 1.550866763 2.674858366 0.219892708 -0.813424728 0.674706995 -0.763297203 31 32 33 34 35 36 -0.151476618 -2.887137436 -1.291975569 0.046076322 -0.713321049 -5.465791955 37 38 39 40 41 42 -4.167983966 -1.325293005 0.708024431 0.955704896 0.831864664 -1.465791955 43 44 45 46 47 48 4.691365713 -0.903796154 -0.539504663 -4.077612540 -2.268239015 -0.482450673 49 50 51 52 53 54 -0.392079247 -0.887137436 -0.251580297 -0.713321049 -2.763297203 0.310415505 55 56 57 58 59 60 -5.746638485 -1.639608341 0.377050377 0.517549327 -0.375269159 0.939046178 61 62 63 64 65 66 -1.556012011 0.798547228 0.922387460 -1.499109391 0.989022332 2.360543029 67 68 69 70 71 72 3.580724473 3.754540859 -1.128696745 0.406756716 -5.121770279 -0.824113660 73 74 75 76 77 78 2.580573102 2.563914384 0.249599048 3.730804305 2.399830250 0.597231820 79 80 81 82 83 84 -0.897826369 1.821175731 2.730804305 1.035387389 -0.121770279 0.366361444 85 86 87 88 89 90 1.349702726 -0.038325319 0.563914384 1.490201676 0.945015963 -1.369148003 91 92 93 94 95 96 1.754389488 1.945015963 -0.750400952 -0.566852314 2.259331300 0.894888439 97 98 99 100 101 102 1.614041909 1.961674681 -0.931295175 1.035387389 0.754389488 5.614041909 103 104 105 106 107 108 -0.262269230 2.854644537 -2.436085616 3.507011765 1.052046107 0.563914384 109 110 111 112 113 114 -2.104960191 0.794936131 1.349702726 3.068856196 -2.469403052 -2.352640656 115 116 117 118 119 120 2.928205875 -0.476480888 -1.452744334 -2.452744334 1.333044008 -1.824113660 121 122 123 124 125 126 1.299726572 -0.409694646 0.563914384 -0.278927947 1.821175731 0.787858295 127 128 129 130 131 132 2.383020162 -0.717083517 -3.526608412 1.506860394 -4.212141705 2.507011765 133 134 135 136 137 138 -3.121770279 -0.021666601 -0.278927947 -1.005007883 2.797590547 -3.666955992 139 140 141 142 143 144 -2.245459141 -3.650297274 1.473542958 0.349702726 1.737730770 1.333044008 145 146 147 148 149 150 -2.921562923 3.680676780 -0.690692546 1.597383191 -0.757327418 -3.492988235 151 152 153 154 155 156 -0.576584566 1.159227622 0.580573102 -1.014588765 -3.897826369 2.945015963 157 158 159 160 161 162 -1.981271329 -0.526608412 1.945015963 -2.857431096 -1.683614710 1.141612223 163 164 165 166 167 168 -0.420383578 3.198514843 1.098562535 -2.627517401 0.479512743 -0.982228009 169 170 171 172 173 174 -0.603780847 2.553376822 -0.784675069 1.569884169 -1.132307842 -3.122726960 175 176 177 178 179 180 -1.015696816 1.305696357 -0.817992505 -0.768016351 0.512830179 -1.727621079 181 182 183 184 185 186 2.008039738 -1.908515301 4.867540788 1.636670412 -5.015696816 2.148538689 187 188 189 190 191 192 3.322355075 -1.346670870 -1.594199965 -2.570463411 0.429536589 1.379409065 193 194 195 196 197 198 -1.660986207 2.272378921 -0.503828539 0.974722302 2.793828080 1.850882070 199 200 201 202 203 204 1.322506445 2.777169362 3.653480500 0.810486798 -2.627668771 -1.222830638 205 206 207 208 209 210 1.181856125 0.867540788 1.850882070 1.727041838 -2.379988306 1.198514843 211 212 213 214 215 216 -0.817992505 -3.530219509 0.958063584 2.198666213 2.586694258 1.596275139 217 218 219 220 221 222 2.743700556 0.322355075 0.008191109 0.396067783 -0.098990406 -0.272958162 223 224 225 226 227 228 2.198666213 -1.503828539 -0.289465510 -6.272958162 -0.065824340 0.603352976 229 230 231 232 233 234 -1.677493555 -1.413305742 -0.049014252 -3.603780847 2.181856125 4.620011694 235 236 237 238 239 240 -0.644327489 -1.784675069 -8.768016351 0.562957704 3.355823881 -2.627517401 241 242 243 244 245 246 -1.082331688 -0.991808891 -2.453701014 -0.239489356 1.653329130 -1.413305742 247 248 249 250 251 252 0.305696357 -3.363329588 0.927400565 0.803560332 -2.818143875 -3.222830638 253 254 255 256 257 258 -1.537145975 2.777169362 -1.239489356 0.760510644 2.553376822 1.339165163 259 260 261 262 263 264 -0.818143875 -8.487018450 1.950985748 -3.446774549 -2.008467609 0.636670412 > postscript(file="/var/wessaorg/rcomp/tmp/6fm4v1351951243.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 -2.970582396 NA 1 0.410367813 -2.970582396 2 3.922387460 0.410367813 3 0.096203846 3.922387460 4 -2.094422628 0.096203846 5 -2.118159183 -2.094422628 6 4.129521282 -2.118159183 7 -0.656267059 4.129521282 8 -1.639608341 -0.656267059 9 -1.111081346 -1.639608341 10 0.708024431 -1.111081346 11 -0.044446474 0.708024431 12 1.062886410 -0.044446474 13 -0.251580297 1.062886410 14 0.624579471 -0.251580297 15 -0.184794054 0.624579471 16 -0.027636386 -0.184794054 17 3.607920753 -0.027636386 18 2.029417604 3.607920753 19 0.691365713 2.029417604 20 0.253210144 0.691365713 21 1.551018133 0.253210144 22 3.029417604 1.551018133 23 0.153257836 3.029417604 24 1.550866763 0.153257836 25 2.674858366 1.550866763 26 0.219892708 2.674858366 27 -0.813424728 0.219892708 28 0.674706995 -0.813424728 29 -0.763297203 0.674706995 30 -0.151476618 -0.763297203 31 -2.887137436 -0.151476618 32 -1.291975569 -2.887137436 33 0.046076322 -1.291975569 34 -0.713321049 0.046076322 35 -5.465791955 -0.713321049 36 -4.167983966 -5.465791955 37 -1.325293005 -4.167983966 38 0.708024431 -1.325293005 39 0.955704896 0.708024431 40 0.831864664 0.955704896 41 -1.465791955 0.831864664 42 4.691365713 -1.465791955 43 -0.903796154 4.691365713 44 -0.539504663 -0.903796154 45 -4.077612540 -0.539504663 46 -2.268239015 -4.077612540 47 -0.482450673 -2.268239015 48 -0.392079247 -0.482450673 49 -0.887137436 -0.392079247 50 -0.251580297 -0.887137436 51 -0.713321049 -0.251580297 52 -2.763297203 -0.713321049 53 0.310415505 -2.763297203 54 -5.746638485 0.310415505 55 -1.639608341 -5.746638485 56 0.377050377 -1.639608341 57 0.517549327 0.377050377 58 -0.375269159 0.517549327 59 0.939046178 -0.375269159 60 -1.556012011 0.939046178 61 0.798547228 -1.556012011 62 0.922387460 0.798547228 63 -1.499109391 0.922387460 64 0.989022332 -1.499109391 65 2.360543029 0.989022332 66 3.580724473 2.360543029 67 3.754540859 3.580724473 68 -1.128696745 3.754540859 69 0.406756716 -1.128696745 70 -5.121770279 0.406756716 71 -0.824113660 -5.121770279 72 2.580573102 -0.824113660 73 2.563914384 2.580573102 74 0.249599048 2.563914384 75 3.730804305 0.249599048 76 2.399830250 3.730804305 77 0.597231820 2.399830250 78 -0.897826369 0.597231820 79 1.821175731 -0.897826369 80 2.730804305 1.821175731 81 1.035387389 2.730804305 82 -0.121770279 1.035387389 83 0.366361444 -0.121770279 84 1.349702726 0.366361444 85 -0.038325319 1.349702726 86 0.563914384 -0.038325319 87 1.490201676 0.563914384 88 0.945015963 1.490201676 89 -1.369148003 0.945015963 90 1.754389488 -1.369148003 91 1.945015963 1.754389488 92 -0.750400952 1.945015963 93 -0.566852314 -0.750400952 94 2.259331300 -0.566852314 95 0.894888439 2.259331300 96 1.614041909 0.894888439 97 1.961674681 1.614041909 98 -0.931295175 1.961674681 99 1.035387389 -0.931295175 100 0.754389488 1.035387389 101 5.614041909 0.754389488 102 -0.262269230 5.614041909 103 2.854644537 -0.262269230 104 -2.436085616 2.854644537 105 3.507011765 -2.436085616 106 1.052046107 3.507011765 107 0.563914384 1.052046107 108 -2.104960191 0.563914384 109 0.794936131 -2.104960191 110 1.349702726 0.794936131 111 3.068856196 1.349702726 112 -2.469403052 3.068856196 113 -2.352640656 -2.469403052 114 2.928205875 -2.352640656 115 -0.476480888 2.928205875 116 -1.452744334 -0.476480888 117 -2.452744334 -1.452744334 118 1.333044008 -2.452744334 119 -1.824113660 1.333044008 120 1.299726572 -1.824113660 121 -0.409694646 1.299726572 122 0.563914384 -0.409694646 123 -0.278927947 0.563914384 124 1.821175731 -0.278927947 125 0.787858295 1.821175731 126 2.383020162 0.787858295 127 -0.717083517 2.383020162 128 -3.526608412 -0.717083517 129 1.506860394 -3.526608412 130 -4.212141705 1.506860394 131 2.507011765 -4.212141705 132 -3.121770279 2.507011765 133 -0.021666601 -3.121770279 134 -0.278927947 -0.021666601 135 -1.005007883 -0.278927947 136 2.797590547 -1.005007883 137 -3.666955992 2.797590547 138 -2.245459141 -3.666955992 139 -3.650297274 -2.245459141 140 1.473542958 -3.650297274 141 0.349702726 1.473542958 142 1.737730770 0.349702726 143 1.333044008 1.737730770 144 -2.921562923 1.333044008 145 3.680676780 -2.921562923 146 -0.690692546 3.680676780 147 1.597383191 -0.690692546 148 -0.757327418 1.597383191 149 -3.492988235 -0.757327418 150 -0.576584566 -3.492988235 151 1.159227622 -0.576584566 152 0.580573102 1.159227622 153 -1.014588765 0.580573102 154 -3.897826369 -1.014588765 155 2.945015963 -3.897826369 156 -1.981271329 2.945015963 157 -0.526608412 -1.981271329 158 1.945015963 -0.526608412 159 -2.857431096 1.945015963 160 -1.683614710 -2.857431096 161 1.141612223 -1.683614710 162 -0.420383578 1.141612223 163 3.198514843 -0.420383578 164 1.098562535 3.198514843 165 -2.627517401 1.098562535 166 0.479512743 -2.627517401 167 -0.982228009 0.479512743 168 -0.603780847 -0.982228009 169 2.553376822 -0.603780847 170 -0.784675069 2.553376822 171 1.569884169 -0.784675069 172 -1.132307842 1.569884169 173 -3.122726960 -1.132307842 174 -1.015696816 -3.122726960 175 1.305696357 -1.015696816 176 -0.817992505 1.305696357 177 -0.768016351 -0.817992505 178 0.512830179 -0.768016351 179 -1.727621079 0.512830179 180 2.008039738 -1.727621079 181 -1.908515301 2.008039738 182 4.867540788 -1.908515301 183 1.636670412 4.867540788 184 -5.015696816 1.636670412 185 2.148538689 -5.015696816 186 3.322355075 2.148538689 187 -1.346670870 3.322355075 188 -1.594199965 -1.346670870 189 -2.570463411 -1.594199965 190 0.429536589 -2.570463411 191 1.379409065 0.429536589 192 -1.660986207 1.379409065 193 2.272378921 -1.660986207 194 -0.503828539 2.272378921 195 0.974722302 -0.503828539 196 2.793828080 0.974722302 197 1.850882070 2.793828080 198 1.322506445 1.850882070 199 2.777169362 1.322506445 200 3.653480500 2.777169362 201 0.810486798 3.653480500 202 -2.627668771 0.810486798 203 -1.222830638 -2.627668771 204 1.181856125 -1.222830638 205 0.867540788 1.181856125 206 1.850882070 0.867540788 207 1.727041838 1.850882070 208 -2.379988306 1.727041838 209 1.198514843 -2.379988306 210 -0.817992505 1.198514843 211 -3.530219509 -0.817992505 212 0.958063584 -3.530219509 213 2.198666213 0.958063584 214 2.586694258 2.198666213 215 1.596275139 2.586694258 216 2.743700556 1.596275139 217 0.322355075 2.743700556 218 0.008191109 0.322355075 219 0.396067783 0.008191109 220 -0.098990406 0.396067783 221 -0.272958162 -0.098990406 222 2.198666213 -0.272958162 223 -1.503828539 2.198666213 224 -0.289465510 -1.503828539 225 -6.272958162 -0.289465510 226 -0.065824340 -6.272958162 227 0.603352976 -0.065824340 228 -1.677493555 0.603352976 229 -1.413305742 -1.677493555 230 -0.049014252 -1.413305742 231 -3.603780847 -0.049014252 232 2.181856125 -3.603780847 233 4.620011694 2.181856125 234 -0.644327489 4.620011694 235 -1.784675069 -0.644327489 236 -8.768016351 -1.784675069 237 0.562957704 -8.768016351 238 3.355823881 0.562957704 239 -2.627517401 3.355823881 240 -1.082331688 -2.627517401 241 -0.991808891 -1.082331688 242 -2.453701014 -0.991808891 243 -0.239489356 -2.453701014 244 1.653329130 -0.239489356 245 -1.413305742 1.653329130 246 0.305696357 -1.413305742 247 -3.363329588 0.305696357 248 0.927400565 -3.363329588 249 0.803560332 0.927400565 250 -2.818143875 0.803560332 251 -3.222830638 -2.818143875 252 -1.537145975 -3.222830638 253 2.777169362 -1.537145975 254 -1.239489356 2.777169362 255 0.760510644 -1.239489356 256 2.553376822 0.760510644 257 1.339165163 2.553376822 258 -0.818143875 1.339165163 259 -8.487018450 -0.818143875 260 1.950985748 -8.487018450 261 -3.446774549 1.950985748 262 -2.008467609 -3.446774549 263 0.636670412 -2.008467609 264 NA 0.636670412 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.410367813 -2.970582396 [2,] 3.922387460 0.410367813 [3,] 0.096203846 3.922387460 [4,] -2.094422628 0.096203846 [5,] -2.118159183 -2.094422628 [6,] 4.129521282 -2.118159183 [7,] -0.656267059 4.129521282 [8,] -1.639608341 -0.656267059 [9,] -1.111081346 -1.639608341 [10,] 0.708024431 -1.111081346 [11,] -0.044446474 0.708024431 [12,] 1.062886410 -0.044446474 [13,] -0.251580297 1.062886410 [14,] 0.624579471 -0.251580297 [15,] -0.184794054 0.624579471 [16,] -0.027636386 -0.184794054 [17,] 3.607920753 -0.027636386 [18,] 2.029417604 3.607920753 [19,] 0.691365713 2.029417604 [20,] 0.253210144 0.691365713 [21,] 1.551018133 0.253210144 [22,] 3.029417604 1.551018133 [23,] 0.153257836 3.029417604 [24,] 1.550866763 0.153257836 [25,] 2.674858366 1.550866763 [26,] 0.219892708 2.674858366 [27,] -0.813424728 0.219892708 [28,] 0.674706995 -0.813424728 [29,] -0.763297203 0.674706995 [30,] -0.151476618 -0.763297203 [31,] -2.887137436 -0.151476618 [32,] -1.291975569 -2.887137436 [33,] 0.046076322 -1.291975569 [34,] -0.713321049 0.046076322 [35,] -5.465791955 -0.713321049 [36,] -4.167983966 -5.465791955 [37,] -1.325293005 -4.167983966 [38,] 0.708024431 -1.325293005 [39,] 0.955704896 0.708024431 [40,] 0.831864664 0.955704896 [41,] -1.465791955 0.831864664 [42,] 4.691365713 -1.465791955 [43,] -0.903796154 4.691365713 [44,] -0.539504663 -0.903796154 [45,] -4.077612540 -0.539504663 [46,] -2.268239015 -4.077612540 [47,] -0.482450673 -2.268239015 [48,] -0.392079247 -0.482450673 [49,] -0.887137436 -0.392079247 [50,] -0.251580297 -0.887137436 [51,] -0.713321049 -0.251580297 [52,] -2.763297203 -0.713321049 [53,] 0.310415505 -2.763297203 [54,] -5.746638485 0.310415505 [55,] -1.639608341 -5.746638485 [56,] 0.377050377 -1.639608341 [57,] 0.517549327 0.377050377 [58,] -0.375269159 0.517549327 [59,] 0.939046178 -0.375269159 [60,] -1.556012011 0.939046178 [61,] 0.798547228 -1.556012011 [62,] 0.922387460 0.798547228 [63,] -1.499109391 0.922387460 [64,] 0.989022332 -1.499109391 [65,] 2.360543029 0.989022332 [66,] 3.580724473 2.360543029 [67,] 3.754540859 3.580724473 [68,] -1.128696745 3.754540859 [69,] 0.406756716 -1.128696745 [70,] -5.121770279 0.406756716 [71,] -0.824113660 -5.121770279 [72,] 2.580573102 -0.824113660 [73,] 2.563914384 2.580573102 [74,] 0.249599048 2.563914384 [75,] 3.730804305 0.249599048 [76,] 2.399830250 3.730804305 [77,] 0.597231820 2.399830250 [78,] -0.897826369 0.597231820 [79,] 1.821175731 -0.897826369 [80,] 2.730804305 1.821175731 [81,] 1.035387389 2.730804305 [82,] -0.121770279 1.035387389 [83,] 0.366361444 -0.121770279 [84,] 1.349702726 0.366361444 [85,] -0.038325319 1.349702726 [86,] 0.563914384 -0.038325319 [87,] 1.490201676 0.563914384 [88,] 0.945015963 1.490201676 [89,] -1.369148003 0.945015963 [90,] 1.754389488 -1.369148003 [91,] 1.945015963 1.754389488 [92,] -0.750400952 1.945015963 [93,] -0.566852314 -0.750400952 [94,] 2.259331300 -0.566852314 [95,] 0.894888439 2.259331300 [96,] 1.614041909 0.894888439 [97,] 1.961674681 1.614041909 [98,] -0.931295175 1.961674681 [99,] 1.035387389 -0.931295175 [100,] 0.754389488 1.035387389 [101,] 5.614041909 0.754389488 [102,] -0.262269230 5.614041909 [103,] 2.854644537 -0.262269230 [104,] -2.436085616 2.854644537 [105,] 3.507011765 -2.436085616 [106,] 1.052046107 3.507011765 [107,] 0.563914384 1.052046107 [108,] -2.104960191 0.563914384 [109,] 0.794936131 -2.104960191 [110,] 1.349702726 0.794936131 [111,] 3.068856196 1.349702726 [112,] -2.469403052 3.068856196 [113,] -2.352640656 -2.469403052 [114,] 2.928205875 -2.352640656 [115,] -0.476480888 2.928205875 [116,] -1.452744334 -0.476480888 [117,] -2.452744334 -1.452744334 [118,] 1.333044008 -2.452744334 [119,] -1.824113660 1.333044008 [120,] 1.299726572 -1.824113660 [121,] -0.409694646 1.299726572 [122,] 0.563914384 -0.409694646 [123,] -0.278927947 0.563914384 [124,] 1.821175731 -0.278927947 [125,] 0.787858295 1.821175731 [126,] 2.383020162 0.787858295 [127,] -0.717083517 2.383020162 [128,] -3.526608412 -0.717083517 [129,] 1.506860394 -3.526608412 [130,] -4.212141705 1.506860394 [131,] 2.507011765 -4.212141705 [132,] -3.121770279 2.507011765 [133,] -0.021666601 -3.121770279 [134,] -0.278927947 -0.021666601 [135,] -1.005007883 -0.278927947 [136,] 2.797590547 -1.005007883 [137,] -3.666955992 2.797590547 [138,] -2.245459141 -3.666955992 [139,] -3.650297274 -2.245459141 [140,] 1.473542958 -3.650297274 [141,] 0.349702726 1.473542958 [142,] 1.737730770 0.349702726 [143,] 1.333044008 1.737730770 [144,] -2.921562923 1.333044008 [145,] 3.680676780 -2.921562923 [146,] -0.690692546 3.680676780 [147,] 1.597383191 -0.690692546 [148,] -0.757327418 1.597383191 [149,] -3.492988235 -0.757327418 [150,] -0.576584566 -3.492988235 [151,] 1.159227622 -0.576584566 [152,] 0.580573102 1.159227622 [153,] -1.014588765 0.580573102 [154,] -3.897826369 -1.014588765 [155,] 2.945015963 -3.897826369 [156,] -1.981271329 2.945015963 [157,] -0.526608412 -1.981271329 [158,] 1.945015963 -0.526608412 [159,] -2.857431096 1.945015963 [160,] -1.683614710 -2.857431096 [161,] 1.141612223 -1.683614710 [162,] -0.420383578 1.141612223 [163,] 3.198514843 -0.420383578 [164,] 1.098562535 3.198514843 [165,] -2.627517401 1.098562535 [166,] 0.479512743 -2.627517401 [167,] -0.982228009 0.479512743 [168,] -0.603780847 -0.982228009 [169,] 2.553376822 -0.603780847 [170,] -0.784675069 2.553376822 [171,] 1.569884169 -0.784675069 [172,] -1.132307842 1.569884169 [173,] -3.122726960 -1.132307842 [174,] -1.015696816 -3.122726960 [175,] 1.305696357 -1.015696816 [176,] -0.817992505 1.305696357 [177,] -0.768016351 -0.817992505 [178,] 0.512830179 -0.768016351 [179,] -1.727621079 0.512830179 [180,] 2.008039738 -1.727621079 [181,] -1.908515301 2.008039738 [182,] 4.867540788 -1.908515301 [183,] 1.636670412 4.867540788 [184,] -5.015696816 1.636670412 [185,] 2.148538689 -5.015696816 [186,] 3.322355075 2.148538689 [187,] -1.346670870 3.322355075 [188,] -1.594199965 -1.346670870 [189,] -2.570463411 -1.594199965 [190,] 0.429536589 -2.570463411 [191,] 1.379409065 0.429536589 [192,] -1.660986207 1.379409065 [193,] 2.272378921 -1.660986207 [194,] -0.503828539 2.272378921 [195,] 0.974722302 -0.503828539 [196,] 2.793828080 0.974722302 [197,] 1.850882070 2.793828080 [198,] 1.322506445 1.850882070 [199,] 2.777169362 1.322506445 [200,] 3.653480500 2.777169362 [201,] 0.810486798 3.653480500 [202,] -2.627668771 0.810486798 [203,] -1.222830638 -2.627668771 [204,] 1.181856125 -1.222830638 [205,] 0.867540788 1.181856125 [206,] 1.850882070 0.867540788 [207,] 1.727041838 1.850882070 [208,] -2.379988306 1.727041838 [209,] 1.198514843 -2.379988306 [210,] -0.817992505 1.198514843 [211,] -3.530219509 -0.817992505 [212,] 0.958063584 -3.530219509 [213,] 2.198666213 0.958063584 [214,] 2.586694258 2.198666213 [215,] 1.596275139 2.586694258 [216,] 2.743700556 1.596275139 [217,] 0.322355075 2.743700556 [218,] 0.008191109 0.322355075 [219,] 0.396067783 0.008191109 [220,] -0.098990406 0.396067783 [221,] -0.272958162 -0.098990406 [222,] 2.198666213 -0.272958162 [223,] -1.503828539 2.198666213 [224,] -0.289465510 -1.503828539 [225,] -6.272958162 -0.289465510 [226,] -0.065824340 -6.272958162 [227,] 0.603352976 -0.065824340 [228,] -1.677493555 0.603352976 [229,] -1.413305742 -1.677493555 [230,] -0.049014252 -1.413305742 [231,] -3.603780847 -0.049014252 [232,] 2.181856125 -3.603780847 [233,] 4.620011694 2.181856125 [234,] -0.644327489 4.620011694 [235,] -1.784675069 -0.644327489 [236,] -8.768016351 -1.784675069 [237,] 0.562957704 -8.768016351 [238,] 3.355823881 0.562957704 [239,] -2.627517401 3.355823881 [240,] -1.082331688 -2.627517401 [241,] -0.991808891 -1.082331688 [242,] -2.453701014 -0.991808891 [243,] -0.239489356 -2.453701014 [244,] 1.653329130 -0.239489356 [245,] -1.413305742 1.653329130 [246,] 0.305696357 -1.413305742 [247,] -3.363329588 0.305696357 [248,] 0.927400565 -3.363329588 [249,] 0.803560332 0.927400565 [250,] -2.818143875 0.803560332 [251,] -3.222830638 -2.818143875 [252,] -1.537145975 -3.222830638 [253,] 2.777169362 -1.537145975 [254,] -1.239489356 2.777169362 [255,] 0.760510644 -1.239489356 [256,] 2.553376822 0.760510644 [257,] 1.339165163 2.553376822 [258,] -0.818143875 1.339165163 [259,] -8.487018450 -0.818143875 [260,] 1.950985748 -8.487018450 [261,] -3.446774549 1.950985748 [262,] -2.008467609 -3.446774549 [263,] 0.636670412 -2.008467609 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.410367813 -2.970582396 2 3.922387460 0.410367813 3 0.096203846 3.922387460 4 -2.094422628 0.096203846 5 -2.118159183 -2.094422628 6 4.129521282 -2.118159183 7 -0.656267059 4.129521282 8 -1.639608341 -0.656267059 9 -1.111081346 -1.639608341 10 0.708024431 -1.111081346 11 -0.044446474 0.708024431 12 1.062886410 -0.044446474 13 -0.251580297 1.062886410 14 0.624579471 -0.251580297 15 -0.184794054 0.624579471 16 -0.027636386 -0.184794054 17 3.607920753 -0.027636386 18 2.029417604 3.607920753 19 0.691365713 2.029417604 20 0.253210144 0.691365713 21 1.551018133 0.253210144 22 3.029417604 1.551018133 23 0.153257836 3.029417604 24 1.550866763 0.153257836 25 2.674858366 1.550866763 26 0.219892708 2.674858366 27 -0.813424728 0.219892708 28 0.674706995 -0.813424728 29 -0.763297203 0.674706995 30 -0.151476618 -0.763297203 31 -2.887137436 -0.151476618 32 -1.291975569 -2.887137436 33 0.046076322 -1.291975569 34 -0.713321049 0.046076322 35 -5.465791955 -0.713321049 36 -4.167983966 -5.465791955 37 -1.325293005 -4.167983966 38 0.708024431 -1.325293005 39 0.955704896 0.708024431 40 0.831864664 0.955704896 41 -1.465791955 0.831864664 42 4.691365713 -1.465791955 43 -0.903796154 4.691365713 44 -0.539504663 -0.903796154 45 -4.077612540 -0.539504663 46 -2.268239015 -4.077612540 47 -0.482450673 -2.268239015 48 -0.392079247 -0.482450673 49 -0.887137436 -0.392079247 50 -0.251580297 -0.887137436 51 -0.713321049 -0.251580297 52 -2.763297203 -0.713321049 53 0.310415505 -2.763297203 54 -5.746638485 0.310415505 55 -1.639608341 -5.746638485 56 0.377050377 -1.639608341 57 0.517549327 0.377050377 58 -0.375269159 0.517549327 59 0.939046178 -0.375269159 60 -1.556012011 0.939046178 61 0.798547228 -1.556012011 62 0.922387460 0.798547228 63 -1.499109391 0.922387460 64 0.989022332 -1.499109391 65 2.360543029 0.989022332 66 3.580724473 2.360543029 67 3.754540859 3.580724473 68 -1.128696745 3.754540859 69 0.406756716 -1.128696745 70 -5.121770279 0.406756716 71 -0.824113660 -5.121770279 72 2.580573102 -0.824113660 73 2.563914384 2.580573102 74 0.249599048 2.563914384 75 3.730804305 0.249599048 76 2.399830250 3.730804305 77 0.597231820 2.399830250 78 -0.897826369 0.597231820 79 1.821175731 -0.897826369 80 2.730804305 1.821175731 81 1.035387389 2.730804305 82 -0.121770279 1.035387389 83 0.366361444 -0.121770279 84 1.349702726 0.366361444 85 -0.038325319 1.349702726 86 0.563914384 -0.038325319 87 1.490201676 0.563914384 88 0.945015963 1.490201676 89 -1.369148003 0.945015963 90 1.754389488 -1.369148003 91 1.945015963 1.754389488 92 -0.750400952 1.945015963 93 -0.566852314 -0.750400952 94 2.259331300 -0.566852314 95 0.894888439 2.259331300 96 1.614041909 0.894888439 97 1.961674681 1.614041909 98 -0.931295175 1.961674681 99 1.035387389 -0.931295175 100 0.754389488 1.035387389 101 5.614041909 0.754389488 102 -0.262269230 5.614041909 103 2.854644537 -0.262269230 104 -2.436085616 2.854644537 105 3.507011765 -2.436085616 106 1.052046107 3.507011765 107 0.563914384 1.052046107 108 -2.104960191 0.563914384 109 0.794936131 -2.104960191 110 1.349702726 0.794936131 111 3.068856196 1.349702726 112 -2.469403052 3.068856196 113 -2.352640656 -2.469403052 114 2.928205875 -2.352640656 115 -0.476480888 2.928205875 116 -1.452744334 -0.476480888 117 -2.452744334 -1.452744334 118 1.333044008 -2.452744334 119 -1.824113660 1.333044008 120 1.299726572 -1.824113660 121 -0.409694646 1.299726572 122 0.563914384 -0.409694646 123 -0.278927947 0.563914384 124 1.821175731 -0.278927947 125 0.787858295 1.821175731 126 2.383020162 0.787858295 127 -0.717083517 2.383020162 128 -3.526608412 -0.717083517 129 1.506860394 -3.526608412 130 -4.212141705 1.506860394 131 2.507011765 -4.212141705 132 -3.121770279 2.507011765 133 -0.021666601 -3.121770279 134 -0.278927947 -0.021666601 135 -1.005007883 -0.278927947 136 2.797590547 -1.005007883 137 -3.666955992 2.797590547 138 -2.245459141 -3.666955992 139 -3.650297274 -2.245459141 140 1.473542958 -3.650297274 141 0.349702726 1.473542958 142 1.737730770 0.349702726 143 1.333044008 1.737730770 144 -2.921562923 1.333044008 145 3.680676780 -2.921562923 146 -0.690692546 3.680676780 147 1.597383191 -0.690692546 148 -0.757327418 1.597383191 149 -3.492988235 -0.757327418 150 -0.576584566 -3.492988235 151 1.159227622 -0.576584566 152 0.580573102 1.159227622 153 -1.014588765 0.580573102 154 -3.897826369 -1.014588765 155 2.945015963 -3.897826369 156 -1.981271329 2.945015963 157 -0.526608412 -1.981271329 158 1.945015963 -0.526608412 159 -2.857431096 1.945015963 160 -1.683614710 -2.857431096 161 1.141612223 -1.683614710 162 -0.420383578 1.141612223 163 3.198514843 -0.420383578 164 1.098562535 3.198514843 165 -2.627517401 1.098562535 166 0.479512743 -2.627517401 167 -0.982228009 0.479512743 168 -0.603780847 -0.982228009 169 2.553376822 -0.603780847 170 -0.784675069 2.553376822 171 1.569884169 -0.784675069 172 -1.132307842 1.569884169 173 -3.122726960 -1.132307842 174 -1.015696816 -3.122726960 175 1.305696357 -1.015696816 176 -0.817992505 1.305696357 177 -0.768016351 -0.817992505 178 0.512830179 -0.768016351 179 -1.727621079 0.512830179 180 2.008039738 -1.727621079 181 -1.908515301 2.008039738 182 4.867540788 -1.908515301 183 1.636670412 4.867540788 184 -5.015696816 1.636670412 185 2.148538689 -5.015696816 186 3.322355075 2.148538689 187 -1.346670870 3.322355075 188 -1.594199965 -1.346670870 189 -2.570463411 -1.594199965 190 0.429536589 -2.570463411 191 1.379409065 0.429536589 192 -1.660986207 1.379409065 193 2.272378921 -1.660986207 194 -0.503828539 2.272378921 195 0.974722302 -0.503828539 196 2.793828080 0.974722302 197 1.850882070 2.793828080 198 1.322506445 1.850882070 199 2.777169362 1.322506445 200 3.653480500 2.777169362 201 0.810486798 3.653480500 202 -2.627668771 0.810486798 203 -1.222830638 -2.627668771 204 1.181856125 -1.222830638 205 0.867540788 1.181856125 206 1.850882070 0.867540788 207 1.727041838 1.850882070 208 -2.379988306 1.727041838 209 1.198514843 -2.379988306 210 -0.817992505 1.198514843 211 -3.530219509 -0.817992505 212 0.958063584 -3.530219509 213 2.198666213 0.958063584 214 2.586694258 2.198666213 215 1.596275139 2.586694258 216 2.743700556 1.596275139 217 0.322355075 2.743700556 218 0.008191109 0.322355075 219 0.396067783 0.008191109 220 -0.098990406 0.396067783 221 -0.272958162 -0.098990406 222 2.198666213 -0.272958162 223 -1.503828539 2.198666213 224 -0.289465510 -1.503828539 225 -6.272958162 -0.289465510 226 -0.065824340 -6.272958162 227 0.603352976 -0.065824340 228 -1.677493555 0.603352976 229 -1.413305742 -1.677493555 230 -0.049014252 -1.413305742 231 -3.603780847 -0.049014252 232 2.181856125 -3.603780847 233 4.620011694 2.181856125 234 -0.644327489 4.620011694 235 -1.784675069 -0.644327489 236 -8.768016351 -1.784675069 237 0.562957704 -8.768016351 238 3.355823881 0.562957704 239 -2.627517401 3.355823881 240 -1.082331688 -2.627517401 241 -0.991808891 -1.082331688 242 -2.453701014 -0.991808891 243 -0.239489356 -2.453701014 244 1.653329130 -0.239489356 245 -1.413305742 1.653329130 246 0.305696357 -1.413305742 247 -3.363329588 0.305696357 248 0.927400565 -3.363329588 249 0.803560332 0.927400565 250 -2.818143875 0.803560332 251 -3.222830638 -2.818143875 252 -1.537145975 -3.222830638 253 2.777169362 -1.537145975 254 -1.239489356 2.777169362 255 0.760510644 -1.239489356 256 2.553376822 0.760510644 257 1.339165163 2.553376822 258 -0.818143875 1.339165163 259 -8.487018450 -0.818143875 260 1.950985748 -8.487018450 261 -3.446774549 1.950985748 262 -2.008467609 -3.446774549 263 0.636670412 -2.008467609 > 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/7tfyh1351951243.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/8d45f1351951243.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/96aih1351951243.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/104eoa1351951243.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/117y6t1351951243.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12a1dd1351951243.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/134wx41351951243.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/149tc31351951243.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/15b47a1351951243.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/160hfh1351951243.tab") + } > > try(system("convert tmp/1pych1351951242.ps tmp/1pych1351951242.png",intern=TRUE)) character(0) > try(system("convert tmp/2islx1351951242.ps tmp/2islx1351951242.png",intern=TRUE)) character(0) > try(system("convert tmp/3gpj01351951242.ps tmp/3gpj01351951242.png",intern=TRUE)) character(0) > try(system("convert tmp/4srev1351951242.ps tmp/4srev1351951242.png",intern=TRUE)) character(0) > try(system("convert tmp/5srwk1351951243.ps tmp/5srwk1351951243.png",intern=TRUE)) character(0) > try(system("convert tmp/6fm4v1351951243.ps tmp/6fm4v1351951243.png",intern=TRUE)) character(0) > try(system("convert tmp/7tfyh1351951243.ps tmp/7tfyh1351951243.png",intern=TRUE)) character(0) > try(system("convert tmp/8d45f1351951243.ps tmp/8d45f1351951243.png",intern=TRUE)) character(0) > try(system("convert tmp/96aih1351951243.ps tmp/96aih1351951243.png",intern=TRUE)) character(0) > try(system("convert tmp/104eoa1351951243.ps tmp/104eoa1351951243.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.487 1.364 13.008