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. 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,dim=c(7 + ,264) + ,dimnames=list(c('Happiness' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Sport1') + ,1:264)) > y <- array(NA,dim=c(7,264),dimnames=list(c('Happiness','Connected','Separate','Learning','Software','Happiness','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 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'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 Connected Separate Learning Software Happiness Sport1 t 1 14 41 38 13 12 14 53 1 2 18 39 32 16 11 18 83 2 3 11 30 35 19 15 11 66 3 4 12 31 33 15 6 12 67 4 5 16 34 37 14 13 16 76 5 6 18 35 29 13 10 18 78 6 7 14 39 31 19 12 14 53 7 8 14 34 36 15 14 14 80 8 9 15 36 35 14 12 15 74 9 10 15 37 38 15 9 15 76 10 11 17 38 31 16 10 17 79 11 12 19 36 34 16 12 19 54 12 13 10 38 35 16 12 10 67 13 14 16 39 38 16 11 16 54 14 15 18 33 37 17 15 18 87 15 16 14 32 33 15 12 14 58 16 17 14 36 32 15 10 14 75 17 18 17 38 38 20 12 17 88 18 19 14 39 38 18 11 14 64 19 20 16 32 32 16 12 16 57 20 21 18 32 33 16 11 18 66 21 22 11 31 31 16 12 11 68 22 23 14 39 38 19 13 14 54 23 24 12 37 39 16 11 12 56 24 25 17 39 32 17 12 17 86 25 26 9 41 32 17 13 9 80 26 27 16 36 35 16 10 16 76 27 28 14 33 37 15 14 14 69 28 29 15 33 33 16 12 15 78 29 30 11 34 33 14 10 11 67 30 31 16 31 31 15 12 16 80 31 32 13 27 32 12 8 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15 35 37 16 12 15 69 70 71 14 38 36 10 8 14 67 71 72 16 31 38 14 11 16 54 72 73 15 34 39 18 14 15 81 73 74 15 38 41 18 14 15 69 74 75 13 34 27 16 12 13 84 75 76 12 39 30 17 9 12 80 76 77 17 37 37 16 13 17 70 77 78 13 34 31 16 11 13 69 78 79 15 28 31 13 12 15 77 79 80 13 37 27 16 12 13 54 80 81 15 33 36 16 12 15 79 81 82 15 35 37 16 12 15 71 82 83 16 37 33 15 12 16 73 83 84 15 32 34 15 11 15 72 84 85 14 33 31 16 10 14 77 85 86 15 38 39 14 9 15 75 86 87 14 33 34 16 12 14 69 87 88 13 29 32 16 12 13 54 88 89 7 33 33 15 12 7 70 89 90 17 31 36 12 9 17 73 90 91 13 36 32 17 15 13 54 91 92 15 35 41 16 12 15 77 92 93 14 32 28 15 12 14 82 93 94 13 29 30 13 12 13 80 94 95 16 39 36 16 10 16 80 95 96 12 37 35 16 13 12 69 96 97 14 35 31 16 9 14 78 97 98 17 37 34 16 12 17 81 98 99 15 32 36 14 10 15 76 99 100 17 38 36 16 14 17 76 100 101 12 37 35 16 11 12 73 101 102 16 36 37 20 15 16 85 102 103 11 32 28 15 11 11 66 103 104 15 33 39 16 11 15 79 104 105 9 40 32 13 12 9 68 105 106 16 38 35 17 12 16 76 106 107 15 41 39 16 12 15 71 107 108 10 36 35 16 11 10 54 108 109 10 43 42 12 7 10 46 109 110 15 30 34 16 12 15 85 110 111 11 31 33 16 14 11 74 111 112 13 32 41 17 11 13 88 112 113 14 32 33 13 11 14 38 113 114 18 37 34 12 10 18 76 114 115 16 37 32 18 13 16 86 115 116 14 33 40 14 13 14 54 116 117 14 34 40 14 8 14 67 117 118 14 33 35 13 11 14 69 118 119 14 38 36 16 12 14 90 119 120 12 33 37 13 11 12 54 120 121 14 31 27 16 13 14 76 121 122 15 38 39 13 12 15 89 122 123 15 37 38 16 14 15 76 123 124 15 36 31 15 13 15 73 124 125 13 31 33 16 15 13 79 125 126 17 39 32 15 10 17 90 126 127 17 44 39 17 11 17 74 127 128 19 33 36 15 9 19 81 128 129 15 35 33 12 11 15 72 129 130 13 32 33 16 10 13 71 130 131 9 28 32 10 11 9 66 131 132 15 40 37 16 8 15 77 132 133 15 27 30 12 11 15 65 133 134 15 37 38 14 12 15 74 134 135 16 32 29 15 12 16 85 135 136 11 28 22 13 9 11 54 136 137 14 34 35 15 11 14 63 137 138 11 30 35 11 10 11 54 138 139 15 35 34 12 8 15 64 139 140 13 31 35 11 9 13 69 140 141 15 32 34 16 8 15 54 141 142 16 30 37 15 9 16 84 142 143 14 30 35 17 15 14 86 143 144 15 31 23 16 11 15 77 144 145 16 40 31 10 8 16 89 145 146 16 32 27 18 13 16 76 146 147 11 36 36 13 12 11 60 147 148 12 32 31 16 12 12 75 148 149 9 35 32 13 9 9 73 149 150 16 38 39 10 7 16 85 150 151 13 42 37 15 13 13 79 151 152 16 34 38 16 9 16 71 152 153 12 35 39 16 6 12 72 153 154 9 38 34 14 8 9 69 154 155 13 33 31 10 8 13 78 155 156 13 36 32 17 15 13 54 156 157 14 32 37 13 6 14 69 157 158 19 33 36 15 9 19 81 158 159 13 34 32 16 11 13 84 159 160 12 32 38 12 8 12 84 160 161 13 34 36 13 8 13 69 161 162 10 27 26 13 10 10 66 162 163 14 31 26 12 8 14 81 163 164 16 38 33 17 14 16 82 164 165 10 34 39 15 10 10 72 165 166 11 24 30 10 8 11 54 166 167 14 30 33 14 11 14 78 167 168 12 26 25 11 12 12 74 168 169 9 34 38 13 12 9 82 169 170 9 27 37 16 12 9 73 170 171 11 37 31 12 5 11 55 171 172 16 36 37 16 12 16 72 172 173 9 41 35 12 10 9 78 173 174 13 29 25 9 7 13 59 174 175 16 36 28 12 12 16 72 175 176 13 32 35 15 11 13 78 176 177 9 37 33 12 8 9 68 177 178 12 30 30 12 9 12 69 178 179 16 31 31 14 10 16 67 179 180 11 38 37 12 9 11 74 180 181 14 36 36 16 12 14 54 181 182 13 35 30 11 6 13 67 182 183 15 31 36 19 15 15 70 183 184 14 38 32 15 12 14 80 184 185 16 22 28 8 12 16 89 185 186 13 32 36 16 12 13 76 186 187 14 36 34 17 11 14 74 187 188 15 39 31 12 7 15 87 188 189 13 28 28 11 7 13 54 189 190 11 32 36 11 5 11 61 190 191 11 32 36 14 12 11 38 191 192 14 38 40 16 12 14 75 192 193 15 32 33 12 3 15 69 193 194 11 35 37 16 11 11 62 194 195 15 32 32 13 10 15 72 195 196 12 37 38 15 12 12 70 196 197 14 34 31 16 9 14 79 197 198 14 33 37 16 12 14 87 198 199 8 33 33 14 9 8 62 199 200 13 26 32 16 12 13 77 200 201 9 30 30 16 12 9 69 201 202 15 24 30 14 10 15 69 202 203 17 34 31 11 9 17 75 203 204 13 34 32 12 12 13 54 204 205 15 33 34 15 8 15 72 205 206 15 34 36 15 11 15 74 206 207 14 35 37 16 11 14 85 207 208 16 35 36 16 12 16 52 208 209 13 36 33 11 10 13 70 209 210 16 34 33 15 10 16 84 210 211 9 34 33 12 12 9 64 211 212 16 41 44 12 12 16 84 212 213 11 32 39 15 11 11 87 213 214 10 30 32 15 8 10 79 214 215 11 35 35 16 12 11 67 215 216 15 28 25 14 10 15 65 216 217 17 33 35 17 11 17 85 217 218 14 39 34 14 10 14 83 218 219 8 36 35 13 8 8 61 219 220 15 36 39 15 12 15 82 220 221 11 35 33 13 12 11 76 221 222 16 38 36 14 10 16 58 222 223 10 33 32 15 12 10 72 223 224 15 31 32 12 9 15 72 224 225 9 34 36 13 9 9 38 225 226 16 32 36 8 6 16 78 226 227 19 31 32 14 10 19 54 227 228 12 33 34 14 9 12 63 228 229 8 34 33 11 9 8 66 229 230 11 34 35 12 9 11 70 230 231 14 34 30 13 6 14 71 231 232 9 33 38 10 10 9 67 232 233 15 32 34 16 6 15 58 233 234 13 41 33 18 14 13 72 234 235 16 34 32 13 10 16 72 235 236 11 36 31 11 10 11 70 236 237 12 37 30 4 6 12 76 237 238 13 36 27 13 12 13 50 238 239 10 29 31 16 12 10 72 239 240 11 37 30 10 7 11 72 240 241 12 27 32 12 8 12 88 241 242 8 35 35 12 11 8 53 242 243 12 28 28 10 3 12 58 243 244 12 35 33 13 6 12 66 244 245 15 37 31 15 10 15 82 245 246 11 29 35 12 8 11 69 246 247 13 32 35 14 9 13 68 247 248 14 36 32 10 9 14 44 248 249 10 19 21 12 8 10 56 249 250 12 21 20 12 9 12 53 250 251 15 31 34 11 7 15 70 251 252 13 33 32 10 7 13 78 252 253 13 36 34 12 6 13 71 253 254 13 33 32 16 9 13 72 254 255 12 37 33 12 10 12 68 255 256 12 34 33 14 11 12 67 256 257 9 35 37 16 12 9 75 257 258 9 31 32 14 8 9 62 258 259 15 37 34 13 11 15 67 259 260 10 35 30 4 3 10 83 260 261 14 27 30 15 11 14 64 261 262 15 34 38 11 12 15 68 262 263 7 40 36 11 7 7 62 263 264 14 29 32 14 9 14 72 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Learning Software Sport1 7.457492 0.016222 0.013316 0.142225 -0.041325 0.062713 t -0.007221 Warning messages: 1: In model.matrix.default(mt, mf, contrasts) : the response appeared on the right-hand side and was dropped 2: In model.matrix.default(mt, mf, contrasts) : problem with term 5 in model.matrix: no columns are assigned > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.817 -1.373 0.292 1.626 7.288 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.457492 1.898849 3.927 0.000110 *** Connected 0.016222 0.042803 0.379 0.705005 Separate 0.013316 0.043690 0.305 0.760784 Learning 0.142225 0.077046 1.846 0.066045 . Software -0.041325 0.079545 -0.520 0.603843 Sport1 0.062713 0.013904 4.510 9.85e-06 *** t -0.007221 0.002068 -3.492 0.000564 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.303 on 257 degrees of freedom Multiple R-squared: 0.1696, Adjusted R-squared: 0.1502 F-statistic: 8.748 on 6 and 257 DF, p-value: 1.114e-08 > 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,] 0.06291417 0.12582835 0.93708583 [2,] 0.50893715 0.98212569 0.49106285 [3,] 0.90332601 0.19334798 0.09667399 [4,] 0.88955795 0.22088411 0.11044205 [5,] 0.89540665 0.20918670 0.10459335 [6,] 0.85978001 0.28043997 0.14021999 [7,] 0.86033728 0.27932544 0.13966272 [8,] 0.81806910 0.36386180 0.18193090 [9,] 0.76549277 0.46901445 0.23450723 [10,] 0.72547664 0.54904673 0.27452336 [11,] 0.73466679 0.53066643 0.26533321 [12,] 0.86574260 0.26851481 0.13425740 [13,] 0.82538665 0.34922669 0.17461335 [14,] 0.81164961 0.37670077 0.18835039 [15,] 0.76826024 0.46347952 0.23173976 [16,] 0.96806882 0.06386235 0.03193118 [17,] 0.95846187 0.08307626 0.04153813 [18,] 0.94285871 0.11428258 0.05714129 [19,] 0.92347526 0.15304948 0.07652474 [20,] 0.93267860 0.13464281 0.06732140 [21,] 0.91980113 0.16039774 0.08019887 [22,] 0.89598775 0.20802449 0.10401225 [23,] 0.89756036 0.20487927 0.10243964 [24,] 0.87022235 0.25955530 0.12977765 [25,] 0.84055187 0.31889625 0.15944813 [26,] 0.81722564 0.36554873 0.18277436 [27,] 0.89021661 0.21956677 0.10978339 [28,] 0.86813132 0.26373736 0.13186868 [29,] 0.87374102 0.25251797 0.12625898 [30,] 0.85184804 0.29630391 0.14815196 [31,] 0.82112666 0.35774667 0.17887334 [32,] 0.80461543 0.39076915 0.19538457 [33,] 0.80287669 0.39424663 0.19712331 [34,] 0.77367015 0.45265969 0.22632985 [35,] 0.73734234 0.52531531 0.26265766 [36,] 0.73689967 0.52620066 0.26310033 [37,] 0.71674081 0.56651839 0.28325919 [38,] 0.67748510 0.64502980 0.32251490 [39,] 0.67722401 0.64555198 0.32277599 [40,] 0.65148137 0.69703727 0.34851863 [41,] 0.62923607 0.74152786 0.37076393 [42,] 0.58466661 0.83066679 0.41533339 [43,] 0.57282417 0.85435166 0.42717583 [44,] 0.54928977 0.90142045 0.45071023 [45,] 0.50827612 0.98344776 0.49172388 [46,] 0.46378320 0.92756639 0.53621680 [47,] 0.43426837 0.86853674 0.56573163 [48,] 0.40026708 0.80053416 0.59973292 [49,] 0.40177875 0.80355749 0.59822125 [50,] 0.39878039 0.79756077 0.60121961 [51,] 0.48292993 0.96585986 0.51707007 [52,] 0.45715395 0.91430791 0.54284605 [53,] 0.50349218 0.99301564 0.49650782 [54,] 0.49572603 0.99145207 0.50427397 [55,] 0.49826451 0.99652902 0.50173549 [56,] 0.60466695 0.79066609 0.39533305 [57,] 0.61077565 0.77844871 0.38922435 [58,] 0.59568453 0.80863094 0.40431547 [59,] 0.59741338 0.80517323 0.40258662 [60,] 0.57709296 0.84581408 0.42290704 [61,] 0.54450404 0.91099193 0.45549596 [62,] 0.59758792 0.80482417 0.40241208 [63,] 0.56047235 0.87905530 0.43952765 [64,] 0.52544345 0.94911311 0.47455655 [65,] 0.49487704 0.98975409 0.50512296 [66,] 0.49380428 0.98760856 0.50619572 [67,] 0.53217608 0.93564784 0.46782392 [68,] 0.49560690 0.99121381 0.50439310 [69,] 0.48010878 0.96021756 0.51989122 [70,] 0.44582277 0.89164555 0.55417723 [71,] 0.41228777 0.82457553 0.58771223 [72,] 0.38277393 0.76554786 0.61722607 [73,] 0.38058739 0.76117478 0.61941261 [74,] 0.35565383 0.71130766 0.64434617 [75,] 0.32148060 0.64296121 0.67851940 [76,] 0.29045087 0.58090175 0.70954913 [77,] 0.25888345 0.51776691 0.74111655 [78,] 0.22948602 0.45897204 0.77051398 [79,] 0.47030603 0.94061206 0.52969397 [80,] 0.51615855 0.96768291 0.48384145 [81,] 0.47999924 0.95999848 0.52000076 [82,] 0.44370317 0.88740634 0.55629683 [83,] 0.41065279 0.82130557 0.58934721 [84,] 0.37794615 0.75589231 0.62205385 [85,] 0.35497464 0.70994928 0.64502536 [86,] 0.33986477 0.67972953 0.66013523 [87,] 0.30838174 0.61676348 0.69161826 [88,] 0.31682431 0.63364862 0.68317569 [89,] 0.28973751 0.57947503 0.71026249 [90,] 0.30277346 0.60554691 0.69722654 [91,] 0.29861598 0.59723196 0.70138402 [92,] 0.27059031 0.54118062 0.72940969 [93,] 0.26481843 0.52963685 0.73518157 [94,] 0.23623698 0.47247396 0.76376302 [95,] 0.30806459 0.61612918 0.69193541 [96,] 0.29301087 0.58602174 0.70698913 [97,] 0.26532937 0.53065874 0.73467063 [98,] 0.28138370 0.56276740 0.71861630 [99,] 0.28385316 0.56770631 0.71614684 [100,] 0.25480299 0.50960598 0.74519701 [101,] 0.26714667 0.53429334 0.73285333 [102,] 0.26823388 0.53646776 0.73176612 [103,] 0.28933543 0.57867087 0.71066457 [104,] 0.37526094 0.75052189 0.62473906 [105,] 0.35183255 0.70366510 0.64816745 [106,] 0.33160853 0.66321706 0.66839147 [107,] 0.30057326 0.60114652 0.69942674 [108,] 0.27291567 0.54583134 0.72708433 [109,] 0.25067380 0.50134760 0.74932620 [110,] 0.22347663 0.44695326 0.77652337 [111,] 0.20163664 0.40327328 0.79836336 [112,] 0.17764892 0.35529784 0.82235108 [113,] 0.15794844 0.31589687 0.84205156 [114,] 0.14505962 0.29011923 0.85494038 [115,] 0.12940763 0.25881525 0.87059237 [116,] 0.12590696 0.25181392 0.87409304 [117,] 0.12851526 0.25703051 0.87148474 [118,] 0.19314524 0.38629047 0.80685476 [119,] 0.18196916 0.36393831 0.81803084 [120,] 0.16133755 0.32267509 0.83866245 [121,] 0.18710441 0.37420882 0.81289559 [122,] 0.16529166 0.33058332 0.83470834 [123,] 0.17028887 0.34057775 0.82971113 [124,] 0.15499187 0.30998373 0.84500813 [125,] 0.14586906 0.29173812 0.85413094 [126,] 0.13201005 0.26402009 0.86798995 [127,] 0.11651092 0.23302185 0.88348908 [128,] 0.10230622 0.20461244 0.89769378 [129,] 0.09982226 0.19964452 0.90017774 [130,] 0.08535459 0.17070919 0.91464541 [131,] 0.08556192 0.17112384 0.91443808 [132,] 0.07891749 0.15783497 0.92108251 [133,] 0.06705847 0.13411695 0.93294153 [134,] 0.05950418 0.11900836 0.94049582 [135,] 0.05523440 0.11046880 0.94476560 [136,] 0.05261597 0.10523194 0.94738403 [137,] 0.04761461 0.09522923 0.95238539 [138,] 0.04401454 0.08802909 0.95598546 [139,] 0.07150506 0.14301013 0.92849494 [140,] 0.07113852 0.14227703 0.92886148 [141,] 0.06238992 0.12477983 0.93761008 [142,] 0.06317007 0.12634013 0.93682993 [143,] 0.06088035 0.12176070 0.93911965 [144,] 0.09256100 0.18512201 0.90743900 [145,] 0.07853240 0.15706480 0.92146760 [146,] 0.06683965 0.13367929 0.93316035 [147,] 0.05815904 0.11631807 0.94184096 [148,] 0.10908249 0.21816498 0.89091751 [149,] 0.09798394 0.19596788 0.90201606 [150,] 0.09186458 0.18372916 0.90813542 [151,] 0.07837944 0.15675887 0.92162056 [152,] 0.08360792 0.16721584 0.91639208 [153,] 0.07109912 0.14219824 0.92890088 [154,] 0.06478896 0.12957792 0.93521104 [155,] 0.07682021 0.15364042 0.92317979 [156,] 0.06498684 0.12997368 0.93501316 [157,] 0.05455227 0.10910453 0.94544773 [158,] 0.04739401 0.09478801 0.95260599 [159,] 0.08019177 0.16038353 0.91980823 [160,] 0.12435142 0.24870285 0.87564858 [161,] 0.11034351 0.22068702 0.88965649 [162,] 0.11173278 0.22346557 0.88826722 [163,] 0.16794884 0.33589767 0.83205116 [164,] 0.15184378 0.30368756 0.84815622 [165,] 0.16085658 0.32171316 0.83914342 [166,] 0.14154372 0.28308744 0.85845628 [167,] 0.18646977 0.37293955 0.81353023 [168,] 0.16941967 0.33883934 0.83058033 [169,] 0.18056898 0.36113797 0.81943102 [170,] 0.18113571 0.36227141 0.81886429 [171,] 0.16584616 0.33169231 0.83415384 [172,] 0.14395597 0.28791194 0.85604403 [173,] 0.12933241 0.25866482 0.87066759 [174,] 0.11031871 0.22063743 0.88968129 [175,] 0.11287730 0.22575459 0.88712270 [176,] 0.09663925 0.19327851 0.90336075 [177,] 0.08105105 0.16210209 0.91894895 [178,] 0.06846840 0.13693680 0.93153160 [179,] 0.05897897 0.11795794 0.94102103 [180,] 0.05220972 0.10441944 0.94779028 [181,] 0.04277979 0.08555958 0.95722021 [182,] 0.03459567 0.06919134 0.96540433 [183,] 0.03143024 0.06286048 0.96856976 [184,] 0.02908784 0.05817567 0.97091216 [185,] 0.02647235 0.05294469 0.97352765 [186,] 0.02226363 0.04452726 0.97773637 [187,] 0.01741955 0.03483909 0.98258045 [188,] 0.01351588 0.02703176 0.98648412 [189,] 0.02909756 0.05819511 0.97090244 [190,] 0.02344290 0.04688580 0.97655710 [191,] 0.04369338 0.08738676 0.95630662 [192,] 0.03919591 0.07839181 0.96080409 [193,] 0.05306734 0.10613468 0.94693266 [194,] 0.04413940 0.08827880 0.95586060 [195,] 0.03782654 0.07565309 0.96217346 [196,] 0.03266381 0.06532762 0.96733619 [197,] 0.02556474 0.05112949 0.97443526 [198,] 0.03404476 0.06808952 0.96595524 [199,] 0.02680033 0.05360066 0.97319967 [200,] 0.02537797 0.05075593 0.97462203 [201,] 0.03089969 0.06179938 0.96910031 [202,] 0.03539050 0.07078099 0.96460950 [203,] 0.03617433 0.07234866 0.96382567 [204,] 0.05093805 0.10187610 0.94906195 [205,] 0.04921779 0.09843558 0.95078221 [206,] 0.04463097 0.08926195 0.95536903 [207,] 0.04687600 0.09375199 0.95312400 [208,] 0.03683497 0.07366994 0.96316503 [209,] 0.06801637 0.13603275 0.93198363 [210,] 0.05893715 0.11787430 0.94106285 [211,] 0.05363108 0.10726216 0.94636892 [212,] 0.06892601 0.13785202 0.93107399 [213,] 0.08188369 0.16376738 0.91811631 [214,] 0.07591703 0.15183407 0.92408297 [215,] 0.07990450 0.15980900 0.92009550 [216,] 0.11525276 0.23050553 0.88474724 [217,] 0.42081226 0.84162453 0.57918774 [218,] 0.36810473 0.73620946 0.63189527 [219,] 0.44406474 0.88812949 0.55593526 [220,] 0.40001512 0.80003024 0.59998488 [221,] 0.35830445 0.71660889 0.64169555 [222,] 0.37982816 0.75965632 0.62017184 [223,] 0.41211175 0.82422350 0.58788825 [224,] 0.35444569 0.70889138 0.64555431 [225,] 0.44098248 0.88196496 0.55901752 [226,] 0.39245694 0.78491389 0.60754306 [227,] 0.33140275 0.66280551 0.66859725 [228,] 0.28912444 0.57824888 0.71087556 [229,] 0.30540895 0.61081789 0.69459105 [230,] 0.25983890 0.51967779 0.74016110 [231,] 0.22975722 0.45951445 0.77024278 [232,] 0.44632222 0.89264444 0.55367778 [233,] 0.39722750 0.79445501 0.60277250 [234,] 0.32614249 0.65228499 0.67385751 [235,] 0.27357543 0.54715087 0.72642457 [236,] 0.29234785 0.58469570 0.70765215 [237,] 0.23197254 0.46394509 0.76802746 [238,] 0.22049632 0.44099264 0.77950368 [239,] 0.24211543 0.48423087 0.75788457 [240,] 0.26236205 0.52472411 0.73763795 [241,] 0.20032883 0.40065765 0.79967117 [242,] 0.12784763 0.25569527 0.87215237 [243,] 0.30404967 0.60809933 0.69595033 There were 50 or more warnings (use warnings() to see the first 50) > postscript(file="/var/fisher/rcomp/tmp/1jlw21384961277.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2pr3o1384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3dmul1384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4yfl31384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5racm1384961277.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 0.701829376 2.472003525 -3.609978018 -2.458089502 1.314290988 3.304637851 7 8 9 10 11 12 0.017460334 -1.002480342 0.421463234 -0.019111433 1.776058796 5.426248890 13 14 15 16 17 18 -4.427556257 2.297437236 2.368861048 0.424712106 -0.768408355 0.682734269 19 20 21 22 23 24 -0.578034454 2.387400678 3.775565623 -3.258459909 0.018404773 -1.736646615 25 26 27 28 29 30 1.349055528 -6.258565135 1.058920083 -0.165306732 0.105885628 -3.011475120 31 32 33 34 35 36 1.196203137 0.146903872 2.737302257 -0.303769214 -0.291760596 2.258369560 37 38 39 40 41 42 -4.074758077 0.845650695 2.312393160 -1.365359938 0.309168585 1.656128973 43 44 45 46 47 48 2.199372426 -0.985906814 0.973108148 -2.524618611 1.215906963 0.518812862 49 50 51 52 53 54 2.344593133 -1.337673612 1.673326374 0.219784658 -2.194988522 -1.751651357 55 56 57 58 59 60 -1.029833438 0.371264019 1.448284921 1.056717444 -2.367846938 -2.296962230 61 62 63 64 65 66 -4.617695045 -1.687524836 -3.734218012 1.238102198 1.881369132 -4.938957930 67 68 69 70 71 72 -3.144247817 -2.496134990 1.713788194 0.880667809 0.666012284 3.130500260 73 74 75 76 77 78 -0.062430205 0.605825066 -1.874539845 -3.003726251 2.877385291 -1.006771549 79 80 81 82 83 84 1.064080779 -0.005715407 0.378733935 0.841897847 1.886736609 0.983140443 85 86 87 88 89 90 -0.483028222 0.705107889 0.075821060 0.115254334 -6.816908510 3.297370654 91 92 93 94 95 96 0.005114525 0.484571685 -0.457776958 -1.018644646 1.237135549 -1.896065925 97 98 99 100 101 102 -0.532855141 2.337813058 0.914877184 2.705617335 -2.193461577 0.647198635 103 104 105 106 107 108 -2.423484371 0.463551736 -4.391731395 1.537384777 0.898466390 -2.935146852 109 110 111 112 113 114 -2.229388889 0.287172544 -2.936020854 -2.195727196 2.622559594 4.253167260 115 116 117 118 119 120 0.930517728 1.471813144 0.440918414 0.671715613 -1.117808166 -0.399780761 121 122 123 124 125 126 0.049334621 0.353296945 0.861298339 1.266988913 -1.107161178 2.029355276 127 128 129 130 131 132 2.622537089 4.610958312 1.699423445 -0.792201627 -3.498536321 0.580273615 133 134 135 136 137 138 2.337022333 1.267957486 1.644063842 -1.086046261 0.884525042 -0.951375008 139 140 141 142 143 144 2.136047018 0.064827800 2.257384027 1.559269023 -0.568801433 1.123323243 145 146 147 148 149 150 1.854840624 1.929195008 -1.575107802 -1.803787282 -4.430423651 2.026392560 151 152 153 154 155 156 -1.091539148 2.226319392 -1.982686246 -4.402314016 -0.269550316 0.474499368 157 158 159 160 161 162 0.736310181 4.827597470 -1.375853591 -1.971157699 -0.171282391 -2.646560795 163 164 165 166 167 168 0.414653830 1.689225709 -3.572281526 -0.525693497 0.394216845 -0.708296703 169 170 171 172 173 174 -4.790107822 -4.518275687 -1.184929845 2.412880358 -4.524405550 1.304879625 175 176 177 178 179 180 3.123284725 -0.742091930 -3.859523351 -0.720187566 3.139796997 -2.242295339 181 182 183 184 185 186 1.620018313 0.371261167 1.409467946 0.174192188 2.925390297 -0.658668420 187 188 189 190 191 192 0.252170557 0.981228972 1.418588572 -1.267244033 0.044975068 0.296776964 193 194 195 196 197 198 2.067788165 -1.826226114 2.054461128 -1.175695383 0.142784728 -0.291391431 199 200 201 202 203 204 -4.502613776 -0.469687699 -3.999021337 2.307332051 4.148089040 1.440715166 205 206 207 208 209 210 1.716719836 1.679638586 -0.174743829 3.956641333 0.487230921 2.080016975 211 212 213 214 215 216 -3.149179405 2.343759143 -3.092581287 -3.581980473 -1.920186130 2.660970888 217 218 219 220 221 222 2.814319654 0.248298925 -4.269872906 1.247968764 -1.987967300 3.834595438 223 224 225 226 227 228 -2.961363641 2.381000716 -1.723696205 3.394605750 7.288370626 -0.369224320 229 230 231 232 233 234 -4.126372882 -1.538859042 1.206025938 -3.034227041 2.588241936 -0.369046303 235 236 237 238 239 240 3.310869033 -1.291162666 0.167148664 1.828999369 -2.909843605 -1.372360984 241 242 243 244 245 246 -1.476078943 -3.319656793 0.534610997 -0.442701941 1.436152996 -1.320820044 247 248 249 250 251 252 0.457322986 3.513610116 -1.135249843 1.082306643 2.734344531 0.376275248 253 254 255 256 257 258 0.421413053 -0.003704616 -0.213610610 -0.338134661 -4.145224977 -3.072122337 259 260 261 262 263 264 2.763772256 -2.197283088 1.897387084 3.043903035 -4.849927856 1.417847142 > postscript(file="/var/fisher/rcomp/tmp/65l6v1384961277.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 0.701829376 NA 1 2.472003525 0.701829376 2 -3.609978018 2.472003525 3 -2.458089502 -3.609978018 4 1.314290988 -2.458089502 5 3.304637851 1.314290988 6 0.017460334 3.304637851 7 -1.002480342 0.017460334 8 0.421463234 -1.002480342 9 -0.019111433 0.421463234 10 1.776058796 -0.019111433 11 5.426248890 1.776058796 12 -4.427556257 5.426248890 13 2.297437236 -4.427556257 14 2.368861048 2.297437236 15 0.424712106 2.368861048 16 -0.768408355 0.424712106 17 0.682734269 -0.768408355 18 -0.578034454 0.682734269 19 2.387400678 -0.578034454 20 3.775565623 2.387400678 21 -3.258459909 3.775565623 22 0.018404773 -3.258459909 23 -1.736646615 0.018404773 24 1.349055528 -1.736646615 25 -6.258565135 1.349055528 26 1.058920083 -6.258565135 27 -0.165306732 1.058920083 28 0.105885628 -0.165306732 29 -3.011475120 0.105885628 30 1.196203137 -3.011475120 31 0.146903872 1.196203137 32 2.737302257 0.146903872 33 -0.303769214 2.737302257 34 -0.291760596 -0.303769214 35 2.258369560 -0.291760596 36 -4.074758077 2.258369560 37 0.845650695 -4.074758077 38 2.312393160 0.845650695 39 -1.365359938 2.312393160 40 0.309168585 -1.365359938 41 1.656128973 0.309168585 42 2.199372426 1.656128973 43 -0.985906814 2.199372426 44 0.973108148 -0.985906814 45 -2.524618611 0.973108148 46 1.215906963 -2.524618611 47 0.518812862 1.215906963 48 2.344593133 0.518812862 49 -1.337673612 2.344593133 50 1.673326374 -1.337673612 51 0.219784658 1.673326374 52 -2.194988522 0.219784658 53 -1.751651357 -2.194988522 54 -1.029833438 -1.751651357 55 0.371264019 -1.029833438 56 1.448284921 0.371264019 57 1.056717444 1.448284921 58 -2.367846938 1.056717444 59 -2.296962230 -2.367846938 60 -4.617695045 -2.296962230 61 -1.687524836 -4.617695045 62 -3.734218012 -1.687524836 63 1.238102198 -3.734218012 64 1.881369132 1.238102198 65 -4.938957930 1.881369132 66 -3.144247817 -4.938957930 67 -2.496134990 -3.144247817 68 1.713788194 -2.496134990 69 0.880667809 1.713788194 70 0.666012284 0.880667809 71 3.130500260 0.666012284 72 -0.062430205 3.130500260 73 0.605825066 -0.062430205 74 -1.874539845 0.605825066 75 -3.003726251 -1.874539845 76 2.877385291 -3.003726251 77 -1.006771549 2.877385291 78 1.064080779 -1.006771549 79 -0.005715407 1.064080779 80 0.378733935 -0.005715407 81 0.841897847 0.378733935 82 1.886736609 0.841897847 83 0.983140443 1.886736609 84 -0.483028222 0.983140443 85 0.705107889 -0.483028222 86 0.075821060 0.705107889 87 0.115254334 0.075821060 88 -6.816908510 0.115254334 89 3.297370654 -6.816908510 90 0.005114525 3.297370654 91 0.484571685 0.005114525 92 -0.457776958 0.484571685 93 -1.018644646 -0.457776958 94 1.237135549 -1.018644646 95 -1.896065925 1.237135549 96 -0.532855141 -1.896065925 97 2.337813058 -0.532855141 98 0.914877184 2.337813058 99 2.705617335 0.914877184 100 -2.193461577 2.705617335 101 0.647198635 -2.193461577 102 -2.423484371 0.647198635 103 0.463551736 -2.423484371 104 -4.391731395 0.463551736 105 1.537384777 -4.391731395 106 0.898466390 1.537384777 107 -2.935146852 0.898466390 108 -2.229388889 -2.935146852 109 0.287172544 -2.229388889 110 -2.936020854 0.287172544 111 -2.195727196 -2.936020854 112 2.622559594 -2.195727196 113 4.253167260 2.622559594 114 0.930517728 4.253167260 115 1.471813144 0.930517728 116 0.440918414 1.471813144 117 0.671715613 0.440918414 118 -1.117808166 0.671715613 119 -0.399780761 -1.117808166 120 0.049334621 -0.399780761 121 0.353296945 0.049334621 122 0.861298339 0.353296945 123 1.266988913 0.861298339 124 -1.107161178 1.266988913 125 2.029355276 -1.107161178 126 2.622537089 2.029355276 127 4.610958312 2.622537089 128 1.699423445 4.610958312 129 -0.792201627 1.699423445 130 -3.498536321 -0.792201627 131 0.580273615 -3.498536321 132 2.337022333 0.580273615 133 1.267957486 2.337022333 134 1.644063842 1.267957486 135 -1.086046261 1.644063842 136 0.884525042 -1.086046261 137 -0.951375008 0.884525042 138 2.136047018 -0.951375008 139 0.064827800 2.136047018 140 2.257384027 0.064827800 141 1.559269023 2.257384027 142 -0.568801433 1.559269023 143 1.123323243 -0.568801433 144 1.854840624 1.123323243 145 1.929195008 1.854840624 146 -1.575107802 1.929195008 147 -1.803787282 -1.575107802 148 -4.430423651 -1.803787282 149 2.026392560 -4.430423651 150 -1.091539148 2.026392560 151 2.226319392 -1.091539148 152 -1.982686246 2.226319392 153 -4.402314016 -1.982686246 154 -0.269550316 -4.402314016 155 0.474499368 -0.269550316 156 0.736310181 0.474499368 157 4.827597470 0.736310181 158 -1.375853591 4.827597470 159 -1.971157699 -1.375853591 160 -0.171282391 -1.971157699 161 -2.646560795 -0.171282391 162 0.414653830 -2.646560795 163 1.689225709 0.414653830 164 -3.572281526 1.689225709 165 -0.525693497 -3.572281526 166 0.394216845 -0.525693497 167 -0.708296703 0.394216845 168 -4.790107822 -0.708296703 169 -4.518275687 -4.790107822 170 -1.184929845 -4.518275687 171 2.412880358 -1.184929845 172 -4.524405550 2.412880358 173 1.304879625 -4.524405550 174 3.123284725 1.304879625 175 -0.742091930 3.123284725 176 -3.859523351 -0.742091930 177 -0.720187566 -3.859523351 178 3.139796997 -0.720187566 179 -2.242295339 3.139796997 180 1.620018313 -2.242295339 181 0.371261167 1.620018313 182 1.409467946 0.371261167 183 0.174192188 1.409467946 184 2.925390297 0.174192188 185 -0.658668420 2.925390297 186 0.252170557 -0.658668420 187 0.981228972 0.252170557 188 1.418588572 0.981228972 189 -1.267244033 1.418588572 190 0.044975068 -1.267244033 191 0.296776964 0.044975068 192 2.067788165 0.296776964 193 -1.826226114 2.067788165 194 2.054461128 -1.826226114 195 -1.175695383 2.054461128 196 0.142784728 -1.175695383 197 -0.291391431 0.142784728 198 -4.502613776 -0.291391431 199 -0.469687699 -4.502613776 200 -3.999021337 -0.469687699 201 2.307332051 -3.999021337 202 4.148089040 2.307332051 203 1.440715166 4.148089040 204 1.716719836 1.440715166 205 1.679638586 1.716719836 206 -0.174743829 1.679638586 207 3.956641333 -0.174743829 208 0.487230921 3.956641333 209 2.080016975 0.487230921 210 -3.149179405 2.080016975 211 2.343759143 -3.149179405 212 -3.092581287 2.343759143 213 -3.581980473 -3.092581287 214 -1.920186130 -3.581980473 215 2.660970888 -1.920186130 216 2.814319654 2.660970888 217 0.248298925 2.814319654 218 -4.269872906 0.248298925 219 1.247968764 -4.269872906 220 -1.987967300 1.247968764 221 3.834595438 -1.987967300 222 -2.961363641 3.834595438 223 2.381000716 -2.961363641 224 -1.723696205 2.381000716 225 3.394605750 -1.723696205 226 7.288370626 3.394605750 227 -0.369224320 7.288370626 228 -4.126372882 -0.369224320 229 -1.538859042 -4.126372882 230 1.206025938 -1.538859042 231 -3.034227041 1.206025938 232 2.588241936 -3.034227041 233 -0.369046303 2.588241936 234 3.310869033 -0.369046303 235 -1.291162666 3.310869033 236 0.167148664 -1.291162666 237 1.828999369 0.167148664 238 -2.909843605 1.828999369 239 -1.372360984 -2.909843605 240 -1.476078943 -1.372360984 241 -3.319656793 -1.476078943 242 0.534610997 -3.319656793 243 -0.442701941 0.534610997 244 1.436152996 -0.442701941 245 -1.320820044 1.436152996 246 0.457322986 -1.320820044 247 3.513610116 0.457322986 248 -1.135249843 3.513610116 249 1.082306643 -1.135249843 250 2.734344531 1.082306643 251 0.376275248 2.734344531 252 0.421413053 0.376275248 253 -0.003704616 0.421413053 254 -0.213610610 -0.003704616 255 -0.338134661 -0.213610610 256 -4.145224977 -0.338134661 257 -3.072122337 -4.145224977 258 2.763772256 -3.072122337 259 -2.197283088 2.763772256 260 1.897387084 -2.197283088 261 3.043903035 1.897387084 262 -4.849927856 3.043903035 263 1.417847142 -4.849927856 264 NA 1.417847142 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.472003525 0.701829376 [2,] -3.609978018 2.472003525 [3,] -2.458089502 -3.609978018 [4,] 1.314290988 -2.458089502 [5,] 3.304637851 1.314290988 [6,] 0.017460334 3.304637851 [7,] -1.002480342 0.017460334 [8,] 0.421463234 -1.002480342 [9,] -0.019111433 0.421463234 [10,] 1.776058796 -0.019111433 [11,] 5.426248890 1.776058796 [12,] -4.427556257 5.426248890 [13,] 2.297437236 -4.427556257 [14,] 2.368861048 2.297437236 [15,] 0.424712106 2.368861048 [16,] -0.768408355 0.424712106 [17,] 0.682734269 -0.768408355 [18,] -0.578034454 0.682734269 [19,] 2.387400678 -0.578034454 [20,] 3.775565623 2.387400678 [21,] -3.258459909 3.775565623 [22,] 0.018404773 -3.258459909 [23,] -1.736646615 0.018404773 [24,] 1.349055528 -1.736646615 [25,] -6.258565135 1.349055528 [26,] 1.058920083 -6.258565135 [27,] -0.165306732 1.058920083 [28,] 0.105885628 -0.165306732 [29,] -3.011475120 0.105885628 [30,] 1.196203137 -3.011475120 [31,] 0.146903872 1.196203137 [32,] 2.737302257 0.146903872 [33,] -0.303769214 2.737302257 [34,] -0.291760596 -0.303769214 [35,] 2.258369560 -0.291760596 [36,] -4.074758077 2.258369560 [37,] 0.845650695 -4.074758077 [38,] 2.312393160 0.845650695 [39,] -1.365359938 2.312393160 [40,] 0.309168585 -1.365359938 [41,] 1.656128973 0.309168585 [42,] 2.199372426 1.656128973 [43,] -0.985906814 2.199372426 [44,] 0.973108148 -0.985906814 [45,] -2.524618611 0.973108148 [46,] 1.215906963 -2.524618611 [47,] 0.518812862 1.215906963 [48,] 2.344593133 0.518812862 [49,] -1.337673612 2.344593133 [50,] 1.673326374 -1.337673612 [51,] 0.219784658 1.673326374 [52,] -2.194988522 0.219784658 [53,] -1.751651357 -2.194988522 [54,] -1.029833438 -1.751651357 [55,] 0.371264019 -1.029833438 [56,] 1.448284921 0.371264019 [57,] 1.056717444 1.448284921 [58,] -2.367846938 1.056717444 [59,] -2.296962230 -2.367846938 [60,] -4.617695045 -2.296962230 [61,] -1.687524836 -4.617695045 [62,] -3.734218012 -1.687524836 [63,] 1.238102198 -3.734218012 [64,] 1.881369132 1.238102198 [65,] -4.938957930 1.881369132 [66,] -3.144247817 -4.938957930 [67,] -2.496134990 -3.144247817 [68,] 1.713788194 -2.496134990 [69,] 0.880667809 1.713788194 [70,] 0.666012284 0.880667809 [71,] 3.130500260 0.666012284 [72,] -0.062430205 3.130500260 [73,] 0.605825066 -0.062430205 [74,] -1.874539845 0.605825066 [75,] -3.003726251 -1.874539845 [76,] 2.877385291 -3.003726251 [77,] -1.006771549 2.877385291 [78,] 1.064080779 -1.006771549 [79,] -0.005715407 1.064080779 [80,] 0.378733935 -0.005715407 [81,] 0.841897847 0.378733935 [82,] 1.886736609 0.841897847 [83,] 0.983140443 1.886736609 [84,] -0.483028222 0.983140443 [85,] 0.705107889 -0.483028222 [86,] 0.075821060 0.705107889 [87,] 0.115254334 0.075821060 [88,] -6.816908510 0.115254334 [89,] 3.297370654 -6.816908510 [90,] 0.005114525 3.297370654 [91,] 0.484571685 0.005114525 [92,] -0.457776958 0.484571685 [93,] -1.018644646 -0.457776958 [94,] 1.237135549 -1.018644646 [95,] -1.896065925 1.237135549 [96,] -0.532855141 -1.896065925 [97,] 2.337813058 -0.532855141 [98,] 0.914877184 2.337813058 [99,] 2.705617335 0.914877184 [100,] -2.193461577 2.705617335 [101,] 0.647198635 -2.193461577 [102,] -2.423484371 0.647198635 [103,] 0.463551736 -2.423484371 [104,] -4.391731395 0.463551736 [105,] 1.537384777 -4.391731395 [106,] 0.898466390 1.537384777 [107,] -2.935146852 0.898466390 [108,] -2.229388889 -2.935146852 [109,] 0.287172544 -2.229388889 [110,] -2.936020854 0.287172544 [111,] -2.195727196 -2.936020854 [112,] 2.622559594 -2.195727196 [113,] 4.253167260 2.622559594 [114,] 0.930517728 4.253167260 [115,] 1.471813144 0.930517728 [116,] 0.440918414 1.471813144 [117,] 0.671715613 0.440918414 [118,] -1.117808166 0.671715613 [119,] -0.399780761 -1.117808166 [120,] 0.049334621 -0.399780761 [121,] 0.353296945 0.049334621 [122,] 0.861298339 0.353296945 [123,] 1.266988913 0.861298339 [124,] -1.107161178 1.266988913 [125,] 2.029355276 -1.107161178 [126,] 2.622537089 2.029355276 [127,] 4.610958312 2.622537089 [128,] 1.699423445 4.610958312 [129,] -0.792201627 1.699423445 [130,] -3.498536321 -0.792201627 [131,] 0.580273615 -3.498536321 [132,] 2.337022333 0.580273615 [133,] 1.267957486 2.337022333 [134,] 1.644063842 1.267957486 [135,] -1.086046261 1.644063842 [136,] 0.884525042 -1.086046261 [137,] -0.951375008 0.884525042 [138,] 2.136047018 -0.951375008 [139,] 0.064827800 2.136047018 [140,] 2.257384027 0.064827800 [141,] 1.559269023 2.257384027 [142,] -0.568801433 1.559269023 [143,] 1.123323243 -0.568801433 [144,] 1.854840624 1.123323243 [145,] 1.929195008 1.854840624 [146,] -1.575107802 1.929195008 [147,] -1.803787282 -1.575107802 [148,] -4.430423651 -1.803787282 [149,] 2.026392560 -4.430423651 [150,] -1.091539148 2.026392560 [151,] 2.226319392 -1.091539148 [152,] -1.982686246 2.226319392 [153,] -4.402314016 -1.982686246 [154,] -0.269550316 -4.402314016 [155,] 0.474499368 -0.269550316 [156,] 0.736310181 0.474499368 [157,] 4.827597470 0.736310181 [158,] -1.375853591 4.827597470 [159,] -1.971157699 -1.375853591 [160,] -0.171282391 -1.971157699 [161,] -2.646560795 -0.171282391 [162,] 0.414653830 -2.646560795 [163,] 1.689225709 0.414653830 [164,] -3.572281526 1.689225709 [165,] -0.525693497 -3.572281526 [166,] 0.394216845 -0.525693497 [167,] -0.708296703 0.394216845 [168,] -4.790107822 -0.708296703 [169,] -4.518275687 -4.790107822 [170,] -1.184929845 -4.518275687 [171,] 2.412880358 -1.184929845 [172,] -4.524405550 2.412880358 [173,] 1.304879625 -4.524405550 [174,] 3.123284725 1.304879625 [175,] -0.742091930 3.123284725 [176,] -3.859523351 -0.742091930 [177,] -0.720187566 -3.859523351 [178,] 3.139796997 -0.720187566 [179,] -2.242295339 3.139796997 [180,] 1.620018313 -2.242295339 [181,] 0.371261167 1.620018313 [182,] 1.409467946 0.371261167 [183,] 0.174192188 1.409467946 [184,] 2.925390297 0.174192188 [185,] -0.658668420 2.925390297 [186,] 0.252170557 -0.658668420 [187,] 0.981228972 0.252170557 [188,] 1.418588572 0.981228972 [189,] -1.267244033 1.418588572 [190,] 0.044975068 -1.267244033 [191,] 0.296776964 0.044975068 [192,] 2.067788165 0.296776964 [193,] -1.826226114 2.067788165 [194,] 2.054461128 -1.826226114 [195,] -1.175695383 2.054461128 [196,] 0.142784728 -1.175695383 [197,] -0.291391431 0.142784728 [198,] -4.502613776 -0.291391431 [199,] -0.469687699 -4.502613776 [200,] -3.999021337 -0.469687699 [201,] 2.307332051 -3.999021337 [202,] 4.148089040 2.307332051 [203,] 1.440715166 4.148089040 [204,] 1.716719836 1.440715166 [205,] 1.679638586 1.716719836 [206,] -0.174743829 1.679638586 [207,] 3.956641333 -0.174743829 [208,] 0.487230921 3.956641333 [209,] 2.080016975 0.487230921 [210,] -3.149179405 2.080016975 [211,] 2.343759143 -3.149179405 [212,] -3.092581287 2.343759143 [213,] -3.581980473 -3.092581287 [214,] -1.920186130 -3.581980473 [215,] 2.660970888 -1.920186130 [216,] 2.814319654 2.660970888 [217,] 0.248298925 2.814319654 [218,] -4.269872906 0.248298925 [219,] 1.247968764 -4.269872906 [220,] -1.987967300 1.247968764 [221,] 3.834595438 -1.987967300 [222,] -2.961363641 3.834595438 [223,] 2.381000716 -2.961363641 [224,] -1.723696205 2.381000716 [225,] 3.394605750 -1.723696205 [226,] 7.288370626 3.394605750 [227,] -0.369224320 7.288370626 [228,] -4.126372882 -0.369224320 [229,] -1.538859042 -4.126372882 [230,] 1.206025938 -1.538859042 [231,] -3.034227041 1.206025938 [232,] 2.588241936 -3.034227041 [233,] -0.369046303 2.588241936 [234,] 3.310869033 -0.369046303 [235,] -1.291162666 3.310869033 [236,] 0.167148664 -1.291162666 [237,] 1.828999369 0.167148664 [238,] -2.909843605 1.828999369 [239,] -1.372360984 -2.909843605 [240,] -1.476078943 -1.372360984 [241,] -3.319656793 -1.476078943 [242,] 0.534610997 -3.319656793 [243,] -0.442701941 0.534610997 [244,] 1.436152996 -0.442701941 [245,] -1.320820044 1.436152996 [246,] 0.457322986 -1.320820044 [247,] 3.513610116 0.457322986 [248,] -1.135249843 3.513610116 [249,] 1.082306643 -1.135249843 [250,] 2.734344531 1.082306643 [251,] 0.376275248 2.734344531 [252,] 0.421413053 0.376275248 [253,] -0.003704616 0.421413053 [254,] -0.213610610 -0.003704616 [255,] -0.338134661 -0.213610610 [256,] -4.145224977 -0.338134661 [257,] -3.072122337 -4.145224977 [258,] 2.763772256 -3.072122337 [259,] -2.197283088 2.763772256 [260,] 1.897387084 -2.197283088 [261,] 3.043903035 1.897387084 [262,] -4.849927856 3.043903035 [263,] 1.417847142 -4.849927856 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.472003525 0.701829376 2 -3.609978018 2.472003525 3 -2.458089502 -3.609978018 4 1.314290988 -2.458089502 5 3.304637851 1.314290988 6 0.017460334 3.304637851 7 -1.002480342 0.017460334 8 0.421463234 -1.002480342 9 -0.019111433 0.421463234 10 1.776058796 -0.019111433 11 5.426248890 1.776058796 12 -4.427556257 5.426248890 13 2.297437236 -4.427556257 14 2.368861048 2.297437236 15 0.424712106 2.368861048 16 -0.768408355 0.424712106 17 0.682734269 -0.768408355 18 -0.578034454 0.682734269 19 2.387400678 -0.578034454 20 3.775565623 2.387400678 21 -3.258459909 3.775565623 22 0.018404773 -3.258459909 23 -1.736646615 0.018404773 24 1.349055528 -1.736646615 25 -6.258565135 1.349055528 26 1.058920083 -6.258565135 27 -0.165306732 1.058920083 28 0.105885628 -0.165306732 29 -3.011475120 0.105885628 30 1.196203137 -3.011475120 31 0.146903872 1.196203137 32 2.737302257 0.146903872 33 -0.303769214 2.737302257 34 -0.291760596 -0.303769214 35 2.258369560 -0.291760596 36 -4.074758077 2.258369560 37 0.845650695 -4.074758077 38 2.312393160 0.845650695 39 -1.365359938 2.312393160 40 0.309168585 -1.365359938 41 1.656128973 0.309168585 42 2.199372426 1.656128973 43 -0.985906814 2.199372426 44 0.973108148 -0.985906814 45 -2.524618611 0.973108148 46 1.215906963 -2.524618611 47 0.518812862 1.215906963 48 2.344593133 0.518812862 49 -1.337673612 2.344593133 50 1.673326374 -1.337673612 51 0.219784658 1.673326374 52 -2.194988522 0.219784658 53 -1.751651357 -2.194988522 54 -1.029833438 -1.751651357 55 0.371264019 -1.029833438 56 1.448284921 0.371264019 57 1.056717444 1.448284921 58 -2.367846938 1.056717444 59 -2.296962230 -2.367846938 60 -4.617695045 -2.296962230 61 -1.687524836 -4.617695045 62 -3.734218012 -1.687524836 63 1.238102198 -3.734218012 64 1.881369132 1.238102198 65 -4.938957930 1.881369132 66 -3.144247817 -4.938957930 67 -2.496134990 -3.144247817 68 1.713788194 -2.496134990 69 0.880667809 1.713788194 70 0.666012284 0.880667809 71 3.130500260 0.666012284 72 -0.062430205 3.130500260 73 0.605825066 -0.062430205 74 -1.874539845 0.605825066 75 -3.003726251 -1.874539845 76 2.877385291 -3.003726251 77 -1.006771549 2.877385291 78 1.064080779 -1.006771549 79 -0.005715407 1.064080779 80 0.378733935 -0.005715407 81 0.841897847 0.378733935 82 1.886736609 0.841897847 83 0.983140443 1.886736609 84 -0.483028222 0.983140443 85 0.705107889 -0.483028222 86 0.075821060 0.705107889 87 0.115254334 0.075821060 88 -6.816908510 0.115254334 89 3.297370654 -6.816908510 90 0.005114525 3.297370654 91 0.484571685 0.005114525 92 -0.457776958 0.484571685 93 -1.018644646 -0.457776958 94 1.237135549 -1.018644646 95 -1.896065925 1.237135549 96 -0.532855141 -1.896065925 97 2.337813058 -0.532855141 98 0.914877184 2.337813058 99 2.705617335 0.914877184 100 -2.193461577 2.705617335 101 0.647198635 -2.193461577 102 -2.423484371 0.647198635 103 0.463551736 -2.423484371 104 -4.391731395 0.463551736 105 1.537384777 -4.391731395 106 0.898466390 1.537384777 107 -2.935146852 0.898466390 108 -2.229388889 -2.935146852 109 0.287172544 -2.229388889 110 -2.936020854 0.287172544 111 -2.195727196 -2.936020854 112 2.622559594 -2.195727196 113 4.253167260 2.622559594 114 0.930517728 4.253167260 115 1.471813144 0.930517728 116 0.440918414 1.471813144 117 0.671715613 0.440918414 118 -1.117808166 0.671715613 119 -0.399780761 -1.117808166 120 0.049334621 -0.399780761 121 0.353296945 0.049334621 122 0.861298339 0.353296945 123 1.266988913 0.861298339 124 -1.107161178 1.266988913 125 2.029355276 -1.107161178 126 2.622537089 2.029355276 127 4.610958312 2.622537089 128 1.699423445 4.610958312 129 -0.792201627 1.699423445 130 -3.498536321 -0.792201627 131 0.580273615 -3.498536321 132 2.337022333 0.580273615 133 1.267957486 2.337022333 134 1.644063842 1.267957486 135 -1.086046261 1.644063842 136 0.884525042 -1.086046261 137 -0.951375008 0.884525042 138 2.136047018 -0.951375008 139 0.064827800 2.136047018 140 2.257384027 0.064827800 141 1.559269023 2.257384027 142 -0.568801433 1.559269023 143 1.123323243 -0.568801433 144 1.854840624 1.123323243 145 1.929195008 1.854840624 146 -1.575107802 1.929195008 147 -1.803787282 -1.575107802 148 -4.430423651 -1.803787282 149 2.026392560 -4.430423651 150 -1.091539148 2.026392560 151 2.226319392 -1.091539148 152 -1.982686246 2.226319392 153 -4.402314016 -1.982686246 154 -0.269550316 -4.402314016 155 0.474499368 -0.269550316 156 0.736310181 0.474499368 157 4.827597470 0.736310181 158 -1.375853591 4.827597470 159 -1.971157699 -1.375853591 160 -0.171282391 -1.971157699 161 -2.646560795 -0.171282391 162 0.414653830 -2.646560795 163 1.689225709 0.414653830 164 -3.572281526 1.689225709 165 -0.525693497 -3.572281526 166 0.394216845 -0.525693497 167 -0.708296703 0.394216845 168 -4.790107822 -0.708296703 169 -4.518275687 -4.790107822 170 -1.184929845 -4.518275687 171 2.412880358 -1.184929845 172 -4.524405550 2.412880358 173 1.304879625 -4.524405550 174 3.123284725 1.304879625 175 -0.742091930 3.123284725 176 -3.859523351 -0.742091930 177 -0.720187566 -3.859523351 178 3.139796997 -0.720187566 179 -2.242295339 3.139796997 180 1.620018313 -2.242295339 181 0.371261167 1.620018313 182 1.409467946 0.371261167 183 0.174192188 1.409467946 184 2.925390297 0.174192188 185 -0.658668420 2.925390297 186 0.252170557 -0.658668420 187 0.981228972 0.252170557 188 1.418588572 0.981228972 189 -1.267244033 1.418588572 190 0.044975068 -1.267244033 191 0.296776964 0.044975068 192 2.067788165 0.296776964 193 -1.826226114 2.067788165 194 2.054461128 -1.826226114 195 -1.175695383 2.054461128 196 0.142784728 -1.175695383 197 -0.291391431 0.142784728 198 -4.502613776 -0.291391431 199 -0.469687699 -4.502613776 200 -3.999021337 -0.469687699 201 2.307332051 -3.999021337 202 4.148089040 2.307332051 203 1.440715166 4.148089040 204 1.716719836 1.440715166 205 1.679638586 1.716719836 206 -0.174743829 1.679638586 207 3.956641333 -0.174743829 208 0.487230921 3.956641333 209 2.080016975 0.487230921 210 -3.149179405 2.080016975 211 2.343759143 -3.149179405 212 -3.092581287 2.343759143 213 -3.581980473 -3.092581287 214 -1.920186130 -3.581980473 215 2.660970888 -1.920186130 216 2.814319654 2.660970888 217 0.248298925 2.814319654 218 -4.269872906 0.248298925 219 1.247968764 -4.269872906 220 -1.987967300 1.247968764 221 3.834595438 -1.987967300 222 -2.961363641 3.834595438 223 2.381000716 -2.961363641 224 -1.723696205 2.381000716 225 3.394605750 -1.723696205 226 7.288370626 3.394605750 227 -0.369224320 7.288370626 228 -4.126372882 -0.369224320 229 -1.538859042 -4.126372882 230 1.206025938 -1.538859042 231 -3.034227041 1.206025938 232 2.588241936 -3.034227041 233 -0.369046303 2.588241936 234 3.310869033 -0.369046303 235 -1.291162666 3.310869033 236 0.167148664 -1.291162666 237 1.828999369 0.167148664 238 -2.909843605 1.828999369 239 -1.372360984 -2.909843605 240 -1.476078943 -1.372360984 241 -3.319656793 -1.476078943 242 0.534610997 -3.319656793 243 -0.442701941 0.534610997 244 1.436152996 -0.442701941 245 -1.320820044 1.436152996 246 0.457322986 -1.320820044 247 3.513610116 0.457322986 248 -1.135249843 3.513610116 249 1.082306643 -1.135249843 250 2.734344531 1.082306643 251 0.376275248 2.734344531 252 0.421413053 0.376275248 253 -0.003704616 0.421413053 254 -0.213610610 -0.003704616 255 -0.338134661 -0.213610610 256 -4.145224977 -0.338134661 257 -3.072122337 -4.145224977 258 2.763772256 -3.072122337 259 -2.197283088 2.763772256 260 1.897387084 -2.197283088 261 3.043903035 1.897387084 262 -4.849927856 3.043903035 263 1.417847142 -4.849927856 > 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/7fcrq1384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8zsje1384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9b4dh1384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: In model.matrix.default(object, data = list(Happiness = c(14, 18, : the response appeared on the right-hand side and was dropped 2: In model.matrix.default(object, data = list(Happiness = c(14, 18, : problem with term 5 in model.matrix: no columns are assigned > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/103ca51384961277.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } Error in mysum$coefficients[i, 1] : subscript out of bounds Execution halted