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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,41 + ,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(9 + ,264) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),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 = '7' > 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 Depression month Connected Separate Learning Software Happiness Belonging 1 12.0 9 41 38 13 12 14 53 2 11.0 9 39 32 16 11 18 83 3 14.0 9 30 35 19 15 11 66 4 12.0 9 31 33 15 6 12 67 5 21.0 9 34 37 14 13 16 76 6 12.0 9 35 29 13 10 18 78 7 22.0 9 39 31 19 12 14 53 8 11.0 9 34 36 15 14 14 80 9 10.0 9 36 35 14 12 15 74 10 13.0 9 37 38 15 9 15 76 11 10.0 9 38 31 16 10 17 79 12 8.0 9 36 34 16 12 19 54 13 15.0 9 38 35 16 12 10 67 14 14.0 9 39 38 16 11 16 54 15 10.0 9 33 37 17 15 18 87 16 14.0 9 32 33 15 12 14 58 17 14.0 9 36 32 15 10 14 75 18 11.0 9 38 38 20 12 17 88 19 10.0 9 39 38 18 11 14 64 20 13.0 9 32 32 16 12 16 57 21 9.5 9 32 33 16 11 18 66 22 14.0 9 31 31 16 12 11 68 23 12.0 9 39 38 19 13 14 54 24 14.0 9 37 39 16 11 12 56 25 11.0 9 39 32 17 12 17 86 26 9.0 9 41 32 17 13 9 80 27 11.0 9 36 35 16 10 16 76 28 15.0 9 33 37 15 14 14 69 29 14.0 9 33 33 16 12 15 78 30 13.0 9 34 33 14 10 11 67 31 9.0 9 31 31 15 12 16 80 32 15.0 9 27 32 12 8 13 54 33 10.0 9 37 31 14 10 17 71 34 11.0 9 34 37 16 12 15 84 35 13.0 9 34 30 14 12 14 74 36 8.0 9 32 33 10 7 16 71 37 20.0 9 29 31 10 9 9 63 38 12.0 9 36 33 14 12 15 71 39 10.0 9 29 31 16 10 17 76 40 10.0 9 35 33 16 10 13 69 41 9.0 9 37 32 16 10 15 74 42 14.0 9 34 33 14 12 16 75 43 8.0 9 38 32 20 15 16 54 44 14.0 9 35 33 14 10 12 52 45 11.0 9 38 28 14 10 15 69 46 13.0 9 37 35 11 12 11 68 47 9.0 9 38 39 14 13 15 65 48 11.0 9 33 34 15 11 15 75 49 15.0 9 36 38 16 11 17 74 50 11.0 9 38 32 14 12 13 75 51 10.0 9 32 38 16 14 16 72 52 14.0 9 32 30 14 10 14 67 53 18.0 9 32 33 12 12 11 63 54 14.0 9 34 38 16 13 12 62 55 11.0 9 32 32 9 5 12 63 56 14.5 9 37 35 14 6 15 76 57 13.0 9 39 34 16 12 16 74 58 9.0 9 29 34 16 12 15 67 59 10.0 9 37 36 15 11 12 73 60 15.0 9 35 34 16 10 12 70 61 20.0 9 30 28 12 7 8 53 62 12.0 9 38 34 16 12 13 77 63 12.0 9 34 35 16 14 11 80 64 14.0 9 31 35 14 11 14 52 65 13.0 9 34 31 16 12 15 54 66 11.0 10 35 37 17 13 10 80 67 17.0 10 36 35 18 14 11 66 68 12.0 10 30 27 18 11 12 73 69 13.0 10 39 40 12 12 15 63 70 14.0 10 35 37 16 12 15 69 71 13.0 10 38 36 10 8 14 67 72 15.0 10 31 38 14 11 16 54 73 13.0 10 34 39 18 14 15 81 74 10.0 10 38 41 18 14 15 69 75 11.0 10 34 27 16 12 13 84 76 19.0 10 39 30 17 9 12 80 77 13.0 10 37 37 16 13 17 70 78 17.0 10 34 31 16 11 13 69 79 13.0 10 28 31 13 12 15 77 80 9.0 10 37 27 16 12 13 54 81 11.0 10 33 36 16 12 15 79 82 9.0 10 35 37 16 12 15 71 83 12.0 10 37 33 15 12 16 73 84 12.0 10 32 34 15 11 15 72 85 13.0 10 33 31 16 10 14 77 86 13.0 10 38 39 14 9 15 75 87 12.0 10 33 34 16 12 14 69 88 15.0 10 29 32 16 12 13 54 89 22.0 10 33 33 15 12 7 70 90 13.0 10 31 36 12 9 17 73 91 15.0 10 36 32 17 15 13 54 92 13.0 10 35 41 16 12 15 77 93 15.0 10 32 28 15 12 14 82 94 12.5 10 29 30 13 12 13 80 95 11.0 10 39 36 16 10 16 80 96 16.0 10 37 35 16 13 12 69 97 11.0 10 35 31 16 9 14 78 98 11.0 10 37 34 16 12 17 81 99 10.0 10 32 36 14 10 15 76 100 10.0 10 38 36 16 14 17 76 101 16.0 10 37 35 16 11 12 73 102 12.0 10 36 37 20 15 16 85 103 11.0 10 32 28 15 11 11 66 104 16.0 10 33 39 16 11 15 79 105 19.0 10 40 32 13 12 9 68 106 11.0 10 38 35 17 12 16 76 107 16.0 10 41 39 16 12 15 71 108 15.0 10 36 35 16 11 10 54 109 24.0 10 43 42 12 7 10 46 110 14.0 10 30 34 16 12 15 85 111 15.0 10 31 33 16 14 11 74 112 11.0 10 32 41 17 11 13 88 113 15.0 10 32 33 13 11 14 38 114 12.0 10 37 34 12 10 18 76 115 10.0 10 37 32 18 13 16 86 116 14.0 10 33 40 14 13 14 54 117 13.0 10 34 40 14 8 14 67 118 9.0 10 33 35 13 11 14 69 119 15.0 10 38 36 16 12 14 90 120 15.0 10 33 37 13 11 12 54 121 14.0 10 31 27 16 13 14 76 122 11.0 10 38 39 13 12 15 89 123 8.0 10 37 38 16 14 15 76 124 11.0 10 36 31 15 13 15 73 125 11.0 10 31 33 16 15 13 79 126 8.0 10 39 32 15 10 17 90 127 10.0 10 44 39 17 11 17 74 128 11.0 10 33 36 15 9 19 81 129 13.0 10 35 33 12 11 15 72 130 11.0 10 32 33 16 10 13 71 131 20.0 10 28 32 10 11 9 66 132 10.0 10 40 37 16 8 15 77 133 15.0 10 27 30 12 11 15 65 134 12.0 10 37 38 14 12 15 74 135 14.0 10 32 29 15 12 16 85 136 23.0 10 28 22 13 9 11 54 137 14.0 10 34 35 15 11 14 63 138 16.0 10 30 35 11 10 11 54 139 11.0 10 35 34 12 8 15 64 140 12.0 10 31 35 11 9 13 69 141 10.0 10 32 34 16 8 15 54 142 14.0 10 30 37 15 9 16 84 143 12.0 10 30 35 17 15 14 86 144 12.0 10 31 23 16 11 15 77 145 11.0 10 40 31 10 8 16 89 146 12.0 10 32 27 18 13 16 76 147 13.0 10 36 36 13 12 11 60 148 11.0 10 32 31 16 12 12 75 149 19.0 10 35 32 13 9 9 73 150 12.0 10 38 39 10 7 16 85 151 17.0 10 42 37 15 13 13 79 152 9.0 10 34 38 16 9 16 71 153 12.0 10 35 39 16 6 12 72 154 19.0 9 38 34 14 8 9 69 155 18.0 10 33 31 10 8 13 78 156 15.0 10 36 32 17 15 13 54 157 14.0 10 32 37 13 6 14 69 158 11.0 10 33 36 15 9 19 81 159 9.0 10 34 32 16 11 13 84 160 18.0 10 32 38 12 8 12 84 161 16.0 10 34 36 13 8 13 69 162 24.0 11 27 26 13 10 10 66 163 14.0 11 31 26 12 8 14 81 164 20.0 11 38 33 17 14 16 82 165 18.0 11 34 39 15 10 10 72 166 23.0 11 24 30 10 8 11 54 167 12.0 11 30 33 14 11 14 78 168 14.0 11 26 25 11 12 12 74 169 16.0 11 34 38 13 12 9 82 170 18.0 11 27 37 16 12 9 73 171 20.0 11 37 31 12 5 11 55 172 12.0 11 36 37 16 12 16 72 173 12.0 11 41 35 12 10 9 78 174 17.0 11 29 25 9 7 13 59 175 13.0 11 36 28 12 12 16 72 176 9.0 11 32 35 15 11 13 78 177 16.0 11 37 33 12 8 9 68 178 18.0 11 30 30 12 9 12 69 179 10.0 11 31 31 14 10 16 67 180 14.0 11 38 37 12 9 11 74 181 11.0 11 36 36 16 12 14 54 182 9.0 11 35 30 11 6 13 67 183 11.0 11 31 36 19 15 15 70 184 10.0 11 38 32 15 12 14 80 185 11.0 11 22 28 8 12 16 89 186 19.0 11 32 36 16 12 13 76 187 14.0 11 36 34 17 11 14 74 188 12.0 11 39 31 12 7 15 87 189 14.0 11 28 28 11 7 13 54 190 21.0 11 32 36 11 5 11 61 191 13.0 11 32 36 14 12 11 38 192 10.0 11 38 40 16 12 14 75 193 15.0 11 32 33 12 3 15 69 194 16.0 11 35 37 16 11 11 62 195 14.0 11 32 32 13 10 15 72 196 12.0 11 37 38 15 12 12 70 197 19.0 11 34 31 16 9 14 79 198 15.0 11 33 37 16 12 14 87 199 19.0 11 33 33 14 9 8 62 200 13.0 11 26 32 16 12 13 77 201 17.0 11 30 30 16 12 9 69 202 12.0 11 24 30 14 10 15 69 203 11.0 11 34 31 11 9 17 75 204 14.0 11 34 32 12 12 13 54 205 11.0 11 33 34 15 8 15 72 206 13.0 11 34 36 15 11 15 74 207 12.0 11 35 37 16 11 14 85 208 15.0 11 35 36 16 12 16 52 209 14.0 11 36 33 11 10 13 70 210 12.0 11 34 33 15 10 16 84 211 17.0 11 34 33 12 12 9 64 212 11.0 11 41 44 12 12 16 84 213 18.0 11 32 39 15 11 11 87 214 13.0 11 30 32 15 8 10 79 215 17.0 11 35 35 16 12 11 67 216 13.0 11 28 25 14 10 15 65 217 11.0 11 33 35 17 11 17 85 218 12.0 11 39 34 14 10 14 83 219 22.0 11 36 35 13 8 8 61 220 14.0 11 36 39 15 12 15 82 221 12.0 11 35 33 13 12 11 76 222 12.0 11 38 36 14 10 16 58 223 17.0 11 33 32 15 12 10 72 224 9.0 11 31 32 12 9 15 72 225 21.0 11 34 36 13 9 9 38 226 10.0 11 32 36 8 6 16 78 227 11.0 11 31 32 14 10 19 54 228 12.0 11 33 34 14 9 12 63 229 23.0 11 34 33 11 9 8 66 230 13.0 11 34 35 12 9 11 70 231 12.0 11 34 30 13 6 14 71 232 16.0 11 33 38 10 10 9 67 233 9.0 11 32 34 16 6 15 58 234 17.0 11 41 33 18 14 13 72 235 9.0 11 34 32 13 10 16 72 236 14.0 11 36 31 11 10 11 70 237 17.0 11 37 30 4 6 12 76 238 13.0 11 36 27 13 12 13 50 239 11.0 11 29 31 16 12 10 72 240 12.0 11 37 30 10 7 11 72 241 10.0 11 27 32 12 8 12 88 242 19.0 11 35 35 12 11 8 53 243 16.0 11 28 28 10 3 12 58 244 16.0 11 35 33 13 6 12 66 245 14.0 11 37 31 15 10 15 82 246 20.0 11 29 35 12 8 11 69 247 15.0 11 32 35 14 9 13 68 248 23.0 11 36 32 10 9 14 44 249 20.0 11 19 21 12 8 10 56 250 16.0 11 21 20 12 9 12 53 251 14.0 11 31 34 11 7 15 70 252 17.0 11 33 32 10 7 13 78 253 11.0 11 36 34 12 6 13 71 254 13.0 11 33 32 16 9 13 72 255 17.0 11 37 33 12 10 12 68 256 15.0 11 34 33 14 11 12 67 257 21.0 11 35 37 16 12 9 75 258 18.0 11 31 32 14 8 9 62 259 15.0 11 37 34 13 11 15 67 260 8.0 11 35 30 4 3 10 83 261 12.0 11 27 30 15 11 14 64 262 12.0 11 34 38 11 12 15 68 263 22.0 11 40 36 11 7 7 62 264 12.0 11 29 32 14 9 14 72 Belonging_Final t 1 32 1 2 51 2 3 42 3 4 41 4 5 46 5 6 47 6 7 37 7 8 49 8 9 45 9 10 47 10 11 49 11 12 33 12 13 42 13 14 33 14 15 53 15 16 36 16 17 45 17 18 54 18 19 41 19 20 36 20 21 41 21 22 44 22 23 33 23 24 37 24 25 52 25 26 47 26 27 43 27 28 44 28 29 45 29 30 44 30 31 49 31 32 33 32 33 43 33 34 54 34 35 42 35 36 44 36 37 37 37 38 43 38 39 46 39 40 42 40 41 45 41 42 44 42 43 33 43 44 31 44 45 42 45 46 40 46 47 43 47 48 46 48 49 42 49 50 45 50 51 44 51 52 40 52 53 37 53 54 46 54 55 36 55 56 47 56 57 45 57 58 42 58 59 43 59 60 43 60 61 32 61 62 45 62 63 48 63 64 31 64 65 33 65 66 49 66 67 42 67 68 41 68 69 38 69 70 42 70 71 44 71 72 33 72 73 48 73 74 40 74 75 50 75 76 49 76 77 43 77 78 44 78 79 47 79 80 33 80 81 46 81 82 45 82 83 43 83 84 44 84 85 47 85 86 45 86 87 42 87 88 33 88 89 43 89 90 46 90 91 33 91 92 46 92 93 48 93 94 47 94 95 47 95 96 43 96 97 46 97 98 48 98 99 46 99 100 45 100 101 45 101 102 52 102 103 42 103 104 47 104 105 41 105 106 47 106 107 43 107 108 33 108 109 30 109 110 52 110 111 44 111 112 55 112 113 11 113 114 47 114 115 53 115 116 33 116 117 44 117 118 42 118 119 55 119 120 33 120 121 46 121 122 54 122 123 47 123 124 45 124 125 47 125 126 55 126 127 44 127 128 53 128 129 44 129 130 42 130 131 40 131 132 46 132 133 40 133 134 46 134 135 53 135 136 33 136 137 42 137 138 35 138 139 40 139 140 41 140 141 33 141 142 51 142 143 53 143 144 46 144 145 55 145 146 47 146 147 38 147 148 46 148 149 46 149 150 53 150 151 47 151 152 41 152 153 44 153 154 43 154 155 51 155 156 33 156 157 43 157 158 53 158 159 51 159 160 50 160 161 46 161 162 43 162 163 47 163 164 50 164 165 43 165 166 33 166 167 48 167 168 44 168 169 50 169 170 41 170 171 34 171 172 44 172 173 47 173 174 35 174 175 44 175 176 44 176 177 43 177 178 41 178 179 41 179 180 42 180 181 33 181 182 41 182 183 44 183 184 48 184 185 55 185 186 44 186 187 43 187 188 52 188 189 30 189 190 39 190 191 11 191 192 44 192 193 42 193 194 41 194 195 44 195 196 44 196 197 48 197 198 53 198 199 37 199 200 44 200 201 44 201 202 40 202 203 42 203 204 35 204 205 43 205 206 45 206 207 55 207 208 31 208 209 44 209 210 50 210 211 40 211 212 53 212 213 54 213 214 49 214 215 40 215 216 41 216 217 52 217 218 52 218 219 36 219 220 52 220 221 46 221 222 31 222 223 44 223 224 44 224 225 11 225 226 46 226 227 33 227 228 34 228 229 42 229 230 43 230 231 43 231 232 44 232 233 36 233 234 46 234 235 44 235 236 43 236 237 50 237 238 33 238 239 43 239 240 44 240 241 53 241 242 34 242 243 35 243 244 40 244 245 53 245 246 42 246 247 43 247 248 29 248 249 36 249 250 30 250 251 42 251 252 47 252 253 44 253 254 45 254 255 44 255 256 43 256 257 43 257 258 40 258 259 41 259 260 52 260 261 38 261 262 41 262 263 39 263 264 43 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Connected Separate 18.939369 1.145774 -0.028251 0.002961 Learning Software Happiness Belonging -0.067509 -0.026789 -0.687128 -0.165979 Belonging_Final t 0.168248 -0.008206 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.2605 -1.7810 -0.1709 1.5673 9.8031 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 18.939369 5.636628 3.360 0.000899 *** month 1.145774 0.599336 1.912 0.057035 . Connected -0.028251 0.051258 -0.551 0.582011 Separate 0.002961 0.052112 0.057 0.954727 Learning -0.067509 0.092538 -0.730 0.466353 Software -0.026789 0.095616 -0.280 0.779573 Happiness -0.687128 0.074436 -9.231 < 2e-16 *** Belonging -0.165979 0.055129 -3.011 0.002869 ** Belonging_Final 0.168248 0.082648 2.036 0.042817 * t -0.008206 0.006404 -1.281 0.201246 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.746 on 254 degrees of freedom Multiple R-squared: 0.3949, Adjusted R-squared: 0.3735 F-statistic: 18.42 on 9 and 254 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.98227830 0.03544340 0.01772170 [2,] 0.98946281 0.02107438 0.01053719 [3,] 0.97870498 0.04259003 0.02129502 [4,] 0.96016006 0.07967989 0.03983994 [5,] 0.98307581 0.03384838 0.01692419 [6,] 0.97420788 0.05158424 0.02579212 [7,] 0.98370902 0.03258195 0.01629098 [8,] 0.97310315 0.05379369 0.02689685 [9,] 0.96318754 0.07362492 0.03681246 [10,] 0.94962270 0.10075461 0.05037730 [11,] 0.93245192 0.13509615 0.06754808 [12,] 0.90712183 0.18575633 0.09287817 [13,] 0.88061951 0.23876098 0.11938049 [14,] 0.86532167 0.26935665 0.13467833 [15,] 0.89167876 0.21664248 0.10832124 [16,] 0.86729771 0.26540458 0.13270229 [17,] 0.90251203 0.19497594 0.09748797 [18,] 0.88278920 0.23442160 0.11721080 [19,] 0.86943246 0.26113508 0.13056754 [20,] 0.85159504 0.29680992 0.14840496 [21,] 0.81456028 0.37087945 0.18543972 [22,] 0.77535526 0.44928947 0.22464474 [23,] 0.75982000 0.48036001 0.24018000 [24,] 0.76104800 0.47790401 0.23895200 [25,] 0.85884208 0.28231584 0.14115792 [26,] 0.82608145 0.34783710 0.17391855 [27,] 0.78878441 0.42243118 0.21121559 [28,] 0.76626005 0.46747991 0.23373995 [29,] 0.73294946 0.53410108 0.26705054 [30,] 0.75030593 0.49938814 0.24969407 [31,] 0.75605910 0.48788180 0.24394090 [32,] 0.72146171 0.55707658 0.27853829 [33,] 0.67841206 0.64317587 0.32158794 [34,] 0.63555560 0.72888880 0.36444440 [35,] 0.62496890 0.75006220 0.37503110 [36,] 0.58028058 0.83943885 0.41971942 [37,] 0.72870510 0.54258981 0.27129490 [38,] 0.69093107 0.61813785 0.30906893 [39,] 0.65263782 0.69472436 0.34736218 [40,] 0.63368432 0.73263137 0.36631568 [41,] 0.66276360 0.67447280 0.33723640 [42,] 0.63901651 0.72196697 0.36098349 [43,] 0.63068940 0.73862120 0.36931060 [44,] 0.69356867 0.61286266 0.30643133 [45,] 0.69329471 0.61341058 0.30670529 [46,] 0.70071127 0.59857746 0.29928873 [47,] 0.69302152 0.61395697 0.30697848 [48,] 0.68858338 0.62283323 0.31141662 [49,] 0.71515387 0.56969227 0.28484613 [50,] 0.67841498 0.64317005 0.32158502 [51,] 0.64605965 0.70788070 0.35394035 [52,] 0.60770318 0.78459364 0.39229682 [53,] 0.56843234 0.86313533 0.43156766 [54,] 0.55912924 0.88174152 0.44087076 [55,] 0.58609018 0.82781964 0.41390982 [56,] 0.56044228 0.87911545 0.43955772 [57,] 0.51986564 0.96026871 0.48013436 [58,] 0.49259339 0.98518678 0.50740661 [59,] 0.45663257 0.91326514 0.54336743 [60,] 0.42588533 0.85177065 0.57411467 [61,] 0.39437732 0.78875463 0.60562268 [62,] 0.37772475 0.75544951 0.62227525 [63,] 0.35183408 0.70366816 0.64816592 [64,] 0.49068410 0.98136821 0.50931590 [65,] 0.46043200 0.92086401 0.53956800 [66,] 0.45327260 0.90654520 0.54672740 [67,] 0.41456706 0.82913412 0.58543294 [68,] 0.55015185 0.89969630 0.44984815 [69,] 0.51613832 0.96772335 0.48386168 [70,] 0.54999460 0.90001080 0.45000540 [71,] 0.51188567 0.97622865 0.48811433 [72,] 0.47516422 0.95032844 0.52483578 [73,] 0.43678923 0.87357846 0.56321077 [74,] 0.40129873 0.80259746 0.59870127 [75,] 0.37183183 0.74366366 0.62816817 [76,] 0.33849440 0.67698880 0.66150560 [77,] 0.41450008 0.82900016 0.58549992 [78,] 0.38127413 0.76254826 0.61872587 [79,] 0.35133151 0.70266302 0.64866849 [80,] 0.32005241 0.64010481 0.67994759 [81,] 0.31310163 0.62620327 0.68689837 [82,] 0.28557651 0.57115303 0.71442349 [83,] 0.25383322 0.50766644 0.74616678 [84,] 0.23590211 0.47180423 0.76409789 [85,] 0.21746963 0.43493926 0.78253037 [86,] 0.19168038 0.38336076 0.80831962 [87,] 0.18765092 0.37530184 0.81234908 [88,] 0.16393182 0.32786365 0.83606818 [89,] 0.15292143 0.30584286 0.84707857 [90,] 0.13662130 0.27324260 0.86337870 [91,] 0.18311087 0.36622174 0.81688913 [92,] 0.21138773 0.42277545 0.78861227 [93,] 0.21517292 0.43034584 0.78482708 [94,] 0.18969701 0.37939402 0.81030299 [95,] 0.20968257 0.41936514 0.79031743 [96,] 0.19757366 0.39514732 0.80242634 [97,] 0.29891324 0.59782648 0.70108676 [98,] 0.28709845 0.57419690 0.71290155 [99,] 0.25753728 0.51507457 0.74246272 [100,] 0.24876574 0.49753148 0.75123426 [101,] 0.22601313 0.45202626 0.77398687 [102,] 0.20593056 0.41186112 0.79406944 [103,] 0.18232206 0.36464412 0.81767794 [104,] 0.15980935 0.31961871 0.84019065 [105,] 0.14343520 0.28687040 0.85656480 [106,] 0.18276719 0.36553437 0.81723281 [107,] 0.18822169 0.37644338 0.81177831 [108,] 0.16723665 0.33447330 0.83276335 [109,] 0.15224756 0.30449512 0.84775244 [110,] 0.13364308 0.26728616 0.86635692 [111,] 0.15985380 0.31970759 0.84014620 [112,] 0.14326512 0.28653024 0.85673488 [113,] 0.13397048 0.26794096 0.86602952 [114,] 0.12381776 0.24763552 0.87618224 [115,] 0.10705138 0.21410277 0.89294862 [116,] 0.09462368 0.18924736 0.90537632 [117,] 0.08069517 0.16139033 0.91930483 [118,] 0.07973960 0.15947920 0.92026040 [119,] 0.08118296 0.16236593 0.91881704 [120,] 0.07402150 0.14804301 0.92597850 [121,] 0.06721223 0.13442447 0.93278777 [122,] 0.05645164 0.11290329 0.94354836 [123,] 0.05845026 0.11690053 0.94154974 [124,] 0.11740406 0.23480811 0.88259594 [125,] 0.10113805 0.20227610 0.89886195 [126,] 0.08919954 0.17839908 0.91080046 [127,] 0.08664628 0.17329255 0.91335372 [128,] 0.08268639 0.16537277 0.91731361 [129,] 0.09617491 0.19234981 0.90382509 [130,] 0.09768729 0.19537458 0.90231271 [131,] 0.08333769 0.16667538 0.91666231 [132,] 0.07026089 0.14052179 0.92973911 [133,] 0.05905611 0.11811223 0.94094389 [134,] 0.04974759 0.09949519 0.95025241 [135,] 0.05654720 0.11309440 0.94345280 [136,] 0.06344911 0.12689821 0.93655089 [137,] 0.05948227 0.11896454 0.94051773 [138,] 0.04963707 0.09927413 0.95036293 [139,] 0.06182789 0.12365577 0.93817211 [140,] 0.05963320 0.11926640 0.94036680 [141,] 0.06119247 0.12238495 0.93880753 [142,] 0.06315941 0.12631881 0.93684059 [143,] 0.06722614 0.13445228 0.93277386 [144,] 0.05730271 0.11460542 0.94269729 [145,] 0.04799511 0.09599023 0.95200489 [146,] 0.04038548 0.08077097 0.95961452 [147,] 0.05571964 0.11143929 0.94428036 [148,] 0.06135768 0.12271536 0.93864232 [149,] 0.05191238 0.10382475 0.94808762 [150,] 0.09075412 0.18150824 0.90924588 [151,] 0.08056482 0.16112965 0.91943518 [152,] 0.27626600 0.55253200 0.72373400 [153,] 0.25113542 0.50227084 0.74886458 [154,] 0.31617719 0.63235439 0.68382281 [155,] 0.30009410 0.60018821 0.69990590 [156,] 0.28305504 0.56611008 0.71694496 [157,] 0.25569332 0.51138664 0.74430668 [158,] 0.23324390 0.46648781 0.76675610 [159,] 0.23128711 0.46257422 0.76871289 [160,] 0.20514679 0.41029359 0.79485321 [161,] 0.25745899 0.51491798 0.74254101 [162,] 0.24814949 0.49629897 0.75185051 [163,] 0.23258588 0.46517176 0.76741412 [164,] 0.27432716 0.54865431 0.72567284 [165,] 0.25594774 0.51189548 0.74405226 [166,] 0.26540612 0.53081223 0.73459388 [167,] 0.26064030 0.52128061 0.73935970 [168,] 0.23439469 0.46878937 0.76560531 [169,] 0.26488420 0.52976840 0.73511580 [170,] 0.38217495 0.76434991 0.61782505 [171,] 0.37372346 0.74744692 0.62627654 [172,] 0.37213882 0.74427765 0.62786118 [173,] 0.35336239 0.70672477 0.64663761 [174,] 0.46254522 0.92509043 0.53745478 [175,] 0.42704787 0.85409573 0.57295213 [176,] 0.39402251 0.78804502 0.60597749 [177,] 0.36192518 0.72385037 0.63807482 [178,] 0.39178946 0.78357892 0.60821054 [179,] 0.42035577 0.84071153 0.57964423 [180,] 0.44129537 0.88259074 0.55870463 [181,] 0.42899901 0.85799803 0.57100099 [182,] 0.39857155 0.79714310 0.60142845 [183,] 0.37055775 0.74111551 0.62944225 [184,] 0.40842318 0.81684636 0.59157682 [185,] 0.61286376 0.77427247 0.38713624 [186,] 0.62218101 0.75563797 0.37781899 [187,] 0.58158422 0.83683156 0.41841578 [188,] 0.54018127 0.91963747 0.45981873 [189,] 0.49697267 0.99394535 0.50302733 [190,] 0.45726953 0.91453907 0.54273047 [191,] 0.43795459 0.87590918 0.56204541 [192,] 0.42636529 0.85273058 0.57363471 [193,] 0.39000440 0.78000880 0.60999560 [194,] 0.34936825 0.69873650 0.65063175 [195,] 0.31105249 0.62210499 0.68894751 [196,] 0.27787576 0.55575151 0.72212424 [197,] 0.24145246 0.48290492 0.75854754 [198,] 0.23791191 0.47582383 0.76208809 [199,] 0.21219776 0.42439551 0.78780224 [200,] 0.18253287 0.36506574 0.81746713 [201,] 0.20975892 0.41951785 0.79024108 [202,] 0.19192341 0.38384682 0.80807659 [203,] 0.16458481 0.32916963 0.83541519 [204,] 0.14035685 0.28071369 0.85964315 [205,] 0.13710300 0.27420600 0.86289700 [206,] 0.11548856 0.23097713 0.88451144 [207,] 0.12819942 0.25639885 0.87180058 [208,] 0.13460223 0.26920446 0.86539777 [209,] 0.12266682 0.24533363 0.87733318 [210,] 0.09847097 0.19694195 0.90152903 [211,] 0.08483527 0.16967054 0.91516473 [212,] 0.07822410 0.15644821 0.92177590 [213,] 0.07234866 0.14469732 0.92765134 [214,] 0.06031428 0.12062856 0.93968572 [215,] 0.04583346 0.09166691 0.95416654 [216,] 0.03782687 0.07565374 0.96217313 [217,] 0.06740715 0.13481429 0.93259285 [218,] 0.05550374 0.11100748 0.94449626 [219,] 0.04269888 0.08539777 0.95730112 [220,] 0.03612996 0.07225993 0.96387004 [221,] 0.06892598 0.13785196 0.93107402 [222,] 0.07476554 0.14953107 0.92523446 [223,] 0.06785872 0.13571744 0.93214128 [224,] 0.05018722 0.10037443 0.94981278 [225,] 0.07007413 0.14014826 0.92992587 [226,] 0.09432158 0.18864316 0.90567842 [227,] 0.18392844 0.36785688 0.81607156 [228,] 0.22589250 0.45178501 0.77410750 [229,] 0.20918514 0.41837028 0.79081486 [230,] 0.63628756 0.72742488 0.36371244 [231,] 0.55193271 0.89613458 0.44806729 [232,] 0.54309221 0.91381559 0.45690779 [233,] 0.47558787 0.95117574 0.52441213 [234,] 0.38920292 0.77840584 0.61079708 [235,] 0.37507133 0.75014266 0.62492867 [236,] 0.32330262 0.64660524 0.67669738 [237,] 0.32787021 0.65574042 0.67212979 [238,] 0.27918104 0.55836209 0.72081896 [239,] 0.17285122 0.34570244 0.82714878 > postscript(file="/var/wessaorg/rcomp/tmp/1umjn1384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2jiq51384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3cjuw1384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/44r9g1384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5lqo01384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -1.965539258 1.710833260 -1.351729121 -2.799131568 9.803078475 2.253316746 7 8 9 10 11 12 7.611735685 -1.290131307 -1.979300593 1.030875586 -0.281942683 -2.368788864 13 14 15 16 17 18 -0.847706575 1.632356430 1.135274867 0.210009241 1.588009632 1.730949316 19 20 21 22 23 24 -3.252051272 0.521580162 -0.973139403 -1.443158893 -1.411944941 -1.434600071 25 26 27 28 29 30 1.636419513 -5.923742857 0.605421950 1.858241817 2.904912938 -2.653257891 31 32 33 34 35 36 -1.850671015 0.047020896 -0.583030942 -0.556013214 1.009964577 -3.911373320 37 38 39 40 41 42 3.111586491 0.103144034 -0.299640350 -3.365222271 -2.598146239 3.262259012 43 44 45 46 47 48 -3.762972270 -1.125456566 -0.985394674 -1.753115745 -3.753354752 -0.702624593 49 50 51 52 53 54 5.327268630 -1.785762885 -1.044540381 1.214023843 2.911349021 -1.735006907 55 56 57 58 59 60 -3.603952208 2.769340606 2.324429411 -3.294113682 -3.393879305 1.106530317 61 62 63 64 65 66 1.921420612 -0.226243087 -1.661490915 0.320772884 0.269974999 -4.574976040 67 68 69 70 71 72 1.102864169 -2.097886558 -0.345840484 1.151160510 -1.620710836 1.601477591 73 74 75 76 77 78 1.312455845 -2.218016588 -2.037009073 4.907915708 1.663878808 2.468780366 79 80 81 82 83 84 0.329082372 -6.030372538 -0.825324161 -3.923159075 0.441467578 -0.742689025 85 86 87 88 89 90 -0.018606727 0.637024187 -1.444091427 -0.205545446 3.695603838 1.220004691 91 92 93 94 95 96 0.164706670 0.974675499 2.675385354 -0.892941807 0.090343098 1.224086893 97 98 99 100 101 102 -1.556199324 0.802817132 -2.392407260 -0.430014713 1.538955660 1.452701632 103 104 105 106 107 108 -4.976904507 4.159483083 2.271391176 -0.387513205 3.782061742 -1.940732924 109 110 111 112 113 114 6.044224161 2.320190852 0.184891683 -1.967969769 1.584995792 1.635975053 115 116 117 118 119 120 -0.588432889 -0.307575640 -1.098068557 -4.421995887 3.252150067 -0.761213027 121 122 123 124 125 126 1.314773580 -0.245246846 -3.986213250 -1.241267416 -1.974034722 -1.710015739 127 128 129 130 131 132 -0.224407533 1.315197266 0.511750480 -2.525290189 2.752694852 -1.651154907 133 134 135 136 137 138 1.838587420 -0.248257382 3.048011453 6.532534224 -0.098692645 -0.877775267 139 140 141 142 143 144 -2.144359789 -2.005452213 -3.424717439 3.115407016 0.046492127 0.314876752 145 146 147 148 149 150 0.232872623 0.889191485 -2.957654025 -3.014294454 2.399466797 0.839498864 151 152 153 154 155 156 4.417142408 -2.100227380 -2.234377891 3.546649162 3.903010062 0.698069695 157 158 159 160 161 162 0.561654765 1.561364816 -3.557583938 4.507066936 1.515646696 6.208933568 163 164 165 166 167 168 0.774259817 8.493265775 1.523701902 5.266915064 -1.692697980 -1.314724374 169 170 171 172 173 174 -0.727031799 1.309326749 2.724630644 -0.280823976 -4.767823902 1.261979701 175 176 177 178 179 180 0.500409091 -4.514895149 -1.882457231 2.527522285 -2.860618828 -1.276268547 181 182 183 184 185 186 -3.715157962 -6.091103850 -2.065049425 -2.897975520 -1.112179790 5.326539932 187 188 189 190 191 192 1.017812666 0.005574726 -1.505707437 3.811596330 -2.896713994 -2.945414492 193 194 195 196 197 198 1.430627733 -0.746026704 0.866472535 -3.206580196 5.919816506 2.448959092 199 200 201 202 203 204 0.673354819 -0.550265524 -0.499475295 -1.053611217 0.038463165 -1.864747282 205 206 207 208 209 210 -1.779454949 0.326907596 -1.115930472 1.856936938 -0.749837781 0.847500674 211 212 213 214 215 216 -0.740236133 -0.624597471 3.213937400 -3.067715016 1.457144003 -0.643083642 217 218 219 220 221 222 0.549183894 -0.892800073 3.824268927 1.666286911 -3.205906492 -0.136154708 223 224 225 226 227 228 0.877434279 -4.018114896 3.916645626 -1.989196546 -0.220066131 -2.672408161 229 230 231 232 233 234 4.567921306 -2.805234707 -1.567716108 -1.974629197 -4.709984827 3.171739357 235 236 237 238 239 240 -3.061672993 -1.728373018 1.236590452 -2.774357545 -4.865563186 -3.648504610 241 242 243 244 245 246 -3.938372124 0.006270271 -0.101724548 0.858917233 1.701543008 4.160278314 247 248 249 250 251 252 0.455075114 7.374245268 2.108477665 0.088745077 1.080964620 3.196419435 253 254 255 256 257 258 -3.265421674 -0.987913293 1.704293656 -0.208178306 5.244684661 0.259538447 259 260 261 262 263 264 2.228603258 -8.260452585 -1.570955649 -0.785629954 3.107657692 -1.130256422 > postscript(file="/var/wessaorg/rcomp/tmp/6mjio1384694160.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 -1.965539258 NA 1 1.710833260 -1.965539258 2 -1.351729121 1.710833260 3 -2.799131568 -1.351729121 4 9.803078475 -2.799131568 5 2.253316746 9.803078475 6 7.611735685 2.253316746 7 -1.290131307 7.611735685 8 -1.979300593 -1.290131307 9 1.030875586 -1.979300593 10 -0.281942683 1.030875586 11 -2.368788864 -0.281942683 12 -0.847706575 -2.368788864 13 1.632356430 -0.847706575 14 1.135274867 1.632356430 15 0.210009241 1.135274867 16 1.588009632 0.210009241 17 1.730949316 1.588009632 18 -3.252051272 1.730949316 19 0.521580162 -3.252051272 20 -0.973139403 0.521580162 21 -1.443158893 -0.973139403 22 -1.411944941 -1.443158893 23 -1.434600071 -1.411944941 24 1.636419513 -1.434600071 25 -5.923742857 1.636419513 26 0.605421950 -5.923742857 27 1.858241817 0.605421950 28 2.904912938 1.858241817 29 -2.653257891 2.904912938 30 -1.850671015 -2.653257891 31 0.047020896 -1.850671015 32 -0.583030942 0.047020896 33 -0.556013214 -0.583030942 34 1.009964577 -0.556013214 35 -3.911373320 1.009964577 36 3.111586491 -3.911373320 37 0.103144034 3.111586491 38 -0.299640350 0.103144034 39 -3.365222271 -0.299640350 40 -2.598146239 -3.365222271 41 3.262259012 -2.598146239 42 -3.762972270 3.262259012 43 -1.125456566 -3.762972270 44 -0.985394674 -1.125456566 45 -1.753115745 -0.985394674 46 -3.753354752 -1.753115745 47 -0.702624593 -3.753354752 48 5.327268630 -0.702624593 49 -1.785762885 5.327268630 50 -1.044540381 -1.785762885 51 1.214023843 -1.044540381 52 2.911349021 1.214023843 53 -1.735006907 2.911349021 54 -3.603952208 -1.735006907 55 2.769340606 -3.603952208 56 2.324429411 2.769340606 57 -3.294113682 2.324429411 58 -3.393879305 -3.294113682 59 1.106530317 -3.393879305 60 1.921420612 1.106530317 61 -0.226243087 1.921420612 62 -1.661490915 -0.226243087 63 0.320772884 -1.661490915 64 0.269974999 0.320772884 65 -4.574976040 0.269974999 66 1.102864169 -4.574976040 67 -2.097886558 1.102864169 68 -0.345840484 -2.097886558 69 1.151160510 -0.345840484 70 -1.620710836 1.151160510 71 1.601477591 -1.620710836 72 1.312455845 1.601477591 73 -2.218016588 1.312455845 74 -2.037009073 -2.218016588 75 4.907915708 -2.037009073 76 1.663878808 4.907915708 77 2.468780366 1.663878808 78 0.329082372 2.468780366 79 -6.030372538 0.329082372 80 -0.825324161 -6.030372538 81 -3.923159075 -0.825324161 82 0.441467578 -3.923159075 83 -0.742689025 0.441467578 84 -0.018606727 -0.742689025 85 0.637024187 -0.018606727 86 -1.444091427 0.637024187 87 -0.205545446 -1.444091427 88 3.695603838 -0.205545446 89 1.220004691 3.695603838 90 0.164706670 1.220004691 91 0.974675499 0.164706670 92 2.675385354 0.974675499 93 -0.892941807 2.675385354 94 0.090343098 -0.892941807 95 1.224086893 0.090343098 96 -1.556199324 1.224086893 97 0.802817132 -1.556199324 98 -2.392407260 0.802817132 99 -0.430014713 -2.392407260 100 1.538955660 -0.430014713 101 1.452701632 1.538955660 102 -4.976904507 1.452701632 103 4.159483083 -4.976904507 104 2.271391176 4.159483083 105 -0.387513205 2.271391176 106 3.782061742 -0.387513205 107 -1.940732924 3.782061742 108 6.044224161 -1.940732924 109 2.320190852 6.044224161 110 0.184891683 2.320190852 111 -1.967969769 0.184891683 112 1.584995792 -1.967969769 113 1.635975053 1.584995792 114 -0.588432889 1.635975053 115 -0.307575640 -0.588432889 116 -1.098068557 -0.307575640 117 -4.421995887 -1.098068557 118 3.252150067 -4.421995887 119 -0.761213027 3.252150067 120 1.314773580 -0.761213027 121 -0.245246846 1.314773580 122 -3.986213250 -0.245246846 123 -1.241267416 -3.986213250 124 -1.974034722 -1.241267416 125 -1.710015739 -1.974034722 126 -0.224407533 -1.710015739 127 1.315197266 -0.224407533 128 0.511750480 1.315197266 129 -2.525290189 0.511750480 130 2.752694852 -2.525290189 131 -1.651154907 2.752694852 132 1.838587420 -1.651154907 133 -0.248257382 1.838587420 134 3.048011453 -0.248257382 135 6.532534224 3.048011453 136 -0.098692645 6.532534224 137 -0.877775267 -0.098692645 138 -2.144359789 -0.877775267 139 -2.005452213 -2.144359789 140 -3.424717439 -2.005452213 141 3.115407016 -3.424717439 142 0.046492127 3.115407016 143 0.314876752 0.046492127 144 0.232872623 0.314876752 145 0.889191485 0.232872623 146 -2.957654025 0.889191485 147 -3.014294454 -2.957654025 148 2.399466797 -3.014294454 149 0.839498864 2.399466797 150 4.417142408 0.839498864 151 -2.100227380 4.417142408 152 -2.234377891 -2.100227380 153 3.546649162 -2.234377891 154 3.903010062 3.546649162 155 0.698069695 3.903010062 156 0.561654765 0.698069695 157 1.561364816 0.561654765 158 -3.557583938 1.561364816 159 4.507066936 -3.557583938 160 1.515646696 4.507066936 161 6.208933568 1.515646696 162 0.774259817 6.208933568 163 8.493265775 0.774259817 164 1.523701902 8.493265775 165 5.266915064 1.523701902 166 -1.692697980 5.266915064 167 -1.314724374 -1.692697980 168 -0.727031799 -1.314724374 169 1.309326749 -0.727031799 170 2.724630644 1.309326749 171 -0.280823976 2.724630644 172 -4.767823902 -0.280823976 173 1.261979701 -4.767823902 174 0.500409091 1.261979701 175 -4.514895149 0.500409091 176 -1.882457231 -4.514895149 177 2.527522285 -1.882457231 178 -2.860618828 2.527522285 179 -1.276268547 -2.860618828 180 -3.715157962 -1.276268547 181 -6.091103850 -3.715157962 182 -2.065049425 -6.091103850 183 -2.897975520 -2.065049425 184 -1.112179790 -2.897975520 185 5.326539932 -1.112179790 186 1.017812666 5.326539932 187 0.005574726 1.017812666 188 -1.505707437 0.005574726 189 3.811596330 -1.505707437 190 -2.896713994 3.811596330 191 -2.945414492 -2.896713994 192 1.430627733 -2.945414492 193 -0.746026704 1.430627733 194 0.866472535 -0.746026704 195 -3.206580196 0.866472535 196 5.919816506 -3.206580196 197 2.448959092 5.919816506 198 0.673354819 2.448959092 199 -0.550265524 0.673354819 200 -0.499475295 -0.550265524 201 -1.053611217 -0.499475295 202 0.038463165 -1.053611217 203 -1.864747282 0.038463165 204 -1.779454949 -1.864747282 205 0.326907596 -1.779454949 206 -1.115930472 0.326907596 207 1.856936938 -1.115930472 208 -0.749837781 1.856936938 209 0.847500674 -0.749837781 210 -0.740236133 0.847500674 211 -0.624597471 -0.740236133 212 3.213937400 -0.624597471 213 -3.067715016 3.213937400 214 1.457144003 -3.067715016 215 -0.643083642 1.457144003 216 0.549183894 -0.643083642 217 -0.892800073 0.549183894 218 3.824268927 -0.892800073 219 1.666286911 3.824268927 220 -3.205906492 1.666286911 221 -0.136154708 -3.205906492 222 0.877434279 -0.136154708 223 -4.018114896 0.877434279 224 3.916645626 -4.018114896 225 -1.989196546 3.916645626 226 -0.220066131 -1.989196546 227 -2.672408161 -0.220066131 228 4.567921306 -2.672408161 229 -2.805234707 4.567921306 230 -1.567716108 -2.805234707 231 -1.974629197 -1.567716108 232 -4.709984827 -1.974629197 233 3.171739357 -4.709984827 234 -3.061672993 3.171739357 235 -1.728373018 -3.061672993 236 1.236590452 -1.728373018 237 -2.774357545 1.236590452 238 -4.865563186 -2.774357545 239 -3.648504610 -4.865563186 240 -3.938372124 -3.648504610 241 0.006270271 -3.938372124 242 -0.101724548 0.006270271 243 0.858917233 -0.101724548 244 1.701543008 0.858917233 245 4.160278314 1.701543008 246 0.455075114 4.160278314 247 7.374245268 0.455075114 248 2.108477665 7.374245268 249 0.088745077 2.108477665 250 1.080964620 0.088745077 251 3.196419435 1.080964620 252 -3.265421674 3.196419435 253 -0.987913293 -3.265421674 254 1.704293656 -0.987913293 255 -0.208178306 1.704293656 256 5.244684661 -0.208178306 257 0.259538447 5.244684661 258 2.228603258 0.259538447 259 -8.260452585 2.228603258 260 -1.570955649 -8.260452585 261 -0.785629954 -1.570955649 262 3.107657692 -0.785629954 263 -1.130256422 3.107657692 264 NA -1.130256422 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.710833260 -1.965539258 [2,] -1.351729121 1.710833260 [3,] -2.799131568 -1.351729121 [4,] 9.803078475 -2.799131568 [5,] 2.253316746 9.803078475 [6,] 7.611735685 2.253316746 [7,] -1.290131307 7.611735685 [8,] -1.979300593 -1.290131307 [9,] 1.030875586 -1.979300593 [10,] -0.281942683 1.030875586 [11,] -2.368788864 -0.281942683 [12,] -0.847706575 -2.368788864 [13,] 1.632356430 -0.847706575 [14,] 1.135274867 1.632356430 [15,] 0.210009241 1.135274867 [16,] 1.588009632 0.210009241 [17,] 1.730949316 1.588009632 [18,] -3.252051272 1.730949316 [19,] 0.521580162 -3.252051272 [20,] -0.973139403 0.521580162 [21,] -1.443158893 -0.973139403 [22,] -1.411944941 -1.443158893 [23,] -1.434600071 -1.411944941 [24,] 1.636419513 -1.434600071 [25,] -5.923742857 1.636419513 [26,] 0.605421950 -5.923742857 [27,] 1.858241817 0.605421950 [28,] 2.904912938 1.858241817 [29,] -2.653257891 2.904912938 [30,] -1.850671015 -2.653257891 [31,] 0.047020896 -1.850671015 [32,] -0.583030942 0.047020896 [33,] -0.556013214 -0.583030942 [34,] 1.009964577 -0.556013214 [35,] -3.911373320 1.009964577 [36,] 3.111586491 -3.911373320 [37,] 0.103144034 3.111586491 [38,] -0.299640350 0.103144034 [39,] -3.365222271 -0.299640350 [40,] -2.598146239 -3.365222271 [41,] 3.262259012 -2.598146239 [42,] -3.762972270 3.262259012 [43,] -1.125456566 -3.762972270 [44,] -0.985394674 -1.125456566 [45,] -1.753115745 -0.985394674 [46,] -3.753354752 -1.753115745 [47,] -0.702624593 -3.753354752 [48,] 5.327268630 -0.702624593 [49,] -1.785762885 5.327268630 [50,] -1.044540381 -1.785762885 [51,] 1.214023843 -1.044540381 [52,] 2.911349021 1.214023843 [53,] -1.735006907 2.911349021 [54,] -3.603952208 -1.735006907 [55,] 2.769340606 -3.603952208 [56,] 2.324429411 2.769340606 [57,] -3.294113682 2.324429411 [58,] -3.393879305 -3.294113682 [59,] 1.106530317 -3.393879305 [60,] 1.921420612 1.106530317 [61,] -0.226243087 1.921420612 [62,] -1.661490915 -0.226243087 [63,] 0.320772884 -1.661490915 [64,] 0.269974999 0.320772884 [65,] -4.574976040 0.269974999 [66,] 1.102864169 -4.574976040 [67,] -2.097886558 1.102864169 [68,] -0.345840484 -2.097886558 [69,] 1.151160510 -0.345840484 [70,] -1.620710836 1.151160510 [71,] 1.601477591 -1.620710836 [72,] 1.312455845 1.601477591 [73,] -2.218016588 1.312455845 [74,] -2.037009073 -2.218016588 [75,] 4.907915708 -2.037009073 [76,] 1.663878808 4.907915708 [77,] 2.468780366 1.663878808 [78,] 0.329082372 2.468780366 [79,] -6.030372538 0.329082372 [80,] -0.825324161 -6.030372538 [81,] -3.923159075 -0.825324161 [82,] 0.441467578 -3.923159075 [83,] -0.742689025 0.441467578 [84,] -0.018606727 -0.742689025 [85,] 0.637024187 -0.018606727 [86,] -1.444091427 0.637024187 [87,] -0.205545446 -1.444091427 [88,] 3.695603838 -0.205545446 [89,] 1.220004691 3.695603838 [90,] 0.164706670 1.220004691 [91,] 0.974675499 0.164706670 [92,] 2.675385354 0.974675499 [93,] -0.892941807 2.675385354 [94,] 0.090343098 -0.892941807 [95,] 1.224086893 0.090343098 [96,] -1.556199324 1.224086893 [97,] 0.802817132 -1.556199324 [98,] -2.392407260 0.802817132 [99,] -0.430014713 -2.392407260 [100,] 1.538955660 -0.430014713 [101,] 1.452701632 1.538955660 [102,] -4.976904507 1.452701632 [103,] 4.159483083 -4.976904507 [104,] 2.271391176 4.159483083 [105,] -0.387513205 2.271391176 [106,] 3.782061742 -0.387513205 [107,] -1.940732924 3.782061742 [108,] 6.044224161 -1.940732924 [109,] 2.320190852 6.044224161 [110,] 0.184891683 2.320190852 [111,] -1.967969769 0.184891683 [112,] 1.584995792 -1.967969769 [113,] 1.635975053 1.584995792 [114,] -0.588432889 1.635975053 [115,] -0.307575640 -0.588432889 [116,] -1.098068557 -0.307575640 [117,] -4.421995887 -1.098068557 [118,] 3.252150067 -4.421995887 [119,] -0.761213027 3.252150067 [120,] 1.314773580 -0.761213027 [121,] -0.245246846 1.314773580 [122,] -3.986213250 -0.245246846 [123,] -1.241267416 -3.986213250 [124,] -1.974034722 -1.241267416 [125,] -1.710015739 -1.974034722 [126,] -0.224407533 -1.710015739 [127,] 1.315197266 -0.224407533 [128,] 0.511750480 1.315197266 [129,] -2.525290189 0.511750480 [130,] 2.752694852 -2.525290189 [131,] -1.651154907 2.752694852 [132,] 1.838587420 -1.651154907 [133,] -0.248257382 1.838587420 [134,] 3.048011453 -0.248257382 [135,] 6.532534224 3.048011453 [136,] -0.098692645 6.532534224 [137,] -0.877775267 -0.098692645 [138,] -2.144359789 -0.877775267 [139,] -2.005452213 -2.144359789 [140,] -3.424717439 -2.005452213 [141,] 3.115407016 -3.424717439 [142,] 0.046492127 3.115407016 [143,] 0.314876752 0.046492127 [144,] 0.232872623 0.314876752 [145,] 0.889191485 0.232872623 [146,] -2.957654025 0.889191485 [147,] -3.014294454 -2.957654025 [148,] 2.399466797 -3.014294454 [149,] 0.839498864 2.399466797 [150,] 4.417142408 0.839498864 [151,] -2.100227380 4.417142408 [152,] -2.234377891 -2.100227380 [153,] 3.546649162 -2.234377891 [154,] 3.903010062 3.546649162 [155,] 0.698069695 3.903010062 [156,] 0.561654765 0.698069695 [157,] 1.561364816 0.561654765 [158,] -3.557583938 1.561364816 [159,] 4.507066936 -3.557583938 [160,] 1.515646696 4.507066936 [161,] 6.208933568 1.515646696 [162,] 0.774259817 6.208933568 [163,] 8.493265775 0.774259817 [164,] 1.523701902 8.493265775 [165,] 5.266915064 1.523701902 [166,] -1.692697980 5.266915064 [167,] -1.314724374 -1.692697980 [168,] -0.727031799 -1.314724374 [169,] 1.309326749 -0.727031799 [170,] 2.724630644 1.309326749 [171,] -0.280823976 2.724630644 [172,] -4.767823902 -0.280823976 [173,] 1.261979701 -4.767823902 [174,] 0.500409091 1.261979701 [175,] -4.514895149 0.500409091 [176,] -1.882457231 -4.514895149 [177,] 2.527522285 -1.882457231 [178,] -2.860618828 2.527522285 [179,] -1.276268547 -2.860618828 [180,] -3.715157962 -1.276268547 [181,] -6.091103850 -3.715157962 [182,] -2.065049425 -6.091103850 [183,] -2.897975520 -2.065049425 [184,] -1.112179790 -2.897975520 [185,] 5.326539932 -1.112179790 [186,] 1.017812666 5.326539932 [187,] 0.005574726 1.017812666 [188,] -1.505707437 0.005574726 [189,] 3.811596330 -1.505707437 [190,] -2.896713994 3.811596330 [191,] -2.945414492 -2.896713994 [192,] 1.430627733 -2.945414492 [193,] -0.746026704 1.430627733 [194,] 0.866472535 -0.746026704 [195,] -3.206580196 0.866472535 [196,] 5.919816506 -3.206580196 [197,] 2.448959092 5.919816506 [198,] 0.673354819 2.448959092 [199,] -0.550265524 0.673354819 [200,] -0.499475295 -0.550265524 [201,] -1.053611217 -0.499475295 [202,] 0.038463165 -1.053611217 [203,] -1.864747282 0.038463165 [204,] -1.779454949 -1.864747282 [205,] 0.326907596 -1.779454949 [206,] -1.115930472 0.326907596 [207,] 1.856936938 -1.115930472 [208,] -0.749837781 1.856936938 [209,] 0.847500674 -0.749837781 [210,] -0.740236133 0.847500674 [211,] -0.624597471 -0.740236133 [212,] 3.213937400 -0.624597471 [213,] -3.067715016 3.213937400 [214,] 1.457144003 -3.067715016 [215,] -0.643083642 1.457144003 [216,] 0.549183894 -0.643083642 [217,] -0.892800073 0.549183894 [218,] 3.824268927 -0.892800073 [219,] 1.666286911 3.824268927 [220,] -3.205906492 1.666286911 [221,] -0.136154708 -3.205906492 [222,] 0.877434279 -0.136154708 [223,] -4.018114896 0.877434279 [224,] 3.916645626 -4.018114896 [225,] -1.989196546 3.916645626 [226,] -0.220066131 -1.989196546 [227,] -2.672408161 -0.220066131 [228,] 4.567921306 -2.672408161 [229,] -2.805234707 4.567921306 [230,] -1.567716108 -2.805234707 [231,] -1.974629197 -1.567716108 [232,] -4.709984827 -1.974629197 [233,] 3.171739357 -4.709984827 [234,] -3.061672993 3.171739357 [235,] -1.728373018 -3.061672993 [236,] 1.236590452 -1.728373018 [237,] -2.774357545 1.236590452 [238,] -4.865563186 -2.774357545 [239,] -3.648504610 -4.865563186 [240,] -3.938372124 -3.648504610 [241,] 0.006270271 -3.938372124 [242,] -0.101724548 0.006270271 [243,] 0.858917233 -0.101724548 [244,] 1.701543008 0.858917233 [245,] 4.160278314 1.701543008 [246,] 0.455075114 4.160278314 [247,] 7.374245268 0.455075114 [248,] 2.108477665 7.374245268 [249,] 0.088745077 2.108477665 [250,] 1.080964620 0.088745077 [251,] 3.196419435 1.080964620 [252,] -3.265421674 3.196419435 [253,] -0.987913293 -3.265421674 [254,] 1.704293656 -0.987913293 [255,] -0.208178306 1.704293656 [256,] 5.244684661 -0.208178306 [257,] 0.259538447 5.244684661 [258,] 2.228603258 0.259538447 [259,] -8.260452585 2.228603258 [260,] -1.570955649 -8.260452585 [261,] -0.785629954 -1.570955649 [262,] 3.107657692 -0.785629954 [263,] -1.130256422 3.107657692 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.710833260 -1.965539258 2 -1.351729121 1.710833260 3 -2.799131568 -1.351729121 4 9.803078475 -2.799131568 5 2.253316746 9.803078475 6 7.611735685 2.253316746 7 -1.290131307 7.611735685 8 -1.979300593 -1.290131307 9 1.030875586 -1.979300593 10 -0.281942683 1.030875586 11 -2.368788864 -0.281942683 12 -0.847706575 -2.368788864 13 1.632356430 -0.847706575 14 1.135274867 1.632356430 15 0.210009241 1.135274867 16 1.588009632 0.210009241 17 1.730949316 1.588009632 18 -3.252051272 1.730949316 19 0.521580162 -3.252051272 20 -0.973139403 0.521580162 21 -1.443158893 -0.973139403 22 -1.411944941 -1.443158893 23 -1.434600071 -1.411944941 24 1.636419513 -1.434600071 25 -5.923742857 1.636419513 26 0.605421950 -5.923742857 27 1.858241817 0.605421950 28 2.904912938 1.858241817 29 -2.653257891 2.904912938 30 -1.850671015 -2.653257891 31 0.047020896 -1.850671015 32 -0.583030942 0.047020896 33 -0.556013214 -0.583030942 34 1.009964577 -0.556013214 35 -3.911373320 1.009964577 36 3.111586491 -3.911373320 37 0.103144034 3.111586491 38 -0.299640350 0.103144034 39 -3.365222271 -0.299640350 40 -2.598146239 -3.365222271 41 3.262259012 -2.598146239 42 -3.762972270 3.262259012 43 -1.125456566 -3.762972270 44 -0.985394674 -1.125456566 45 -1.753115745 -0.985394674 46 -3.753354752 -1.753115745 47 -0.702624593 -3.753354752 48 5.327268630 -0.702624593 49 -1.785762885 5.327268630 50 -1.044540381 -1.785762885 51 1.214023843 -1.044540381 52 2.911349021 1.214023843 53 -1.735006907 2.911349021 54 -3.603952208 -1.735006907 55 2.769340606 -3.603952208 56 2.324429411 2.769340606 57 -3.294113682 2.324429411 58 -3.393879305 -3.294113682 59 1.106530317 -3.393879305 60 1.921420612 1.106530317 61 -0.226243087 1.921420612 62 -1.661490915 -0.226243087 63 0.320772884 -1.661490915 64 0.269974999 0.320772884 65 -4.574976040 0.269974999 66 1.102864169 -4.574976040 67 -2.097886558 1.102864169 68 -0.345840484 -2.097886558 69 1.151160510 -0.345840484 70 -1.620710836 1.151160510 71 1.601477591 -1.620710836 72 1.312455845 1.601477591 73 -2.218016588 1.312455845 74 -2.037009073 -2.218016588 75 4.907915708 -2.037009073 76 1.663878808 4.907915708 77 2.468780366 1.663878808 78 0.329082372 2.468780366 79 -6.030372538 0.329082372 80 -0.825324161 -6.030372538 81 -3.923159075 -0.825324161 82 0.441467578 -3.923159075 83 -0.742689025 0.441467578 84 -0.018606727 -0.742689025 85 0.637024187 -0.018606727 86 -1.444091427 0.637024187 87 -0.205545446 -1.444091427 88 3.695603838 -0.205545446 89 1.220004691 3.695603838 90 0.164706670 1.220004691 91 0.974675499 0.164706670 92 2.675385354 0.974675499 93 -0.892941807 2.675385354 94 0.090343098 -0.892941807 95 1.224086893 0.090343098 96 -1.556199324 1.224086893 97 0.802817132 -1.556199324 98 -2.392407260 0.802817132 99 -0.430014713 -2.392407260 100 1.538955660 -0.430014713 101 1.452701632 1.538955660 102 -4.976904507 1.452701632 103 4.159483083 -4.976904507 104 2.271391176 4.159483083 105 -0.387513205 2.271391176 106 3.782061742 -0.387513205 107 -1.940732924 3.782061742 108 6.044224161 -1.940732924 109 2.320190852 6.044224161 110 0.184891683 2.320190852 111 -1.967969769 0.184891683 112 1.584995792 -1.967969769 113 1.635975053 1.584995792 114 -0.588432889 1.635975053 115 -0.307575640 -0.588432889 116 -1.098068557 -0.307575640 117 -4.421995887 -1.098068557 118 3.252150067 -4.421995887 119 -0.761213027 3.252150067 120 1.314773580 -0.761213027 121 -0.245246846 1.314773580 122 -3.986213250 -0.245246846 123 -1.241267416 -3.986213250 124 -1.974034722 -1.241267416 125 -1.710015739 -1.974034722 126 -0.224407533 -1.710015739 127 1.315197266 -0.224407533 128 0.511750480 1.315197266 129 -2.525290189 0.511750480 130 2.752694852 -2.525290189 131 -1.651154907 2.752694852 132 1.838587420 -1.651154907 133 -0.248257382 1.838587420 134 3.048011453 -0.248257382 135 6.532534224 3.048011453 136 -0.098692645 6.532534224 137 -0.877775267 -0.098692645 138 -2.144359789 -0.877775267 139 -2.005452213 -2.144359789 140 -3.424717439 -2.005452213 141 3.115407016 -3.424717439 142 0.046492127 3.115407016 143 0.314876752 0.046492127 144 0.232872623 0.314876752 145 0.889191485 0.232872623 146 -2.957654025 0.889191485 147 -3.014294454 -2.957654025 148 2.399466797 -3.014294454 149 0.839498864 2.399466797 150 4.417142408 0.839498864 151 -2.100227380 4.417142408 152 -2.234377891 -2.100227380 153 3.546649162 -2.234377891 154 3.903010062 3.546649162 155 0.698069695 3.903010062 156 0.561654765 0.698069695 157 1.561364816 0.561654765 158 -3.557583938 1.561364816 159 4.507066936 -3.557583938 160 1.515646696 4.507066936 161 6.208933568 1.515646696 162 0.774259817 6.208933568 163 8.493265775 0.774259817 164 1.523701902 8.493265775 165 5.266915064 1.523701902 166 -1.692697980 5.266915064 167 -1.314724374 -1.692697980 168 -0.727031799 -1.314724374 169 1.309326749 -0.727031799 170 2.724630644 1.309326749 171 -0.280823976 2.724630644 172 -4.767823902 -0.280823976 173 1.261979701 -4.767823902 174 0.500409091 1.261979701 175 -4.514895149 0.500409091 176 -1.882457231 -4.514895149 177 2.527522285 -1.882457231 178 -2.860618828 2.527522285 179 -1.276268547 -2.860618828 180 -3.715157962 -1.276268547 181 -6.091103850 -3.715157962 182 -2.065049425 -6.091103850 183 -2.897975520 -2.065049425 184 -1.112179790 -2.897975520 185 5.326539932 -1.112179790 186 1.017812666 5.326539932 187 0.005574726 1.017812666 188 -1.505707437 0.005574726 189 3.811596330 -1.505707437 190 -2.896713994 3.811596330 191 -2.945414492 -2.896713994 192 1.430627733 -2.945414492 193 -0.746026704 1.430627733 194 0.866472535 -0.746026704 195 -3.206580196 0.866472535 196 5.919816506 -3.206580196 197 2.448959092 5.919816506 198 0.673354819 2.448959092 199 -0.550265524 0.673354819 200 -0.499475295 -0.550265524 201 -1.053611217 -0.499475295 202 0.038463165 -1.053611217 203 -1.864747282 0.038463165 204 -1.779454949 -1.864747282 205 0.326907596 -1.779454949 206 -1.115930472 0.326907596 207 1.856936938 -1.115930472 208 -0.749837781 1.856936938 209 0.847500674 -0.749837781 210 -0.740236133 0.847500674 211 -0.624597471 -0.740236133 212 3.213937400 -0.624597471 213 -3.067715016 3.213937400 214 1.457144003 -3.067715016 215 -0.643083642 1.457144003 216 0.549183894 -0.643083642 217 -0.892800073 0.549183894 218 3.824268927 -0.892800073 219 1.666286911 3.824268927 220 -3.205906492 1.666286911 221 -0.136154708 -3.205906492 222 0.877434279 -0.136154708 223 -4.018114896 0.877434279 224 3.916645626 -4.018114896 225 -1.989196546 3.916645626 226 -0.220066131 -1.989196546 227 -2.672408161 -0.220066131 228 4.567921306 -2.672408161 229 -2.805234707 4.567921306 230 -1.567716108 -2.805234707 231 -1.974629197 -1.567716108 232 -4.709984827 -1.974629197 233 3.171739357 -4.709984827 234 -3.061672993 3.171739357 235 -1.728373018 -3.061672993 236 1.236590452 -1.728373018 237 -2.774357545 1.236590452 238 -4.865563186 -2.774357545 239 -3.648504610 -4.865563186 240 -3.938372124 -3.648504610 241 0.006270271 -3.938372124 242 -0.101724548 0.006270271 243 0.858917233 -0.101724548 244 1.701543008 0.858917233 245 4.160278314 1.701543008 246 0.455075114 4.160278314 247 7.374245268 0.455075114 248 2.108477665 7.374245268 249 0.088745077 2.108477665 250 1.080964620 0.088745077 251 3.196419435 1.080964620 252 -3.265421674 3.196419435 253 -0.987913293 -3.265421674 254 1.704293656 -0.987913293 255 -0.208178306 1.704293656 256 5.244684661 -0.208178306 257 0.259538447 5.244684661 258 2.228603258 0.259538447 259 -8.260452585 2.228603258 260 -1.570955649 -8.260452585 261 -0.785629954 -1.570955649 262 3.107657692 -0.785629954 263 -1.130256422 3.107657692 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7pj8l1384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8ikt51384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9yxa11384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/106tjh1384694160.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/117r4l1384694160.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12dz661384694160.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ihbc1384694160.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14zvfb1384694160.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15zyme1384694160.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16vipi1384694160.tab") + } > > try(system("convert tmp/1umjn1384694160.ps tmp/1umjn1384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/2jiq51384694160.ps tmp/2jiq51384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/3cjuw1384694160.ps tmp/3cjuw1384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/44r9g1384694160.ps tmp/44r9g1384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/5lqo01384694160.ps tmp/5lqo01384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/6mjio1384694160.ps tmp/6mjio1384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/7pj8l1384694160.ps tmp/7pj8l1384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/8ikt51384694160.ps tmp/8ikt51384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/9yxa11384694160.ps tmp/9yxa11384694160.png",intern=TRUE)) character(0) > try(system("convert tmp/106tjh1384694160.ps tmp/106tjh1384694160.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.575 2.960 18.515