R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing 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(13 + ,12 + ,14 + ,12 + ,53 + ,16 + ,11 + ,18 + ,11 + ,83 + ,19 + ,15 + ,11 + ,14 + ,66 + ,15 + ,6 + ,12 + ,12 + ,67 + ,14 + ,13 + ,16 + ,21 + ,76 + ,13 + ,10 + ,18 + ,12 + ,78 + ,19 + ,12 + ,14 + ,22 + ,53 + ,15 + ,14 + ,14 + ,11 + ,80 + ,14 + ,12 + ,15 + ,10 + ,74 + ,15 + ,9 + ,15 + ,13 + ,76 + ,16 + ,10 + ,17 + ,10 + ,79 + ,16 + ,12 + ,19 + ,8 + ,54 + ,16 + ,12 + ,10 + ,15 + ,67 + ,16 + ,11 + ,16 + ,14 + ,54 + ,17 + ,15 + ,18 + ,10 + ,87 + ,15 + ,12 + ,14 + ,14 + ,58 + ,15 + ,10 + ,14 + ,14 + ,75 + ,20 + ,12 + ,17 + ,11 + ,88 + ,18 + ,11 + ,14 + ,10 + ,64 + ,16 + ,12 + ,16 + ,13 + ,57 + ,16 + ,11 + ,18 + ,9.5 + ,66 + ,16 + ,12 + ,11 + ,14 + ,68 + ,19 + ,13 + ,14 + ,12 + ,54 + ,16 + ,11 + ,12 + ,14 + ,56 + ,17 + ,12 + ,17 + ,11 + ,86 + ,17 + ,13 + ,9 + ,9 + ,80 + ,16 + ,10 + ,16 + ,11 + ,76 + ,15 + ,14 + ,14 + ,15 + ,69 + ,16 + ,12 + ,15 + ,14 + ,78 + ,14 + ,10 + ,11 + ,13 + ,67 + ,15 + ,12 + ,16 + ,9 + ,80 + ,12 + ,8 + ,13 + ,15 + ,54 + ,14 + 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array(NA,dim=c(5,264),dimnames=list(c('Learning','Software','Happiness','Depression','Sport1'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects 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 Happiness Learning Software Depression Sport1 1 14 13 12 12.0 53 2 18 16 11 11.0 83 3 11 19 15 14.0 66 4 12 15 6 12.0 67 5 16 14 13 21.0 76 6 18 13 10 12.0 78 7 14 19 12 22.0 53 8 14 15 14 11.0 80 9 15 14 12 10.0 74 10 15 15 9 13.0 76 11 17 16 10 10.0 79 12 19 16 12 8.0 54 13 10 16 12 15.0 67 14 16 16 11 14.0 54 15 18 17 15 10.0 87 16 14 15 12 14.0 58 17 14 15 10 14.0 75 18 17 20 12 11.0 88 19 14 18 11 10.0 64 20 16 16 12 13.0 57 21 18 16 11 9.5 66 22 11 16 12 14.0 68 23 14 19 13 12.0 54 24 12 16 11 14.0 56 25 17 17 12 11.0 86 26 9 17 13 9.0 80 27 16 16 10 11.0 76 28 14 15 14 15.0 69 29 15 16 12 14.0 78 30 11 14 10 13.0 67 31 16 15 12 9.0 80 32 13 12 8 15.0 54 33 17 14 10 10.0 71 34 15 16 12 11.0 84 35 14 14 12 13.0 74 36 16 10 7 8.0 71 37 9 10 9 20.0 63 38 15 14 12 12.0 71 39 17 16 10 10.0 76 40 13 16 10 10.0 69 41 15 16 10 9.0 74 42 16 14 12 14.0 75 43 16 20 15 8.0 54 44 12 14 10 14.0 52 45 15 14 10 11.0 69 46 11 11 12 13.0 68 47 15 14 13 9.0 65 48 15 15 11 11.0 75 49 17 16 11 15.0 74 50 13 14 12 11.0 75 51 16 16 14 10.0 72 52 14 14 10 14.0 67 53 11 12 12 18.0 63 54 12 16 13 14.0 62 55 12 9 5 11.0 63 56 15 14 6 14.5 76 57 16 16 12 13.0 74 58 15 16 12 9.0 67 59 12 15 11 10.0 73 60 12 16 10 15.0 70 61 8 12 7 20.0 53 62 13 16 12 12.0 77 63 11 16 14 12.0 80 64 14 14 11 14.0 52 65 15 16 12 13.0 54 66 10 17 13 11.0 80 67 11 18 14 17.0 66 68 12 18 11 12.0 73 69 15 12 12 13.0 63 70 15 16 12 14.0 69 71 14 10 8 13.0 67 72 16 14 11 15.0 54 73 15 18 14 13.0 81 74 15 18 14 10.0 69 75 13 16 12 11.0 84 76 12 17 9 19.0 80 77 17 16 13 13.0 70 78 13 16 11 17.0 69 79 15 13 12 13.0 77 80 13 16 12 9.0 54 81 15 16 12 11.0 79 82 15 16 12 9.0 71 83 16 15 12 12.0 73 84 15 15 11 12.0 72 85 14 16 10 13.0 77 86 15 14 9 13.0 75 87 14 16 12 12.0 69 88 13 16 12 15.0 54 89 7 15 12 22.0 70 90 17 12 9 13.0 73 91 13 17 15 15.0 54 92 15 16 12 13.0 77 93 14 15 12 15.0 82 94 13 13 12 12.5 80 95 16 16 10 11.0 80 96 12 16 13 16.0 69 97 14 16 9 11.0 78 98 17 16 12 11.0 81 99 15 14 10 10.0 76 100 17 16 14 10.0 76 101 12 16 11 16.0 73 102 16 20 15 12.0 85 103 11 15 11 11.0 66 104 15 16 11 16.0 79 105 9 13 12 19.0 68 106 16 17 12 11.0 76 107 15 16 12 16.0 71 108 10 16 11 15.0 54 109 10 12 7 24.0 46 110 15 16 12 14.0 85 111 11 16 14 15.0 74 112 13 17 11 11.0 88 113 14 13 11 15.0 38 114 18 12 10 12.0 76 115 16 18 13 10.0 86 116 14 14 13 14.0 54 117 14 14 8 13.0 67 118 14 13 11 9.0 69 119 14 16 12 15.0 90 120 12 13 11 15.0 54 121 14 16 13 14.0 76 122 15 13 12 11.0 89 123 15 16 14 8.0 76 124 15 15 13 11.0 73 125 13 16 15 11.0 79 126 17 15 10 8.0 90 127 17 17 11 10.0 74 128 19 15 9 11.0 81 129 15 12 11 13.0 72 130 13 16 10 11.0 71 131 9 10 11 20.0 66 132 15 16 8 10.0 77 133 15 12 11 15.0 65 134 15 14 12 12.0 74 135 16 15 12 14.0 85 136 11 13 9 23.0 54 137 14 15 11 14.0 63 138 11 11 10 16.0 54 139 15 12 8 11.0 64 140 13 11 9 12.0 69 141 15 16 8 10.0 54 142 16 15 9 14.0 84 143 14 17 15 12.0 86 144 15 16 11 12.0 77 145 16 10 8 11.0 89 146 16 18 13 12.0 76 147 11 13 12 13.0 60 148 12 16 12 11.0 75 149 9 13 9 19.0 73 150 16 10 7 12.0 85 151 13 15 13 17.0 79 152 16 16 9 9.0 71 153 12 16 6 12.0 72 154 9 14 8 19.0 69 155 13 10 8 18.0 78 156 13 17 15 15.0 54 157 14 13 6 14.0 69 158 19 15 9 11.0 81 159 13 16 11 9.0 84 160 12 12 8 18.0 84 161 13 13 8 16.0 69 162 10 13 10 24.0 66 163 14 12 8 14.0 81 164 16 17 14 20.0 82 165 10 15 10 18.0 72 166 11 10 8 23.0 54 167 14 14 11 12.0 78 168 12 11 12 14.0 74 169 9 13 12 16.0 82 170 9 16 12 18.0 73 171 11 12 5 20.0 55 172 16 16 12 12.0 72 173 9 12 10 12.0 78 174 13 9 7 17.0 59 175 16 12 12 13.0 72 176 13 15 11 9.0 78 177 9 12 8 16.0 68 178 12 12 9 18.0 69 179 16 14 10 10.0 67 180 11 12 9 14.0 74 181 14 16 12 11.0 54 182 13 11 6 9.0 67 183 15 19 15 11.0 70 184 14 15 12 10.0 80 185 16 8 12 11.0 89 186 13 16 12 19.0 76 187 14 17 11 14.0 74 188 15 12 7 12.0 87 189 13 11 7 14.0 54 190 11 11 5 21.0 61 191 11 14 12 13.0 38 192 14 16 12 10.0 75 193 15 12 3 15.0 69 194 11 16 11 16.0 62 195 15 13 10 14.0 72 196 12 15 12 12.0 70 197 14 16 9 19.0 79 198 14 16 12 15.0 87 199 8 14 9 19.0 62 200 13 16 12 13.0 77 201 9 16 12 17.0 69 202 15 14 10 12.0 69 203 17 11 9 11.0 75 204 13 12 12 14.0 54 205 15 15 8 11.0 72 206 15 15 11 13.0 74 207 14 16 11 12.0 85 208 16 16 12 15.0 52 209 13 11 10 14.0 70 210 16 15 10 12.0 84 211 9 12 12 17.0 64 212 16 12 12 11.0 84 213 11 15 11 18.0 87 214 10 15 8 13.0 79 215 11 16 12 17.0 67 216 15 14 10 13.0 65 217 17 17 11 11.0 85 218 14 14 10 12.0 83 219 8 13 8 22.0 61 220 15 15 12 14.0 82 221 11 13 12 12.0 76 222 16 14 10 12.0 58 223 10 15 12 17.0 72 224 15 12 9 9.0 72 225 9 13 9 21.0 38 226 16 8 6 10.0 78 227 19 14 10 11.0 54 228 12 14 9 12.0 63 229 8 11 9 23.0 66 230 11 12 9 13.0 70 231 14 13 6 12.0 71 232 9 10 10 16.0 67 233 15 16 6 9.0 58 234 13 18 14 17.0 72 235 16 13 10 9.0 72 236 11 11 10 14.0 70 237 12 4 6 17.0 76 238 13 13 12 13.0 50 239 10 16 12 11.0 72 240 11 10 7 12.0 72 241 12 12 8 10.0 88 242 8 12 11 19.0 53 243 12 10 3 16.0 58 244 12 13 6 16.0 66 245 15 15 10 14.0 82 246 11 12 8 20.0 69 247 13 14 9 15.0 68 248 14 10 9 23.0 44 249 10 12 8 20.0 56 250 12 12 9 16.0 53 251 15 11 7 14.0 70 252 13 10 7 17.0 78 253 13 12 6 11.0 71 254 13 16 9 13.0 72 255 12 12 10 17.0 68 256 12 14 11 15.0 67 257 9 16 12 21.0 75 258 9 14 8 18.0 62 259 15 13 11 15.0 67 260 10 4 3 8.0 83 261 14 15 11 12.0 64 262 15 11 12 12.0 68 263 7 11 7 22.0 62 264 14 14 9 12.0 72 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Learning Software Depression Sport1 15.356954 0.118945 -0.006095 -0.377726 0.023610 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.7891 -1.3735 0.2189 1.2750 5.1949 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 15.356954 1.415240 10.851 <2e-16 *** Learning 0.118945 0.065559 1.814 0.0708 . Software -0.006095 0.068405 -0.089 0.9291 Depression -0.377726 0.038613 -9.782 <2e-16 *** Sport1 0.023610 0.012671 1.863 0.0636 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.011 on 259 degrees of freedom Multiple R-squared: 0.3621, Adjusted R-squared: 0.3522 F-statistic: 36.75 on 4 and 259 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,] 0.8838786 0.232242889 0.116121445 [2,] 0.7930527 0.413894687 0.206947343 [3,] 0.6875459 0.624908157 0.312454079 [4,] 0.6754442 0.649111665 0.324555832 [5,] 0.9503914 0.099217289 0.049608644 [6,] 0.9865659 0.026868250 0.013434125 [7,] 0.9831989 0.033602189 0.016801095 [8,] 0.9815073 0.036985439 0.018492719 [9,] 0.9710950 0.057810052 0.028905026 [10,] 0.9589186 0.082162810 0.041081405 [11,] 0.9458808 0.108238482 0.054119241 [12,] 0.9281926 0.143614892 0.071807446 [13,] 0.9165229 0.166954274 0.083477137 [14,] 0.9210378 0.157924366 0.078962183 [15,] 0.9563402 0.087319693 0.043659846 [16,] 0.9391599 0.121680140 0.060840070 [17,] 0.9343744 0.131251169 0.065625585 [18,] 0.9166094 0.166781149 0.083390574 [19,] 0.9965643 0.006871311 0.003435655 [20,] 0.9949707 0.010058628 0.005029314 [21,] 0.9926377 0.014724632 0.007362316 [22,] 0.9894551 0.021089893 0.010544947 [23,] 0.9942090 0.011581941 0.005790971 [24,] 0.9915683 0.016863440 0.008431720 [25,] 0.9883452 0.023309648 0.011654824 [26,] 0.9867840 0.026432068 0.013216034 [27,] 0.9819586 0.036082881 0.018041441 [28,] 0.9762921 0.047415717 0.023707858 [29,] 0.9682824 0.063435224 0.031717612 [30,] 0.9772464 0.045507220 0.022753610 [31,] 0.9699951 0.060009823 0.030004911 [32,] 0.9651555 0.069689059 0.034844529 [33,] 0.9659053 0.068189319 0.034094660 [34,] 0.9566053 0.086789455 0.043394728 [35,] 0.9540423 0.091915385 0.045957692 [36,] 0.9419763 0.116047388 0.058023694 [37,] 0.9319603 0.136079421 0.068039710 [38,] 0.9153619 0.169276139 0.084638069 [39,] 0.9280701 0.143859705 0.071929852 [40,] 0.9106636 0.178672882 0.089336441 [41,] 0.8906584 0.218683103 0.109341552 [42,] 0.9123680 0.175263943 0.087631972 [43,] 0.9078241 0.184351755 0.092175877 [44,] 0.8911218 0.217756354 0.108878177 [45,] 0.8696015 0.260797054 0.130398527 [46,] 0.8513017 0.297396504 0.148698252 [47,] 0.8412910 0.317418078 0.158709039 [48,] 0.8306189 0.338762210 0.169381105 [49,] 0.8093365 0.381326991 0.190663495 [50,] 0.7955648 0.408870480 0.204435240 [51,] 0.7640432 0.471913509 0.235956755 [52,] 0.8051186 0.389762799 0.194881400 [53,] 0.8002648 0.399470420 0.199735210 [54,] 0.8232807 0.353438559 0.176719279 [55,] 0.8189697 0.362060623 0.181030311 [56,] 0.8802622 0.239475612 0.119737806 [57,] 0.8663151 0.267369713 0.133684856 [58,] 0.8546317 0.290736612 0.145368306 [59,] 0.9446556 0.110688731 0.055344366 [60,] 0.9428866 0.114226772 0.057113386 [61,] 0.9510231 0.097953741 0.048976871 [62,] 0.9473851 0.105229842 0.052614921 [63,] 0.9407471 0.118505749 0.059252874 [64,] 0.9295530 0.140893901 0.070446951 [65,] 0.9468540 0.106292059 0.053146030 [66,] 0.9360173 0.127965383 0.063982692 [67,] 0.9228515 0.154296913 0.077148456 [68,] 0.9220807 0.155838527 0.077919263 [69,] 0.9079521 0.184095765 0.092047883 [70,] 0.9238651 0.152269848 0.076134924 [71,] 0.9097244 0.180551260 0.090275630 [72,] 0.8979983 0.204003395 0.102001697 [73,] 0.8959140 0.208171989 0.104085994 [74,] 0.8775367 0.244926637 0.122463318 [75,] 0.8576307 0.284738565 0.142369282 [76,] 0.8499971 0.300005832 0.150002916 [77,] 0.8294207 0.341158648 0.170579324 [78,] 0.8045841 0.390831800 0.195415900 [79,] 0.7844687 0.431062509 0.215531254 [80,] 0.7566067 0.486786628 0.243393314 [81,] 0.7265047 0.546990682 0.273495341 [82,] 0.8024462 0.395107537 0.197553769 [83,] 0.8395534 0.320893217 0.160446608 [84,] 0.8158885 0.368223078 0.184111539 [85,] 0.7945296 0.410940827 0.205470414 [86,] 0.7689697 0.462060690 0.231030345 [87,] 0.7498878 0.500224462 0.250112231 [88,] 0.7272115 0.545577093 0.272788547 [89,] 0.7017174 0.596565146 0.298282573 [90,] 0.6763697 0.647260626 0.323630313 [91,] 0.6760514 0.647897257 0.323948628 [92,] 0.6425457 0.714908619 0.357454310 [93,] 0.6358350 0.728330081 0.364165041 [94,] 0.6095544 0.780891196 0.390445598 [95,] 0.5820795 0.835840933 0.417920466 [96,] 0.6503392 0.699321543 0.349660771 [97,] 0.6447339 0.710532125 0.355266062 [98,] 0.6614836 0.677032726 0.338516363 [99,] 0.6370164 0.725967110 0.362983555 [100,] 0.6399350 0.720129963 0.360064982 [101,] 0.6685711 0.662857875 0.331428938 [102,] 0.6446327 0.710734578 0.355367289 [103,] 0.6191863 0.761627478 0.380813739 [104,] 0.6291941 0.741611708 0.370805854 [105,] 0.6388040 0.722392070 0.361196035 [106,] 0.6349754 0.730049192 0.365024596 [107,] 0.7188369 0.562326217 0.281163109 [108,] 0.6890775 0.621844954 0.310922477 [109,] 0.6659749 0.668050105 0.334025052 [110,] 0.6336328 0.732734347 0.366367174 [111,] 0.6104277 0.779144652 0.389572326 [112,] 0.5768284 0.846343219 0.423171609 [113,] 0.5444781 0.911043858 0.455521929 [114,] 0.5100708 0.979858474 0.489929237 [115,] 0.4753535 0.950707027 0.524646487 [116,] 0.4461771 0.892354210 0.553822895 [117,] 0.4126720 0.825344048 0.587327976 [118,] 0.4057563 0.811512679 0.594243661 [119,] 0.3767295 0.753459051 0.623270474 [120,] 0.3691439 0.738287873 0.630856064 [121,] 0.4761618 0.952323622 0.523838189 [122,] 0.4579754 0.915950886 0.542024557 [123,] 0.4484444 0.896888700 0.551555650 [124,] 0.4399577 0.879915466 0.560042267 [125,] 0.4068974 0.813794785 0.593102607 [126,] 0.4183685 0.836737035 0.581631483 [127,] 0.3904397 0.780879482 0.609560259 [128,] 0.3978025 0.795604957 0.602197521 [129,] 0.3800062 0.760012493 0.619993753 [130,] 0.3516060 0.703211952 0.648394024 [131,] 0.3271994 0.654398868 0.672800566 [132,] 0.3012492 0.602498400 0.698750800 [133,] 0.2770525 0.554104962 0.722947519 [134,] 0.2490735 0.498147097 0.750926451 [135,] 0.2537723 0.507544558 0.746227721 [136,] 0.2288581 0.457716182 0.771141909 [137,] 0.2056073 0.411214596 0.794392702 [138,] 0.1938918 0.387783651 0.806108175 [139,] 0.1843040 0.368608094 0.815695953 [140,] 0.1916057 0.383211352 0.808394324 [141,] 0.2109169 0.421833708 0.789083146 [142,] 0.2282796 0.456559163 0.771720418 [143,] 0.2267362 0.453472365 0.773263818 [144,] 0.2032597 0.406519430 0.796740285 [145,] 0.1816502 0.363300456 0.818349772 [146,] 0.1928303 0.385660620 0.807169690 [147,] 0.2070980 0.414196091 0.792901954 [148,] 0.1945027 0.389005413 0.805497294 [149,] 0.1710329 0.342065890 0.828967055 [150,] 0.1523431 0.304686283 0.847656858 [151,] 0.2398391 0.479678131 0.760160935 [152,] 0.2581718 0.516343544 0.741828228 [153,] 0.2325967 0.465193400 0.767403300 [154,] 0.2086757 0.417351434 0.791324283 [155,] 0.1865485 0.373096980 0.813451510 [156,] 0.1678123 0.335624669 0.832187665 [157,] 0.2875048 0.575009574 0.712495213 [158,] 0.2828929 0.565785777 0.717107111 [159,] 0.2788742 0.557748367 0.721125816 [160,] 0.2501839 0.500367859 0.749816070 [161,] 0.2292769 0.458553891 0.770723055 [162,] 0.2894220 0.578843961 0.710578020 [163,] 0.3232360 0.646471956 0.676764022 [164,] 0.2936948 0.587389555 0.706305222 [165,] 0.2879256 0.575851107 0.712074447 [166,] 0.4608439 0.921687781 0.539156109 [167,] 0.4483944 0.896788884 0.551605558 [168,] 0.4737581 0.947516286 0.526241857 [169,] 0.4885934 0.977186856 0.511406572 [170,] 0.5457249 0.908550287 0.454275144 [171,] 0.5126128 0.974774448 0.487387224 [172,] 0.4901967 0.980393476 0.509803262 [173,] 0.4917811 0.983562162 0.508218919 [174,] 0.4541814 0.908362725 0.545818637 [175,] 0.4477518 0.895503561 0.552248219 [176,] 0.4099950 0.819989936 0.590005032 [177,] 0.3822532 0.764506449 0.617746775 [178,] 0.3870685 0.774136983 0.612931509 [179,] 0.3747109 0.749421830 0.625289085 [180,] 0.3403515 0.680703038 0.659648481 [181,] 0.3147678 0.629535566 0.685232217 [182,] 0.2812144 0.562428746 0.718785627 [183,] 0.2583728 0.516745515 0.741627242 [184,] 0.2693701 0.538740243 0.730629878 [185,] 0.2465940 0.493188062 0.753405969 [186,] 0.2680433 0.536086683 0.731956659 [187,] 0.2542868 0.508573573 0.745713214 [188,] 0.2525191 0.505038198 0.747480901 [189,] 0.2589447 0.517889490 0.741055255 [190,] 0.3058649 0.611729786 0.694135107 [191,] 0.2868901 0.573780275 0.713109863 [192,] 0.3233854 0.646770864 0.676614568 [193,] 0.2929856 0.585971282 0.707014359 [194,] 0.3450390 0.690077942 0.654961029 [195,] 0.3145774 0.629154744 0.685422628 [196,] 0.3602805 0.720560974 0.639719513 [197,] 0.3225495 0.645099003 0.677450498 [198,] 0.2885888 0.577177520 0.711411240 [199,] 0.2681061 0.536212213 0.731893893 [200,] 0.2352817 0.470563458 0.764718271 [201,] 0.2682914 0.536582736 0.731708632 [202,] 0.2344520 0.468904051 0.765547974 [203,] 0.2423508 0.484701638 0.757649181 [204,] 0.2731422 0.546284326 0.726857837 [205,] 0.2718248 0.543649664 0.728175168 [206,] 0.2416495 0.483298999 0.758350500 [207,] 0.3001891 0.600378127 0.699810936 [208,] 0.2718480 0.543696067 0.728151966 [209,] 0.2537731 0.507546113 0.746226943 [210,] 0.2878875 0.575775086 0.712112457 [211,] 0.2559232 0.511846404 0.744076798 [212,] 0.2408806 0.481761276 0.759119362 [213,] 0.2618544 0.523708850 0.738145575 [214,] 0.2772886 0.554577172 0.722711414 [215,] 0.2752490 0.550498047 0.724750977 [216,] 0.2612687 0.522537381 0.738731309 [217,] 0.2241655 0.448331085 0.775834457 [218,] 0.2208869 0.441773770 0.779113115 [219,] 0.2542999 0.508599845 0.745700077 [220,] 0.4675602 0.935120371 0.532439814 [221,] 0.4422722 0.884544345 0.557727828 [222,] 0.4168865 0.833772981 0.583113509 [223,] 0.4053499 0.810699750 0.594650125 [224,] 0.3660894 0.732178842 0.633910579 [225,] 0.4189144 0.837828820 0.581085590 [226,] 0.3773657 0.754731305 0.622634347 [227,] 0.3290958 0.658191678 0.670904161 [228,] 0.3325755 0.665150941 0.667424530 [229,] 0.3086732 0.617346440 0.691326780 [230,] 0.2678666 0.535733201 0.732133400 [231,] 0.2221071 0.444214194 0.777892903 [232,] 0.3918379 0.783675856 0.608162072 [233,] 0.3781581 0.756316267 0.621841866 [234,] 0.3520169 0.704033852 0.647983074 [235,] 0.5838132 0.832373670 0.416186835 [236,] 0.5462143 0.907571359 0.453785680 [237,] 0.4931767 0.986353347 0.506823327 [238,] 0.5503165 0.899366972 0.449683486 [239,] 0.4919901 0.983980108 0.508009946 [240,] 0.4219816 0.843963148 0.578018426 [241,] 0.6242854 0.751429180 0.375714590 [242,] 0.5378524 0.924295149 0.462147575 [243,] 0.4417488 0.883497586 0.558251207 [244,] 0.6505649 0.698870102 0.349435051 [245,] 0.9202969 0.159406130 0.079703065 [246,] 0.8911069 0.217786291 0.108893146 [247,] 0.8257913 0.348417389 0.174208695 [248,] 0.7347173 0.530565386 0.265282693 [249,] 0.6256410 0.748717994 0.374358997 > postscript(file="/var/fisher/rcomp/tmp/1rh1o1384952168.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/28phi1384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3n9x81384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4ibl11384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5b6xf1384952168.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 0.45125987 3.00229393 -2.79560820 -2.15374355 5.19490279 3.84881122 7 8 9 10 11 12 3.51484788 -0.78964582 0.08104596 1.02977320 1.71291491 3.55991214 13 14 15 16 17 18 -3.10294264 2.82017162 2.43556101 0.85076977 0.43720404 1.41455751 19 20 21 22 23 24 -1.16472454 2.37770959 2.83708156 -2.50427871 -0.27992479 -1.22704911 25 26 27 28 29 30 1.81861277 -6.78908169 1.16147171 0.98097102 1.25961765 -2.63269391 31 32 33 34 35 36 0.44271322 0.65539238 2.13968751 -0.01522166 0.21422308 0.84173115 37 38 39 40 41 42 -1.42448790 0.90732847 1.78374600 -2.05098145 -0.54675898 2.56833843 43 44 45 46 47 48 0.10241708 -0.90081274 0.56463395 -2.28728020 -0.07809170 0.31012169 49 50 51 52 53 54 3.72569004 -1.56483870 0.90256654 0.74503180 -0.39954469 -1.35652176 55 56 57 58 59 60 -1.72945351 1.69702229 1.97633340 -0.36929689 -3.02038329 -1.18596328 61 62 63 64 65 66 -2.43846349 -1.47222340 -3.53086495 1.10528203 1.44854068 -5.03363028 67 68 69 70 71 72 -1.54958100 -2.62176641 1.71182678 1.47211092 0.83089592 3.43578701 73 74 75 76 77 78 0.58536070 -0.26449205 -2.01522166 -0.03620370 3.07686962 0.59919328 79 80 81 82 83 84 1.26233683 -2.06236215 0.10283016 -0.46373834 1.74116290 0.75867849 85 86 87 88 89 90 -0.10668724 1.17232840 -0.28334049 0.20399209 -3.41074894 3.45743882 91 92 93 94 95 96 0.10333156 0.90550230 0.66184674 -0.99735711 1.06703025 -0.76634289 97 98 99 100 101 102 -0.89184379 2.05560943 0.02163569 1.80812509 -0.87297389 0.88139862 103 104 105 106 107 108 -3.47738503 1.98536393 -2.25881564 1.05471641 2.18034161 -2.80210268 109 110 111 112 113 114 1.23771189 1.09434510 -2.25602565 -2.23470273 1.93249768 4.01497679 115 116 117 118 119 120 0.32803699 1.07025085 0.35511655 -1.06577785 0.35401898 -0.44526815 121 122 123 124 125 126 0.31293315 0.22356105 -0.94732633 0.36953196 -1.87888553 0.81669433 127 128 129 130 131 132 1.71811666 4.15626996 1.49323873 -1.72047647 -1.48312945 -0.25205391 133 134 135 136 137 138 2.41396269 0.83649737 2.21328994 1.56434797 0.72662318 -0.83574752 139 140 141 142 143 144 0.90838591 -0.70690059 0.29098447 2.21861599 -0.78537721 0.52168182 145 146 147 148 149 150 1.55601649 1.31959204 -2.33628698 -2.80272838 -2.39515178 2.02208889 151 152 153 154 155 156 0.49422402 0.51797734 -2.39074021 -2.42574994 1.45981045 0.10333156 157 158 159 160 161 162 0.79237683 4.15626996 -2.77676785 0.08025858 0.56001778 0.66484408 163 164 165 166 167 168 0.64018684 4.32477513 -1.98106204 1.91508773 -0.26403885 -1.05121668 169 170 171 172 173 174 -3.72253786 -3.11142770 0.50212624 1.64582842 -5.03224394 1.64353174 175 176 177 178 179 180 2.49933350 -2.51616082 -3.29742701 0.44050881 1.23412897 -2.18844584 181 182 183 184 185 186 -0.30691074 -1.81114130 -0.02322678 -1.17956107 1.81828527 1.19546691 187 188 189 190 191 192 0.22901949 0.73697847 0.39051675 0.85713461 -1.93580381 -1.18045409 193 194 195 196 197 198 2.27076306 -1.61325988 1.74592482 -2.18800601 2.10635151 0.42485008 199 200 201 202 203 204 -3.25438262 -1.09449770 -3.39471195 0.94235965 2.77371152 0.30204576 205 206 207 208 209 210 0.36266847 1.08918347 -0.66720109 3.25121282 0.03103524 1.46925935 211 212 213 214 215 216 -2.80088076 1.46055771 -1.32912273 -4.04715267 -1.34749122 1.41452682 217 218 219 220 221 222 1.83612836 -0.38818545 -1.98474506 1.28412103 -3.09177851 2.20207366 223 224 225 226 227 228 -2.34659820 -0.02985365 -0.81333762 1.66370494 4.91878941 -1.92207293 229 230 231 232 233 234 -1.48108671 -2.47173009 -0.01029532 -3.02373741 -0.19337224 0.30875681 235 236 237 238 239 240 0.85729628 -1.96896476 0.83078499 -0.10018333 -4.73189729 -2.67097638 241 242 243 244 245 246 -3.03598854 -2.79181011 0.14609246 -0.38134067 1.27193149 0.18986546 247 248 249 250 251 252 0.09305237 5.15728614 -0.50319981 0.06282323 2.01275092 1.07598997 253 254 255 256 257 258 -1.26907618 -0.99473019 0.09248824 -0.87114772 -2.02547131 -2.63820309 259 260 261 262 263 264 2.24779712 -4.75230324 -0.05243860 1.33499409 -2.77656050 -0.13456621 > postscript(file="/var/fisher/rcomp/tmp/6rwe01384952168.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 0.45125987 NA 1 3.00229393 0.45125987 2 -2.79560820 3.00229393 3 -2.15374355 -2.79560820 4 5.19490279 -2.15374355 5 3.84881122 5.19490279 6 3.51484788 3.84881122 7 -0.78964582 3.51484788 8 0.08104596 -0.78964582 9 1.02977320 0.08104596 10 1.71291491 1.02977320 11 3.55991214 1.71291491 12 -3.10294264 3.55991214 13 2.82017162 -3.10294264 14 2.43556101 2.82017162 15 0.85076977 2.43556101 16 0.43720404 0.85076977 17 1.41455751 0.43720404 18 -1.16472454 1.41455751 19 2.37770959 -1.16472454 20 2.83708156 2.37770959 21 -2.50427871 2.83708156 22 -0.27992479 -2.50427871 23 -1.22704911 -0.27992479 24 1.81861277 -1.22704911 25 -6.78908169 1.81861277 26 1.16147171 -6.78908169 27 0.98097102 1.16147171 28 1.25961765 0.98097102 29 -2.63269391 1.25961765 30 0.44271322 -2.63269391 31 0.65539238 0.44271322 32 2.13968751 0.65539238 33 -0.01522166 2.13968751 34 0.21422308 -0.01522166 35 0.84173115 0.21422308 36 -1.42448790 0.84173115 37 0.90732847 -1.42448790 38 1.78374600 0.90732847 39 -2.05098145 1.78374600 40 -0.54675898 -2.05098145 41 2.56833843 -0.54675898 42 0.10241708 2.56833843 43 -0.90081274 0.10241708 44 0.56463395 -0.90081274 45 -2.28728020 0.56463395 46 -0.07809170 -2.28728020 47 0.31012169 -0.07809170 48 3.72569004 0.31012169 49 -1.56483870 3.72569004 50 0.90256654 -1.56483870 51 0.74503180 0.90256654 52 -0.39954469 0.74503180 53 -1.35652176 -0.39954469 54 -1.72945351 -1.35652176 55 1.69702229 -1.72945351 56 1.97633340 1.69702229 57 -0.36929689 1.97633340 58 -3.02038329 -0.36929689 59 -1.18596328 -3.02038329 60 -2.43846349 -1.18596328 61 -1.47222340 -2.43846349 62 -3.53086495 -1.47222340 63 1.10528203 -3.53086495 64 1.44854068 1.10528203 65 -5.03363028 1.44854068 66 -1.54958100 -5.03363028 67 -2.62176641 -1.54958100 68 1.71182678 -2.62176641 69 1.47211092 1.71182678 70 0.83089592 1.47211092 71 3.43578701 0.83089592 72 0.58536070 3.43578701 73 -0.26449205 0.58536070 74 -2.01522166 -0.26449205 75 -0.03620370 -2.01522166 76 3.07686962 -0.03620370 77 0.59919328 3.07686962 78 1.26233683 0.59919328 79 -2.06236215 1.26233683 80 0.10283016 -2.06236215 81 -0.46373834 0.10283016 82 1.74116290 -0.46373834 83 0.75867849 1.74116290 84 -0.10668724 0.75867849 85 1.17232840 -0.10668724 86 -0.28334049 1.17232840 87 0.20399209 -0.28334049 88 -3.41074894 0.20399209 89 3.45743882 -3.41074894 90 0.10333156 3.45743882 91 0.90550230 0.10333156 92 0.66184674 0.90550230 93 -0.99735711 0.66184674 94 1.06703025 -0.99735711 95 -0.76634289 1.06703025 96 -0.89184379 -0.76634289 97 2.05560943 -0.89184379 98 0.02163569 2.05560943 99 1.80812509 0.02163569 100 -0.87297389 1.80812509 101 0.88139862 -0.87297389 102 -3.47738503 0.88139862 103 1.98536393 -3.47738503 104 -2.25881564 1.98536393 105 1.05471641 -2.25881564 106 2.18034161 1.05471641 107 -2.80210268 2.18034161 108 1.23771189 -2.80210268 109 1.09434510 1.23771189 110 -2.25602565 1.09434510 111 -2.23470273 -2.25602565 112 1.93249768 -2.23470273 113 4.01497679 1.93249768 114 0.32803699 4.01497679 115 1.07025085 0.32803699 116 0.35511655 1.07025085 117 -1.06577785 0.35511655 118 0.35401898 -1.06577785 119 -0.44526815 0.35401898 120 0.31293315 -0.44526815 121 0.22356105 0.31293315 122 -0.94732633 0.22356105 123 0.36953196 -0.94732633 124 -1.87888553 0.36953196 125 0.81669433 -1.87888553 126 1.71811666 0.81669433 127 4.15626996 1.71811666 128 1.49323873 4.15626996 129 -1.72047647 1.49323873 130 -1.48312945 -1.72047647 131 -0.25205391 -1.48312945 132 2.41396269 -0.25205391 133 0.83649737 2.41396269 134 2.21328994 0.83649737 135 1.56434797 2.21328994 136 0.72662318 1.56434797 137 -0.83574752 0.72662318 138 0.90838591 -0.83574752 139 -0.70690059 0.90838591 140 0.29098447 -0.70690059 141 2.21861599 0.29098447 142 -0.78537721 2.21861599 143 0.52168182 -0.78537721 144 1.55601649 0.52168182 145 1.31959204 1.55601649 146 -2.33628698 1.31959204 147 -2.80272838 -2.33628698 148 -2.39515178 -2.80272838 149 2.02208889 -2.39515178 150 0.49422402 2.02208889 151 0.51797734 0.49422402 152 -2.39074021 0.51797734 153 -2.42574994 -2.39074021 154 1.45981045 -2.42574994 155 0.10333156 1.45981045 156 0.79237683 0.10333156 157 4.15626996 0.79237683 158 -2.77676785 4.15626996 159 0.08025858 -2.77676785 160 0.56001778 0.08025858 161 0.66484408 0.56001778 162 0.64018684 0.66484408 163 4.32477513 0.64018684 164 -1.98106204 4.32477513 165 1.91508773 -1.98106204 166 -0.26403885 1.91508773 167 -1.05121668 -0.26403885 168 -3.72253786 -1.05121668 169 -3.11142770 -3.72253786 170 0.50212624 -3.11142770 171 1.64582842 0.50212624 172 -5.03224394 1.64582842 173 1.64353174 -5.03224394 174 2.49933350 1.64353174 175 -2.51616082 2.49933350 176 -3.29742701 -2.51616082 177 0.44050881 -3.29742701 178 1.23412897 0.44050881 179 -2.18844584 1.23412897 180 -0.30691074 -2.18844584 181 -1.81114130 -0.30691074 182 -0.02322678 -1.81114130 183 -1.17956107 -0.02322678 184 1.81828527 -1.17956107 185 1.19546691 1.81828527 186 0.22901949 1.19546691 187 0.73697847 0.22901949 188 0.39051675 0.73697847 189 0.85713461 0.39051675 190 -1.93580381 0.85713461 191 -1.18045409 -1.93580381 192 2.27076306 -1.18045409 193 -1.61325988 2.27076306 194 1.74592482 -1.61325988 195 -2.18800601 1.74592482 196 2.10635151 -2.18800601 197 0.42485008 2.10635151 198 -3.25438262 0.42485008 199 -1.09449770 -3.25438262 200 -3.39471195 -1.09449770 201 0.94235965 -3.39471195 202 2.77371152 0.94235965 203 0.30204576 2.77371152 204 0.36266847 0.30204576 205 1.08918347 0.36266847 206 -0.66720109 1.08918347 207 3.25121282 -0.66720109 208 0.03103524 3.25121282 209 1.46925935 0.03103524 210 -2.80088076 1.46925935 211 1.46055771 -2.80088076 212 -1.32912273 1.46055771 213 -4.04715267 -1.32912273 214 -1.34749122 -4.04715267 215 1.41452682 -1.34749122 216 1.83612836 1.41452682 217 -0.38818545 1.83612836 218 -1.98474506 -0.38818545 219 1.28412103 -1.98474506 220 -3.09177851 1.28412103 221 2.20207366 -3.09177851 222 -2.34659820 2.20207366 223 -0.02985365 -2.34659820 224 -0.81333762 -0.02985365 225 1.66370494 -0.81333762 226 4.91878941 1.66370494 227 -1.92207293 4.91878941 228 -1.48108671 -1.92207293 229 -2.47173009 -1.48108671 230 -0.01029532 -2.47173009 231 -3.02373741 -0.01029532 232 -0.19337224 -3.02373741 233 0.30875681 -0.19337224 234 0.85729628 0.30875681 235 -1.96896476 0.85729628 236 0.83078499 -1.96896476 237 -0.10018333 0.83078499 238 -4.73189729 -0.10018333 239 -2.67097638 -4.73189729 240 -3.03598854 -2.67097638 241 -2.79181011 -3.03598854 242 0.14609246 -2.79181011 243 -0.38134067 0.14609246 244 1.27193149 -0.38134067 245 0.18986546 1.27193149 246 0.09305237 0.18986546 247 5.15728614 0.09305237 248 -0.50319981 5.15728614 249 0.06282323 -0.50319981 250 2.01275092 0.06282323 251 1.07598997 2.01275092 252 -1.26907618 1.07598997 253 -0.99473019 -1.26907618 254 0.09248824 -0.99473019 255 -0.87114772 0.09248824 256 -2.02547131 -0.87114772 257 -2.63820309 -2.02547131 258 2.24779712 -2.63820309 259 -4.75230324 2.24779712 260 -0.05243860 -4.75230324 261 1.33499409 -0.05243860 262 -2.77656050 1.33499409 263 -0.13456621 -2.77656050 264 NA -0.13456621 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 3.00229393 0.45125987 [2,] -2.79560820 3.00229393 [3,] -2.15374355 -2.79560820 [4,] 5.19490279 -2.15374355 [5,] 3.84881122 5.19490279 [6,] 3.51484788 3.84881122 [7,] -0.78964582 3.51484788 [8,] 0.08104596 -0.78964582 [9,] 1.02977320 0.08104596 [10,] 1.71291491 1.02977320 [11,] 3.55991214 1.71291491 [12,] -3.10294264 3.55991214 [13,] 2.82017162 -3.10294264 [14,] 2.43556101 2.82017162 [15,] 0.85076977 2.43556101 [16,] 0.43720404 0.85076977 [17,] 1.41455751 0.43720404 [18,] -1.16472454 1.41455751 [19,] 2.37770959 -1.16472454 [20,] 2.83708156 2.37770959 [21,] -2.50427871 2.83708156 [22,] -0.27992479 -2.50427871 [23,] -1.22704911 -0.27992479 [24,] 1.81861277 -1.22704911 [25,] -6.78908169 1.81861277 [26,] 1.16147171 -6.78908169 [27,] 0.98097102 1.16147171 [28,] 1.25961765 0.98097102 [29,] -2.63269391 1.25961765 [30,] 0.44271322 -2.63269391 [31,] 0.65539238 0.44271322 [32,] 2.13968751 0.65539238 [33,] -0.01522166 2.13968751 [34,] 0.21422308 -0.01522166 [35,] 0.84173115 0.21422308 [36,] -1.42448790 0.84173115 [37,] 0.90732847 -1.42448790 [38,] 1.78374600 0.90732847 [39,] -2.05098145 1.78374600 [40,] -0.54675898 -2.05098145 [41,] 2.56833843 -0.54675898 [42,] 0.10241708 2.56833843 [43,] -0.90081274 0.10241708 [44,] 0.56463395 -0.90081274 [45,] -2.28728020 0.56463395 [46,] -0.07809170 -2.28728020 [47,] 0.31012169 -0.07809170 [48,] 3.72569004 0.31012169 [49,] -1.56483870 3.72569004 [50,] 0.90256654 -1.56483870 [51,] 0.74503180 0.90256654 [52,] -0.39954469 0.74503180 [53,] -1.35652176 -0.39954469 [54,] -1.72945351 -1.35652176 [55,] 1.69702229 -1.72945351 [56,] 1.97633340 1.69702229 [57,] -0.36929689 1.97633340 [58,] -3.02038329 -0.36929689 [59,] -1.18596328 -3.02038329 [60,] -2.43846349 -1.18596328 [61,] -1.47222340 -2.43846349 [62,] -3.53086495 -1.47222340 [63,] 1.10528203 -3.53086495 [64,] 1.44854068 1.10528203 [65,] -5.03363028 1.44854068 [66,] -1.54958100 -5.03363028 [67,] -2.62176641 -1.54958100 [68,] 1.71182678 -2.62176641 [69,] 1.47211092 1.71182678 [70,] 0.83089592 1.47211092 [71,] 3.43578701 0.83089592 [72,] 0.58536070 3.43578701 [73,] -0.26449205 0.58536070 [74,] -2.01522166 -0.26449205 [75,] -0.03620370 -2.01522166 [76,] 3.07686962 -0.03620370 [77,] 0.59919328 3.07686962 [78,] 1.26233683 0.59919328 [79,] -2.06236215 1.26233683 [80,] 0.10283016 -2.06236215 [81,] -0.46373834 0.10283016 [82,] 1.74116290 -0.46373834 [83,] 0.75867849 1.74116290 [84,] -0.10668724 0.75867849 [85,] 1.17232840 -0.10668724 [86,] -0.28334049 1.17232840 [87,] 0.20399209 -0.28334049 [88,] -3.41074894 0.20399209 [89,] 3.45743882 -3.41074894 [90,] 0.10333156 3.45743882 [91,] 0.90550230 0.10333156 [92,] 0.66184674 0.90550230 [93,] -0.99735711 0.66184674 [94,] 1.06703025 -0.99735711 [95,] -0.76634289 1.06703025 [96,] -0.89184379 -0.76634289 [97,] 2.05560943 -0.89184379 [98,] 0.02163569 2.05560943 [99,] 1.80812509 0.02163569 [100,] -0.87297389 1.80812509 [101,] 0.88139862 -0.87297389 [102,] -3.47738503 0.88139862 [103,] 1.98536393 -3.47738503 [104,] -2.25881564 1.98536393 [105,] 1.05471641 -2.25881564 [106,] 2.18034161 1.05471641 [107,] -2.80210268 2.18034161 [108,] 1.23771189 -2.80210268 [109,] 1.09434510 1.23771189 [110,] -2.25602565 1.09434510 [111,] -2.23470273 -2.25602565 [112,] 1.93249768 -2.23470273 [113,] 4.01497679 1.93249768 [114,] 0.32803699 4.01497679 [115,] 1.07025085 0.32803699 [116,] 0.35511655 1.07025085 [117,] -1.06577785 0.35511655 [118,] 0.35401898 -1.06577785 [119,] -0.44526815 0.35401898 [120,] 0.31293315 -0.44526815 [121,] 0.22356105 0.31293315 [122,] -0.94732633 0.22356105 [123,] 0.36953196 -0.94732633 [124,] -1.87888553 0.36953196 [125,] 0.81669433 -1.87888553 [126,] 1.71811666 0.81669433 [127,] 4.15626996 1.71811666 [128,] 1.49323873 4.15626996 [129,] -1.72047647 1.49323873 [130,] -1.48312945 -1.72047647 [131,] -0.25205391 -1.48312945 [132,] 2.41396269 -0.25205391 [133,] 0.83649737 2.41396269 [134,] 2.21328994 0.83649737 [135,] 1.56434797 2.21328994 [136,] 0.72662318 1.56434797 [137,] -0.83574752 0.72662318 [138,] 0.90838591 -0.83574752 [139,] -0.70690059 0.90838591 [140,] 0.29098447 -0.70690059 [141,] 2.21861599 0.29098447 [142,] -0.78537721 2.21861599 [143,] 0.52168182 -0.78537721 [144,] 1.55601649 0.52168182 [145,] 1.31959204 1.55601649 [146,] -2.33628698 1.31959204 [147,] -2.80272838 -2.33628698 [148,] -2.39515178 -2.80272838 [149,] 2.02208889 -2.39515178 [150,] 0.49422402 2.02208889 [151,] 0.51797734 0.49422402 [152,] -2.39074021 0.51797734 [153,] -2.42574994 -2.39074021 [154,] 1.45981045 -2.42574994 [155,] 0.10333156 1.45981045 [156,] 0.79237683 0.10333156 [157,] 4.15626996 0.79237683 [158,] -2.77676785 4.15626996 [159,] 0.08025858 -2.77676785 [160,] 0.56001778 0.08025858 [161,] 0.66484408 0.56001778 [162,] 0.64018684 0.66484408 [163,] 4.32477513 0.64018684 [164,] -1.98106204 4.32477513 [165,] 1.91508773 -1.98106204 [166,] -0.26403885 1.91508773 [167,] -1.05121668 -0.26403885 [168,] -3.72253786 -1.05121668 [169,] -3.11142770 -3.72253786 [170,] 0.50212624 -3.11142770 [171,] 1.64582842 0.50212624 [172,] -5.03224394 1.64582842 [173,] 1.64353174 -5.03224394 [174,] 2.49933350 1.64353174 [175,] -2.51616082 2.49933350 [176,] -3.29742701 -2.51616082 [177,] 0.44050881 -3.29742701 [178,] 1.23412897 0.44050881 [179,] -2.18844584 1.23412897 [180,] -0.30691074 -2.18844584 [181,] -1.81114130 -0.30691074 [182,] -0.02322678 -1.81114130 [183,] -1.17956107 -0.02322678 [184,] 1.81828527 -1.17956107 [185,] 1.19546691 1.81828527 [186,] 0.22901949 1.19546691 [187,] 0.73697847 0.22901949 [188,] 0.39051675 0.73697847 [189,] 0.85713461 0.39051675 [190,] -1.93580381 0.85713461 [191,] -1.18045409 -1.93580381 [192,] 2.27076306 -1.18045409 [193,] -1.61325988 2.27076306 [194,] 1.74592482 -1.61325988 [195,] -2.18800601 1.74592482 [196,] 2.10635151 -2.18800601 [197,] 0.42485008 2.10635151 [198,] -3.25438262 0.42485008 [199,] -1.09449770 -3.25438262 [200,] -3.39471195 -1.09449770 [201,] 0.94235965 -3.39471195 [202,] 2.77371152 0.94235965 [203,] 0.30204576 2.77371152 [204,] 0.36266847 0.30204576 [205,] 1.08918347 0.36266847 [206,] -0.66720109 1.08918347 [207,] 3.25121282 -0.66720109 [208,] 0.03103524 3.25121282 [209,] 1.46925935 0.03103524 [210,] -2.80088076 1.46925935 [211,] 1.46055771 -2.80088076 [212,] -1.32912273 1.46055771 [213,] -4.04715267 -1.32912273 [214,] -1.34749122 -4.04715267 [215,] 1.41452682 -1.34749122 [216,] 1.83612836 1.41452682 [217,] -0.38818545 1.83612836 [218,] -1.98474506 -0.38818545 [219,] 1.28412103 -1.98474506 [220,] -3.09177851 1.28412103 [221,] 2.20207366 -3.09177851 [222,] -2.34659820 2.20207366 [223,] -0.02985365 -2.34659820 [224,] -0.81333762 -0.02985365 [225,] 1.66370494 -0.81333762 [226,] 4.91878941 1.66370494 [227,] -1.92207293 4.91878941 [228,] -1.48108671 -1.92207293 [229,] -2.47173009 -1.48108671 [230,] -0.01029532 -2.47173009 [231,] -3.02373741 -0.01029532 [232,] -0.19337224 -3.02373741 [233,] 0.30875681 -0.19337224 [234,] 0.85729628 0.30875681 [235,] -1.96896476 0.85729628 [236,] 0.83078499 -1.96896476 [237,] -0.10018333 0.83078499 [238,] -4.73189729 -0.10018333 [239,] -2.67097638 -4.73189729 [240,] -3.03598854 -2.67097638 [241,] -2.79181011 -3.03598854 [242,] 0.14609246 -2.79181011 [243,] -0.38134067 0.14609246 [244,] 1.27193149 -0.38134067 [245,] 0.18986546 1.27193149 [246,] 0.09305237 0.18986546 [247,] 5.15728614 0.09305237 [248,] -0.50319981 5.15728614 [249,] 0.06282323 -0.50319981 [250,] 2.01275092 0.06282323 [251,] 1.07598997 2.01275092 [252,] -1.26907618 1.07598997 [253,] -0.99473019 -1.26907618 [254,] 0.09248824 -0.99473019 [255,] -0.87114772 0.09248824 [256,] -2.02547131 -0.87114772 [257,] -2.63820309 -2.02547131 [258,] 2.24779712 -2.63820309 [259,] -4.75230324 2.24779712 [260,] -0.05243860 -4.75230324 [261,] 1.33499409 -0.05243860 [262,] -2.77656050 1.33499409 [263,] -0.13456621 -2.77656050 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 3.00229393 0.45125987 2 -2.79560820 3.00229393 3 -2.15374355 -2.79560820 4 5.19490279 -2.15374355 5 3.84881122 5.19490279 6 3.51484788 3.84881122 7 -0.78964582 3.51484788 8 0.08104596 -0.78964582 9 1.02977320 0.08104596 10 1.71291491 1.02977320 11 3.55991214 1.71291491 12 -3.10294264 3.55991214 13 2.82017162 -3.10294264 14 2.43556101 2.82017162 15 0.85076977 2.43556101 16 0.43720404 0.85076977 17 1.41455751 0.43720404 18 -1.16472454 1.41455751 19 2.37770959 -1.16472454 20 2.83708156 2.37770959 21 -2.50427871 2.83708156 22 -0.27992479 -2.50427871 23 -1.22704911 -0.27992479 24 1.81861277 -1.22704911 25 -6.78908169 1.81861277 26 1.16147171 -6.78908169 27 0.98097102 1.16147171 28 1.25961765 0.98097102 29 -2.63269391 1.25961765 30 0.44271322 -2.63269391 31 0.65539238 0.44271322 32 2.13968751 0.65539238 33 -0.01522166 2.13968751 34 0.21422308 -0.01522166 35 0.84173115 0.21422308 36 -1.42448790 0.84173115 37 0.90732847 -1.42448790 38 1.78374600 0.90732847 39 -2.05098145 1.78374600 40 -0.54675898 -2.05098145 41 2.56833843 -0.54675898 42 0.10241708 2.56833843 43 -0.90081274 0.10241708 44 0.56463395 -0.90081274 45 -2.28728020 0.56463395 46 -0.07809170 -2.28728020 47 0.31012169 -0.07809170 48 3.72569004 0.31012169 49 -1.56483870 3.72569004 50 0.90256654 -1.56483870 51 0.74503180 0.90256654 52 -0.39954469 0.74503180 53 -1.35652176 -0.39954469 54 -1.72945351 -1.35652176 55 1.69702229 -1.72945351 56 1.97633340 1.69702229 57 -0.36929689 1.97633340 58 -3.02038329 -0.36929689 59 -1.18596328 -3.02038329 60 -2.43846349 -1.18596328 61 -1.47222340 -2.43846349 62 -3.53086495 -1.47222340 63 1.10528203 -3.53086495 64 1.44854068 1.10528203 65 -5.03363028 1.44854068 66 -1.54958100 -5.03363028 67 -2.62176641 -1.54958100 68 1.71182678 -2.62176641 69 1.47211092 1.71182678 70 0.83089592 1.47211092 71 3.43578701 0.83089592 72 0.58536070 3.43578701 73 -0.26449205 0.58536070 74 -2.01522166 -0.26449205 75 -0.03620370 -2.01522166 76 3.07686962 -0.03620370 77 0.59919328 3.07686962 78 1.26233683 0.59919328 79 -2.06236215 1.26233683 80 0.10283016 -2.06236215 81 -0.46373834 0.10283016 82 1.74116290 -0.46373834 83 0.75867849 1.74116290 84 -0.10668724 0.75867849 85 1.17232840 -0.10668724 86 -0.28334049 1.17232840 87 0.20399209 -0.28334049 88 -3.41074894 0.20399209 89 3.45743882 -3.41074894 90 0.10333156 3.45743882 91 0.90550230 0.10333156 92 0.66184674 0.90550230 93 -0.99735711 0.66184674 94 1.06703025 -0.99735711 95 -0.76634289 1.06703025 96 -0.89184379 -0.76634289 97 2.05560943 -0.89184379 98 0.02163569 2.05560943 99 1.80812509 0.02163569 100 -0.87297389 1.80812509 101 0.88139862 -0.87297389 102 -3.47738503 0.88139862 103 1.98536393 -3.47738503 104 -2.25881564 1.98536393 105 1.05471641 -2.25881564 106 2.18034161 1.05471641 107 -2.80210268 2.18034161 108 1.23771189 -2.80210268 109 1.09434510 1.23771189 110 -2.25602565 1.09434510 111 -2.23470273 -2.25602565 112 1.93249768 -2.23470273 113 4.01497679 1.93249768 114 0.32803699 4.01497679 115 1.07025085 0.32803699 116 0.35511655 1.07025085 117 -1.06577785 0.35511655 118 0.35401898 -1.06577785 119 -0.44526815 0.35401898 120 0.31293315 -0.44526815 121 0.22356105 0.31293315 122 -0.94732633 0.22356105 123 0.36953196 -0.94732633 124 -1.87888553 0.36953196 125 0.81669433 -1.87888553 126 1.71811666 0.81669433 127 4.15626996 1.71811666 128 1.49323873 4.15626996 129 -1.72047647 1.49323873 130 -1.48312945 -1.72047647 131 -0.25205391 -1.48312945 132 2.41396269 -0.25205391 133 0.83649737 2.41396269 134 2.21328994 0.83649737 135 1.56434797 2.21328994 136 0.72662318 1.56434797 137 -0.83574752 0.72662318 138 0.90838591 -0.83574752 139 -0.70690059 0.90838591 140 0.29098447 -0.70690059 141 2.21861599 0.29098447 142 -0.78537721 2.21861599 143 0.52168182 -0.78537721 144 1.55601649 0.52168182 145 1.31959204 1.55601649 146 -2.33628698 1.31959204 147 -2.80272838 -2.33628698 148 -2.39515178 -2.80272838 149 2.02208889 -2.39515178 150 0.49422402 2.02208889 151 0.51797734 0.49422402 152 -2.39074021 0.51797734 153 -2.42574994 -2.39074021 154 1.45981045 -2.42574994 155 0.10333156 1.45981045 156 0.79237683 0.10333156 157 4.15626996 0.79237683 158 -2.77676785 4.15626996 159 0.08025858 -2.77676785 160 0.56001778 0.08025858 161 0.66484408 0.56001778 162 0.64018684 0.66484408 163 4.32477513 0.64018684 164 -1.98106204 4.32477513 165 1.91508773 -1.98106204 166 -0.26403885 1.91508773 167 -1.05121668 -0.26403885 168 -3.72253786 -1.05121668 169 -3.11142770 -3.72253786 170 0.50212624 -3.11142770 171 1.64582842 0.50212624 172 -5.03224394 1.64582842 173 1.64353174 -5.03224394 174 2.49933350 1.64353174 175 -2.51616082 2.49933350 176 -3.29742701 -2.51616082 177 0.44050881 -3.29742701 178 1.23412897 0.44050881 179 -2.18844584 1.23412897 180 -0.30691074 -2.18844584 181 -1.81114130 -0.30691074 182 -0.02322678 -1.81114130 183 -1.17956107 -0.02322678 184 1.81828527 -1.17956107 185 1.19546691 1.81828527 186 0.22901949 1.19546691 187 0.73697847 0.22901949 188 0.39051675 0.73697847 189 0.85713461 0.39051675 190 -1.93580381 0.85713461 191 -1.18045409 -1.93580381 192 2.27076306 -1.18045409 193 -1.61325988 2.27076306 194 1.74592482 -1.61325988 195 -2.18800601 1.74592482 196 2.10635151 -2.18800601 197 0.42485008 2.10635151 198 -3.25438262 0.42485008 199 -1.09449770 -3.25438262 200 -3.39471195 -1.09449770 201 0.94235965 -3.39471195 202 2.77371152 0.94235965 203 0.30204576 2.77371152 204 0.36266847 0.30204576 205 1.08918347 0.36266847 206 -0.66720109 1.08918347 207 3.25121282 -0.66720109 208 0.03103524 3.25121282 209 1.46925935 0.03103524 210 -2.80088076 1.46925935 211 1.46055771 -2.80088076 212 -1.32912273 1.46055771 213 -4.04715267 -1.32912273 214 -1.34749122 -4.04715267 215 1.41452682 -1.34749122 216 1.83612836 1.41452682 217 -0.38818545 1.83612836 218 -1.98474506 -0.38818545 219 1.28412103 -1.98474506 220 -3.09177851 1.28412103 221 2.20207366 -3.09177851 222 -2.34659820 2.20207366 223 -0.02985365 -2.34659820 224 -0.81333762 -0.02985365 225 1.66370494 -0.81333762 226 4.91878941 1.66370494 227 -1.92207293 4.91878941 228 -1.48108671 -1.92207293 229 -2.47173009 -1.48108671 230 -0.01029532 -2.47173009 231 -3.02373741 -0.01029532 232 -0.19337224 -3.02373741 233 0.30875681 -0.19337224 234 0.85729628 0.30875681 235 -1.96896476 0.85729628 236 0.83078499 -1.96896476 237 -0.10018333 0.83078499 238 -4.73189729 -0.10018333 239 -2.67097638 -4.73189729 240 -3.03598854 -2.67097638 241 -2.79181011 -3.03598854 242 0.14609246 -2.79181011 243 -0.38134067 0.14609246 244 1.27193149 -0.38134067 245 0.18986546 1.27193149 246 0.09305237 0.18986546 247 5.15728614 0.09305237 248 -0.50319981 5.15728614 249 0.06282323 -0.50319981 250 2.01275092 0.06282323 251 1.07598997 2.01275092 252 -1.26907618 1.07598997 253 -0.99473019 -1.26907618 254 0.09248824 -0.99473019 255 -0.87114772 0.09248824 256 -2.02547131 -0.87114772 257 -2.63820309 -2.02547131 258 2.24779712 -2.63820309 259 -4.75230324 2.24779712 260 -0.05243860 -4.75230324 261 1.33499409 -0.05243860 262 -2.77656050 1.33499409 263 -0.13456621 -2.77656050 > 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/fisher/rcomp/tmp/7ni2n1384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8rsqc1384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9xu701384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10euu11384952168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$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/fisher/rcomp/tmp/11hax21384952168.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$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12b2gy1384952168.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, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/134gk61384952168.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,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14kci11384952168.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,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/1586xq1384952168.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,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + 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,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + 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,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + 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/fisher/rcomp/tmp/16j7w01384952168.tab") + } > > try(system("convert tmp/1rh1o1384952168.ps tmp/1rh1o1384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/28phi1384952168.ps tmp/28phi1384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/3n9x81384952168.ps tmp/3n9x81384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/4ibl11384952168.ps tmp/4ibl11384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/5b6xf1384952168.ps tmp/5b6xf1384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/6rwe01384952168.ps tmp/6rwe01384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/7ni2n1384952168.ps tmp/7ni2n1384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/8rsqc1384952168.ps tmp/8rsqc1384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/9xu701384952168.ps tmp/9xu701384952168.png",intern=TRUE)) character(0) > try(system("convert tmp/10euu11384952168.ps tmp/10euu11384952168.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.650 1.597 12.235