R version 2.12.1 (2010-12-16) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,0 + ,258 + ,0 + ,14 + ,32 + ,0 + ,9 + ,0 + ,0 + ,259 + ,0 + ,13 + ,34 + ,0 + ,15 + ,0 + ,0 + ,260 + ,0 + ,4 + ,30 + ,0 + ,10 + ,0 + ,0 + ,261 + ,0 + ,15 + ,30 + ,0 + ,14 + ,0 + ,0 + ,262 + ,0 + ,11 + ,38 + ,0 + ,15 + ,0 + ,0 + ,263 + ,0 + ,11 + ,36 + ,0 + ,7 + ,0 + ,0 + ,264 + ,0 + ,14 + ,32 + ,0 + ,14 + ,0) + ,dim=c(8 + ,264) + ,dimnames=list(c('Pop' + ,'t' + ,'Pop_t' + ,'Doorzettingsvermogen' + ,'Zelfstandig' + ,'Zelfstandig_p' + ,'Stressbestendig' + ,'Stressbestendig_p') + ,1:264)) > y <- array(NA,dim=c(8,264),dimnames=list(c('Pop','t','Pop_t','Doorzettingsvermogen','Zelfstandig','Zelfstandig_p','Stressbestendig','Stressbestendig_p'),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 = '4' > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Doorzettingsvermogen Pop t Pop_t Zelfstandig Zelfstandig_p 1 13 1 1 1 38 38 2 16 1 2 2 32 32 3 19 1 3 3 35 35 4 15 1 4 4 33 33 5 14 1 5 5 37 37 6 13 1 6 6 29 29 7 19 1 7 7 31 31 8 15 1 8 8 36 36 9 14 1 9 9 35 35 10 15 1 10 10 38 38 11 16 1 11 11 31 31 12 16 1 12 12 34 34 13 16 1 13 13 35 35 14 16 1 14 14 38 38 15 17 1 15 15 37 37 16 15 1 16 16 33 33 17 15 1 17 17 32 32 18 20 1 18 18 38 38 19 18 1 19 19 38 38 20 16 1 20 20 32 32 21 16 1 21 21 33 33 22 16 1 22 22 31 31 23 19 1 23 23 38 38 24 16 1 24 24 39 39 25 17 1 25 25 32 32 26 17 1 26 26 32 32 27 16 1 27 27 35 35 28 15 1 28 28 37 37 29 16 1 29 29 33 33 30 14 1 30 30 33 33 31 15 1 31 31 31 31 32 12 1 32 32 32 32 33 14 1 33 33 31 31 34 16 1 34 34 37 37 35 14 1 35 35 30 30 36 10 1 36 36 33 33 37 10 1 37 37 31 31 38 14 1 38 38 33 33 39 16 1 39 39 31 31 40 16 1 40 40 33 33 41 16 1 41 41 32 32 42 14 1 42 42 33 33 43 20 1 43 43 32 32 44 14 1 44 44 33 33 45 14 1 45 45 28 28 46 11 1 46 46 35 35 47 14 1 47 47 39 39 48 15 1 48 48 34 34 49 16 1 49 49 38 38 50 14 1 50 50 32 32 51 16 1 51 51 38 38 52 14 1 52 52 30 30 53 12 1 53 53 33 33 54 16 1 54 54 38 38 55 9 1 55 55 32 32 56 14 1 56 56 35 35 57 16 1 57 57 34 34 58 16 1 58 58 34 34 59 15 1 59 59 36 36 60 16 1 60 60 34 34 61 12 1 61 61 28 28 62 16 1 62 62 34 34 63 16 1 63 63 35 35 64 14 1 64 64 35 35 65 16 1 65 65 31 31 66 17 1 66 66 37 37 67 18 1 67 67 35 35 68 18 1 68 68 27 27 69 12 1 69 69 40 40 70 16 1 70 70 37 37 71 10 1 71 71 36 36 72 14 1 72 72 38 38 73 18 1 73 73 39 39 74 18 1 74 74 41 41 75 16 1 75 75 27 27 76 17 1 76 76 30 30 77 16 1 77 77 37 37 78 16 1 78 78 31 31 79 13 1 79 79 31 31 80 16 1 80 80 27 27 81 16 1 81 81 36 36 82 16 1 82 82 37 37 83 15 1 83 83 33 33 84 15 1 84 84 34 34 85 16 1 85 85 31 31 86 14 1 86 86 39 39 87 16 1 87 87 34 34 88 16 1 88 88 32 32 89 15 1 89 89 33 33 90 12 1 90 90 36 36 91 17 1 91 91 32 32 92 16 1 92 92 41 41 93 15 1 93 93 28 28 94 13 1 94 94 30 30 95 16 1 95 95 36 36 96 16 1 96 96 35 35 97 16 1 97 97 31 31 98 16 1 98 98 34 34 99 14 1 99 99 36 36 100 16 1 100 100 36 36 101 16 1 101 101 35 35 102 20 1 102 102 37 37 103 15 1 103 103 28 28 104 16 1 104 104 39 39 105 13 1 105 105 32 32 106 17 1 106 106 35 35 107 16 1 107 107 39 39 108 16 1 108 108 35 35 109 12 1 109 109 42 42 110 16 1 110 110 34 34 111 16 1 111 111 33 33 112 17 1 112 112 41 41 113 13 1 113 113 33 33 114 12 1 114 114 34 34 115 18 1 115 115 32 32 116 14 1 116 116 40 40 117 14 1 117 117 40 40 118 13 1 118 118 35 35 119 16 1 119 119 36 36 120 13 1 120 120 37 37 121 16 1 121 121 27 27 122 13 1 122 122 39 39 123 16 1 123 123 38 38 124 15 1 124 124 31 31 125 16 1 125 125 33 33 126 15 1 126 126 32 32 127 17 1 127 127 39 39 128 15 1 128 128 36 36 129 12 1 129 129 33 33 130 16 1 130 130 33 33 131 10 1 131 131 32 32 132 16 1 132 132 37 37 133 12 1 133 133 30 30 134 14 1 134 134 38 38 135 15 1 135 135 29 29 136 13 1 136 136 22 22 137 15 1 137 137 35 35 138 11 1 138 138 35 35 139 12 1 139 139 34 34 140 11 1 140 140 35 35 141 16 1 141 141 34 34 142 15 1 142 142 37 37 143 17 1 143 143 35 35 144 16 1 144 144 23 23 145 10 1 145 145 31 31 146 18 1 146 146 27 27 147 13 1 147 147 36 36 148 16 1 148 148 31 31 149 13 1 149 149 32 32 150 10 1 150 150 39 39 151 15 1 151 151 37 37 152 16 1 152 152 38 38 153 16 1 153 153 39 39 154 14 1 154 154 34 34 155 10 1 155 155 31 31 156 17 1 156 156 32 32 157 13 1 157 157 37 37 158 15 1 158 158 36 36 159 16 1 159 159 32 32 160 12 1 160 160 38 38 161 13 1 161 161 36 36 162 13 0 162 0 26 0 163 12 0 163 0 26 0 164 17 0 164 0 33 0 165 15 0 165 0 39 0 166 10 0 166 0 30 0 167 14 0 167 0 33 0 168 11 0 168 0 25 0 169 13 0 169 0 38 0 170 16 0 170 0 37 0 171 12 0 171 0 31 0 172 16 0 172 0 37 0 173 12 0 173 0 35 0 174 9 0 174 0 25 0 175 12 0 175 0 28 0 176 15 0 176 0 35 0 177 12 0 177 0 33 0 178 12 0 178 0 30 0 179 14 0 179 0 31 0 180 12 0 180 0 37 0 181 16 0 181 0 36 0 182 11 0 182 0 30 0 183 19 0 183 0 36 0 184 15 0 184 0 32 0 185 8 0 185 0 28 0 186 16 0 186 0 36 0 187 17 0 187 0 34 0 188 12 0 188 0 31 0 189 11 0 189 0 28 0 190 11 0 190 0 36 0 191 14 0 191 0 36 0 192 16 0 192 0 40 0 193 12 0 193 0 33 0 194 16 0 194 0 37 0 195 13 0 195 0 32 0 196 15 0 196 0 38 0 197 16 0 197 0 31 0 198 16 0 198 0 37 0 199 14 0 199 0 33 0 200 16 0 200 0 32 0 201 16 0 201 0 30 0 202 14 0 202 0 30 0 203 11 0 203 0 31 0 204 12 0 204 0 32 0 205 15 0 205 0 34 0 206 15 0 206 0 36 0 207 16 0 207 0 37 0 208 16 0 208 0 36 0 209 11 0 209 0 33 0 210 15 0 210 0 33 0 211 12 0 211 0 33 0 212 12 0 212 0 44 0 213 15 0 213 0 39 0 214 15 0 214 0 32 0 215 16 0 215 0 35 0 216 14 0 216 0 25 0 217 17 0 217 0 35 0 218 14 0 218 0 34 0 219 13 0 219 0 35 0 220 15 0 220 0 39 0 221 13 0 221 0 33 0 222 14 0 222 0 36 0 223 15 0 223 0 32 0 224 12 0 224 0 32 0 225 13 0 225 0 36 0 226 8 0 226 0 36 0 227 14 0 227 0 32 0 228 14 0 228 0 34 0 229 11 0 229 0 33 0 230 12 0 230 0 35 0 231 13 0 231 0 30 0 232 10 0 232 0 38 0 233 16 0 233 0 34 0 234 18 0 234 0 33 0 235 13 0 235 0 32 0 236 11 0 236 0 31 0 237 4 0 237 0 30 0 238 13 0 238 0 27 0 239 16 0 239 0 31 0 240 10 0 240 0 30 0 241 12 0 241 0 32 0 242 12 0 242 0 35 0 243 10 0 243 0 28 0 244 13 0 244 0 33 0 245 15 0 245 0 31 0 246 12 0 246 0 35 0 247 14 0 247 0 35 0 248 10 0 248 0 32 0 249 12 0 249 0 21 0 250 12 0 250 0 20 0 251 11 0 251 0 34 0 252 10 0 252 0 32 0 253 12 0 253 0 34 0 254 16 0 254 0 32 0 255 12 0 255 0 33 0 256 14 0 256 0 33 0 257 16 0 257 0 37 0 258 14 0 258 0 32 0 259 13 0 259 0 34 0 260 4 0 260 0 30 0 261 15 0 261 0 30 0 262 11 0 262 0 38 0 263 11 0 263 0 36 0 264 14 0 264 0 32 0 Stressbestendig Stressbestendig_p 1 14 14 2 18 18 3 11 11 4 12 12 5 16 16 6 18 18 7 14 14 8 14 14 9 15 15 10 15 15 11 17 17 12 19 19 13 10 10 14 16 16 15 18 18 16 14 14 17 14 14 18 17 17 19 14 14 20 16 16 21 18 18 22 11 11 23 14 14 24 12 12 25 17 17 26 9 9 27 16 16 28 14 14 29 15 15 30 11 11 31 16 16 32 13 13 33 17 17 34 15 15 35 14 14 36 16 16 37 9 9 38 15 15 39 17 17 40 13 13 41 15 15 42 16 16 43 16 16 44 12 12 45 15 15 46 11 11 47 15 15 48 15 15 49 17 17 50 13 13 51 16 16 52 14 14 53 11 11 54 12 12 55 12 12 56 15 15 57 16 16 58 15 15 59 12 12 60 12 12 61 8 8 62 13 13 63 11 11 64 14 14 65 15 15 66 10 10 67 11 11 68 12 12 69 15 15 70 15 15 71 14 14 72 16 16 73 15 15 74 15 15 75 13 13 76 12 12 77 17 17 78 13 13 79 15 15 80 13 13 81 15 15 82 15 15 83 16 16 84 15 15 85 14 14 86 15 15 87 14 14 88 13 13 89 7 7 90 17 17 91 13 13 92 15 15 93 14 14 94 13 13 95 16 16 96 12 12 97 14 14 98 17 17 99 15 15 100 17 17 101 12 12 102 16 16 103 11 11 104 15 15 105 9 9 106 16 16 107 15 15 108 10 10 109 10 10 110 15 15 111 11 11 112 13 13 113 14 14 114 18 18 115 16 16 116 14 14 117 14 14 118 14 14 119 14 14 120 12 12 121 14 14 122 15 15 123 15 15 124 15 15 125 13 13 126 17 17 127 17 17 128 19 19 129 15 15 130 13 13 131 9 9 132 15 15 133 15 15 134 15 15 135 16 16 136 11 11 137 14 14 138 11 11 139 15 15 140 13 13 141 15 15 142 16 16 143 14 14 144 15 15 145 16 16 146 16 16 147 11 11 148 12 12 149 9 9 150 16 16 151 13 13 152 16 16 153 12 12 154 9 9 155 13 13 156 13 13 157 14 14 158 19 19 159 13 13 160 12 12 161 13 13 162 10 0 163 14 0 164 16 0 165 10 0 166 11 0 167 14 0 168 12 0 169 9 0 170 9 0 171 11 0 172 16 0 173 9 0 174 13 0 175 16 0 176 13 0 177 9 0 178 12 0 179 16 0 180 11 0 181 14 0 182 13 0 183 15 0 184 14 0 185 16 0 186 13 0 187 14 0 188 15 0 189 13 0 190 11 0 191 11 0 192 14 0 193 15 0 194 11 0 195 15 0 196 12 0 197 14 0 198 14 0 199 8 0 200 13 0 201 9 0 202 15 0 203 17 0 204 13 0 205 15 0 206 15 0 207 14 0 208 16 0 209 13 0 210 16 0 211 9 0 212 16 0 213 11 0 214 10 0 215 11 0 216 15 0 217 17 0 218 14 0 219 8 0 220 15 0 221 11 0 222 16 0 223 10 0 224 15 0 225 9 0 226 16 0 227 19 0 228 12 0 229 8 0 230 11 0 231 14 0 232 9 0 233 15 0 234 13 0 235 16 0 236 11 0 237 12 0 238 13 0 239 10 0 240 11 0 241 12 0 242 8 0 243 12 0 244 12 0 245 15 0 246 11 0 247 13 0 248 14 0 249 10 0 250 12 0 251 15 0 252 13 0 253 13 0 254 13 0 255 12 0 256 12 0 257 9 0 258 9 0 259 15 0 260 10 0 261 14 0 262 15 0 263 7 0 264 14 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop t Pop_t 7.059653 5.739575 -0.013936 0.005345 Zelfstandig Zelfstandig_p Stressbestendig Stressbestendig_p 0.216451 -0.191550 0.156511 -0.013882 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.128 -1.244 0.265 1.366 5.024 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.059653 2.784902 2.535 0.011842 * Pop 5.739575 3.372763 1.702 0.090018 . t -0.013936 0.007309 -1.907 0.057686 . Pop_t 0.005345 0.008215 0.651 0.515862 Zelfstandig 0.216451 0.056610 3.824 0.000165 *** Zelfstandig_p -0.191550 0.075243 -2.546 0.011491 * Stressbestendig 0.156511 0.086322 1.813 0.070987 . Stressbestendig_p -0.013882 0.114746 -0.121 0.903800 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.197 on 256 degrees of freedom Multiple R-squared: 0.2214, Adjusted R-squared: 0.2001 F-statistic: 10.4 on 7 and 256 DF, p-value: 1.748e-11 > 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,] 0.8504721314 0.2990557371 0.1495279 [2,] 0.8103210256 0.3793579489 0.1896790 [3,] 0.7524979525 0.4950040951 0.2475020 [4,] 0.6747179141 0.6505641719 0.3252821 [5,] 0.6346776511 0.7306446977 0.3653223 [6,] 0.5933333665 0.8133332670 0.4066666 [7,] 0.5315741374 0.9368517251 0.4684259 [8,] 0.7091620339 0.5816759321 0.2908380 [9,] 0.6438708833 0.7122582334 0.3561291 [10,] 0.5707961727 0.8584076545 0.4292038 [11,] 0.4928301609 0.9856603219 0.5071698 [12,] 0.4285214595 0.8570429190 0.5714785 [13,] 0.3973277295 0.7946554591 0.6026723 [14,] 0.3723786310 0.7447572621 0.6276214 [15,] 0.3054808136 0.6109616273 0.6945192 [16,] 0.2506433854 0.5012867708 0.7493566 [17,] 0.2124850413 0.4249700826 0.7875150 [18,] 0.2168872442 0.4337744883 0.7831128 [19,] 0.1754674861 0.3509349723 0.8245325 [20,] 0.2001414357 0.4002828714 0.7998586 [21,] 0.1682141973 0.3364283947 0.8317858 [22,] 0.2774001841 0.5548003682 0.7225998 [23,] 0.2482824383 0.4965648766 0.7517176 [24,] 0.2026604237 0.4053208473 0.7973396 [25,] 0.1716011407 0.3432022814 0.8283989 [26,] 0.4037720297 0.8075440593 0.5962280 [27,] 0.5536693212 0.8926613576 0.4463307 [28,] 0.5028538897 0.9942922206 0.4971461 [29,] 0.4833851474 0.9667702947 0.5166149 [30,] 0.4546121385 0.9092242770 0.5453879 [31,] 0.4242590791 0.8485181582 0.5757409 [32,] 0.3821457307 0.7642914614 0.6178543 [33,] 0.6046919857 0.7906160287 0.3953080 [34,] 0.5608704782 0.8782590436 0.4391295 [35,] 0.5142742900 0.9714514199 0.4857257 [36,] 0.6011257569 0.7977484862 0.3988742 [37,] 0.5726073863 0.8547852274 0.4273926 [38,] 0.5258538088 0.9482923825 0.4741462 [39,] 0.4797244314 0.9594488629 0.5202756 [40,] 0.4347816310 0.8695632619 0.5652184 [41,] 0.3915041019 0.7830082037 0.6084959 [42,] 0.3504324841 0.7008649682 0.6495675 [43,] 0.3463409918 0.6926819836 0.6536590 [44,] 0.3184884062 0.6369768125 0.6815116 [45,] 0.5046464763 0.9907070475 0.4953535 [46,] 0.4673376573 0.9346753145 0.5326623 [47,] 0.4421737554 0.8843475109 0.5578262 [48,] 0.4196798256 0.8393596512 0.5803202 [49,] 0.3824807177 0.7649614355 0.6175193 [50,] 0.3736247984 0.7472495967 0.6263752 [51,] 0.3518665537 0.7037331074 0.6481334 [52,] 0.3358064701 0.6716129402 0.6641935 [53,] 0.3218162512 0.6436325024 0.6781837 [54,] 0.2921024903 0.5842049807 0.7078975 [55,] 0.2791434455 0.5582868911 0.7208566 [56,] 0.2873402012 0.5746804024 0.7126598 [57,] 0.3403057003 0.6806114006 0.6596943 [58,] 0.4505757903 0.9011515806 0.5494242 [59,] 0.5295933600 0.9408132799 0.4704066 [60,] 0.4919856391 0.9839712782 0.5080144 [61,] 0.6632421052 0.6735157896 0.3367579 [62,] 0.6414679041 0.7170641918 0.3585321 [63,] 0.6586523070 0.6826953861 0.3413477 [64,] 0.6624003002 0.6751993995 0.3375997 [65,] 0.6530081405 0.6939837189 0.3469919 [66,] 0.6624136753 0.6751726494 0.3375863 [67,] 0.6261087198 0.7477825604 0.3738913 [68,] 0.5996325006 0.8007349988 0.4003675 [69,] 0.5972623121 0.8054753759 0.4027377 [70,] 0.5769891991 0.8460216017 0.4230108 [71,] 0.5409693015 0.9180613969 0.4590307 [72,] 0.5038712234 0.9922575532 0.4961288 [73,] 0.4671885632 0.9343771263 0.5328114 [74,] 0.4300779503 0.8601559007 0.5699220 [75,] 0.4003162939 0.8006325878 0.5996837 [76,] 0.3822321195 0.7644642390 0.6177679 [77,] 0.3510849820 0.7021699640 0.6489150 [78,] 0.3237355210 0.6474710421 0.6762645 [79,] 0.2934614018 0.5869228036 0.7065386 [80,] 0.3553928188 0.7107856377 0.6446072 [81,] 0.3504839251 0.7009678502 0.6495161 [82,] 0.3167980250 0.6335960499 0.6832020 [83,] 0.2850935605 0.5701871210 0.7149064 [84,] 0.2786554481 0.5573108962 0.7213446 [85,] 0.2497976184 0.4995952368 0.7502024 [86,] 0.2266046417 0.4532092834 0.7733954 [87,] 0.2043957423 0.4087914846 0.7956043 [88,] 0.1802259857 0.3604519715 0.8197740 [89,] 0.1666290955 0.3332581910 0.8333709 [90,] 0.1451678775 0.2903357550 0.8548321 [91,] 0.1289066673 0.2578133347 0.8710933 [92,] 0.1977177487 0.3954354975 0.8022823 [93,] 0.1736777647 0.3473555293 0.8263222 [94,] 0.1521837897 0.3043675794 0.8478162 [95,] 0.1402645083 0.2805290166 0.8597355 [96,] 0.1295175694 0.2590351387 0.8704824 [97,] 0.1126557914 0.2253115828 0.8873442 [98,] 0.1033996455 0.2067992909 0.8966004 [99,] 0.1161510176 0.2323020351 0.8838490 [100,] 0.1012865744 0.2025731489 0.8987134 [101,] 0.0930208589 0.1860417178 0.9069791 [102,] 0.0932409220 0.1864818439 0.9067591 [103,] 0.0903639934 0.1807279868 0.9096360 [104,] 0.1183260659 0.2366521319 0.8816739 [105,] 0.1304308798 0.2608617595 0.8695691 [106,] 0.1176913663 0.2353827327 0.8823086 [107,] 0.1052767924 0.2105535847 0.8947232 [108,] 0.1007011834 0.2014023667 0.8992988 [109,] 0.0905753009 0.1811506018 0.9094247 [110,] 0.0836390636 0.1672781271 0.9163609 [111,] 0.0753301582 0.1506603164 0.9246698 [112,] 0.0730679913 0.1461359826 0.9269320 [113,] 0.0646701368 0.1293402737 0.9353299 [114,] 0.0542375387 0.1084750774 0.9457625 [115,] 0.0505155358 0.1010310715 0.9494845 [116,] 0.0419269648 0.0838539297 0.9580730 [117,] 0.0415416630 0.0830833261 0.9584583 [118,] 0.0346906759 0.0693813518 0.9653093 [119,] 0.0378229825 0.0756459650 0.9621770 [120,] 0.0364412886 0.0728825771 0.9635587 [121,] 0.0500489860 0.1000979721 0.9499510 [122,] 0.0471186052 0.0942372103 0.9528814 [123,] 0.0493605857 0.0987211714 0.9506394 [124,] 0.0423332798 0.0846665597 0.9576667 [125,] 0.0350568130 0.0701136261 0.9649432 [126,] 0.0304370632 0.0608741263 0.9695629 [127,] 0.0260154629 0.0520309257 0.9739845 [128,] 0.0297512060 0.0595024120 0.9702488 [129,] 0.0313393934 0.0626787867 0.9686606 [130,] 0.0407387247 0.0814774494 0.9592613 [131,] 0.0357957257 0.0715914515 0.9642043 [132,] 0.0295287818 0.0590575636 0.9704712 [133,] 0.0332406311 0.0664812623 0.9667594 [134,] 0.0293694686 0.0587389371 0.9706305 [135,] 0.0600611006 0.1201222013 0.9399389 [136,] 0.0667351383 0.1334702765 0.9332649 [137,] 0.0577523607 0.1155047214 0.9422476 [138,] 0.0545259018 0.1090518035 0.9454741 [139,] 0.0461874267 0.0923748533 0.9538126 [140,] 0.0871642900 0.1743285800 0.9128357 [141,] 0.0744130531 0.1488261062 0.9255869 [142,] 0.0643307780 0.1286615561 0.9356692 [143,] 0.0627957888 0.1255915777 0.9372042 [144,] 0.0572561123 0.1145122247 0.9427439 [145,] 0.1031420603 0.2062841205 0.8968579 [146,] 0.0996538413 0.1993076826 0.9003462 [147,] 0.0875241509 0.1750483018 0.9124758 [148,] 0.0738746679 0.1477493359 0.9261253 [149,] 0.0655066085 0.1310132170 0.9344934 [150,] 0.0589767681 0.1179535362 0.9410232 [151,] 0.0501194740 0.1002389479 0.9498805 [152,] 0.0422905563 0.0845811125 0.9577094 [153,] 0.0348660863 0.0697321726 0.9651339 [154,] 0.0331351758 0.0662703517 0.9668648 [155,] 0.0269437568 0.0538875136 0.9730562 [156,] 0.0274617933 0.0549235865 0.9725382 [157,] 0.0232524907 0.0465049813 0.9767475 [158,] 0.0208127664 0.0416255329 0.9791872 [159,] 0.0175488967 0.0350977934 0.9824511 [160,] 0.0182211306 0.0364422611 0.9817789 [161,] 0.0150047860 0.0300095720 0.9849952 [162,] 0.0120715558 0.0241431117 0.9879284 [163,] 0.0103306586 0.0206613172 0.9896693 [164,] 0.0114317202 0.0228634403 0.9885683 [165,] 0.0096282091 0.0192564182 0.9903718 [166,] 0.0079853412 0.0159706823 0.9920147 [167,] 0.0067927159 0.0135854317 0.9932073 [168,] 0.0057150148 0.0114300296 0.9942850 [169,] 0.0044193531 0.0088387061 0.9955806 [170,] 0.0044149391 0.0088298782 0.9955851 [171,] 0.0037477057 0.0074954113 0.9962523 [172,] 0.0036919863 0.0073839726 0.9963080 [173,] 0.0067725871 0.0135451742 0.9932274 [174,] 0.0056127547 0.0112255094 0.9943872 [175,] 0.0176944705 0.0353889410 0.9823055 [176,] 0.0155322624 0.0310645247 0.9844677 [177,] 0.0173613553 0.0347227107 0.9826386 [178,] 0.0160883833 0.0321767666 0.9839116 [179,] 0.0163546277 0.0327092554 0.9836454 [180,] 0.0207215143 0.0414430286 0.9792785 [181,] 0.0167168401 0.0334336802 0.9832832 [182,] 0.0133021044 0.0266042088 0.9866979 [183,] 0.0135898157 0.0271796314 0.9864102 [184,] 0.0124375375 0.0248750750 0.9875625 [185,] 0.0105037155 0.0210074311 0.9894963 [186,] 0.0080483315 0.0160966631 0.9919517 [187,] 0.0086068857 0.0172137713 0.9913931 [188,] 0.0069791396 0.0139582792 0.9930209 [189,] 0.0059296555 0.0118593110 0.9940703 [190,] 0.0059029021 0.0118058042 0.9940971 [191,] 0.0079548384 0.0159096768 0.9920452 [192,] 0.0059745644 0.0119491288 0.9940254 [193,] 0.0078886047 0.0157772093 0.9921114 [194,] 0.0070692767 0.0141385535 0.9929307 [195,] 0.0053209735 0.0106419470 0.9946790 [196,] 0.0039501928 0.0079003856 0.9960498 [197,] 0.0032526237 0.0065052475 0.9967474 [198,] 0.0026381389 0.0052762779 0.9973619 [199,] 0.0029893913 0.0059787825 0.9970106 [200,] 0.0022152112 0.0044304224 0.9977848 [201,] 0.0016977687 0.0033955374 0.9983022 [202,] 0.0032421532 0.0064843064 0.9967578 [203,] 0.0023833939 0.0047667878 0.9976166 [204,] 0.0021059667 0.0042119335 0.9978940 [205,] 0.0021583158 0.0043166316 0.9978417 [206,] 0.0016966323 0.0033932646 0.9983034 [207,] 0.0019106983 0.0038213965 0.9980893 [208,] 0.0013600681 0.0027201362 0.9986399 [209,] 0.0009558665 0.0019117330 0.9990441 [210,] 0.0007202225 0.0014404450 0.9992798 [211,] 0.0004880823 0.0009761646 0.9995119 [212,] 0.0003348793 0.0006697585 0.9996651 [213,] 0.0003774766 0.0007549532 0.9996225 [214,] 0.0002631397 0.0005262794 0.9997369 [215,] 0.0001849144 0.0003698287 0.9998151 [216,] 0.0012846066 0.0025692132 0.9987154 [217,] 0.0008467921 0.0016935842 0.9991532 [218,] 0.0006007349 0.0012014698 0.9993993 [219,] 0.0004057126 0.0008114252 0.9995943 [220,] 0.0002647232 0.0005294464 0.9997353 [221,] 0.0001613984 0.0003227968 0.9998386 [222,] 0.0001886588 0.0003773176 0.9998113 [223,] 0.0001825643 0.0003651285 0.9998174 [224,] 0.0010847266 0.0021694533 0.9989153 [225,] 0.0007073145 0.0014146291 0.9992927 [226,] 0.0004418380 0.0008836761 0.9995582 [227,] 0.0231808429 0.0463616858 0.9768192 [228,] 0.0164974413 0.0329948826 0.9835026 [229,] 0.0296484638 0.0592969276 0.9703515 [230,] 0.0249586716 0.0499173432 0.9750413 [231,] 0.0166798293 0.0333596586 0.9833202 [232,] 0.0106868909 0.0213737818 0.9893131 [233,] 0.0092927888 0.0185855776 0.9907072 [234,] 0.0055905191 0.0111810383 0.9944095 [235,] 0.0047243655 0.0094487311 0.9952756 [236,] 0.0026975623 0.0053951246 0.9973024 [237,] 0.0017095973 0.0034191947 0.9982904 [238,] 0.0014690038 0.0029380075 0.9985310 [239,] 0.0007708824 0.0015417649 0.9992291 [240,] 0.0004115050 0.0008230100 0.9995885 [241,] 0.0002951439 0.0005902878 0.9997049 [242,] 0.0003362187 0.0006724374 0.9996638 [243,] 0.0002364059 0.0004728118 0.9997636 > postscript(file="/var/www/rcomp/tmp/1m2ig1321986910.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/www/rcomp/tmp/24u8l1321986910.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3z9fe1321986910.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4svyk1321986910.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5npbn1321986910.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 264 Frequency = 1 1 2 3 4 5 6 -2.733665502 -0.146185050 3.786105280 -0.298130828 -1.959657311 -3.037117996 7 8 9 10 11 12 3.492186813 -0.623725410 -1.732862212 -0.798973048 0.098665573 -0.252702740 13 14 15 16 17 18 1.014646453 0.092763188 0.840997648 -0.480293382 -0.446801445 3.984499423 19 20 21 22 23 24 2.420976881 0.293714809 -0.007852117 1.048941680 3.455341855 0.724289882 25 26 27 28 29 30 1.194042288 2.343663437 0.279151433 -0.476801234 0.488764045 -0.932129759 31 32 33 34 35 36 -0.586880819 -3.175304054 -1.712327070 0.432117489 -1.242357675 -5.593725988 37 38 39 40 41 42 -4.536932191 -1.433914763 0.339220391 0.868525200 0.616759661 -1.542178527 43 44 45 46 47 48 4.491313410 -0.954481088 -1.249272592 -3.844471249 -1.505997732 -0.372903022 49 50 51 52 53 54 0.250827972 -1.020661671 0.410639197 -1.096306536 -2.734531158 1.006927880 55 56 57 58 59 60 -5.835076716 -1.329073767 0.561789432 0.713009413 0.099685485 1.158078115 61 62 63 64 65 66 -2.113411528 1.032631864 1.301579890 -1.117715081 0.847850198 2.420180972 67 68 69 70 71 72 3.335944864 3.401112917 -3.341891068 0.741402255 -5.082477070 -1.408944689 73 74 75 76 77 78 2.717374599 2.676164456 1.318622883 2.395140785 0.516283484 1.244793840 79 80 81 82 83 84 -2.031872393 1.361579101 0.860806627 0.844497177 -0.189937544 -0.063618255 85 86 87 88 89 90 1.162303806 -1.170939235 1.104784213 1.305805582 1.145268561 -3.347129657 91 92 93 94 95 96 2.331579312 0.830806839 0.305735835 -1.592845570 0.838455298 1.442462188 97 98 99 100 101 102 1.265398728 0.771401677 -0.984550990 0.738782778 1.485418405 4.873693309 103 104 105 106 107 108 0.819534484 0.983703148 -0.977628326 1.957859670 1.009476878 1.830814586 109 110 111 112 113 114 -2.334899024 1.159754076 1.763760965 2.287889185 -1.646942762 -3.233767165 115 116 117 118 119 120 3.109882942 -0.795473885 -0.786882642 -1.653787931 1.329902619 -1.401149355 121 122 123 124 125 126 1.571191346 -1.861654469 1.171837468 0.354733565 1.598780898 0.061757882 127 128 129 130 131 132 1.896044272 -0.305919881 -2.652111604 1.641737116 -3.754255995 1.274059353 133 134 135 136 137 138 -2.543044550 -0.733658854 0.356409892 -0.747550320 0.509445695 -3.054076847 139 140 141 142 143 144 -2.591099863 -3.322151836 1.426082624 0.217343050 2.560993156 1.725763982 145 146 147 148 149 150 -4.607479060 3.500714957 -1.001656349 1.988809624 -0.599613612 -4.763728389 151 152 153 154 155 156 0.722550456 1.278354791 1.832560294 0.393541219 -4.093680410 2.890010140 157 158 159 160 161 162 -1.368530822 -0.048182576 1.915783871 -2.082400308 -1.166636416 1.005198586 163 164 165 166 167 168 -0.606908951 2.578848954 0.233145519 -2.961370774 -0.066320127 -1.007754448 169 170 171 172 173 174 -1.338147273 1.892239983 -1.108140058 0.824535875 -1.633049177 -3.080647442 175 176 177 178 179 180 -1.185596795 0.782715943 -1.144402008 -0.950645797 0.220795738 -2.281418664 181 182 183 184 185 186 1.479435693 -2.051411449 4.350797383 1.387048387 -5.046233508 1.705628302 187 188 189 190 191 192 2.995955519 -1.497266337 -1.520955294 -2.925604450 0.088331879 0.766931599 193 194 195 196 197 198 -1.860486549 1.913689937 -0.616162964 0.568600701 2.784671587 1.499902352 199 200 201 202 203 204 1.318708189 2.766540612 3.839422661 0.914293191 -2.601243341 -1.177714073 205 206 207 208 209 210 1.090298468 0.671332942 1.625329310 1.542694633 -2.324483357 1.219920072 211 212 213 214 215 216 -0.670566834 -4.133167470 0.745578327 2.431182113 2.639254693 2.191656428 217 218 219 220 221 222 2.728061552 0.427981707 0.164532907 0.217088762 0.155774520 -0.262196766 223 224 225 226 227 228 2.556609071 -1.212009433 -0.124811014 -6.206451451 0.203755687 0.880366926 229 230 231 232 233 234 -1.263201952 -1.151700377 0.474957688 -3.460158568 2.480515670 5.023924859 235 236 237 238 239 240 -0.215220785 -1.202278696 -8.128402407 1.378375737 3.996041256 -1.930082454 241 242 243 244 245 246 -0.505558947 -0.514931534 -1.611882581 0.319799112 2.297104396 -0.928719119 247 248 249 250 251 252 0.772195277 -2.721026579 2.299913815 2.217279137 -2.268630414 -2.508770298 253 254 255 256 257 258 -0.927735824 3.519102359 -0.526901273 1.487035056 3.104700575 2.200891540 259 260 261 262 263 264 -0.157139785 -7.494844915 2.893047548 -2.981134508 -1.282208593 1.501954679 > postscript(file="/var/www/rcomp/tmp/6v12u1321986910.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.733665502 NA 1 -0.146185050 -2.733665502 2 3.786105280 -0.146185050 3 -0.298130828 3.786105280 4 -1.959657311 -0.298130828 5 -3.037117996 -1.959657311 6 3.492186813 -3.037117996 7 -0.623725410 3.492186813 8 -1.732862212 -0.623725410 9 -0.798973048 -1.732862212 10 0.098665573 -0.798973048 11 -0.252702740 0.098665573 12 1.014646453 -0.252702740 13 0.092763188 1.014646453 14 0.840997648 0.092763188 15 -0.480293382 0.840997648 16 -0.446801445 -0.480293382 17 3.984499423 -0.446801445 18 2.420976881 3.984499423 19 0.293714809 2.420976881 20 -0.007852117 0.293714809 21 1.048941680 -0.007852117 22 3.455341855 1.048941680 23 0.724289882 3.455341855 24 1.194042288 0.724289882 25 2.343663437 1.194042288 26 0.279151433 2.343663437 27 -0.476801234 0.279151433 28 0.488764045 -0.476801234 29 -0.932129759 0.488764045 30 -0.586880819 -0.932129759 31 -3.175304054 -0.586880819 32 -1.712327070 -3.175304054 33 0.432117489 -1.712327070 34 -1.242357675 0.432117489 35 -5.593725988 -1.242357675 36 -4.536932191 -5.593725988 37 -1.433914763 -4.536932191 38 0.339220391 -1.433914763 39 0.868525200 0.339220391 40 0.616759661 0.868525200 41 -1.542178527 0.616759661 42 4.491313410 -1.542178527 43 -0.954481088 4.491313410 44 -1.249272592 -0.954481088 45 -3.844471249 -1.249272592 46 -1.505997732 -3.844471249 47 -0.372903022 -1.505997732 48 0.250827972 -0.372903022 49 -1.020661671 0.250827972 50 0.410639197 -1.020661671 51 -1.096306536 0.410639197 52 -2.734531158 -1.096306536 53 1.006927880 -2.734531158 54 -5.835076716 1.006927880 55 -1.329073767 -5.835076716 56 0.561789432 -1.329073767 57 0.713009413 0.561789432 58 0.099685485 0.713009413 59 1.158078115 0.099685485 60 -2.113411528 1.158078115 61 1.032631864 -2.113411528 62 1.301579890 1.032631864 63 -1.117715081 1.301579890 64 0.847850198 -1.117715081 65 2.420180972 0.847850198 66 3.335944864 2.420180972 67 3.401112917 3.335944864 68 -3.341891068 3.401112917 69 0.741402255 -3.341891068 70 -5.082477070 0.741402255 71 -1.408944689 -5.082477070 72 2.717374599 -1.408944689 73 2.676164456 2.717374599 74 1.318622883 2.676164456 75 2.395140785 1.318622883 76 0.516283484 2.395140785 77 1.244793840 0.516283484 78 -2.031872393 1.244793840 79 1.361579101 -2.031872393 80 0.860806627 1.361579101 81 0.844497177 0.860806627 82 -0.189937544 0.844497177 83 -0.063618255 -0.189937544 84 1.162303806 -0.063618255 85 -1.170939235 1.162303806 86 1.104784213 -1.170939235 87 1.305805582 1.104784213 88 1.145268561 1.305805582 89 -3.347129657 1.145268561 90 2.331579312 -3.347129657 91 0.830806839 2.331579312 92 0.305735835 0.830806839 93 -1.592845570 0.305735835 94 0.838455298 -1.592845570 95 1.442462188 0.838455298 96 1.265398728 1.442462188 97 0.771401677 1.265398728 98 -0.984550990 0.771401677 99 0.738782778 -0.984550990 100 1.485418405 0.738782778 101 4.873693309 1.485418405 102 0.819534484 4.873693309 103 0.983703148 0.819534484 104 -0.977628326 0.983703148 105 1.957859670 -0.977628326 106 1.009476878 1.957859670 107 1.830814586 1.009476878 108 -2.334899024 1.830814586 109 1.159754076 -2.334899024 110 1.763760965 1.159754076 111 2.287889185 1.763760965 112 -1.646942762 2.287889185 113 -3.233767165 -1.646942762 114 3.109882942 -3.233767165 115 -0.795473885 3.109882942 116 -0.786882642 -0.795473885 117 -1.653787931 -0.786882642 118 1.329902619 -1.653787931 119 -1.401149355 1.329902619 120 1.571191346 -1.401149355 121 -1.861654469 1.571191346 122 1.171837468 -1.861654469 123 0.354733565 1.171837468 124 1.598780898 0.354733565 125 0.061757882 1.598780898 126 1.896044272 0.061757882 127 -0.305919881 1.896044272 128 -2.652111604 -0.305919881 129 1.641737116 -2.652111604 130 -3.754255995 1.641737116 131 1.274059353 -3.754255995 132 -2.543044550 1.274059353 133 -0.733658854 -2.543044550 134 0.356409892 -0.733658854 135 -0.747550320 0.356409892 136 0.509445695 -0.747550320 137 -3.054076847 0.509445695 138 -2.591099863 -3.054076847 139 -3.322151836 -2.591099863 140 1.426082624 -3.322151836 141 0.217343050 1.426082624 142 2.560993156 0.217343050 143 1.725763982 2.560993156 144 -4.607479060 1.725763982 145 3.500714957 -4.607479060 146 -1.001656349 3.500714957 147 1.988809624 -1.001656349 148 -0.599613612 1.988809624 149 -4.763728389 -0.599613612 150 0.722550456 -4.763728389 151 1.278354791 0.722550456 152 1.832560294 1.278354791 153 0.393541219 1.832560294 154 -4.093680410 0.393541219 155 2.890010140 -4.093680410 156 -1.368530822 2.890010140 157 -0.048182576 -1.368530822 158 1.915783871 -0.048182576 159 -2.082400308 1.915783871 160 -1.166636416 -2.082400308 161 1.005198586 -1.166636416 162 -0.606908951 1.005198586 163 2.578848954 -0.606908951 164 0.233145519 2.578848954 165 -2.961370774 0.233145519 166 -0.066320127 -2.961370774 167 -1.007754448 -0.066320127 168 -1.338147273 -1.007754448 169 1.892239983 -1.338147273 170 -1.108140058 1.892239983 171 0.824535875 -1.108140058 172 -1.633049177 0.824535875 173 -3.080647442 -1.633049177 174 -1.185596795 -3.080647442 175 0.782715943 -1.185596795 176 -1.144402008 0.782715943 177 -0.950645797 -1.144402008 178 0.220795738 -0.950645797 179 -2.281418664 0.220795738 180 1.479435693 -2.281418664 181 -2.051411449 1.479435693 182 4.350797383 -2.051411449 183 1.387048387 4.350797383 184 -5.046233508 1.387048387 185 1.705628302 -5.046233508 186 2.995955519 1.705628302 187 -1.497266337 2.995955519 188 -1.520955294 -1.497266337 189 -2.925604450 -1.520955294 190 0.088331879 -2.925604450 191 0.766931599 0.088331879 192 -1.860486549 0.766931599 193 1.913689937 -1.860486549 194 -0.616162964 1.913689937 195 0.568600701 -0.616162964 196 2.784671587 0.568600701 197 1.499902352 2.784671587 198 1.318708189 1.499902352 199 2.766540612 1.318708189 200 3.839422661 2.766540612 201 0.914293191 3.839422661 202 -2.601243341 0.914293191 203 -1.177714073 -2.601243341 204 1.090298468 -1.177714073 205 0.671332942 1.090298468 206 1.625329310 0.671332942 207 1.542694633 1.625329310 208 -2.324483357 1.542694633 209 1.219920072 -2.324483357 210 -0.670566834 1.219920072 211 -4.133167470 -0.670566834 212 0.745578327 -4.133167470 213 2.431182113 0.745578327 214 2.639254693 2.431182113 215 2.191656428 2.639254693 216 2.728061552 2.191656428 217 0.427981707 2.728061552 218 0.164532907 0.427981707 219 0.217088762 0.164532907 220 0.155774520 0.217088762 221 -0.262196766 0.155774520 222 2.556609071 -0.262196766 223 -1.212009433 2.556609071 224 -0.124811014 -1.212009433 225 -6.206451451 -0.124811014 226 0.203755687 -6.206451451 227 0.880366926 0.203755687 228 -1.263201952 0.880366926 229 -1.151700377 -1.263201952 230 0.474957688 -1.151700377 231 -3.460158568 0.474957688 232 2.480515670 -3.460158568 233 5.023924859 2.480515670 234 -0.215220785 5.023924859 235 -1.202278696 -0.215220785 236 -8.128402407 -1.202278696 237 1.378375737 -8.128402407 238 3.996041256 1.378375737 239 -1.930082454 3.996041256 240 -0.505558947 -1.930082454 241 -0.514931534 -0.505558947 242 -1.611882581 -0.514931534 243 0.319799112 -1.611882581 244 2.297104396 0.319799112 245 -0.928719119 2.297104396 246 0.772195277 -0.928719119 247 -2.721026579 0.772195277 248 2.299913815 -2.721026579 249 2.217279137 2.299913815 250 -2.268630414 2.217279137 251 -2.508770298 -2.268630414 252 -0.927735824 -2.508770298 253 3.519102359 -0.927735824 254 -0.526901273 3.519102359 255 1.487035056 -0.526901273 256 3.104700575 1.487035056 257 2.200891540 3.104700575 258 -0.157139785 2.200891540 259 -7.494844915 -0.157139785 260 2.893047548 -7.494844915 261 -2.981134508 2.893047548 262 -1.282208593 -2.981134508 263 1.501954679 -1.282208593 264 NA 1.501954679 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.146185050 -2.733665502 [2,] 3.786105280 -0.146185050 [3,] -0.298130828 3.786105280 [4,] -1.959657311 -0.298130828 [5,] -3.037117996 -1.959657311 [6,] 3.492186813 -3.037117996 [7,] -0.623725410 3.492186813 [8,] -1.732862212 -0.623725410 [9,] -0.798973048 -1.732862212 [10,] 0.098665573 -0.798973048 [11,] -0.252702740 0.098665573 [12,] 1.014646453 -0.252702740 [13,] 0.092763188 1.014646453 [14,] 0.840997648 0.092763188 [15,] -0.480293382 0.840997648 [16,] -0.446801445 -0.480293382 [17,] 3.984499423 -0.446801445 [18,] 2.420976881 3.984499423 [19,] 0.293714809 2.420976881 [20,] -0.007852117 0.293714809 [21,] 1.048941680 -0.007852117 [22,] 3.455341855 1.048941680 [23,] 0.724289882 3.455341855 [24,] 1.194042288 0.724289882 [25,] 2.343663437 1.194042288 [26,] 0.279151433 2.343663437 [27,] -0.476801234 0.279151433 [28,] 0.488764045 -0.476801234 [29,] -0.932129759 0.488764045 [30,] -0.586880819 -0.932129759 [31,] -3.175304054 -0.586880819 [32,] -1.712327070 -3.175304054 [33,] 0.432117489 -1.712327070 [34,] -1.242357675 0.432117489 [35,] -5.593725988 -1.242357675 [36,] -4.536932191 -5.593725988 [37,] -1.433914763 -4.536932191 [38,] 0.339220391 -1.433914763 [39,] 0.868525200 0.339220391 [40,] 0.616759661 0.868525200 [41,] -1.542178527 0.616759661 [42,] 4.491313410 -1.542178527 [43,] -0.954481088 4.491313410 [44,] -1.249272592 -0.954481088 [45,] -3.844471249 -1.249272592 [46,] -1.505997732 -3.844471249 [47,] -0.372903022 -1.505997732 [48,] 0.250827972 -0.372903022 [49,] -1.020661671 0.250827972 [50,] 0.410639197 -1.020661671 [51,] -1.096306536 0.410639197 [52,] -2.734531158 -1.096306536 [53,] 1.006927880 -2.734531158 [54,] -5.835076716 1.006927880 [55,] -1.329073767 -5.835076716 [56,] 0.561789432 -1.329073767 [57,] 0.713009413 0.561789432 [58,] 0.099685485 0.713009413 [59,] 1.158078115 0.099685485 [60,] -2.113411528 1.158078115 [61,] 1.032631864 -2.113411528 [62,] 1.301579890 1.032631864 [63,] -1.117715081 1.301579890 [64,] 0.847850198 -1.117715081 [65,] 2.420180972 0.847850198 [66,] 3.335944864 2.420180972 [67,] 3.401112917 3.335944864 [68,] -3.341891068 3.401112917 [69,] 0.741402255 -3.341891068 [70,] -5.082477070 0.741402255 [71,] -1.408944689 -5.082477070 [72,] 2.717374599 -1.408944689 [73,] 2.676164456 2.717374599 [74,] 1.318622883 2.676164456 [75,] 2.395140785 1.318622883 [76,] 0.516283484 2.395140785 [77,] 1.244793840 0.516283484 [78,] -2.031872393 1.244793840 [79,] 1.361579101 -2.031872393 [80,] 0.860806627 1.361579101 [81,] 0.844497177 0.860806627 [82,] -0.189937544 0.844497177 [83,] -0.063618255 -0.189937544 [84,] 1.162303806 -0.063618255 [85,] -1.170939235 1.162303806 [86,] 1.104784213 -1.170939235 [87,] 1.305805582 1.104784213 [88,] 1.145268561 1.305805582 [89,] -3.347129657 1.145268561 [90,] 2.331579312 -3.347129657 [91,] 0.830806839 2.331579312 [92,] 0.305735835 0.830806839 [93,] -1.592845570 0.305735835 [94,] 0.838455298 -1.592845570 [95,] 1.442462188 0.838455298 [96,] 1.265398728 1.442462188 [97,] 0.771401677 1.265398728 [98,] -0.984550990 0.771401677 [99,] 0.738782778 -0.984550990 [100,] 1.485418405 0.738782778 [101,] 4.873693309 1.485418405 [102,] 0.819534484 4.873693309 [103,] 0.983703148 0.819534484 [104,] -0.977628326 0.983703148 [105,] 1.957859670 -0.977628326 [106,] 1.009476878 1.957859670 [107,] 1.830814586 1.009476878 [108,] -2.334899024 1.830814586 [109,] 1.159754076 -2.334899024 [110,] 1.763760965 1.159754076 [111,] 2.287889185 1.763760965 [112,] -1.646942762 2.287889185 [113,] -3.233767165 -1.646942762 [114,] 3.109882942 -3.233767165 [115,] -0.795473885 3.109882942 [116,] -0.786882642 -0.795473885 [117,] -1.653787931 -0.786882642 [118,] 1.329902619 -1.653787931 [119,] -1.401149355 1.329902619 [120,] 1.571191346 -1.401149355 [121,] -1.861654469 1.571191346 [122,] 1.171837468 -1.861654469 [123,] 0.354733565 1.171837468 [124,] 1.598780898 0.354733565 [125,] 0.061757882 1.598780898 [126,] 1.896044272 0.061757882 [127,] -0.305919881 1.896044272 [128,] -2.652111604 -0.305919881 [129,] 1.641737116 -2.652111604 [130,] -3.754255995 1.641737116 [131,] 1.274059353 -3.754255995 [132,] -2.543044550 1.274059353 [133,] -0.733658854 -2.543044550 [134,] 0.356409892 -0.733658854 [135,] -0.747550320 0.356409892 [136,] 0.509445695 -0.747550320 [137,] -3.054076847 0.509445695 [138,] -2.591099863 -3.054076847 [139,] -3.322151836 -2.591099863 [140,] 1.426082624 -3.322151836 [141,] 0.217343050 1.426082624 [142,] 2.560993156 0.217343050 [143,] 1.725763982 2.560993156 [144,] -4.607479060 1.725763982 [145,] 3.500714957 -4.607479060 [146,] -1.001656349 3.500714957 [147,] 1.988809624 -1.001656349 [148,] -0.599613612 1.988809624 [149,] -4.763728389 -0.599613612 [150,] 0.722550456 -4.763728389 [151,] 1.278354791 0.722550456 [152,] 1.832560294 1.278354791 [153,] 0.393541219 1.832560294 [154,] -4.093680410 0.393541219 [155,] 2.890010140 -4.093680410 [156,] -1.368530822 2.890010140 [157,] -0.048182576 -1.368530822 [158,] 1.915783871 -0.048182576 [159,] -2.082400308 1.915783871 [160,] -1.166636416 -2.082400308 [161,] 1.005198586 -1.166636416 [162,] -0.606908951 1.005198586 [163,] 2.578848954 -0.606908951 [164,] 0.233145519 2.578848954 [165,] -2.961370774 0.233145519 [166,] -0.066320127 -2.961370774 [167,] -1.007754448 -0.066320127 [168,] -1.338147273 -1.007754448 [169,] 1.892239983 -1.338147273 [170,] -1.108140058 1.892239983 [171,] 0.824535875 -1.108140058 [172,] -1.633049177 0.824535875 [173,] -3.080647442 -1.633049177 [174,] -1.185596795 -3.080647442 [175,] 0.782715943 -1.185596795 [176,] -1.144402008 0.782715943 [177,] -0.950645797 -1.144402008 [178,] 0.220795738 -0.950645797 [179,] -2.281418664 0.220795738 [180,] 1.479435693 -2.281418664 [181,] -2.051411449 1.479435693 [182,] 4.350797383 -2.051411449 [183,] 1.387048387 4.350797383 [184,] -5.046233508 1.387048387 [185,] 1.705628302 -5.046233508 [186,] 2.995955519 1.705628302 [187,] -1.497266337 2.995955519 [188,] -1.520955294 -1.497266337 [189,] -2.925604450 -1.520955294 [190,] 0.088331879 -2.925604450 [191,] 0.766931599 0.088331879 [192,] -1.860486549 0.766931599 [193,] 1.913689937 -1.860486549 [194,] -0.616162964 1.913689937 [195,] 0.568600701 -0.616162964 [196,] 2.784671587 0.568600701 [197,] 1.499902352 2.784671587 [198,] 1.318708189 1.499902352 [199,] 2.766540612 1.318708189 [200,] 3.839422661 2.766540612 [201,] 0.914293191 3.839422661 [202,] -2.601243341 0.914293191 [203,] -1.177714073 -2.601243341 [204,] 1.090298468 -1.177714073 [205,] 0.671332942 1.090298468 [206,] 1.625329310 0.671332942 [207,] 1.542694633 1.625329310 [208,] -2.324483357 1.542694633 [209,] 1.219920072 -2.324483357 [210,] -0.670566834 1.219920072 [211,] -4.133167470 -0.670566834 [212,] 0.745578327 -4.133167470 [213,] 2.431182113 0.745578327 [214,] 2.639254693 2.431182113 [215,] 2.191656428 2.639254693 [216,] 2.728061552 2.191656428 [217,] 0.427981707 2.728061552 [218,] 0.164532907 0.427981707 [219,] 0.217088762 0.164532907 [220,] 0.155774520 0.217088762 [221,] -0.262196766 0.155774520 [222,] 2.556609071 -0.262196766 [223,] -1.212009433 2.556609071 [224,] -0.124811014 -1.212009433 [225,] -6.206451451 -0.124811014 [226,] 0.203755687 -6.206451451 [227,] 0.880366926 0.203755687 [228,] -1.263201952 0.880366926 [229,] -1.151700377 -1.263201952 [230,] 0.474957688 -1.151700377 [231,] -3.460158568 0.474957688 [232,] 2.480515670 -3.460158568 [233,] 5.023924859 2.480515670 [234,] -0.215220785 5.023924859 [235,] -1.202278696 -0.215220785 [236,] -8.128402407 -1.202278696 [237,] 1.378375737 -8.128402407 [238,] 3.996041256 1.378375737 [239,] -1.930082454 3.996041256 [240,] -0.505558947 -1.930082454 [241,] -0.514931534 -0.505558947 [242,] -1.611882581 -0.514931534 [243,] 0.319799112 -1.611882581 [244,] 2.297104396 0.319799112 [245,] -0.928719119 2.297104396 [246,] 0.772195277 -0.928719119 [247,] -2.721026579 0.772195277 [248,] 2.299913815 -2.721026579 [249,] 2.217279137 2.299913815 [250,] -2.268630414 2.217279137 [251,] -2.508770298 -2.268630414 [252,] -0.927735824 -2.508770298 [253,] 3.519102359 -0.927735824 [254,] -0.526901273 3.519102359 [255,] 1.487035056 -0.526901273 [256,] 3.104700575 1.487035056 [257,] 2.200891540 3.104700575 [258,] -0.157139785 2.200891540 [259,] -7.494844915 -0.157139785 [260,] 2.893047548 -7.494844915 [261,] -2.981134508 2.893047548 [262,] -1.282208593 -2.981134508 [263,] 1.501954679 -1.282208593 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.146185050 -2.733665502 2 3.786105280 -0.146185050 3 -0.298130828 3.786105280 4 -1.959657311 -0.298130828 5 -3.037117996 -1.959657311 6 3.492186813 -3.037117996 7 -0.623725410 3.492186813 8 -1.732862212 -0.623725410 9 -0.798973048 -1.732862212 10 0.098665573 -0.798973048 11 -0.252702740 0.098665573 12 1.014646453 -0.252702740 13 0.092763188 1.014646453 14 0.840997648 0.092763188 15 -0.480293382 0.840997648 16 -0.446801445 -0.480293382 17 3.984499423 -0.446801445 18 2.420976881 3.984499423 19 0.293714809 2.420976881 20 -0.007852117 0.293714809 21 1.048941680 -0.007852117 22 3.455341855 1.048941680 23 0.724289882 3.455341855 24 1.194042288 0.724289882 25 2.343663437 1.194042288 26 0.279151433 2.343663437 27 -0.476801234 0.279151433 28 0.488764045 -0.476801234 29 -0.932129759 0.488764045 30 -0.586880819 -0.932129759 31 -3.175304054 -0.586880819 32 -1.712327070 -3.175304054 33 0.432117489 -1.712327070 34 -1.242357675 0.432117489 35 -5.593725988 -1.242357675 36 -4.536932191 -5.593725988 37 -1.433914763 -4.536932191 38 0.339220391 -1.433914763 39 0.868525200 0.339220391 40 0.616759661 0.868525200 41 -1.542178527 0.616759661 42 4.491313410 -1.542178527 43 -0.954481088 4.491313410 44 -1.249272592 -0.954481088 45 -3.844471249 -1.249272592 46 -1.505997732 -3.844471249 47 -0.372903022 -1.505997732 48 0.250827972 -0.372903022 49 -1.020661671 0.250827972 50 0.410639197 -1.020661671 51 -1.096306536 0.410639197 52 -2.734531158 -1.096306536 53 1.006927880 -2.734531158 54 -5.835076716 1.006927880 55 -1.329073767 -5.835076716 56 0.561789432 -1.329073767 57 0.713009413 0.561789432 58 0.099685485 0.713009413 59 1.158078115 0.099685485 60 -2.113411528 1.158078115 61 1.032631864 -2.113411528 62 1.301579890 1.032631864 63 -1.117715081 1.301579890 64 0.847850198 -1.117715081 65 2.420180972 0.847850198 66 3.335944864 2.420180972 67 3.401112917 3.335944864 68 -3.341891068 3.401112917 69 0.741402255 -3.341891068 70 -5.082477070 0.741402255 71 -1.408944689 -5.082477070 72 2.717374599 -1.408944689 73 2.676164456 2.717374599 74 1.318622883 2.676164456 75 2.395140785 1.318622883 76 0.516283484 2.395140785 77 1.244793840 0.516283484 78 -2.031872393 1.244793840 79 1.361579101 -2.031872393 80 0.860806627 1.361579101 81 0.844497177 0.860806627 82 -0.189937544 0.844497177 83 -0.063618255 -0.189937544 84 1.162303806 -0.063618255 85 -1.170939235 1.162303806 86 1.104784213 -1.170939235 87 1.305805582 1.104784213 88 1.145268561 1.305805582 89 -3.347129657 1.145268561 90 2.331579312 -3.347129657 91 0.830806839 2.331579312 92 0.305735835 0.830806839 93 -1.592845570 0.305735835 94 0.838455298 -1.592845570 95 1.442462188 0.838455298 96 1.265398728 1.442462188 97 0.771401677 1.265398728 98 -0.984550990 0.771401677 99 0.738782778 -0.984550990 100 1.485418405 0.738782778 101 4.873693309 1.485418405 102 0.819534484 4.873693309 103 0.983703148 0.819534484 104 -0.977628326 0.983703148 105 1.957859670 -0.977628326 106 1.009476878 1.957859670 107 1.830814586 1.009476878 108 -2.334899024 1.830814586 109 1.159754076 -2.334899024 110 1.763760965 1.159754076 111 2.287889185 1.763760965 112 -1.646942762 2.287889185 113 -3.233767165 -1.646942762 114 3.109882942 -3.233767165 115 -0.795473885 3.109882942 116 -0.786882642 -0.795473885 117 -1.653787931 -0.786882642 118 1.329902619 -1.653787931 119 -1.401149355 1.329902619 120 1.571191346 -1.401149355 121 -1.861654469 1.571191346 122 1.171837468 -1.861654469 123 0.354733565 1.171837468 124 1.598780898 0.354733565 125 0.061757882 1.598780898 126 1.896044272 0.061757882 127 -0.305919881 1.896044272 128 -2.652111604 -0.305919881 129 1.641737116 -2.652111604 130 -3.754255995 1.641737116 131 1.274059353 -3.754255995 132 -2.543044550 1.274059353 133 -0.733658854 -2.543044550 134 0.356409892 -0.733658854 135 -0.747550320 0.356409892 136 0.509445695 -0.747550320 137 -3.054076847 0.509445695 138 -2.591099863 -3.054076847 139 -3.322151836 -2.591099863 140 1.426082624 -3.322151836 141 0.217343050 1.426082624 142 2.560993156 0.217343050 143 1.725763982 2.560993156 144 -4.607479060 1.725763982 145 3.500714957 -4.607479060 146 -1.001656349 3.500714957 147 1.988809624 -1.001656349 148 -0.599613612 1.988809624 149 -4.763728389 -0.599613612 150 0.722550456 -4.763728389 151 1.278354791 0.722550456 152 1.832560294 1.278354791 153 0.393541219 1.832560294 154 -4.093680410 0.393541219 155 2.890010140 -4.093680410 156 -1.368530822 2.890010140 157 -0.048182576 -1.368530822 158 1.915783871 -0.048182576 159 -2.082400308 1.915783871 160 -1.166636416 -2.082400308 161 1.005198586 -1.166636416 162 -0.606908951 1.005198586 163 2.578848954 -0.606908951 164 0.233145519 2.578848954 165 -2.961370774 0.233145519 166 -0.066320127 -2.961370774 167 -1.007754448 -0.066320127 168 -1.338147273 -1.007754448 169 1.892239983 -1.338147273 170 -1.108140058 1.892239983 171 0.824535875 -1.108140058 172 -1.633049177 0.824535875 173 -3.080647442 -1.633049177 174 -1.185596795 -3.080647442 175 0.782715943 -1.185596795 176 -1.144402008 0.782715943 177 -0.950645797 -1.144402008 178 0.220795738 -0.950645797 179 -2.281418664 0.220795738 180 1.479435693 -2.281418664 181 -2.051411449 1.479435693 182 4.350797383 -2.051411449 183 1.387048387 4.350797383 184 -5.046233508 1.387048387 185 1.705628302 -5.046233508 186 2.995955519 1.705628302 187 -1.497266337 2.995955519 188 -1.520955294 -1.497266337 189 -2.925604450 -1.520955294 190 0.088331879 -2.925604450 191 0.766931599 0.088331879 192 -1.860486549 0.766931599 193 1.913689937 -1.860486549 194 -0.616162964 1.913689937 195 0.568600701 -0.616162964 196 2.784671587 0.568600701 197 1.499902352 2.784671587 198 1.318708189 1.499902352 199 2.766540612 1.318708189 200 3.839422661 2.766540612 201 0.914293191 3.839422661 202 -2.601243341 0.914293191 203 -1.177714073 -2.601243341 204 1.090298468 -1.177714073 205 0.671332942 1.090298468 206 1.625329310 0.671332942 207 1.542694633 1.625329310 208 -2.324483357 1.542694633 209 1.219920072 -2.324483357 210 -0.670566834 1.219920072 211 -4.133167470 -0.670566834 212 0.745578327 -4.133167470 213 2.431182113 0.745578327 214 2.639254693 2.431182113 215 2.191656428 2.639254693 216 2.728061552 2.191656428 217 0.427981707 2.728061552 218 0.164532907 0.427981707 219 0.217088762 0.164532907 220 0.155774520 0.217088762 221 -0.262196766 0.155774520 222 2.556609071 -0.262196766 223 -1.212009433 2.556609071 224 -0.124811014 -1.212009433 225 -6.206451451 -0.124811014 226 0.203755687 -6.206451451 227 0.880366926 0.203755687 228 -1.263201952 0.880366926 229 -1.151700377 -1.263201952 230 0.474957688 -1.151700377 231 -3.460158568 0.474957688 232 2.480515670 -3.460158568 233 5.023924859 2.480515670 234 -0.215220785 5.023924859 235 -1.202278696 -0.215220785 236 -8.128402407 -1.202278696 237 1.378375737 -8.128402407 238 3.996041256 1.378375737 239 -1.930082454 3.996041256 240 -0.505558947 -1.930082454 241 -0.514931534 -0.505558947 242 -1.611882581 -0.514931534 243 0.319799112 -1.611882581 244 2.297104396 0.319799112 245 -0.928719119 2.297104396 246 0.772195277 -0.928719119 247 -2.721026579 0.772195277 248 2.299913815 -2.721026579 249 2.217279137 2.299913815 250 -2.268630414 2.217279137 251 -2.508770298 -2.268630414 252 -0.927735824 -2.508770298 253 3.519102359 -0.927735824 254 -0.526901273 3.519102359 255 1.487035056 -0.526901273 256 3.104700575 1.487035056 257 2.200891540 3.104700575 258 -0.157139785 2.200891540 259 -7.494844915 -0.157139785 260 2.893047548 -7.494844915 261 -2.981134508 2.893047548 262 -1.282208593 -2.981134508 263 1.501954679 -1.282208593 > 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/www/rcomp/tmp/7rz641321986910.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/www/rcomp/tmp/89apw1321986910.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/www/rcomp/tmp/9d8dh1321986910.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/www/rcomp/tmp/102l211321986910.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/11uifn1321986910.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/12de711321986910.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/134joz1321986910.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/14kquc1321986910.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/15q0g01321986910.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/16b5wo1321986910.tab") + } > > try(system("convert tmp/1m2ig1321986910.ps tmp/1m2ig1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/24u8l1321986910.ps tmp/24u8l1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/3z9fe1321986910.ps tmp/3z9fe1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/4svyk1321986910.ps tmp/4svyk1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/5npbn1321986910.ps tmp/5npbn1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/6v12u1321986910.ps tmp/6v12u1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/7rz641321986910.ps tmp/7rz641321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/89apw1321986910.ps tmp/89apw1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/9d8dh1321986910.ps tmp/9d8dh1321986910.png",intern=TRUE)) character(0) > try(system("convert tmp/102l211321986910.ps tmp/102l211321986910.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.928 0.712 11.816