R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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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 = '5' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Software month Connected Separate Learning Happiness Depression Belonging 1 12 9 41 38 13 14 12.0 53 2 11 9 39 32 16 18 11.0 83 3 15 9 30 35 19 11 14.0 66 4 6 9 31 33 15 12 12.0 67 5 13 9 34 37 14 16 21.0 76 6 10 9 35 29 13 18 12.0 78 7 12 9 39 31 19 14 22.0 53 8 14 9 34 36 15 14 11.0 80 9 12 9 36 35 14 15 10.0 74 10 9 9 37 38 15 15 13.0 76 11 10 9 38 31 16 17 10.0 79 12 12 9 36 34 16 19 8.0 54 13 12 9 38 35 16 10 15.0 67 14 11 9 39 38 16 16 14.0 54 15 15 9 33 37 17 18 10.0 87 16 12 9 32 33 15 14 14.0 58 17 10 9 36 32 15 14 14.0 75 18 12 9 38 38 20 17 11.0 88 19 11 9 39 38 18 14 10.0 64 20 12 9 32 32 16 16 13.0 57 21 11 9 32 33 16 18 9.5 66 22 12 9 31 31 16 11 14.0 68 23 13 9 39 38 19 14 12.0 54 24 11 9 37 39 16 12 14.0 56 25 12 9 39 32 17 17 11.0 86 26 13 9 41 32 17 9 9.0 80 27 10 9 36 35 16 16 11.0 76 28 14 9 33 37 15 14 15.0 69 29 12 9 33 33 16 15 14.0 78 30 10 9 34 33 14 11 13.0 67 31 12 9 31 31 15 16 9.0 80 32 8 9 27 32 12 13 15.0 54 33 10 9 37 31 14 17 10.0 71 34 12 9 34 37 16 15 11.0 84 35 12 9 34 30 14 14 13.0 74 36 7 9 32 33 10 16 8.0 71 37 9 9 29 31 10 9 20.0 63 38 12 9 36 33 14 15 12.0 71 39 10 9 29 31 16 17 10.0 76 40 10 9 35 33 16 13 10.0 69 41 10 9 37 32 16 15 9.0 74 42 12 9 34 33 14 16 14.0 75 43 15 9 38 32 20 16 8.0 54 44 10 9 35 33 14 12 14.0 52 45 10 9 38 28 14 15 11.0 69 46 12 9 37 35 11 11 13.0 68 47 13 9 38 39 14 15 9.0 65 48 11 9 33 34 15 15 11.0 75 49 11 9 36 38 16 17 15.0 74 50 12 9 38 32 14 13 11.0 75 51 14 9 32 38 16 16 10.0 72 52 10 9 32 30 14 14 14.0 67 53 12 9 32 33 12 11 18.0 63 54 13 9 34 38 16 12 14.0 62 55 5 9 32 32 9 12 11.0 63 56 6 9 37 35 14 15 14.5 76 57 12 9 39 34 16 16 13.0 74 58 12 9 29 34 16 15 9.0 67 59 11 9 37 36 15 12 10.0 73 60 10 9 35 34 16 12 15.0 70 61 7 9 30 28 12 8 20.0 53 62 12 9 38 34 16 13 12.0 77 63 14 9 34 35 16 11 12.0 80 64 11 9 31 35 14 14 14.0 52 65 12 9 34 31 16 15 13.0 54 66 13 10 35 37 17 10 11.0 80 67 14 10 36 35 18 11 17.0 66 68 11 10 30 27 18 12 12.0 73 69 12 10 39 40 12 15 13.0 63 70 12 10 35 37 16 15 14.0 69 71 8 10 38 36 10 14 13.0 67 72 11 10 31 38 14 16 15.0 54 73 14 10 34 39 18 15 13.0 81 74 14 10 38 41 18 15 10.0 69 75 12 10 34 27 16 13 11.0 84 76 9 10 39 30 17 12 19.0 80 77 13 10 37 37 16 17 13.0 70 78 11 10 34 31 16 13 17.0 69 79 12 10 28 31 13 15 13.0 77 80 12 10 37 27 16 13 9.0 54 81 12 10 33 36 16 15 11.0 79 82 12 10 35 37 16 15 9.0 71 83 12 10 37 33 15 16 12.0 73 84 11 10 32 34 15 15 12.0 72 85 10 10 33 31 16 14 13.0 77 86 9 10 38 39 14 15 13.0 75 87 12 10 33 34 16 14 12.0 69 88 12 10 29 32 16 13 15.0 54 89 12 10 33 33 15 7 22.0 70 90 9 10 31 36 12 17 13.0 73 91 15 10 36 32 17 13 15.0 54 92 12 10 35 41 16 15 13.0 77 93 12 10 32 28 15 14 15.0 82 94 12 10 29 30 13 13 12.5 80 95 10 10 39 36 16 16 11.0 80 96 13 10 37 35 16 12 16.0 69 97 9 10 35 31 16 14 11.0 78 98 12 10 37 34 16 17 11.0 81 99 10 10 32 36 14 15 10.0 76 100 14 10 38 36 16 17 10.0 76 101 11 10 37 35 16 12 16.0 73 102 15 10 36 37 20 16 12.0 85 103 11 10 32 28 15 11 11.0 66 104 11 10 33 39 16 15 16.0 79 105 12 10 40 32 13 9 19.0 68 106 12 10 38 35 17 16 11.0 76 107 12 10 41 39 16 15 16.0 71 108 11 10 36 35 16 10 15.0 54 109 7 10 43 42 12 10 24.0 46 110 12 10 30 34 16 15 14.0 85 111 14 10 31 33 16 11 15.0 74 112 11 10 32 41 17 13 11.0 88 113 11 10 32 33 13 14 15.0 38 114 10 10 37 34 12 18 12.0 76 115 13 10 37 32 18 16 10.0 86 116 13 10 33 40 14 14 14.0 54 117 8 10 34 40 14 14 13.0 67 118 11 10 33 35 13 14 9.0 69 119 12 10 38 36 16 14 15.0 90 120 11 10 33 37 13 12 15.0 54 121 13 10 31 27 16 14 14.0 76 122 12 10 38 39 13 15 11.0 89 123 14 10 37 38 16 15 8.0 76 124 13 10 36 31 15 15 11.0 73 125 15 10 31 33 16 13 11.0 79 126 10 10 39 32 15 17 8.0 90 127 11 10 44 39 17 17 10.0 74 128 9 10 33 36 15 19 11.0 81 129 11 10 35 33 12 15 13.0 72 130 10 10 32 33 16 13 11.0 71 131 11 10 28 32 10 9 20.0 66 132 8 10 40 37 16 15 10.0 77 133 11 10 27 30 12 15 15.0 65 134 12 10 37 38 14 15 12.0 74 135 12 10 32 29 15 16 14.0 85 136 9 10 28 22 13 11 23.0 54 137 11 10 34 35 15 14 14.0 63 138 10 10 30 35 11 11 16.0 54 139 8 10 35 34 12 15 11.0 64 140 9 10 31 35 11 13 12.0 69 141 8 10 32 34 16 15 10.0 54 142 9 10 30 37 15 16 14.0 84 143 15 10 30 35 17 14 12.0 86 144 11 10 31 23 16 15 12.0 77 145 8 10 40 31 10 16 11.0 89 146 13 10 32 27 18 16 12.0 76 147 12 10 36 36 13 11 13.0 60 148 12 10 32 31 16 12 11.0 75 149 9 10 35 32 13 9 19.0 73 150 7 10 38 39 10 16 12.0 85 151 13 10 42 37 15 13 17.0 79 152 9 10 34 38 16 16 9.0 71 153 6 10 35 39 16 12 12.0 72 154 8 9 38 34 14 9 19.0 69 155 8 10 33 31 10 13 18.0 78 156 15 10 36 32 17 13 15.0 54 157 6 10 32 37 13 14 14.0 69 158 9 10 33 36 15 19 11.0 81 159 11 10 34 32 16 13 9.0 84 160 8 10 32 38 12 12 18.0 84 161 8 10 34 36 13 13 16.0 69 162 10 11 27 26 13 10 24.0 66 163 8 11 31 26 12 14 14.0 81 164 14 11 38 33 17 16 20.0 82 165 10 11 34 39 15 10 18.0 72 166 8 11 24 30 10 11 23.0 54 167 11 11 30 33 14 14 12.0 78 168 12 11 26 25 11 12 14.0 74 169 12 11 34 38 13 9 16.0 82 170 12 11 27 37 16 9 18.0 73 171 5 11 37 31 12 11 20.0 55 172 12 11 36 37 16 16 12.0 72 173 10 11 41 35 12 9 12.0 78 174 7 11 29 25 9 13 17.0 59 175 12 11 36 28 12 16 13.0 72 176 11 11 32 35 15 13 9.0 78 177 8 11 37 33 12 9 16.0 68 178 9 11 30 30 12 12 18.0 69 179 10 11 31 31 14 16 10.0 67 180 9 11 38 37 12 11 14.0 74 181 12 11 36 36 16 14 11.0 54 182 6 11 35 30 11 13 9.0 67 183 15 11 31 36 19 15 11.0 70 184 12 11 38 32 15 14 10.0 80 185 12 11 22 28 8 16 11.0 89 186 12 11 32 36 16 13 19.0 76 187 11 11 36 34 17 14 14.0 74 188 7 11 39 31 12 15 12.0 87 189 7 11 28 28 11 13 14.0 54 190 5 11 32 36 11 11 21.0 61 191 12 11 32 36 14 11 13.0 38 192 12 11 38 40 16 14 10.0 75 193 3 11 32 33 12 15 15.0 69 194 11 11 35 37 16 11 16.0 62 195 10 11 32 32 13 15 14.0 72 196 12 11 37 38 15 12 12.0 70 197 9 11 34 31 16 14 19.0 79 198 12 11 33 37 16 14 15.0 87 199 9 11 33 33 14 8 19.0 62 200 12 11 26 32 16 13 13.0 77 201 12 11 30 30 16 9 17.0 69 202 10 11 24 30 14 15 12.0 69 203 9 11 34 31 11 17 11.0 75 204 12 11 34 32 12 13 14.0 54 205 8 11 33 34 15 15 11.0 72 206 11 11 34 36 15 15 13.0 74 207 11 11 35 37 16 14 12.0 85 208 12 11 35 36 16 16 15.0 52 209 10 11 36 33 11 13 14.0 70 210 10 11 34 33 15 16 12.0 84 211 12 11 34 33 12 9 17.0 64 212 12 11 41 44 12 16 11.0 84 213 11 11 32 39 15 11 18.0 87 214 8 11 30 32 15 10 13.0 79 215 12 11 35 35 16 11 17.0 67 216 10 11 28 25 14 15 13.0 65 217 11 11 33 35 17 17 11.0 85 218 10 11 39 34 14 14 12.0 83 219 8 11 36 35 13 8 22.0 61 220 12 11 36 39 15 15 14.0 82 221 12 11 35 33 13 11 12.0 76 222 10 11 38 36 14 16 12.0 58 223 12 11 33 32 15 10 17.0 72 224 9 11 31 32 12 15 9.0 72 225 9 11 34 36 13 9 21.0 38 226 6 11 32 36 8 16 10.0 78 227 10 11 31 32 14 19 11.0 54 228 9 11 33 34 14 12 12.0 63 229 9 11 34 33 11 8 23.0 66 230 9 11 34 35 12 11 13.0 70 231 6 11 34 30 13 14 12.0 71 232 10 11 33 38 10 9 16.0 67 233 6 11 32 34 16 15 9.0 58 234 14 11 41 33 18 13 17.0 72 235 10 11 34 32 13 16 9.0 72 236 10 11 36 31 11 11 14.0 70 237 6 11 37 30 4 12 17.0 76 238 12 11 36 27 13 13 13.0 50 239 12 11 29 31 16 10 11.0 72 240 7 11 37 30 10 11 12.0 72 241 8 11 27 32 12 12 10.0 88 242 11 11 35 35 12 8 19.0 53 243 3 11 28 28 10 12 16.0 58 244 6 11 35 33 13 12 16.0 66 245 10 11 37 31 15 15 14.0 82 246 8 11 29 35 12 11 20.0 69 247 9 11 32 35 14 13 15.0 68 248 9 11 36 32 10 14 23.0 44 249 8 11 19 21 12 10 20.0 56 250 9 11 21 20 12 12 16.0 53 251 7 11 31 34 11 15 14.0 70 252 7 11 33 32 10 13 17.0 78 253 6 11 36 34 12 13 11.0 71 254 9 11 33 32 16 13 13.0 72 255 10 11 37 33 12 12 17.0 68 256 11 11 34 33 14 12 15.0 67 257 12 11 35 37 16 9 21.0 75 258 8 11 31 32 14 9 18.0 62 259 11 11 37 34 13 15 15.0 67 260 3 11 35 30 4 10 8.0 83 261 11 11 27 30 15 14 12.0 64 262 12 11 34 38 11 15 12.0 68 263 7 11 40 36 11 7 22.0 62 264 9 11 29 32 14 14 12.0 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 -3.584040 0.769535 -0.017943 0.036920 Learning Happiness Depression Belonging 0.519756 -0.033210 -0.011533 0.008105 Belonging_Final t -0.003066 -0.011815 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.2411 -0.9752 0.1003 1.1854 4.6134 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.584040 3.772925 -0.950 0.34305 month 0.769535 0.393103 1.958 0.05137 . Connected -0.017943 0.033633 -0.533 0.59416 Separate 0.036920 0.034113 1.082 0.28016 Learning 0.519756 0.051290 10.134 < 2e-16 *** Happiness -0.033210 0.056401 -0.589 0.55651 Depression -0.011533 0.041162 -0.280 0.77957 Belonging 0.008105 0.036808 0.220 0.82590 Belonging_Final -0.003066 0.054668 -0.056 0.95532 t -0.011815 0.004150 -2.847 0.00477 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.802 on 254 degrees of freedom Multiple R-squared: 0.4174, Adjusted R-squared: 0.3968 F-statistic: 20.22 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,] 0.996070086 0.007859828 0.003929914 [2,] 0.994410440 0.011179121 0.005589560 [3,] 0.990357101 0.019285798 0.009642899 [4,] 0.985904924 0.028190152 0.014095076 [5,] 0.974704347 0.050591305 0.025295653 [6,] 0.972064845 0.055870310 0.027935155 [7,] 0.962069876 0.075860249 0.037930124 [8,] 0.941283315 0.117433371 0.058716685 [9,] 0.918028270 0.163943461 0.081971730 [10,] 0.891081780 0.217836441 0.108918220 [11,] 0.861945474 0.276109051 0.138054526 [12,] 0.829339395 0.341321209 0.170660605 [13,] 0.793946078 0.412107843 0.206053922 [14,] 0.790648948 0.418702103 0.209351052 [15,] 0.802159470 0.395681060 0.197840530 [16,] 0.803763511 0.392472979 0.196236489 [17,] 0.754001111 0.491997779 0.245998889 [18,] 0.717802053 0.564395894 0.282197947 [19,] 0.669155772 0.661688455 0.330844228 [20,] 0.686294827 0.627410346 0.313705173 [21,] 0.629774820 0.740450359 0.370225180 [22,] 0.572970924 0.854058151 0.427029076 [23,] 0.554508210 0.890983581 0.445491790 [24,] 0.541379294 0.917241411 0.458620706 [25,] 0.483971618 0.967943236 0.516028382 [26,] 0.465457456 0.930914913 0.534542544 [27,] 0.443503933 0.887007866 0.556496067 [28,] 0.412731704 0.825463407 0.587268296 [29,] 0.374617132 0.749234264 0.625382868 [30,] 0.343910733 0.687821465 0.656089267 [31,] 0.392552919 0.785105838 0.607447081 [32,] 0.346645719 0.693291438 0.653354281 [33,] 0.300287793 0.600575587 0.699712207 [34,] 0.336356213 0.672712426 0.663643787 [35,] 0.338441894 0.676883788 0.661558106 [36,] 0.296978979 0.593957957 0.703021021 [37,] 0.297017734 0.594035467 0.702982266 [38,] 0.272246999 0.544493998 0.727753001 [39,] 0.269452834 0.538905669 0.730547166 [40,] 0.234319873 0.468639745 0.765680127 [41,] 0.231109020 0.462218040 0.768890980 [42,] 0.199381697 0.398763395 0.800618303 [43,] 0.301057738 0.602115476 0.698942262 [44,] 0.619997247 0.760005506 0.380002753 [45,] 0.577618892 0.844762216 0.422381108 [46,] 0.533706364 0.932587271 0.466293636 [47,] 0.491198656 0.982397312 0.508801344 [48,] 0.487218461 0.974436921 0.512781539 [49,] 0.495898535 0.991797070 0.504101465 [50,] 0.454151569 0.908303138 0.545848431 [51,] 0.465507781 0.931015562 0.534492219 [52,] 0.423959828 0.847919657 0.576040172 [53,] 0.393913531 0.787827062 0.606086469 [54,] 0.352532328 0.705064656 0.647467672 [55,] 0.319173381 0.638346762 0.680826619 [56,] 0.305833401 0.611666802 0.694166599 [57,] 0.286172957 0.572345914 0.713827043 [58,] 0.254943759 0.509887519 0.745056241 [59,] 0.235799170 0.471598339 0.764200830 [60,] 0.205623684 0.411247368 0.794376316 [61,] 0.177933745 0.355867490 0.822066255 [62,] 0.152954325 0.305908650 0.847045675 [63,] 0.136917378 0.273834755 0.863082622 [64,] 0.180048014 0.360096029 0.819951986 [65,] 0.160718142 0.321436283 0.839281858 [66,] 0.139357405 0.278714809 0.860642595 [67,] 0.139495917 0.278991835 0.860504083 [68,] 0.128947727 0.257895454 0.871052273 [69,] 0.110239902 0.220479804 0.889760098 [70,] 0.093476065 0.186952129 0.906523935 [71,] 0.079874037 0.159748074 0.920125963 [72,] 0.067385240 0.134770481 0.932614760 [73,] 0.066619143 0.133238287 0.933380857 [74,] 0.079053285 0.158106569 0.920946715 [75,] 0.065420290 0.130840581 0.934579710 [76,] 0.054021546 0.108043092 0.945978454 [77,] 0.045739857 0.091479713 0.954260143 [78,] 0.039812521 0.079625042 0.960187479 [79,] 0.056214878 0.112429756 0.943785122 [80,] 0.047645074 0.095290148 0.952354926 [81,] 0.042536087 0.085072175 0.957463913 [82,] 0.042173432 0.084346864 0.957826568 [83,] 0.044833191 0.089666381 0.955166809 [84,] 0.039235092 0.078470184 0.960764908 [85,] 0.051804492 0.103608985 0.948195508 [86,] 0.042635030 0.085270060 0.957364970 [87,] 0.037578114 0.075156228 0.962421886 [88,] 0.040796256 0.081592512 0.959203744 [89,] 0.035149702 0.070299404 0.964850298 [90,] 0.029744256 0.059488512 0.970255744 [91,] 0.024093703 0.048187406 0.975906297 [92,] 0.021916526 0.043833051 0.978083474 [93,] 0.022442701 0.044885401 0.977557299 [94,] 0.018010719 0.036021437 0.981989281 [95,] 0.014298818 0.028597636 0.985701182 [96,] 0.012225494 0.024450988 0.987774506 [97,] 0.018350678 0.036701357 0.981649322 [98,] 0.014529249 0.029058497 0.985470751 [99,] 0.015616658 0.031233315 0.984383342 [100,] 0.016603467 0.033206933 0.983396533 [101,] 0.013667132 0.027334263 0.986332868 [102,] 0.011037424 0.022074847 0.988962576 [103,] 0.008708058 0.017416116 0.991291942 [104,] 0.009331829 0.018663658 0.990668171 [105,] 0.014152530 0.028305061 0.985847470 [106,] 0.011605861 0.023211723 0.988394139 [107,] 0.009146855 0.018293710 0.990853145 [108,] 0.007444903 0.014889807 0.992555097 [109,] 0.006996318 0.013992635 0.993003682 [110,] 0.006792383 0.013584765 0.993207617 [111,] 0.007457950 0.014915901 0.992542050 [112,] 0.007881190 0.015762381 0.992118810 [113,] 0.012984537 0.025969074 0.987015463 [114,] 0.011271971 0.022543941 0.988728029 [115,] 0.010048926 0.020097851 0.989951074 [116,] 0.010958016 0.021916031 0.989041984 [117,] 0.010499512 0.020999024 0.989500488 [118,] 0.010737117 0.021474233 0.989262883 [119,] 0.013332402 0.026664804 0.986667598 [120,] 0.026286245 0.052572490 0.973713755 [121,] 0.025787998 0.051575995 0.974212002 [122,] 0.024290552 0.048581103 0.975709448 [123,] 0.021554065 0.043108130 0.978445935 [124,] 0.017815176 0.035630352 0.982184824 [125,] 0.014350881 0.028701762 0.985649119 [126,] 0.013145901 0.026291802 0.986854099 [127,] 0.011827652 0.023655303 0.988172348 [128,] 0.009964917 0.019929834 0.990035083 [129,] 0.017221632 0.034443264 0.982778368 [130,] 0.018515716 0.037031432 0.981484284 [131,] 0.027472198 0.054944397 0.972527802 [132,] 0.022265191 0.044530383 0.977734809 [133,] 0.017901190 0.035802380 0.982098810 [134,] 0.015403444 0.030806888 0.984596556 [135,] 0.018725219 0.037450439 0.981274781 [136,] 0.016486824 0.032973648 0.983513176 [137,] 0.013796577 0.027593153 0.986203423 [138,] 0.012015125 0.024030249 0.987984875 [139,] 0.015214137 0.030428275 0.984785863 [140,] 0.017143491 0.034286981 0.982856509 [141,] 0.070019201 0.140038402 0.929980799 [142,] 0.061795221 0.123590443 0.938204779 [143,] 0.052570835 0.105141671 0.947429165 [144,] 0.108845162 0.217690323 0.891154838 [145,] 0.143779167 0.287558333 0.856220833 [146,] 0.130696238 0.261392476 0.869303762 [147,] 0.116362543 0.232725086 0.883637457 [148,] 0.104041713 0.208083425 0.895958287 [149,] 0.091841554 0.183683109 0.908158446 [150,] 0.078081213 0.156162426 0.921918787 [151,] 0.073794828 0.147589657 0.926205172 [152,] 0.074846842 0.149693684 0.925153158 [153,] 0.071041741 0.142083482 0.928958259 [154,] 0.060496994 0.120993988 0.939503006 [155,] 0.050195103 0.100390206 0.949804897 [156,] 0.073638867 0.147277733 0.926361133 [157,] 0.070093941 0.140187882 0.929906059 [158,] 0.059532542 0.119065085 0.940467458 [159,] 0.125236877 0.250473753 0.874763123 [160,] 0.107010969 0.214021938 0.892989031 [161,] 0.091337325 0.182674650 0.908662675 [162,] 0.078092185 0.156184370 0.921907815 [163,] 0.101404188 0.202808376 0.898595812 [164,] 0.086483320 0.172966640 0.913516680 [165,] 0.080250928 0.160501856 0.919749072 [166,] 0.067464776 0.134929553 0.932535224 [167,] 0.056223588 0.112447177 0.943776412 [168,] 0.046827330 0.093654661 0.953172670 [169,] 0.038364397 0.076728793 0.961635603 [170,] 0.049166651 0.098333301 0.950833349 [171,] 0.047766970 0.095533940 0.952233030 [172,] 0.042443732 0.084887464 0.957556268 [173,] 0.167157484 0.334314969 0.832842516 [174,] 0.151701611 0.303403222 0.848298389 [175,] 0.133138458 0.266276916 0.866861542 [176,] 0.133855446 0.267710891 0.866144554 [177,] 0.123290483 0.246580966 0.876709517 [178,] 0.205282329 0.410564658 0.794717671 [179,] 0.204812768 0.409625537 0.795187232 [180,] 0.177904219 0.355808437 0.822095781 [181,] 0.582449294 0.835101412 0.417550706 [182,] 0.566107787 0.867784426 0.433892213 [183,] 0.524501265 0.950997470 0.475498735 [184,] 0.487836271 0.975672543 0.512163729 [185,] 0.520449315 0.959101370 0.479550685 [186,] 0.479366636 0.958733271 0.520633364 [187,] 0.474925829 0.949851659 0.525074171 [188,] 0.456382317 0.912764633 0.543617683 [189,] 0.416956304 0.833912608 0.583043696 [190,] 0.381077367 0.762154733 0.618922633 [191,] 0.348064398 0.696128795 0.651935602 [192,] 0.377697364 0.755394729 0.622302636 [193,] 0.422626237 0.845252473 0.577373763 [194,] 0.379125058 0.758250116 0.620874942 [195,] 0.344795814 0.689591628 0.655204186 [196,] 0.307171398 0.614342795 0.692828602 [197,] 0.280607710 0.561215419 0.719392290 [198,] 0.245561105 0.491122211 0.754438895 [199,] 0.285626931 0.571253863 0.714373069 [200,] 0.297247964 0.594495928 0.702752036 [201,] 0.257616253 0.515232506 0.742383747 [202,] 0.282513781 0.565027561 0.717486219 [203,] 0.247344219 0.494688438 0.752655781 [204,] 0.211720765 0.423441529 0.788279235 [205,] 0.181216168 0.362432337 0.818783832 [206,] 0.152237204 0.304474408 0.847762796 [207,] 0.161042594 0.322085188 0.838957406 [208,] 0.136710695 0.273421390 0.863289305 [209,] 0.160934238 0.321868475 0.839065762 [210,] 0.131412001 0.262824002 0.868587999 [211,] 0.122291482 0.244582964 0.877708518 [212,] 0.102359746 0.204719492 0.897640254 [213,] 0.081069220 0.162138441 0.918930780 [214,] 0.063556262 0.127112524 0.936443738 [215,] 0.049325391 0.098650783 0.950674609 [216,] 0.037552901 0.075105802 0.962447099 [217,] 0.027736436 0.055472871 0.972263564 [218,] 0.020416552 0.040833105 0.979583448 [219,] 0.029494055 0.058988110 0.970505945 [220,] 0.037887820 0.075775639 0.962112180 [221,] 0.170004873 0.340009745 0.829995127 [222,] 0.146751309 0.293502618 0.853248691 [223,] 0.113363647 0.226727294 0.886636353 [224,] 0.109105846 0.218211692 0.890894154 [225,] 0.137211516 0.274423032 0.862788484 [226,] 0.162032097 0.324064193 0.837967903 [227,] 0.181429763 0.362859526 0.818570237 [228,] 0.168107884 0.336215767 0.831892116 [229,] 0.170488037 0.340976075 0.829511963 [230,] 0.609955315 0.780089371 0.390044685 [231,] 0.667243182 0.665513636 0.332756818 [232,] 0.632452026 0.735095949 0.367547974 [233,] 0.560362908 0.879274183 0.439637092 [234,] 0.458000591 0.916001183 0.541999409 [235,] 0.362291158 0.724582315 0.637708842 [236,] 0.275878798 0.551757597 0.724121202 [237,] 0.188346782 0.376693564 0.811653218 [238,] 0.234009101 0.468018201 0.765990899 [239,] 0.168658093 0.337316185 0.831341907 > postscript(file="/var/wessaorg/rcomp/tmp/1rm8x1353260106.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/232vb1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3dede1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/48tka1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/50jzl1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 1.517814577 -0.907584206 1.185028394 -5.633375170 1.983367835 -0.222276570 7 8 9 10 11 12 -1.176625117 2.331008790 0.993426942 -2.582810296 -1.800729966 0.261355599 13 14 15 16 17 18 -0.023793772 -0.839300087 2.396177101 0.673443342 -1.316235890 -2.101568709 19 20 21 22 23 24 -1.988807739 0.300858698 -0.755801889 0.124323573 -0.381720930 -0.930743250 25 26 27 28 29 30 -0.210057498 0.582194117 -1.811017225 2.632394649 0.223933605 -0.765084007 31 32 33 34 35 36 0.776875286 -1.629502228 -0.472792763 0.097640214 1.441515026 -1.575086539 37 38 39 40 41 42 0.406034064 1.451143821 -1.616284618 -1.659024166 -1.550841317 1.489440073 43 44 45 46 47 48 1.558685125 -0.455278918 -0.244057107 2.942842304 2.385875010 -0.075965688 49 50 51 52 53 54 -0.569366144 1.561487651 2.313960293 -0.331385176 2.578903304 1.385759230 55 56 57 58 59 60 -2.851862608 -4.391513515 0.679582928 0.480163034 -0.052222477 -1.440233058 61 62 63 64 65 66 -2.188713793 0.585236893 2.406824115 0.701827175 0.887237480 0.055403853 67 68 69 70 71 72 0.833654000 -2.051082240 1.943811347 -0.109242441 -0.910543045 -0.016072803 73 74 75 76 77 78 0.704514573 0.752391550 0.103026078 -3.337556819 1.059196345 -0.836838669 79 80 81 82 83 84 1.591238207 0.383881645 -0.081625831 -0.032138652 0.728463674 -0.408395102 85 86 87 88 89 90 -1.840637315 -1.951666056 0.110207808 0.219454144 0.568329375 -0.794039294 91 92 93 94 95 96 2.860742581 -0.061101012 0.852061732 1.726822541 -1.780389279 1.234169151 97 98 99 100 101 102 -2.697209493 0.321180521 -0.834607706 2.308706295 -0.733043351 1.118880598 103 104 105 106 107 108 -0.064272136 -0.859914802 2.001299036 -0.118786685 0.371646112 -0.629038850 109 110 111 112 113 114 -2.511604257 0.285381188 2.295373624 -1.549431983 1.186434735 0.671443098 115 116 117 118 119 120 0.486423706 2.387873178 -2.665536137 0.964218868 0.408415346 1.010760180 121 122 123 124 125 126 1.713061194 1.884487789 2.405311616 2.230157813 3.449746009 -0.804600283 127 128 129 130 131 132 -0.882008016 -1.868478500 1.784822189 -1.423729643 2.677114822 -3.385712033 133 134 135 136 137 138 1.866831560 1.634061030 1.357269062 -0.277070090 0.273422319 1.267405047 139 140 141 142 143 144 -1.104441062 0.226093491 -3.165613047 -1.889298244 3.057280692 0.134521683 145 146 147 148 149 150 0.083017719 1.033283731 2.330935803 0.809412106 -0.593755845 -1.151332560 151 152 153 154 155 156 2.395576290 -2.239016875 -5.343328459 -1.281692983 0.133550917 3.628706839 157 158 159 160 161 162 -3.606055885 -1.514033458 -0.109125677 -1.208172958 -1.486937866 0.006685188 163 164 165 166 167 168 -1.481763618 1.935141460 -1.469563110 -0.500020970 0.253917081 3.025386425 169 170 171 172 173 174 1.538268479 -0.029452893 -4.323758003 0.336241453 0.318733687 -0.648599471 175 176 177 178 179 180 2.794521246 -0.277531626 -1.517025859 -0.411592949 -0.401478042 -0.619656487 181 182 183 184 185 186 0.513699612 -2.809236760 1.825607820 1.076201233 4.613375789 0.415476143 187 188 189 190 191 192 -0.958163706 -2.250601307 -1.648996165 -3.875598333 1.585245370 0.383864958 193 194 195 196 197 198 -6.241145016 -0.469846902 0.269933576 1.003945425 -2.212915832 0.463799948 199 200 201 202 203 204 -1.336764082 0.543605209 0.679158219 -0.247837737 0.478146655 2.983778718 205 206 207 208 209 210 -2.744991038 0.223915112 -0.406237064 0.937385371 1.459494512 -0.662105392 211 212 213 214 215 216 2.865603092 2.637941561 0.007029574 -2.799969062 0.820047170 0.220957660 217 218 219 220 221 222 -0.690991384 -0.047219407 -1.561085543 1.282606754 2.411835463 0.112908199 223 224 225 226 227 228 1.447726098 0.056714137 -0.431567848 -0.968120577 0.320710117 -0.996243867 229 230 231 232 233 234 0.623934482 -0.002894539 -3.246243891 1.927103039 -4.882936631 2.230806608 235 236 237 238 239 240 0.753959932 1.782848063 1.528461472 3.100965421 1.009893757 -0.631482267 241 242 243 244 245 246 -1.004383873 2.236576682 -4.518472351 -3.174432093 -0.105655472 -0.817804746 247 248 249 250 251 252 -0.771745583 1.778683952 -0.391158112 0.719669988 -1.110628286 -0.550661858 253 254 255 256 257 258 -2.620031253 -1.649203988 1.518760539 1.419207674 1.166501314 -1.607779068 259 260 261 262 263 264 2.084815836 -1.456497698 0.984491079 3.915562696 -0.998985274 -0.547769191 > postscript(file="/var/wessaorg/rcomp/tmp/66xmb1353260106.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 1.517814577 NA 1 -0.907584206 1.517814577 2 1.185028394 -0.907584206 3 -5.633375170 1.185028394 4 1.983367835 -5.633375170 5 -0.222276570 1.983367835 6 -1.176625117 -0.222276570 7 2.331008790 -1.176625117 8 0.993426942 2.331008790 9 -2.582810296 0.993426942 10 -1.800729966 -2.582810296 11 0.261355599 -1.800729966 12 -0.023793772 0.261355599 13 -0.839300087 -0.023793772 14 2.396177101 -0.839300087 15 0.673443342 2.396177101 16 -1.316235890 0.673443342 17 -2.101568709 -1.316235890 18 -1.988807739 -2.101568709 19 0.300858698 -1.988807739 20 -0.755801889 0.300858698 21 0.124323573 -0.755801889 22 -0.381720930 0.124323573 23 -0.930743250 -0.381720930 24 -0.210057498 -0.930743250 25 0.582194117 -0.210057498 26 -1.811017225 0.582194117 27 2.632394649 -1.811017225 28 0.223933605 2.632394649 29 -0.765084007 0.223933605 30 0.776875286 -0.765084007 31 -1.629502228 0.776875286 32 -0.472792763 -1.629502228 33 0.097640214 -0.472792763 34 1.441515026 0.097640214 35 -1.575086539 1.441515026 36 0.406034064 -1.575086539 37 1.451143821 0.406034064 38 -1.616284618 1.451143821 39 -1.659024166 -1.616284618 40 -1.550841317 -1.659024166 41 1.489440073 -1.550841317 42 1.558685125 1.489440073 43 -0.455278918 1.558685125 44 -0.244057107 -0.455278918 45 2.942842304 -0.244057107 46 2.385875010 2.942842304 47 -0.075965688 2.385875010 48 -0.569366144 -0.075965688 49 1.561487651 -0.569366144 50 2.313960293 1.561487651 51 -0.331385176 2.313960293 52 2.578903304 -0.331385176 53 1.385759230 2.578903304 54 -2.851862608 1.385759230 55 -4.391513515 -2.851862608 56 0.679582928 -4.391513515 57 0.480163034 0.679582928 58 -0.052222477 0.480163034 59 -1.440233058 -0.052222477 60 -2.188713793 -1.440233058 61 0.585236893 -2.188713793 62 2.406824115 0.585236893 63 0.701827175 2.406824115 64 0.887237480 0.701827175 65 0.055403853 0.887237480 66 0.833654000 0.055403853 67 -2.051082240 0.833654000 68 1.943811347 -2.051082240 69 -0.109242441 1.943811347 70 -0.910543045 -0.109242441 71 -0.016072803 -0.910543045 72 0.704514573 -0.016072803 73 0.752391550 0.704514573 74 0.103026078 0.752391550 75 -3.337556819 0.103026078 76 1.059196345 -3.337556819 77 -0.836838669 1.059196345 78 1.591238207 -0.836838669 79 0.383881645 1.591238207 80 -0.081625831 0.383881645 81 -0.032138652 -0.081625831 82 0.728463674 -0.032138652 83 -0.408395102 0.728463674 84 -1.840637315 -0.408395102 85 -1.951666056 -1.840637315 86 0.110207808 -1.951666056 87 0.219454144 0.110207808 88 0.568329375 0.219454144 89 -0.794039294 0.568329375 90 2.860742581 -0.794039294 91 -0.061101012 2.860742581 92 0.852061732 -0.061101012 93 1.726822541 0.852061732 94 -1.780389279 1.726822541 95 1.234169151 -1.780389279 96 -2.697209493 1.234169151 97 0.321180521 -2.697209493 98 -0.834607706 0.321180521 99 2.308706295 -0.834607706 100 -0.733043351 2.308706295 101 1.118880598 -0.733043351 102 -0.064272136 1.118880598 103 -0.859914802 -0.064272136 104 2.001299036 -0.859914802 105 -0.118786685 2.001299036 106 0.371646112 -0.118786685 107 -0.629038850 0.371646112 108 -2.511604257 -0.629038850 109 0.285381188 -2.511604257 110 2.295373624 0.285381188 111 -1.549431983 2.295373624 112 1.186434735 -1.549431983 113 0.671443098 1.186434735 114 0.486423706 0.671443098 115 2.387873178 0.486423706 116 -2.665536137 2.387873178 117 0.964218868 -2.665536137 118 0.408415346 0.964218868 119 1.010760180 0.408415346 120 1.713061194 1.010760180 121 1.884487789 1.713061194 122 2.405311616 1.884487789 123 2.230157813 2.405311616 124 3.449746009 2.230157813 125 -0.804600283 3.449746009 126 -0.882008016 -0.804600283 127 -1.868478500 -0.882008016 128 1.784822189 -1.868478500 129 -1.423729643 1.784822189 130 2.677114822 -1.423729643 131 -3.385712033 2.677114822 132 1.866831560 -3.385712033 133 1.634061030 1.866831560 134 1.357269062 1.634061030 135 -0.277070090 1.357269062 136 0.273422319 -0.277070090 137 1.267405047 0.273422319 138 -1.104441062 1.267405047 139 0.226093491 -1.104441062 140 -3.165613047 0.226093491 141 -1.889298244 -3.165613047 142 3.057280692 -1.889298244 143 0.134521683 3.057280692 144 0.083017719 0.134521683 145 1.033283731 0.083017719 146 2.330935803 1.033283731 147 0.809412106 2.330935803 148 -0.593755845 0.809412106 149 -1.151332560 -0.593755845 150 2.395576290 -1.151332560 151 -2.239016875 2.395576290 152 -5.343328459 -2.239016875 153 -1.281692983 -5.343328459 154 0.133550917 -1.281692983 155 3.628706839 0.133550917 156 -3.606055885 3.628706839 157 -1.514033458 -3.606055885 158 -0.109125677 -1.514033458 159 -1.208172958 -0.109125677 160 -1.486937866 -1.208172958 161 0.006685188 -1.486937866 162 -1.481763618 0.006685188 163 1.935141460 -1.481763618 164 -1.469563110 1.935141460 165 -0.500020970 -1.469563110 166 0.253917081 -0.500020970 167 3.025386425 0.253917081 168 1.538268479 3.025386425 169 -0.029452893 1.538268479 170 -4.323758003 -0.029452893 171 0.336241453 -4.323758003 172 0.318733687 0.336241453 173 -0.648599471 0.318733687 174 2.794521246 -0.648599471 175 -0.277531626 2.794521246 176 -1.517025859 -0.277531626 177 -0.411592949 -1.517025859 178 -0.401478042 -0.411592949 179 -0.619656487 -0.401478042 180 0.513699612 -0.619656487 181 -2.809236760 0.513699612 182 1.825607820 -2.809236760 183 1.076201233 1.825607820 184 4.613375789 1.076201233 185 0.415476143 4.613375789 186 -0.958163706 0.415476143 187 -2.250601307 -0.958163706 188 -1.648996165 -2.250601307 189 -3.875598333 -1.648996165 190 1.585245370 -3.875598333 191 0.383864958 1.585245370 192 -6.241145016 0.383864958 193 -0.469846902 -6.241145016 194 0.269933576 -0.469846902 195 1.003945425 0.269933576 196 -2.212915832 1.003945425 197 0.463799948 -2.212915832 198 -1.336764082 0.463799948 199 0.543605209 -1.336764082 200 0.679158219 0.543605209 201 -0.247837737 0.679158219 202 0.478146655 -0.247837737 203 2.983778718 0.478146655 204 -2.744991038 2.983778718 205 0.223915112 -2.744991038 206 -0.406237064 0.223915112 207 0.937385371 -0.406237064 208 1.459494512 0.937385371 209 -0.662105392 1.459494512 210 2.865603092 -0.662105392 211 2.637941561 2.865603092 212 0.007029574 2.637941561 213 -2.799969062 0.007029574 214 0.820047170 -2.799969062 215 0.220957660 0.820047170 216 -0.690991384 0.220957660 217 -0.047219407 -0.690991384 218 -1.561085543 -0.047219407 219 1.282606754 -1.561085543 220 2.411835463 1.282606754 221 0.112908199 2.411835463 222 1.447726098 0.112908199 223 0.056714137 1.447726098 224 -0.431567848 0.056714137 225 -0.968120577 -0.431567848 226 0.320710117 -0.968120577 227 -0.996243867 0.320710117 228 0.623934482 -0.996243867 229 -0.002894539 0.623934482 230 -3.246243891 -0.002894539 231 1.927103039 -3.246243891 232 -4.882936631 1.927103039 233 2.230806608 -4.882936631 234 0.753959932 2.230806608 235 1.782848063 0.753959932 236 1.528461472 1.782848063 237 3.100965421 1.528461472 238 1.009893757 3.100965421 239 -0.631482267 1.009893757 240 -1.004383873 -0.631482267 241 2.236576682 -1.004383873 242 -4.518472351 2.236576682 243 -3.174432093 -4.518472351 244 -0.105655472 -3.174432093 245 -0.817804746 -0.105655472 246 -0.771745583 -0.817804746 247 1.778683952 -0.771745583 248 -0.391158112 1.778683952 249 0.719669988 -0.391158112 250 -1.110628286 0.719669988 251 -0.550661858 -1.110628286 252 -2.620031253 -0.550661858 253 -1.649203988 -2.620031253 254 1.518760539 -1.649203988 255 1.419207674 1.518760539 256 1.166501314 1.419207674 257 -1.607779068 1.166501314 258 2.084815836 -1.607779068 259 -1.456497698 2.084815836 260 0.984491079 -1.456497698 261 3.915562696 0.984491079 262 -0.998985274 3.915562696 263 -0.547769191 -0.998985274 264 NA -0.547769191 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.907584206 1.517814577 [2,] 1.185028394 -0.907584206 [3,] -5.633375170 1.185028394 [4,] 1.983367835 -5.633375170 [5,] -0.222276570 1.983367835 [6,] -1.176625117 -0.222276570 [7,] 2.331008790 -1.176625117 [8,] 0.993426942 2.331008790 [9,] -2.582810296 0.993426942 [10,] -1.800729966 -2.582810296 [11,] 0.261355599 -1.800729966 [12,] -0.023793772 0.261355599 [13,] -0.839300087 -0.023793772 [14,] 2.396177101 -0.839300087 [15,] 0.673443342 2.396177101 [16,] -1.316235890 0.673443342 [17,] -2.101568709 -1.316235890 [18,] -1.988807739 -2.101568709 [19,] 0.300858698 -1.988807739 [20,] -0.755801889 0.300858698 [21,] 0.124323573 -0.755801889 [22,] -0.381720930 0.124323573 [23,] -0.930743250 -0.381720930 [24,] -0.210057498 -0.930743250 [25,] 0.582194117 -0.210057498 [26,] -1.811017225 0.582194117 [27,] 2.632394649 -1.811017225 [28,] 0.223933605 2.632394649 [29,] -0.765084007 0.223933605 [30,] 0.776875286 -0.765084007 [31,] -1.629502228 0.776875286 [32,] -0.472792763 -1.629502228 [33,] 0.097640214 -0.472792763 [34,] 1.441515026 0.097640214 [35,] -1.575086539 1.441515026 [36,] 0.406034064 -1.575086539 [37,] 1.451143821 0.406034064 [38,] -1.616284618 1.451143821 [39,] -1.659024166 -1.616284618 [40,] -1.550841317 -1.659024166 [41,] 1.489440073 -1.550841317 [42,] 1.558685125 1.489440073 [43,] -0.455278918 1.558685125 [44,] -0.244057107 -0.455278918 [45,] 2.942842304 -0.244057107 [46,] 2.385875010 2.942842304 [47,] -0.075965688 2.385875010 [48,] -0.569366144 -0.075965688 [49,] 1.561487651 -0.569366144 [50,] 2.313960293 1.561487651 [51,] -0.331385176 2.313960293 [52,] 2.578903304 -0.331385176 [53,] 1.385759230 2.578903304 [54,] -2.851862608 1.385759230 [55,] -4.391513515 -2.851862608 [56,] 0.679582928 -4.391513515 [57,] 0.480163034 0.679582928 [58,] -0.052222477 0.480163034 [59,] -1.440233058 -0.052222477 [60,] -2.188713793 -1.440233058 [61,] 0.585236893 -2.188713793 [62,] 2.406824115 0.585236893 [63,] 0.701827175 2.406824115 [64,] 0.887237480 0.701827175 [65,] 0.055403853 0.887237480 [66,] 0.833654000 0.055403853 [67,] -2.051082240 0.833654000 [68,] 1.943811347 -2.051082240 [69,] -0.109242441 1.943811347 [70,] -0.910543045 -0.109242441 [71,] -0.016072803 -0.910543045 [72,] 0.704514573 -0.016072803 [73,] 0.752391550 0.704514573 [74,] 0.103026078 0.752391550 [75,] -3.337556819 0.103026078 [76,] 1.059196345 -3.337556819 [77,] -0.836838669 1.059196345 [78,] 1.591238207 -0.836838669 [79,] 0.383881645 1.591238207 [80,] -0.081625831 0.383881645 [81,] -0.032138652 -0.081625831 [82,] 0.728463674 -0.032138652 [83,] -0.408395102 0.728463674 [84,] -1.840637315 -0.408395102 [85,] -1.951666056 -1.840637315 [86,] 0.110207808 -1.951666056 [87,] 0.219454144 0.110207808 [88,] 0.568329375 0.219454144 [89,] -0.794039294 0.568329375 [90,] 2.860742581 -0.794039294 [91,] -0.061101012 2.860742581 [92,] 0.852061732 -0.061101012 [93,] 1.726822541 0.852061732 [94,] -1.780389279 1.726822541 [95,] 1.234169151 -1.780389279 [96,] -2.697209493 1.234169151 [97,] 0.321180521 -2.697209493 [98,] -0.834607706 0.321180521 [99,] 2.308706295 -0.834607706 [100,] -0.733043351 2.308706295 [101,] 1.118880598 -0.733043351 [102,] -0.064272136 1.118880598 [103,] -0.859914802 -0.064272136 [104,] 2.001299036 -0.859914802 [105,] -0.118786685 2.001299036 [106,] 0.371646112 -0.118786685 [107,] -0.629038850 0.371646112 [108,] -2.511604257 -0.629038850 [109,] 0.285381188 -2.511604257 [110,] 2.295373624 0.285381188 [111,] -1.549431983 2.295373624 [112,] 1.186434735 -1.549431983 [113,] 0.671443098 1.186434735 [114,] 0.486423706 0.671443098 [115,] 2.387873178 0.486423706 [116,] -2.665536137 2.387873178 [117,] 0.964218868 -2.665536137 [118,] 0.408415346 0.964218868 [119,] 1.010760180 0.408415346 [120,] 1.713061194 1.010760180 [121,] 1.884487789 1.713061194 [122,] 2.405311616 1.884487789 [123,] 2.230157813 2.405311616 [124,] 3.449746009 2.230157813 [125,] -0.804600283 3.449746009 [126,] -0.882008016 -0.804600283 [127,] -1.868478500 -0.882008016 [128,] 1.784822189 -1.868478500 [129,] -1.423729643 1.784822189 [130,] 2.677114822 -1.423729643 [131,] -3.385712033 2.677114822 [132,] 1.866831560 -3.385712033 [133,] 1.634061030 1.866831560 [134,] 1.357269062 1.634061030 [135,] -0.277070090 1.357269062 [136,] 0.273422319 -0.277070090 [137,] 1.267405047 0.273422319 [138,] -1.104441062 1.267405047 [139,] 0.226093491 -1.104441062 [140,] -3.165613047 0.226093491 [141,] -1.889298244 -3.165613047 [142,] 3.057280692 -1.889298244 [143,] 0.134521683 3.057280692 [144,] 0.083017719 0.134521683 [145,] 1.033283731 0.083017719 [146,] 2.330935803 1.033283731 [147,] 0.809412106 2.330935803 [148,] -0.593755845 0.809412106 [149,] -1.151332560 -0.593755845 [150,] 2.395576290 -1.151332560 [151,] -2.239016875 2.395576290 [152,] -5.343328459 -2.239016875 [153,] -1.281692983 -5.343328459 [154,] 0.133550917 -1.281692983 [155,] 3.628706839 0.133550917 [156,] -3.606055885 3.628706839 [157,] -1.514033458 -3.606055885 [158,] -0.109125677 -1.514033458 [159,] -1.208172958 -0.109125677 [160,] -1.486937866 -1.208172958 [161,] 0.006685188 -1.486937866 [162,] -1.481763618 0.006685188 [163,] 1.935141460 -1.481763618 [164,] -1.469563110 1.935141460 [165,] -0.500020970 -1.469563110 [166,] 0.253917081 -0.500020970 [167,] 3.025386425 0.253917081 [168,] 1.538268479 3.025386425 [169,] -0.029452893 1.538268479 [170,] -4.323758003 -0.029452893 [171,] 0.336241453 -4.323758003 [172,] 0.318733687 0.336241453 [173,] -0.648599471 0.318733687 [174,] 2.794521246 -0.648599471 [175,] -0.277531626 2.794521246 [176,] -1.517025859 -0.277531626 [177,] -0.411592949 -1.517025859 [178,] -0.401478042 -0.411592949 [179,] -0.619656487 -0.401478042 [180,] 0.513699612 -0.619656487 [181,] -2.809236760 0.513699612 [182,] 1.825607820 -2.809236760 [183,] 1.076201233 1.825607820 [184,] 4.613375789 1.076201233 [185,] 0.415476143 4.613375789 [186,] -0.958163706 0.415476143 [187,] -2.250601307 -0.958163706 [188,] -1.648996165 -2.250601307 [189,] -3.875598333 -1.648996165 [190,] 1.585245370 -3.875598333 [191,] 0.383864958 1.585245370 [192,] -6.241145016 0.383864958 [193,] -0.469846902 -6.241145016 [194,] 0.269933576 -0.469846902 [195,] 1.003945425 0.269933576 [196,] -2.212915832 1.003945425 [197,] 0.463799948 -2.212915832 [198,] -1.336764082 0.463799948 [199,] 0.543605209 -1.336764082 [200,] 0.679158219 0.543605209 [201,] -0.247837737 0.679158219 [202,] 0.478146655 -0.247837737 [203,] 2.983778718 0.478146655 [204,] -2.744991038 2.983778718 [205,] 0.223915112 -2.744991038 [206,] -0.406237064 0.223915112 [207,] 0.937385371 -0.406237064 [208,] 1.459494512 0.937385371 [209,] -0.662105392 1.459494512 [210,] 2.865603092 -0.662105392 [211,] 2.637941561 2.865603092 [212,] 0.007029574 2.637941561 [213,] -2.799969062 0.007029574 [214,] 0.820047170 -2.799969062 [215,] 0.220957660 0.820047170 [216,] -0.690991384 0.220957660 [217,] -0.047219407 -0.690991384 [218,] -1.561085543 -0.047219407 [219,] 1.282606754 -1.561085543 [220,] 2.411835463 1.282606754 [221,] 0.112908199 2.411835463 [222,] 1.447726098 0.112908199 [223,] 0.056714137 1.447726098 [224,] -0.431567848 0.056714137 [225,] -0.968120577 -0.431567848 [226,] 0.320710117 -0.968120577 [227,] -0.996243867 0.320710117 [228,] 0.623934482 -0.996243867 [229,] -0.002894539 0.623934482 [230,] -3.246243891 -0.002894539 [231,] 1.927103039 -3.246243891 [232,] -4.882936631 1.927103039 [233,] 2.230806608 -4.882936631 [234,] 0.753959932 2.230806608 [235,] 1.782848063 0.753959932 [236,] 1.528461472 1.782848063 [237,] 3.100965421 1.528461472 [238,] 1.009893757 3.100965421 [239,] -0.631482267 1.009893757 [240,] -1.004383873 -0.631482267 [241,] 2.236576682 -1.004383873 [242,] -4.518472351 2.236576682 [243,] -3.174432093 -4.518472351 [244,] -0.105655472 -3.174432093 [245,] -0.817804746 -0.105655472 [246,] -0.771745583 -0.817804746 [247,] 1.778683952 -0.771745583 [248,] -0.391158112 1.778683952 [249,] 0.719669988 -0.391158112 [250,] -1.110628286 0.719669988 [251,] -0.550661858 -1.110628286 [252,] -2.620031253 -0.550661858 [253,] -1.649203988 -2.620031253 [254,] 1.518760539 -1.649203988 [255,] 1.419207674 1.518760539 [256,] 1.166501314 1.419207674 [257,] -1.607779068 1.166501314 [258,] 2.084815836 -1.607779068 [259,] -1.456497698 2.084815836 [260,] 0.984491079 -1.456497698 [261,] 3.915562696 0.984491079 [262,] -0.998985274 3.915562696 [263,] -0.547769191 -0.998985274 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.907584206 1.517814577 2 1.185028394 -0.907584206 3 -5.633375170 1.185028394 4 1.983367835 -5.633375170 5 -0.222276570 1.983367835 6 -1.176625117 -0.222276570 7 2.331008790 -1.176625117 8 0.993426942 2.331008790 9 -2.582810296 0.993426942 10 -1.800729966 -2.582810296 11 0.261355599 -1.800729966 12 -0.023793772 0.261355599 13 -0.839300087 -0.023793772 14 2.396177101 -0.839300087 15 0.673443342 2.396177101 16 -1.316235890 0.673443342 17 -2.101568709 -1.316235890 18 -1.988807739 -2.101568709 19 0.300858698 -1.988807739 20 -0.755801889 0.300858698 21 0.124323573 -0.755801889 22 -0.381720930 0.124323573 23 -0.930743250 -0.381720930 24 -0.210057498 -0.930743250 25 0.582194117 -0.210057498 26 -1.811017225 0.582194117 27 2.632394649 -1.811017225 28 0.223933605 2.632394649 29 -0.765084007 0.223933605 30 0.776875286 -0.765084007 31 -1.629502228 0.776875286 32 -0.472792763 -1.629502228 33 0.097640214 -0.472792763 34 1.441515026 0.097640214 35 -1.575086539 1.441515026 36 0.406034064 -1.575086539 37 1.451143821 0.406034064 38 -1.616284618 1.451143821 39 -1.659024166 -1.616284618 40 -1.550841317 -1.659024166 41 1.489440073 -1.550841317 42 1.558685125 1.489440073 43 -0.455278918 1.558685125 44 -0.244057107 -0.455278918 45 2.942842304 -0.244057107 46 2.385875010 2.942842304 47 -0.075965688 2.385875010 48 -0.569366144 -0.075965688 49 1.561487651 -0.569366144 50 2.313960293 1.561487651 51 -0.331385176 2.313960293 52 2.578903304 -0.331385176 53 1.385759230 2.578903304 54 -2.851862608 1.385759230 55 -4.391513515 -2.851862608 56 0.679582928 -4.391513515 57 0.480163034 0.679582928 58 -0.052222477 0.480163034 59 -1.440233058 -0.052222477 60 -2.188713793 -1.440233058 61 0.585236893 -2.188713793 62 2.406824115 0.585236893 63 0.701827175 2.406824115 64 0.887237480 0.701827175 65 0.055403853 0.887237480 66 0.833654000 0.055403853 67 -2.051082240 0.833654000 68 1.943811347 -2.051082240 69 -0.109242441 1.943811347 70 -0.910543045 -0.109242441 71 -0.016072803 -0.910543045 72 0.704514573 -0.016072803 73 0.752391550 0.704514573 74 0.103026078 0.752391550 75 -3.337556819 0.103026078 76 1.059196345 -3.337556819 77 -0.836838669 1.059196345 78 1.591238207 -0.836838669 79 0.383881645 1.591238207 80 -0.081625831 0.383881645 81 -0.032138652 -0.081625831 82 0.728463674 -0.032138652 83 -0.408395102 0.728463674 84 -1.840637315 -0.408395102 85 -1.951666056 -1.840637315 86 0.110207808 -1.951666056 87 0.219454144 0.110207808 88 0.568329375 0.219454144 89 -0.794039294 0.568329375 90 2.860742581 -0.794039294 91 -0.061101012 2.860742581 92 0.852061732 -0.061101012 93 1.726822541 0.852061732 94 -1.780389279 1.726822541 95 1.234169151 -1.780389279 96 -2.697209493 1.234169151 97 0.321180521 -2.697209493 98 -0.834607706 0.321180521 99 2.308706295 -0.834607706 100 -0.733043351 2.308706295 101 1.118880598 -0.733043351 102 -0.064272136 1.118880598 103 -0.859914802 -0.064272136 104 2.001299036 -0.859914802 105 -0.118786685 2.001299036 106 0.371646112 -0.118786685 107 -0.629038850 0.371646112 108 -2.511604257 -0.629038850 109 0.285381188 -2.511604257 110 2.295373624 0.285381188 111 -1.549431983 2.295373624 112 1.186434735 -1.549431983 113 0.671443098 1.186434735 114 0.486423706 0.671443098 115 2.387873178 0.486423706 116 -2.665536137 2.387873178 117 0.964218868 -2.665536137 118 0.408415346 0.964218868 119 1.010760180 0.408415346 120 1.713061194 1.010760180 121 1.884487789 1.713061194 122 2.405311616 1.884487789 123 2.230157813 2.405311616 124 3.449746009 2.230157813 125 -0.804600283 3.449746009 126 -0.882008016 -0.804600283 127 -1.868478500 -0.882008016 128 1.784822189 -1.868478500 129 -1.423729643 1.784822189 130 2.677114822 -1.423729643 131 -3.385712033 2.677114822 132 1.866831560 -3.385712033 133 1.634061030 1.866831560 134 1.357269062 1.634061030 135 -0.277070090 1.357269062 136 0.273422319 -0.277070090 137 1.267405047 0.273422319 138 -1.104441062 1.267405047 139 0.226093491 -1.104441062 140 -3.165613047 0.226093491 141 -1.889298244 -3.165613047 142 3.057280692 -1.889298244 143 0.134521683 3.057280692 144 0.083017719 0.134521683 145 1.033283731 0.083017719 146 2.330935803 1.033283731 147 0.809412106 2.330935803 148 -0.593755845 0.809412106 149 -1.151332560 -0.593755845 150 2.395576290 -1.151332560 151 -2.239016875 2.395576290 152 -5.343328459 -2.239016875 153 -1.281692983 -5.343328459 154 0.133550917 -1.281692983 155 3.628706839 0.133550917 156 -3.606055885 3.628706839 157 -1.514033458 -3.606055885 158 -0.109125677 -1.514033458 159 -1.208172958 -0.109125677 160 -1.486937866 -1.208172958 161 0.006685188 -1.486937866 162 -1.481763618 0.006685188 163 1.935141460 -1.481763618 164 -1.469563110 1.935141460 165 -0.500020970 -1.469563110 166 0.253917081 -0.500020970 167 3.025386425 0.253917081 168 1.538268479 3.025386425 169 -0.029452893 1.538268479 170 -4.323758003 -0.029452893 171 0.336241453 -4.323758003 172 0.318733687 0.336241453 173 -0.648599471 0.318733687 174 2.794521246 -0.648599471 175 -0.277531626 2.794521246 176 -1.517025859 -0.277531626 177 -0.411592949 -1.517025859 178 -0.401478042 -0.411592949 179 -0.619656487 -0.401478042 180 0.513699612 -0.619656487 181 -2.809236760 0.513699612 182 1.825607820 -2.809236760 183 1.076201233 1.825607820 184 4.613375789 1.076201233 185 0.415476143 4.613375789 186 -0.958163706 0.415476143 187 -2.250601307 -0.958163706 188 -1.648996165 -2.250601307 189 -3.875598333 -1.648996165 190 1.585245370 -3.875598333 191 0.383864958 1.585245370 192 -6.241145016 0.383864958 193 -0.469846902 -6.241145016 194 0.269933576 -0.469846902 195 1.003945425 0.269933576 196 -2.212915832 1.003945425 197 0.463799948 -2.212915832 198 -1.336764082 0.463799948 199 0.543605209 -1.336764082 200 0.679158219 0.543605209 201 -0.247837737 0.679158219 202 0.478146655 -0.247837737 203 2.983778718 0.478146655 204 -2.744991038 2.983778718 205 0.223915112 -2.744991038 206 -0.406237064 0.223915112 207 0.937385371 -0.406237064 208 1.459494512 0.937385371 209 -0.662105392 1.459494512 210 2.865603092 -0.662105392 211 2.637941561 2.865603092 212 0.007029574 2.637941561 213 -2.799969062 0.007029574 214 0.820047170 -2.799969062 215 0.220957660 0.820047170 216 -0.690991384 0.220957660 217 -0.047219407 -0.690991384 218 -1.561085543 -0.047219407 219 1.282606754 -1.561085543 220 2.411835463 1.282606754 221 0.112908199 2.411835463 222 1.447726098 0.112908199 223 0.056714137 1.447726098 224 -0.431567848 0.056714137 225 -0.968120577 -0.431567848 226 0.320710117 -0.968120577 227 -0.996243867 0.320710117 228 0.623934482 -0.996243867 229 -0.002894539 0.623934482 230 -3.246243891 -0.002894539 231 1.927103039 -3.246243891 232 -4.882936631 1.927103039 233 2.230806608 -4.882936631 234 0.753959932 2.230806608 235 1.782848063 0.753959932 236 1.528461472 1.782848063 237 3.100965421 1.528461472 238 1.009893757 3.100965421 239 -0.631482267 1.009893757 240 -1.004383873 -0.631482267 241 2.236576682 -1.004383873 242 -4.518472351 2.236576682 243 -3.174432093 -4.518472351 244 -0.105655472 -3.174432093 245 -0.817804746 -0.105655472 246 -0.771745583 -0.817804746 247 1.778683952 -0.771745583 248 -0.391158112 1.778683952 249 0.719669988 -0.391158112 250 -1.110628286 0.719669988 251 -0.550661858 -1.110628286 252 -2.620031253 -0.550661858 253 -1.649203988 -2.620031253 254 1.518760539 -1.649203988 255 1.419207674 1.518760539 256 1.166501314 1.419207674 257 -1.607779068 1.166501314 258 2.084815836 -1.607779068 259 -1.456497698 2.084815836 260 0.984491079 -1.456497698 261 3.915562696 0.984491079 262 -0.998985274 3.915562696 263 -0.547769191 -0.998985274 > 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/7x9k11353260106.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/8icmq1353260106.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/9w9x01353260106.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/10brzd1353260106.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11s4ix1353260106.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12bedh1353260106.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/138tqs1353260106.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14hgkh1353260106.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/15i0mw1353260106.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/162i751353260106.tab") + } > > try(system("convert tmp/1rm8x1353260106.ps tmp/1rm8x1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/232vb1353260106.ps tmp/232vb1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/3dede1353260106.ps tmp/3dede1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/48tka1353260106.ps tmp/48tka1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/50jzl1353260106.ps tmp/50jzl1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/66xmb1353260106.ps tmp/66xmb1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/7x9k11353260106.ps tmp/7x9k11353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/8icmq1353260106.ps tmp/8icmq1353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/9w9x01353260106.ps tmp/9w9x01353260106.png",intern=TRUE)) character(0) > try(system("convert tmp/10brzd1353260106.ps tmp/10brzd1353260106.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 13.310 1.288 14.583