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 = '1' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > 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 month Connected Separate Learning Software Happiness Depression Belonging 1 9 41 38 13 12 14 12.0 53 2 9 39 32 16 11 18 11.0 83 3 9 30 35 19 15 11 14.0 66 4 9 31 33 15 6 12 12.0 67 5 9 34 37 14 13 16 21.0 76 6 9 35 29 13 10 18 12.0 78 7 9 39 31 19 12 14 22.0 53 8 9 34 36 15 14 14 11.0 80 9 9 36 35 14 12 15 10.0 74 10 9 37 38 15 9 15 13.0 76 11 9 38 31 16 10 17 10.0 79 12 9 36 34 16 12 19 8.0 54 13 9 38 35 16 12 10 15.0 67 14 9 39 38 16 11 16 14.0 54 15 9 33 37 17 15 18 10.0 87 16 9 32 33 15 12 14 14.0 58 17 9 36 32 15 10 14 14.0 75 18 9 38 38 20 12 17 11.0 88 19 9 39 38 18 11 14 10.0 64 20 9 32 32 16 12 16 13.0 57 21 9 32 33 16 11 18 9.5 66 22 9 31 31 16 12 11 14.0 68 23 9 39 38 19 13 14 12.0 54 24 9 37 39 16 11 12 14.0 56 25 9 39 32 17 12 17 11.0 86 26 9 41 32 17 13 9 9.0 80 27 9 36 35 16 10 16 11.0 76 28 9 33 37 15 14 14 15.0 69 29 9 33 33 16 12 15 14.0 78 30 9 34 33 14 10 11 13.0 67 31 9 31 31 15 12 16 9.0 80 32 9 27 32 12 8 13 15.0 54 33 9 37 31 14 10 17 10.0 71 34 9 34 37 16 12 15 11.0 84 35 9 34 30 14 12 14 13.0 74 36 9 32 33 10 7 16 8.0 71 37 9 29 31 10 9 9 20.0 63 38 9 36 33 14 12 15 12.0 71 39 9 29 31 16 10 17 10.0 76 40 9 35 33 16 10 13 10.0 69 41 9 37 32 16 10 15 9.0 74 42 9 34 33 14 12 16 14.0 75 43 9 38 32 20 15 16 8.0 54 44 9 35 33 14 10 12 14.0 52 45 9 38 28 14 10 15 11.0 69 46 9 37 35 11 12 11 13.0 68 47 9 38 39 14 13 15 9.0 65 48 9 33 34 15 11 15 11.0 75 49 9 36 38 16 11 17 15.0 74 50 9 38 32 14 12 13 11.0 75 51 9 32 38 16 14 16 10.0 72 52 9 32 30 14 10 14 14.0 67 53 9 32 33 12 12 11 18.0 63 54 9 34 38 16 13 12 14.0 62 55 9 32 32 9 5 12 11.0 63 56 9 37 35 14 6 15 14.5 76 57 9 39 34 16 12 16 13.0 74 58 9 29 34 16 12 15 9.0 67 59 9 37 36 15 11 12 10.0 73 60 9 35 34 16 10 12 15.0 70 61 9 30 28 12 7 8 20.0 53 62 9 38 34 16 12 13 12.0 77 63 9 34 35 16 14 11 12.0 80 64 9 31 35 14 11 14 14.0 52 65 9 34 31 16 12 15 13.0 54 66 10 35 37 17 13 10 11.0 80 67 10 36 35 18 14 11 17.0 66 68 10 30 27 18 11 12 12.0 73 69 10 39 40 12 12 15 13.0 63 70 10 35 37 16 12 15 14.0 69 71 10 38 36 10 8 14 13.0 67 72 10 31 38 14 11 16 15.0 54 73 10 34 39 18 14 15 13.0 81 74 10 38 41 18 14 15 10.0 69 75 10 34 27 16 12 13 11.0 84 76 10 39 30 17 9 12 19.0 80 77 10 37 37 16 13 17 13.0 70 78 10 34 31 16 11 13 17.0 69 79 10 28 31 13 12 15 13.0 77 80 10 37 27 16 12 13 9.0 54 81 10 33 36 16 12 15 11.0 79 82 10 35 37 16 12 15 9.0 71 83 10 37 33 15 12 16 12.0 73 84 10 32 34 15 11 15 12.0 72 85 10 33 31 16 10 14 13.0 77 86 10 38 39 14 9 15 13.0 75 87 10 33 34 16 12 14 12.0 69 88 10 29 32 16 12 13 15.0 54 89 10 33 33 15 12 7 22.0 70 90 10 31 36 12 9 17 13.0 73 91 10 36 32 17 15 13 15.0 54 92 10 35 41 16 12 15 13.0 77 93 10 32 28 15 12 14 15.0 82 94 10 29 30 13 12 13 12.5 80 95 10 39 36 16 10 16 11.0 80 96 10 37 35 16 13 12 16.0 69 97 10 35 31 16 9 14 11.0 78 98 10 37 34 16 12 17 11.0 81 99 10 32 36 14 10 15 10.0 76 100 10 38 36 16 14 17 10.0 76 101 10 37 35 16 11 12 16.0 73 102 10 36 37 20 15 16 12.0 85 103 10 32 28 15 11 11 11.0 66 104 10 33 39 16 11 15 16.0 79 105 10 40 32 13 12 9 19.0 68 106 10 38 35 17 12 16 11.0 76 107 10 41 39 16 12 15 16.0 71 108 10 36 35 16 11 10 15.0 54 109 10 43 42 12 7 10 24.0 46 110 10 30 34 16 12 15 14.0 85 111 10 31 33 16 14 11 15.0 74 112 10 32 41 17 11 13 11.0 88 113 10 32 33 13 11 14 15.0 38 114 10 37 34 12 10 18 12.0 76 115 10 37 32 18 13 16 10.0 86 116 10 33 40 14 13 14 14.0 54 117 10 34 40 14 8 14 13.0 67 118 10 33 35 13 11 14 9.0 69 119 10 38 36 16 12 14 15.0 90 120 10 33 37 13 11 12 15.0 54 121 10 31 27 16 13 14 14.0 76 122 10 38 39 13 12 15 11.0 89 123 10 37 38 16 14 15 8.0 76 124 10 36 31 15 13 15 11.0 73 125 10 31 33 16 15 13 11.0 79 126 10 39 32 15 10 17 8.0 90 127 10 44 39 17 11 17 10.0 74 128 10 33 36 15 9 19 11.0 81 129 10 35 33 12 11 15 13.0 72 130 10 32 33 16 10 13 11.0 71 131 10 28 32 10 11 9 20.0 66 132 10 40 37 16 8 15 10.0 77 133 10 27 30 12 11 15 15.0 65 134 10 37 38 14 12 15 12.0 74 135 10 32 29 15 12 16 14.0 85 136 10 28 22 13 9 11 23.0 54 137 10 34 35 15 11 14 14.0 63 138 10 30 35 11 10 11 16.0 54 139 10 35 34 12 8 15 11.0 64 140 10 31 35 11 9 13 12.0 69 141 10 32 34 16 8 15 10.0 54 142 10 30 37 15 9 16 14.0 84 143 10 30 35 17 15 14 12.0 86 144 10 31 23 16 11 15 12.0 77 145 10 40 31 10 8 16 11.0 89 146 10 32 27 18 13 16 12.0 76 147 10 36 36 13 12 11 13.0 60 148 10 32 31 16 12 12 11.0 75 149 10 35 32 13 9 9 19.0 73 150 10 38 39 10 7 16 12.0 85 151 10 42 37 15 13 13 17.0 79 152 10 34 38 16 9 16 9.0 71 153 10 35 39 16 6 12 12.0 72 154 9 38 34 14 8 9 19.0 69 155 10 33 31 10 8 13 18.0 78 156 10 36 32 17 15 13 15.0 54 157 10 32 37 13 6 14 14.0 69 158 10 33 36 15 9 19 11.0 81 159 10 34 32 16 11 13 9.0 84 160 10 32 38 12 8 12 18.0 84 161 10 34 36 13 8 13 16.0 69 162 11 27 26 13 10 10 24.0 66 163 11 31 26 12 8 14 14.0 81 164 11 38 33 17 14 16 20.0 82 165 11 34 39 15 10 10 18.0 72 166 11 24 30 10 8 11 23.0 54 167 11 30 33 14 11 14 12.0 78 168 11 26 25 11 12 12 14.0 74 169 11 34 38 13 12 9 16.0 82 170 11 27 37 16 12 9 18.0 73 171 11 37 31 12 5 11 20.0 55 172 11 36 37 16 12 16 12.0 72 173 11 41 35 12 10 9 12.0 78 174 11 29 25 9 7 13 17.0 59 175 11 36 28 12 12 16 13.0 72 176 11 32 35 15 11 13 9.0 78 177 11 37 33 12 8 9 16.0 68 178 11 30 30 12 9 12 18.0 69 179 11 31 31 14 10 16 10.0 67 180 11 38 37 12 9 11 14.0 74 181 11 36 36 16 12 14 11.0 54 182 11 35 30 11 6 13 9.0 67 183 11 31 36 19 15 15 11.0 70 184 11 38 32 15 12 14 10.0 80 185 11 22 28 8 12 16 11.0 89 186 11 32 36 16 12 13 19.0 76 187 11 36 34 17 11 14 14.0 74 188 11 39 31 12 7 15 12.0 87 189 11 28 28 11 7 13 14.0 54 190 11 32 36 11 5 11 21.0 61 191 11 32 36 14 12 11 13.0 38 192 11 38 40 16 12 14 10.0 75 193 11 32 33 12 3 15 15.0 69 194 11 35 37 16 11 11 16.0 62 195 11 32 32 13 10 15 14.0 72 196 11 37 38 15 12 12 12.0 70 197 11 34 31 16 9 14 19.0 79 198 11 33 37 16 12 14 15.0 87 199 11 33 33 14 9 8 19.0 62 200 11 26 32 16 12 13 13.0 77 201 11 30 30 16 12 9 17.0 69 202 11 24 30 14 10 15 12.0 69 203 11 34 31 11 9 17 11.0 75 204 11 34 32 12 12 13 14.0 54 205 11 33 34 15 8 15 11.0 72 206 11 34 36 15 11 15 13.0 74 207 11 35 37 16 11 14 12.0 85 208 11 35 36 16 12 16 15.0 52 209 11 36 33 11 10 13 14.0 70 210 11 34 33 15 10 16 12.0 84 211 11 34 33 12 12 9 17.0 64 212 11 41 44 12 12 16 11.0 84 213 11 32 39 15 11 11 18.0 87 214 11 30 32 15 8 10 13.0 79 215 11 35 35 16 12 11 17.0 67 216 11 28 25 14 10 15 13.0 65 217 11 33 35 17 11 17 11.0 85 218 11 39 34 14 10 14 12.0 83 219 11 36 35 13 8 8 22.0 61 220 11 36 39 15 12 15 14.0 82 221 11 35 33 13 12 11 12.0 76 222 11 38 36 14 10 16 12.0 58 223 11 33 32 15 12 10 17.0 72 224 11 31 32 12 9 15 9.0 72 225 11 34 36 13 9 9 21.0 38 226 11 32 36 8 6 16 10.0 78 227 11 31 32 14 10 19 11.0 54 228 11 33 34 14 9 12 12.0 63 229 11 34 33 11 9 8 23.0 66 230 11 34 35 12 9 11 13.0 70 231 11 34 30 13 6 14 12.0 71 232 11 33 38 10 10 9 16.0 67 233 11 32 34 16 6 15 9.0 58 234 11 41 33 18 14 13 17.0 72 235 11 34 32 13 10 16 9.0 72 236 11 36 31 11 10 11 14.0 70 237 11 37 30 4 6 12 17.0 76 238 11 36 27 13 12 13 13.0 50 239 11 29 31 16 12 10 11.0 72 240 11 37 30 10 7 11 12.0 72 241 11 27 32 12 8 12 10.0 88 242 11 35 35 12 11 8 19.0 53 243 11 28 28 10 3 12 16.0 58 244 11 35 33 13 6 12 16.0 66 245 11 37 31 15 10 15 14.0 82 246 11 29 35 12 8 11 20.0 69 247 11 32 35 14 9 13 15.0 68 248 11 36 32 10 9 14 23.0 44 249 11 19 21 12 8 10 20.0 56 250 11 21 20 12 9 12 16.0 53 251 11 31 34 11 7 15 14.0 70 252 11 33 32 10 7 13 17.0 78 253 11 36 34 12 6 13 11.0 71 254 11 33 32 16 9 13 13.0 72 255 11 37 33 12 10 12 17.0 68 256 11 34 33 14 11 12 15.0 67 257 11 35 37 16 12 9 21.0 75 258 11 31 32 14 8 9 18.0 62 259 11 37 34 13 11 15 15.0 67 260 11 35 30 4 3 10 8.0 83 261 11 27 30 15 11 14 12.0 64 262 11 34 38 11 12 15 12.0 68 263 11 40 36 11 7 7 22.0 62 264 11 29 32 14 9 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) Connected Separate Learning 8.202389 -0.003467 0.001352 0.003810 Software Happiness Depression Belonging 0.019314 0.007948 0.012380 0.015397 Belonging_Final t -0.021062 0.009782 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.29437 -0.21266 -0.02041 0.19546 0.61648 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.2023892 0.3060543 26.800 < 2e-16 *** Connected -0.0034669 0.0053269 -0.651 0.51574 Separate 0.0013519 0.0054162 0.250 0.80310 Learning 0.0038098 0.0096262 0.396 0.69260 Software 0.0193143 0.0098664 1.958 0.05137 . Happiness 0.0079476 0.0089276 0.890 0.37419 Depression 0.0123800 0.0064758 1.912 0.05703 . Belonging 0.0153971 0.0057513 2.677 0.00791 ** Belonging_Final -0.0210622 0.0085594 -2.461 0.01453 * t 0.0097820 0.0002632 37.171 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2855 on 254 degrees of freedom Multiple R-squared: 0.8736, Adjusted R-squared: 0.8692 F-statistic: 195.1 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,] 2.280314e-45 4.560629e-45 1.000000e+00 [2,] 3.520748e-58 7.041496e-58 1.000000e+00 [3,] 1.372402e-71 2.744804e-71 1.000000e+00 [4,] 0.000000e+00 0.000000e+00 1.000000e+00 [5,] 2.446611e-100 4.893222e-100 1.000000e+00 [6,] 1.174623e-115 2.349245e-115 1.000000e+00 [7,] 8.406288e-131 1.681258e-130 1.000000e+00 [8,] 1.573626e-151 3.147251e-151 1.000000e+00 [9,] 2.224920e-161 4.449839e-161 1.000000e+00 [10,] 7.260871e-174 1.452174e-173 1.000000e+00 [11,] 1.225645e-185 2.451290e-185 1.000000e+00 [12,] 3.292103e-207 6.584207e-207 1.000000e+00 [13,] 1.079954e-220 2.159908e-220 1.000000e+00 [14,] 3.147537e-238 6.295075e-238 1.000000e+00 [15,] 9.972773e-249 1.994555e-248 1.000000e+00 [16,] 6.081782e-262 1.216356e-261 1.000000e+00 [17,] 5.666677e-286 1.133335e-285 1.000000e+00 [18,] 2.555065e-283 5.110130e-283 1.000000e+00 [19,] 1.496870e-306 2.993741e-306 1.000000e+00 [20,] 4.940656e-324 9.881313e-324 1.000000e+00 [21,] 0.000000e+00 0.000000e+00 1.000000e+00 [22,] 0.000000e+00 0.000000e+00 1.000000e+00 [23,] 0.000000e+00 0.000000e+00 1.000000e+00 [24,] 0.000000e+00 0.000000e+00 1.000000e+00 [25,] 0.000000e+00 0.000000e+00 1.000000e+00 [26,] 0.000000e+00 0.000000e+00 1.000000e+00 [27,] 0.000000e+00 0.000000e+00 1.000000e+00 [28,] 0.000000e+00 0.000000e+00 1.000000e+00 [29,] 0.000000e+00 0.000000e+00 1.000000e+00 [30,] 0.000000e+00 0.000000e+00 1.000000e+00 [31,] 0.000000e+00 0.000000e+00 1.000000e+00 [32,] 0.000000e+00 0.000000e+00 1.000000e+00 [33,] 0.000000e+00 0.000000e+00 1.000000e+00 [34,] 0.000000e+00 0.000000e+00 1.000000e+00 [35,] 0.000000e+00 0.000000e+00 1.000000e+00 [36,] 0.000000e+00 0.000000e+00 1.000000e+00 [37,] 0.000000e+00 0.000000e+00 1.000000e+00 [38,] 0.000000e+00 0.000000e+00 1.000000e+00 [39,] 0.000000e+00 0.000000e+00 1.000000e+00 [40,] 0.000000e+00 0.000000e+00 1.000000e+00 [41,] 0.000000e+00 0.000000e+00 1.000000e+00 [42,] 0.000000e+00 0.000000e+00 1.000000e+00 [43,] 0.000000e+00 0.000000e+00 1.000000e+00 [44,] 0.000000e+00 0.000000e+00 1.000000e+00 [45,] 0.000000e+00 0.000000e+00 1.000000e+00 [46,] 0.000000e+00 0.000000e+00 1.000000e+00 [47,] 0.000000e+00 0.000000e+00 1.000000e+00 [48,] 0.000000e+00 0.000000e+00 1.000000e+00 [49,] 0.000000e+00 0.000000e+00 1.000000e+00 [50,] 0.000000e+00 0.000000e+00 1.000000e+00 [51,] 0.000000e+00 0.000000e+00 1.000000e+00 [52,] 0.000000e+00 0.000000e+00 1.000000e+00 [53,] 0.000000e+00 0.000000e+00 1.000000e+00 [54,] 2.792190e-10 5.584379e-10 1.000000e+00 [55,] 1.500292e-05 3.000583e-05 9.999850e-01 [56,] 9.622506e-04 1.924501e-03 9.990377e-01 [57,] 4.062353e-02 8.124706e-02 9.593765e-01 [58,] 1.291982e-01 2.583963e-01 8.708018e-01 [59,] 3.255282e-01 6.510564e-01 6.744718e-01 [60,] 4.591952e-01 9.183903e-01 5.408048e-01 [61,] 5.084726e-01 9.830548e-01 4.915274e-01 [62,] 5.363217e-01 9.273566e-01 4.636783e-01 [63,] 6.627445e-01 6.745110e-01 3.372555e-01 [64,] 6.800624e-01 6.398751e-01 3.199376e-01 [65,] 6.908002e-01 6.183997e-01 3.091998e-01 [66,] 7.067530e-01 5.864940e-01 2.932470e-01 [67,] 7.374428e-01 5.251145e-01 2.625572e-01 [68,] 7.845009e-01 4.309982e-01 2.154991e-01 [69,] 7.802859e-01 4.394282e-01 2.197141e-01 [70,] 7.799334e-01 4.401333e-01 2.200666e-01 [71,] 7.670617e-01 4.658765e-01 2.329383e-01 [72,] 7.571912e-01 4.856176e-01 2.428088e-01 [73,] 7.414311e-01 5.171378e-01 2.585689e-01 [74,] 7.218094e-01 5.563811e-01 2.781906e-01 [75,] 7.014138e-01 5.971724e-01 2.985862e-01 [76,] 6.822136e-01 6.355728e-01 3.177864e-01 [77,] 6.515280e-01 6.969440e-01 3.484720e-01 [78,] 6.262286e-01 7.475428e-01 3.737714e-01 [79,] 5.932941e-01 8.134118e-01 4.067059e-01 [80,] 5.581900e-01 8.836199e-01 4.418100e-01 [81,] 5.206999e-01 9.586002e-01 4.793001e-01 [82,] 4.888525e-01 9.777051e-01 5.111475e-01 [83,] 4.536816e-01 9.073632e-01 5.463184e-01 [84,] 4.196268e-01 8.392537e-01 5.803732e-01 [85,] 3.855905e-01 7.711810e-01 6.144095e-01 [86,] 3.528487e-01 7.056974e-01 6.471513e-01 [87,] 3.205757e-01 6.411513e-01 6.794243e-01 [88,] 2.905633e-01 5.811265e-01 7.094367e-01 [89,] 2.637478e-01 5.274956e-01 7.362522e-01 [90,] 2.517694e-01 5.035388e-01 7.482306e-01 [91,] 2.262083e-01 4.524167e-01 7.737917e-01 [92,] 2.066013e-01 4.132026e-01 7.933987e-01 [93,] 1.816647e-01 3.633294e-01 8.183353e-01 [94,] 1.644829e-01 3.289658e-01 8.355171e-01 [95,] 1.503890e-01 3.007780e-01 8.496110e-01 [96,] 1.309363e-01 2.618727e-01 8.690637e-01 [97,] 1.164673e-01 2.329346e-01 8.835327e-01 [98,] 1.061849e-01 2.123698e-01 8.938151e-01 [99,] 9.348154e-02 1.869631e-01 9.065185e-01 [100,] 8.304278e-02 1.660856e-01 9.169572e-01 [101,] 7.652908e-02 1.530582e-01 9.234709e-01 [102,] 6.667663e-02 1.333533e-01 9.333234e-01 [103,] 6.159429e-02 1.231886e-01 9.384057e-01 [104,] 5.300993e-02 1.060199e-01 9.469901e-01 [105,] 4.589169e-02 9.178338e-02 9.541083e-01 [106,] 3.803925e-02 7.607851e-02 9.619607e-01 [107,] 3.634898e-02 7.269796e-02 9.636510e-01 [108,] 3.043474e-02 6.086949e-02 9.695653e-01 [109,] 2.826913e-02 5.653826e-02 9.717309e-01 [110,] 2.498120e-02 4.996240e-02 9.750188e-01 [111,] 2.183087e-02 4.366174e-02 9.781691e-01 [112,] 1.925683e-02 3.851367e-02 9.807432e-01 [113,] 1.823482e-02 3.646964e-02 9.817652e-01 [114,] 1.596976e-02 3.193952e-02 9.840302e-01 [115,] 1.485747e-02 2.971494e-02 9.851425e-01 [116,] 1.336318e-02 2.672636e-02 9.866368e-01 [117,] 1.177673e-02 2.355346e-02 9.882233e-01 [118,] 1.058661e-02 2.117322e-02 9.894134e-01 [119,] 1.013523e-02 2.027046e-02 9.898648e-01 [120,] 8.962044e-03 1.792409e-02 9.910380e-01 [121,] 8.623199e-03 1.724640e-02 9.913768e-01 [122,] 8.200126e-03 1.640025e-02 9.917999e-01 [123,] 8.796218e-03 1.759244e-02 9.912038e-01 [124,] 9.152507e-03 1.830501e-02 9.908475e-01 [125,] 8.353325e-03 1.670665e-02 9.916467e-01 [126,] 7.533503e-03 1.506701e-02 9.924665e-01 [127,] 6.555940e-03 1.311188e-02 9.934441e-01 [128,] 6.668329e-03 1.333666e-02 9.933317e-01 [129,] 6.147065e-03 1.229413e-02 9.938529e-01 [130,] 7.423994e-03 1.484799e-02 9.925760e-01 [131,] 1.118795e-02 2.237589e-02 9.888121e-01 [132,] 1.418062e-02 2.836124e-02 9.858194e-01 [133,] 1.520264e-02 3.040528e-02 9.847974e-01 [134,] 2.267362e-02 4.534723e-02 9.773264e-01 [135,] 2.684798e-02 5.369596e-02 9.731520e-01 [136,] 3.851900e-02 7.703800e-02 9.614810e-01 [137,] 4.975231e-02 9.950462e-02 9.502477e-01 [138,] 5.725780e-02 1.145156e-01 9.427422e-01 [139,] 1.080702e-01 2.161404e-01 8.919298e-01 [140,] 1.514500e-01 3.029001e-01 8.485500e-01 [141,] 1.762480e-01 3.524960e-01 8.237520e-01 [142,] 9.860929e-01 2.781415e-02 1.390707e-02 [143,] 9.964338e-01 7.132302e-03 3.566151e-03 [144,] 9.998857e-01 2.286060e-04 1.143030e-04 [145,] 9.999915e-01 1.694109e-05 8.470543e-06 [146,] 9.999999e-01 1.697252e-07 8.486260e-08 [147,] 1.000000e+00 1.987449e-12 9.937245e-13 [148,] 1.000000e+00 6.227295e-22 3.113648e-22 [149,] 1.000000e+00 0.000000e+00 0.000000e+00 [150,] 1.000000e+00 0.000000e+00 0.000000e+00 [151,] 1.000000e+00 0.000000e+00 0.000000e+00 [152,] 1.000000e+00 0.000000e+00 0.000000e+00 [153,] 1.000000e+00 0.000000e+00 0.000000e+00 [154,] 1.000000e+00 0.000000e+00 0.000000e+00 [155,] 1.000000e+00 0.000000e+00 0.000000e+00 [156,] 1.000000e+00 0.000000e+00 0.000000e+00 [157,] 1.000000e+00 0.000000e+00 0.000000e+00 [158,] 1.000000e+00 0.000000e+00 0.000000e+00 [159,] 1.000000e+00 0.000000e+00 0.000000e+00 [160,] 1.000000e+00 0.000000e+00 0.000000e+00 [161,] 1.000000e+00 0.000000e+00 0.000000e+00 [162,] 1.000000e+00 0.000000e+00 0.000000e+00 [163,] 1.000000e+00 0.000000e+00 0.000000e+00 [164,] 1.000000e+00 0.000000e+00 0.000000e+00 [165,] 1.000000e+00 0.000000e+00 0.000000e+00 [166,] 1.000000e+00 0.000000e+00 0.000000e+00 [167,] 1.000000e+00 0.000000e+00 0.000000e+00 [168,] 1.000000e+00 0.000000e+00 0.000000e+00 [169,] 1.000000e+00 0.000000e+00 0.000000e+00 [170,] 1.000000e+00 0.000000e+00 0.000000e+00 [171,] 1.000000e+00 0.000000e+00 0.000000e+00 [172,] 1.000000e+00 0.000000e+00 0.000000e+00 [173,] 1.000000e+00 0.000000e+00 0.000000e+00 [174,] 1.000000e+00 0.000000e+00 0.000000e+00 [175,] 1.000000e+00 0.000000e+00 0.000000e+00 [176,] 1.000000e+00 0.000000e+00 0.000000e+00 [177,] 1.000000e+00 0.000000e+00 0.000000e+00 [178,] 1.000000e+00 0.000000e+00 0.000000e+00 [179,] 1.000000e+00 0.000000e+00 0.000000e+00 [180,] 1.000000e+00 0.000000e+00 0.000000e+00 [181,] 1.000000e+00 0.000000e+00 0.000000e+00 [182,] 1.000000e+00 0.000000e+00 0.000000e+00 [183,] 1.000000e+00 0.000000e+00 0.000000e+00 [184,] 1.000000e+00 0.000000e+00 0.000000e+00 [185,] 1.000000e+00 0.000000e+00 0.000000e+00 [186,] 1.000000e+00 0.000000e+00 0.000000e+00 [187,] 1.000000e+00 0.000000e+00 0.000000e+00 [188,] 1.000000e+00 0.000000e+00 0.000000e+00 [189,] 1.000000e+00 0.000000e+00 0.000000e+00 [190,] 1.000000e+00 0.000000e+00 0.000000e+00 [191,] 1.000000e+00 0.000000e+00 0.000000e+00 [192,] 1.000000e+00 0.000000e+00 0.000000e+00 [193,] 1.000000e+00 0.000000e+00 0.000000e+00 [194,] 1.000000e+00 0.000000e+00 0.000000e+00 [195,] 1.000000e+00 0.000000e+00 0.000000e+00 [196,] 1.000000e+00 0.000000e+00 0.000000e+00 [197,] 1.000000e+00 0.000000e+00 0.000000e+00 [198,] 1.000000e+00 0.000000e+00 0.000000e+00 [199,] 1.000000e+00 0.000000e+00 0.000000e+00 [200,] 1.000000e+00 0.000000e+00 0.000000e+00 [201,] 1.000000e+00 0.000000e+00 0.000000e+00 [202,] 1.000000e+00 0.000000e+00 0.000000e+00 [203,] 1.000000e+00 0.000000e+00 0.000000e+00 [204,] 1.000000e+00 0.000000e+00 0.000000e+00 [205,] 1.000000e+00 0.000000e+00 0.000000e+00 [206,] 1.000000e+00 0.000000e+00 0.000000e+00 [207,] 1.000000e+00 0.000000e+00 0.000000e+00 [208,] 1.000000e+00 0.000000e+00 0.000000e+00 [209,] 1.000000e+00 0.000000e+00 0.000000e+00 [210,] 1.000000e+00 0.000000e+00 0.000000e+00 [211,] 1.000000e+00 0.000000e+00 0.000000e+00 [212,] 1.000000e+00 0.000000e+00 0.000000e+00 [213,] 1.000000e+00 0.000000e+00 0.000000e+00 [214,] 1.000000e+00 0.000000e+00 0.000000e+00 [215,] 1.000000e+00 0.000000e+00 0.000000e+00 [216,] 1.000000e+00 0.000000e+00 0.000000e+00 [217,] 1.000000e+00 0.000000e+00 0.000000e+00 [218,] 1.000000e+00 0.000000e+00 0.000000e+00 [219,] 1.000000e+00 8.893182e-323 4.446591e-323 [220,] 1.000000e+00 0.000000e+00 0.000000e+00 [221,] 1.000000e+00 1.425088e-301 7.125442e-302 [222,] 1.000000e+00 6.588852e-282 3.294426e-282 [223,] 1.000000e+00 4.531229e-278 2.265615e-278 [224,] 1.000000e+00 8.185120e-255 4.092560e-255 [225,] 1.000000e+00 3.304471e-246 1.652236e-246 [226,] 1.000000e+00 4.403412e-229 2.201706e-229 [227,] 1.000000e+00 2.743249e-210 1.371624e-210 [228,] 1.000000e+00 5.994997e-213 2.997498e-213 [229,] 1.000000e+00 1.125575e-191 5.627877e-192 [230,] 1.000000e+00 5.583106e-170 2.791553e-170 [231,] 1.000000e+00 3.818049e-165 1.909024e-165 [232,] 1.000000e+00 2.657864e-150 1.328932e-150 [233,] 1.000000e+00 3.763362e-130 1.881681e-130 [234,] 1.000000e+00 6.165419e-126 3.082709e-126 [235,] 1.000000e+00 8.412953e-101 4.206476e-101 [236,] 1.000000e+00 0.000000e+00 0.000000e+00 [237,] 1.000000e+00 7.029661e-73 3.514831e-73 [238,] 1.000000e+00 1.133751e-56 5.668757e-57 [239,] 1.000000e+00 3.950626e-47 1.975313e-47 > postscript(file="/var/wessaorg/rcomp/tmp/14ia01356106677.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/281xq1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/34i4r1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/476uj1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5ab8v1356106677.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 0.1954218383 0.1135610982 0.0705180288 0.2363277668 -0.0763242743 6 7 8 9 10 0.0757211554 0.0979110351 0.0138506581 0.0673588988 0.0853115227 11 12 13 14 15 0.0825134298 0.0799100718 0.0499758314 0.0342163316 -0.1293173962 16 17 18 19 20 -0.0008680448 -0.0289924641 -0.0949345117 0.0576283191 -0.0305722102 21 22 23 24 25 -0.0282197309 -0.0257638926 -0.0632249307 0.0133549462 -0.1517314772 26 27 28 29 30 -0.0984819050 -0.1709548767 -0.1720721298 -0.2547068258 -0.0222979616 31 32 33 34 35 -0.1672843135 -0.0907076157 -0.1317353890 -0.1712405376 -0.2795276438 36 37 38 39 40 -0.0541681489 -0.2274614340 -0.2343097595 -0.2395815045 -0.1759446657 41 42 43 44 45 -0.1947549323 -0.3536054371 -0.2630350629 -0.2189594075 -0.2283502605 46 47 48 49 50 -0.2979586079 -0.2133175034 -0.3144001213 -0.4572653566 -0.3345973542 51 52 53 54 55 -0.4058741132 -0.3608525247 -0.4329747461 -0.2306068094 -0.2469072425 56 57 58 59 60 -0.3174241705 -0.4431336751 -0.3855245575 -0.4070083173 -0.4212247173 61 62 63 64 65 -0.3670915270 -0.5054793321 -0.5362191556 -0.4663812123 -0.4715262099 66 67 68 69 70 0.4920921328 0.4512528976 0.4145379700 0.4764897914 0.4211427889 71 72 73 74 75 0.6164753826 0.4343618864 0.2933660046 0.3481553464 0.3728608697 76 77 78 79 80 0.3799247156 0.3424379963 0.3877245545 0.3228907957 0.4629665181 81 82 83 84 85 0.2753763122 0.3980505810 0.2864136935 0.3216662169 0.3166800050 86 87 88 89 90 0.3210737228 0.2846773463 0.2759353837 0.2077730717 0.3053133883 91 92 93 94 95 0.2091050638 0.1739824524 0.1235103847 0.1568728728 0.1955754306 96 97 98 99 100 0.1772778231 0.2138443950 0.1210881086 0.2006519518 0.0898373979 101 102 103 104 105 0.1475322916 0.0194832841 0.2383469332 0.0248101804 0.0944149710 106 107 108 109 110 0.1050077401 0.0428130400 0.1436643928 0.1897542081 -0.0191493795 111 112 113 114 115 -0.0424605840 0.0442921932 -0.1537853314 0.0540387574 -0.0207837091 116 117 118 119 120 -0.0021714480 0.1319869601 0.0479657356 -0.1003873247 0.0087095437 121 122 123 124 125 -0.1129879669 -0.0864524275 -0.0585408624 -0.0722757257 -0.1788975010 126 127 128 129 130 -0.0547315584 -0.0936667440 -0.0377762964 -0.1077237397 -0.1099035758 131 132 133 134 135 -0.1734257237 -0.0801599609 -0.1717609415 -0.1596831648 -0.2330828590 136 137 138 139 140 -0.1973234340 -0.1115633240 -0.1104385109 -0.0852653101 -0.1781794893 141 142 143 144 145 -0.1115538752 -0.2880903879 -0.3666887794 -0.2925235177 -0.1918893879 146 147 148 149 150 -0.3317647035 -0.2173312972 -0.2912972633 -0.2670612101 -0.2321502418 151 152 153 154 155 -0.4323434566 -0.3257646429 -0.2330489880 -1.2943681119 -0.2916765561 156 157 158 159 160 -0.4267278228 -0.2839709345 -0.3312376287 -0.3904537857 -0.4666333986 161 162 163 164 165 -0.3070679060 0.5415791365 0.5334055751 0.3611084982 0.4932051262 166 167 168 169 170 0.5152761682 0.5077981774 0.4555532103 0.4505918561 0.3307179039 171 172 173 174 175 0.6032126029 0.4395867478 0.5301479508 0.5077617733 0.4252666566 176 177 178 179 180 0.3810189775 0.5386864224 0.3832531035 0.4466960265 0.3835057129 181 182 183 184 185 0.4266386698 0.5574787733 0.2977508784 0.3414325120 0.2888420411 186 187 188 189 190 0.1657167803 0.2516950852 0.3588857080 0.3547034359 0.3976179638 191 192 193 194 195 0.1046381614 0.2412880171 0.3896459565 0.3212311498 0.2407369391 196 197 198 199 200 0.2733269147 0.1598603621 0.1122114925 0.2194968176 0.0722569551 201 202 203 204 205 0.1844931971 0.1301236500 0.1306295240 0.2282963623 0.1907665213 206 207 208 209 210 0.1103749578 0.1604796950 0.0823112398 0.1706130012 0.0503893322 211 212 213 214 215 0.1044606564 0.0885903652 -0.0098008735 0.1286024732 -0.0112298131 216 217 218 219 220 0.0840725643 -0.0200298506 0.0653420728 0.0118719915 -0.0311310760 221 222 223 224 225 -0.0060895894 -0.0532308077 -0.0733437063 0.0386147690 -0.2424095741 226 227 228 229 230 0.0197065706 -0.0287521667 -0.0892482323 -0.0648656113 -0.0217300705 231 232 233 234 235 0.0025206291 -0.0145029363 0.0411048866 -0.1863389588 -0.0896585844 236 237 238 239 240 -0.0959654804 0.0129622610 -0.0625657774 -0.1929642743 -0.0534941219 241 242 243 244 245 -0.1675642273 -0.1525229438 -0.0655497896 -0.1450606562 -0.2017094671 246 247 248 249 250 -0.2685881715 -0.2124392492 -0.2213870752 -0.2319409581 -0.2993085406 251 252 253 254 255 -0.2589959400 -0.2944411046 -0.1659584789 -0.2757145163 -0.2781021774 256 257 258 259 260 -0.3061240182 -0.5183942755 -0.2762925601 -0.3885784735 -0.0993557668 261 262 263 264 -0.4169317168 -0.4236847285 -0.3233512312 -0.4174750184 > postscript(file="/var/wessaorg/rcomp/tmp/6ythl1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 0.1954218383 NA 1 0.1135610982 0.1954218383 2 0.0705180288 0.1135610982 3 0.2363277668 0.0705180288 4 -0.0763242743 0.2363277668 5 0.0757211554 -0.0763242743 6 0.0979110351 0.0757211554 7 0.0138506581 0.0979110351 8 0.0673588988 0.0138506581 9 0.0853115227 0.0673588988 10 0.0825134298 0.0853115227 11 0.0799100718 0.0825134298 12 0.0499758314 0.0799100718 13 0.0342163316 0.0499758314 14 -0.1293173962 0.0342163316 15 -0.0008680448 -0.1293173962 16 -0.0289924641 -0.0008680448 17 -0.0949345117 -0.0289924641 18 0.0576283191 -0.0949345117 19 -0.0305722102 0.0576283191 20 -0.0282197309 -0.0305722102 21 -0.0257638926 -0.0282197309 22 -0.0632249307 -0.0257638926 23 0.0133549462 -0.0632249307 24 -0.1517314772 0.0133549462 25 -0.0984819050 -0.1517314772 26 -0.1709548767 -0.0984819050 27 -0.1720721298 -0.1709548767 28 -0.2547068258 -0.1720721298 29 -0.0222979616 -0.2547068258 30 -0.1672843135 -0.0222979616 31 -0.0907076157 -0.1672843135 32 -0.1317353890 -0.0907076157 33 -0.1712405376 -0.1317353890 34 -0.2795276438 -0.1712405376 35 -0.0541681489 -0.2795276438 36 -0.2274614340 -0.0541681489 37 -0.2343097595 -0.2274614340 38 -0.2395815045 -0.2343097595 39 -0.1759446657 -0.2395815045 40 -0.1947549323 -0.1759446657 41 -0.3536054371 -0.1947549323 42 -0.2630350629 -0.3536054371 43 -0.2189594075 -0.2630350629 44 -0.2283502605 -0.2189594075 45 -0.2979586079 -0.2283502605 46 -0.2133175034 -0.2979586079 47 -0.3144001213 -0.2133175034 48 -0.4572653566 -0.3144001213 49 -0.3345973542 -0.4572653566 50 -0.4058741132 -0.3345973542 51 -0.3608525247 -0.4058741132 52 -0.4329747461 -0.3608525247 53 -0.2306068094 -0.4329747461 54 -0.2469072425 -0.2306068094 55 -0.3174241705 -0.2469072425 56 -0.4431336751 -0.3174241705 57 -0.3855245575 -0.4431336751 58 -0.4070083173 -0.3855245575 59 -0.4212247173 -0.4070083173 60 -0.3670915270 -0.4212247173 61 -0.5054793321 -0.3670915270 62 -0.5362191556 -0.5054793321 63 -0.4663812123 -0.5362191556 64 -0.4715262099 -0.4663812123 65 0.4920921328 -0.4715262099 66 0.4512528976 0.4920921328 67 0.4145379700 0.4512528976 68 0.4764897914 0.4145379700 69 0.4211427889 0.4764897914 70 0.6164753826 0.4211427889 71 0.4343618864 0.6164753826 72 0.2933660046 0.4343618864 73 0.3481553464 0.2933660046 74 0.3728608697 0.3481553464 75 0.3799247156 0.3728608697 76 0.3424379963 0.3799247156 77 0.3877245545 0.3424379963 78 0.3228907957 0.3877245545 79 0.4629665181 0.3228907957 80 0.2753763122 0.4629665181 81 0.3980505810 0.2753763122 82 0.2864136935 0.3980505810 83 0.3216662169 0.2864136935 84 0.3166800050 0.3216662169 85 0.3210737228 0.3166800050 86 0.2846773463 0.3210737228 87 0.2759353837 0.2846773463 88 0.2077730717 0.2759353837 89 0.3053133883 0.2077730717 90 0.2091050638 0.3053133883 91 0.1739824524 0.2091050638 92 0.1235103847 0.1739824524 93 0.1568728728 0.1235103847 94 0.1955754306 0.1568728728 95 0.1772778231 0.1955754306 96 0.2138443950 0.1772778231 97 0.1210881086 0.2138443950 98 0.2006519518 0.1210881086 99 0.0898373979 0.2006519518 100 0.1475322916 0.0898373979 101 0.0194832841 0.1475322916 102 0.2383469332 0.0194832841 103 0.0248101804 0.2383469332 104 0.0944149710 0.0248101804 105 0.1050077401 0.0944149710 106 0.0428130400 0.1050077401 107 0.1436643928 0.0428130400 108 0.1897542081 0.1436643928 109 -0.0191493795 0.1897542081 110 -0.0424605840 -0.0191493795 111 0.0442921932 -0.0424605840 112 -0.1537853314 0.0442921932 113 0.0540387574 -0.1537853314 114 -0.0207837091 0.0540387574 115 -0.0021714480 -0.0207837091 116 0.1319869601 -0.0021714480 117 0.0479657356 0.1319869601 118 -0.1003873247 0.0479657356 119 0.0087095437 -0.1003873247 120 -0.1129879669 0.0087095437 121 -0.0864524275 -0.1129879669 122 -0.0585408624 -0.0864524275 123 -0.0722757257 -0.0585408624 124 -0.1788975010 -0.0722757257 125 -0.0547315584 -0.1788975010 126 -0.0936667440 -0.0547315584 127 -0.0377762964 -0.0936667440 128 -0.1077237397 -0.0377762964 129 -0.1099035758 -0.1077237397 130 -0.1734257237 -0.1099035758 131 -0.0801599609 -0.1734257237 132 -0.1717609415 -0.0801599609 133 -0.1596831648 -0.1717609415 134 -0.2330828590 -0.1596831648 135 -0.1973234340 -0.2330828590 136 -0.1115633240 -0.1973234340 137 -0.1104385109 -0.1115633240 138 -0.0852653101 -0.1104385109 139 -0.1781794893 -0.0852653101 140 -0.1115538752 -0.1781794893 141 -0.2880903879 -0.1115538752 142 -0.3666887794 -0.2880903879 143 -0.2925235177 -0.3666887794 144 -0.1918893879 -0.2925235177 145 -0.3317647035 -0.1918893879 146 -0.2173312972 -0.3317647035 147 -0.2912972633 -0.2173312972 148 -0.2670612101 -0.2912972633 149 -0.2321502418 -0.2670612101 150 -0.4323434566 -0.2321502418 151 -0.3257646429 -0.4323434566 152 -0.2330489880 -0.3257646429 153 -1.2943681119 -0.2330489880 154 -0.2916765561 -1.2943681119 155 -0.4267278228 -0.2916765561 156 -0.2839709345 -0.4267278228 157 -0.3312376287 -0.2839709345 158 -0.3904537857 -0.3312376287 159 -0.4666333986 -0.3904537857 160 -0.3070679060 -0.4666333986 161 0.5415791365 -0.3070679060 162 0.5334055751 0.5415791365 163 0.3611084982 0.5334055751 164 0.4932051262 0.3611084982 165 0.5152761682 0.4932051262 166 0.5077981774 0.5152761682 167 0.4555532103 0.5077981774 168 0.4505918561 0.4555532103 169 0.3307179039 0.4505918561 170 0.6032126029 0.3307179039 171 0.4395867478 0.6032126029 172 0.5301479508 0.4395867478 173 0.5077617733 0.5301479508 174 0.4252666566 0.5077617733 175 0.3810189775 0.4252666566 176 0.5386864224 0.3810189775 177 0.3832531035 0.5386864224 178 0.4466960265 0.3832531035 179 0.3835057129 0.4466960265 180 0.4266386698 0.3835057129 181 0.5574787733 0.4266386698 182 0.2977508784 0.5574787733 183 0.3414325120 0.2977508784 184 0.2888420411 0.3414325120 185 0.1657167803 0.2888420411 186 0.2516950852 0.1657167803 187 0.3588857080 0.2516950852 188 0.3547034359 0.3588857080 189 0.3976179638 0.3547034359 190 0.1046381614 0.3976179638 191 0.2412880171 0.1046381614 192 0.3896459565 0.2412880171 193 0.3212311498 0.3896459565 194 0.2407369391 0.3212311498 195 0.2733269147 0.2407369391 196 0.1598603621 0.2733269147 197 0.1122114925 0.1598603621 198 0.2194968176 0.1122114925 199 0.0722569551 0.2194968176 200 0.1844931971 0.0722569551 201 0.1301236500 0.1844931971 202 0.1306295240 0.1301236500 203 0.2282963623 0.1306295240 204 0.1907665213 0.2282963623 205 0.1103749578 0.1907665213 206 0.1604796950 0.1103749578 207 0.0823112398 0.1604796950 208 0.1706130012 0.0823112398 209 0.0503893322 0.1706130012 210 0.1044606564 0.0503893322 211 0.0885903652 0.1044606564 212 -0.0098008735 0.0885903652 213 0.1286024732 -0.0098008735 214 -0.0112298131 0.1286024732 215 0.0840725643 -0.0112298131 216 -0.0200298506 0.0840725643 217 0.0653420728 -0.0200298506 218 0.0118719915 0.0653420728 219 -0.0311310760 0.0118719915 220 -0.0060895894 -0.0311310760 221 -0.0532308077 -0.0060895894 222 -0.0733437063 -0.0532308077 223 0.0386147690 -0.0733437063 224 -0.2424095741 0.0386147690 225 0.0197065706 -0.2424095741 226 -0.0287521667 0.0197065706 227 -0.0892482323 -0.0287521667 228 -0.0648656113 -0.0892482323 229 -0.0217300705 -0.0648656113 230 0.0025206291 -0.0217300705 231 -0.0145029363 0.0025206291 232 0.0411048866 -0.0145029363 233 -0.1863389588 0.0411048866 234 -0.0896585844 -0.1863389588 235 -0.0959654804 -0.0896585844 236 0.0129622610 -0.0959654804 237 -0.0625657774 0.0129622610 238 -0.1929642743 -0.0625657774 239 -0.0534941219 -0.1929642743 240 -0.1675642273 -0.0534941219 241 -0.1525229438 -0.1675642273 242 -0.0655497896 -0.1525229438 243 -0.1450606562 -0.0655497896 244 -0.2017094671 -0.1450606562 245 -0.2685881715 -0.2017094671 246 -0.2124392492 -0.2685881715 247 -0.2213870752 -0.2124392492 248 -0.2319409581 -0.2213870752 249 -0.2993085406 -0.2319409581 250 -0.2589959400 -0.2993085406 251 -0.2944411046 -0.2589959400 252 -0.1659584789 -0.2944411046 253 -0.2757145163 -0.1659584789 254 -0.2781021774 -0.2757145163 255 -0.3061240182 -0.2781021774 256 -0.5183942755 -0.3061240182 257 -0.2762925601 -0.5183942755 258 -0.3885784735 -0.2762925601 259 -0.0993557668 -0.3885784735 260 -0.4169317168 -0.0993557668 261 -0.4236847285 -0.4169317168 262 -0.3233512312 -0.4236847285 263 -0.4174750184 -0.3233512312 264 NA -0.4174750184 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.1135610982 0.1954218383 [2,] 0.0705180288 0.1135610982 [3,] 0.2363277668 0.0705180288 [4,] -0.0763242743 0.2363277668 [5,] 0.0757211554 -0.0763242743 [6,] 0.0979110351 0.0757211554 [7,] 0.0138506581 0.0979110351 [8,] 0.0673588988 0.0138506581 [9,] 0.0853115227 0.0673588988 [10,] 0.0825134298 0.0853115227 [11,] 0.0799100718 0.0825134298 [12,] 0.0499758314 0.0799100718 [13,] 0.0342163316 0.0499758314 [14,] -0.1293173962 0.0342163316 [15,] -0.0008680448 -0.1293173962 [16,] -0.0289924641 -0.0008680448 [17,] -0.0949345117 -0.0289924641 [18,] 0.0576283191 -0.0949345117 [19,] -0.0305722102 0.0576283191 [20,] -0.0282197309 -0.0305722102 [21,] -0.0257638926 -0.0282197309 [22,] -0.0632249307 -0.0257638926 [23,] 0.0133549462 -0.0632249307 [24,] -0.1517314772 0.0133549462 [25,] -0.0984819050 -0.1517314772 [26,] -0.1709548767 -0.0984819050 [27,] -0.1720721298 -0.1709548767 [28,] -0.2547068258 -0.1720721298 [29,] -0.0222979616 -0.2547068258 [30,] -0.1672843135 -0.0222979616 [31,] -0.0907076157 -0.1672843135 [32,] -0.1317353890 -0.0907076157 [33,] -0.1712405376 -0.1317353890 [34,] -0.2795276438 -0.1712405376 [35,] -0.0541681489 -0.2795276438 [36,] -0.2274614340 -0.0541681489 [37,] -0.2343097595 -0.2274614340 [38,] -0.2395815045 -0.2343097595 [39,] -0.1759446657 -0.2395815045 [40,] -0.1947549323 -0.1759446657 [41,] -0.3536054371 -0.1947549323 [42,] -0.2630350629 -0.3536054371 [43,] -0.2189594075 -0.2630350629 [44,] -0.2283502605 -0.2189594075 [45,] -0.2979586079 -0.2283502605 [46,] -0.2133175034 -0.2979586079 [47,] -0.3144001213 -0.2133175034 [48,] -0.4572653566 -0.3144001213 [49,] -0.3345973542 -0.4572653566 [50,] -0.4058741132 -0.3345973542 [51,] -0.3608525247 -0.4058741132 [52,] -0.4329747461 -0.3608525247 [53,] -0.2306068094 -0.4329747461 [54,] -0.2469072425 -0.2306068094 [55,] -0.3174241705 -0.2469072425 [56,] -0.4431336751 -0.3174241705 [57,] -0.3855245575 -0.4431336751 [58,] -0.4070083173 -0.3855245575 [59,] -0.4212247173 -0.4070083173 [60,] -0.3670915270 -0.4212247173 [61,] -0.5054793321 -0.3670915270 [62,] -0.5362191556 -0.5054793321 [63,] -0.4663812123 -0.5362191556 [64,] -0.4715262099 -0.4663812123 [65,] 0.4920921328 -0.4715262099 [66,] 0.4512528976 0.4920921328 [67,] 0.4145379700 0.4512528976 [68,] 0.4764897914 0.4145379700 [69,] 0.4211427889 0.4764897914 [70,] 0.6164753826 0.4211427889 [71,] 0.4343618864 0.6164753826 [72,] 0.2933660046 0.4343618864 [73,] 0.3481553464 0.2933660046 [74,] 0.3728608697 0.3481553464 [75,] 0.3799247156 0.3728608697 [76,] 0.3424379963 0.3799247156 [77,] 0.3877245545 0.3424379963 [78,] 0.3228907957 0.3877245545 [79,] 0.4629665181 0.3228907957 [80,] 0.2753763122 0.4629665181 [81,] 0.3980505810 0.2753763122 [82,] 0.2864136935 0.3980505810 [83,] 0.3216662169 0.2864136935 [84,] 0.3166800050 0.3216662169 [85,] 0.3210737228 0.3166800050 [86,] 0.2846773463 0.3210737228 [87,] 0.2759353837 0.2846773463 [88,] 0.2077730717 0.2759353837 [89,] 0.3053133883 0.2077730717 [90,] 0.2091050638 0.3053133883 [91,] 0.1739824524 0.2091050638 [92,] 0.1235103847 0.1739824524 [93,] 0.1568728728 0.1235103847 [94,] 0.1955754306 0.1568728728 [95,] 0.1772778231 0.1955754306 [96,] 0.2138443950 0.1772778231 [97,] 0.1210881086 0.2138443950 [98,] 0.2006519518 0.1210881086 [99,] 0.0898373979 0.2006519518 [100,] 0.1475322916 0.0898373979 [101,] 0.0194832841 0.1475322916 [102,] 0.2383469332 0.0194832841 [103,] 0.0248101804 0.2383469332 [104,] 0.0944149710 0.0248101804 [105,] 0.1050077401 0.0944149710 [106,] 0.0428130400 0.1050077401 [107,] 0.1436643928 0.0428130400 [108,] 0.1897542081 0.1436643928 [109,] -0.0191493795 0.1897542081 [110,] -0.0424605840 -0.0191493795 [111,] 0.0442921932 -0.0424605840 [112,] -0.1537853314 0.0442921932 [113,] 0.0540387574 -0.1537853314 [114,] -0.0207837091 0.0540387574 [115,] -0.0021714480 -0.0207837091 [116,] 0.1319869601 -0.0021714480 [117,] 0.0479657356 0.1319869601 [118,] -0.1003873247 0.0479657356 [119,] 0.0087095437 -0.1003873247 [120,] -0.1129879669 0.0087095437 [121,] -0.0864524275 -0.1129879669 [122,] -0.0585408624 -0.0864524275 [123,] -0.0722757257 -0.0585408624 [124,] -0.1788975010 -0.0722757257 [125,] -0.0547315584 -0.1788975010 [126,] -0.0936667440 -0.0547315584 [127,] -0.0377762964 -0.0936667440 [128,] -0.1077237397 -0.0377762964 [129,] -0.1099035758 -0.1077237397 [130,] -0.1734257237 -0.1099035758 [131,] -0.0801599609 -0.1734257237 [132,] -0.1717609415 -0.0801599609 [133,] -0.1596831648 -0.1717609415 [134,] -0.2330828590 -0.1596831648 [135,] -0.1973234340 -0.2330828590 [136,] -0.1115633240 -0.1973234340 [137,] -0.1104385109 -0.1115633240 [138,] -0.0852653101 -0.1104385109 [139,] -0.1781794893 -0.0852653101 [140,] -0.1115538752 -0.1781794893 [141,] -0.2880903879 -0.1115538752 [142,] -0.3666887794 -0.2880903879 [143,] -0.2925235177 -0.3666887794 [144,] -0.1918893879 -0.2925235177 [145,] -0.3317647035 -0.1918893879 [146,] -0.2173312972 -0.3317647035 [147,] -0.2912972633 -0.2173312972 [148,] -0.2670612101 -0.2912972633 [149,] -0.2321502418 -0.2670612101 [150,] -0.4323434566 -0.2321502418 [151,] -0.3257646429 -0.4323434566 [152,] -0.2330489880 -0.3257646429 [153,] -1.2943681119 -0.2330489880 [154,] -0.2916765561 -1.2943681119 [155,] -0.4267278228 -0.2916765561 [156,] -0.2839709345 -0.4267278228 [157,] -0.3312376287 -0.2839709345 [158,] -0.3904537857 -0.3312376287 [159,] -0.4666333986 -0.3904537857 [160,] -0.3070679060 -0.4666333986 [161,] 0.5415791365 -0.3070679060 [162,] 0.5334055751 0.5415791365 [163,] 0.3611084982 0.5334055751 [164,] 0.4932051262 0.3611084982 [165,] 0.5152761682 0.4932051262 [166,] 0.5077981774 0.5152761682 [167,] 0.4555532103 0.5077981774 [168,] 0.4505918561 0.4555532103 [169,] 0.3307179039 0.4505918561 [170,] 0.6032126029 0.3307179039 [171,] 0.4395867478 0.6032126029 [172,] 0.5301479508 0.4395867478 [173,] 0.5077617733 0.5301479508 [174,] 0.4252666566 0.5077617733 [175,] 0.3810189775 0.4252666566 [176,] 0.5386864224 0.3810189775 [177,] 0.3832531035 0.5386864224 [178,] 0.4466960265 0.3832531035 [179,] 0.3835057129 0.4466960265 [180,] 0.4266386698 0.3835057129 [181,] 0.5574787733 0.4266386698 [182,] 0.2977508784 0.5574787733 [183,] 0.3414325120 0.2977508784 [184,] 0.2888420411 0.3414325120 [185,] 0.1657167803 0.2888420411 [186,] 0.2516950852 0.1657167803 [187,] 0.3588857080 0.2516950852 [188,] 0.3547034359 0.3588857080 [189,] 0.3976179638 0.3547034359 [190,] 0.1046381614 0.3976179638 [191,] 0.2412880171 0.1046381614 [192,] 0.3896459565 0.2412880171 [193,] 0.3212311498 0.3896459565 [194,] 0.2407369391 0.3212311498 [195,] 0.2733269147 0.2407369391 [196,] 0.1598603621 0.2733269147 [197,] 0.1122114925 0.1598603621 [198,] 0.2194968176 0.1122114925 [199,] 0.0722569551 0.2194968176 [200,] 0.1844931971 0.0722569551 [201,] 0.1301236500 0.1844931971 [202,] 0.1306295240 0.1301236500 [203,] 0.2282963623 0.1306295240 [204,] 0.1907665213 0.2282963623 [205,] 0.1103749578 0.1907665213 [206,] 0.1604796950 0.1103749578 [207,] 0.0823112398 0.1604796950 [208,] 0.1706130012 0.0823112398 [209,] 0.0503893322 0.1706130012 [210,] 0.1044606564 0.0503893322 [211,] 0.0885903652 0.1044606564 [212,] -0.0098008735 0.0885903652 [213,] 0.1286024732 -0.0098008735 [214,] -0.0112298131 0.1286024732 [215,] 0.0840725643 -0.0112298131 [216,] -0.0200298506 0.0840725643 [217,] 0.0653420728 -0.0200298506 [218,] 0.0118719915 0.0653420728 [219,] -0.0311310760 0.0118719915 [220,] -0.0060895894 -0.0311310760 [221,] -0.0532308077 -0.0060895894 [222,] -0.0733437063 -0.0532308077 [223,] 0.0386147690 -0.0733437063 [224,] -0.2424095741 0.0386147690 [225,] 0.0197065706 -0.2424095741 [226,] -0.0287521667 0.0197065706 [227,] -0.0892482323 -0.0287521667 [228,] -0.0648656113 -0.0892482323 [229,] -0.0217300705 -0.0648656113 [230,] 0.0025206291 -0.0217300705 [231,] -0.0145029363 0.0025206291 [232,] 0.0411048866 -0.0145029363 [233,] -0.1863389588 0.0411048866 [234,] -0.0896585844 -0.1863389588 [235,] -0.0959654804 -0.0896585844 [236,] 0.0129622610 -0.0959654804 [237,] -0.0625657774 0.0129622610 [238,] -0.1929642743 -0.0625657774 [239,] -0.0534941219 -0.1929642743 [240,] -0.1675642273 -0.0534941219 [241,] -0.1525229438 -0.1675642273 [242,] -0.0655497896 -0.1525229438 [243,] -0.1450606562 -0.0655497896 [244,] -0.2017094671 -0.1450606562 [245,] -0.2685881715 -0.2017094671 [246,] -0.2124392492 -0.2685881715 [247,] -0.2213870752 -0.2124392492 [248,] -0.2319409581 -0.2213870752 [249,] -0.2993085406 -0.2319409581 [250,] -0.2589959400 -0.2993085406 [251,] -0.2944411046 -0.2589959400 [252,] -0.1659584789 -0.2944411046 [253,] -0.2757145163 -0.1659584789 [254,] -0.2781021774 -0.2757145163 [255,] -0.3061240182 -0.2781021774 [256,] -0.5183942755 -0.3061240182 [257,] -0.2762925601 -0.5183942755 [258,] -0.3885784735 -0.2762925601 [259,] -0.0993557668 -0.3885784735 [260,] -0.4169317168 -0.0993557668 [261,] -0.4236847285 -0.4169317168 [262,] -0.3233512312 -0.4236847285 [263,] -0.4174750184 -0.3233512312 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.1135610982 0.1954218383 2 0.0705180288 0.1135610982 3 0.2363277668 0.0705180288 4 -0.0763242743 0.2363277668 5 0.0757211554 -0.0763242743 6 0.0979110351 0.0757211554 7 0.0138506581 0.0979110351 8 0.0673588988 0.0138506581 9 0.0853115227 0.0673588988 10 0.0825134298 0.0853115227 11 0.0799100718 0.0825134298 12 0.0499758314 0.0799100718 13 0.0342163316 0.0499758314 14 -0.1293173962 0.0342163316 15 -0.0008680448 -0.1293173962 16 -0.0289924641 -0.0008680448 17 -0.0949345117 -0.0289924641 18 0.0576283191 -0.0949345117 19 -0.0305722102 0.0576283191 20 -0.0282197309 -0.0305722102 21 -0.0257638926 -0.0282197309 22 -0.0632249307 -0.0257638926 23 0.0133549462 -0.0632249307 24 -0.1517314772 0.0133549462 25 -0.0984819050 -0.1517314772 26 -0.1709548767 -0.0984819050 27 -0.1720721298 -0.1709548767 28 -0.2547068258 -0.1720721298 29 -0.0222979616 -0.2547068258 30 -0.1672843135 -0.0222979616 31 -0.0907076157 -0.1672843135 32 -0.1317353890 -0.0907076157 33 -0.1712405376 -0.1317353890 34 -0.2795276438 -0.1712405376 35 -0.0541681489 -0.2795276438 36 -0.2274614340 -0.0541681489 37 -0.2343097595 -0.2274614340 38 -0.2395815045 -0.2343097595 39 -0.1759446657 -0.2395815045 40 -0.1947549323 -0.1759446657 41 -0.3536054371 -0.1947549323 42 -0.2630350629 -0.3536054371 43 -0.2189594075 -0.2630350629 44 -0.2283502605 -0.2189594075 45 -0.2979586079 -0.2283502605 46 -0.2133175034 -0.2979586079 47 -0.3144001213 -0.2133175034 48 -0.4572653566 -0.3144001213 49 -0.3345973542 -0.4572653566 50 -0.4058741132 -0.3345973542 51 -0.3608525247 -0.4058741132 52 -0.4329747461 -0.3608525247 53 -0.2306068094 -0.4329747461 54 -0.2469072425 -0.2306068094 55 -0.3174241705 -0.2469072425 56 -0.4431336751 -0.3174241705 57 -0.3855245575 -0.4431336751 58 -0.4070083173 -0.3855245575 59 -0.4212247173 -0.4070083173 60 -0.3670915270 -0.4212247173 61 -0.5054793321 -0.3670915270 62 -0.5362191556 -0.5054793321 63 -0.4663812123 -0.5362191556 64 -0.4715262099 -0.4663812123 65 0.4920921328 -0.4715262099 66 0.4512528976 0.4920921328 67 0.4145379700 0.4512528976 68 0.4764897914 0.4145379700 69 0.4211427889 0.4764897914 70 0.6164753826 0.4211427889 71 0.4343618864 0.6164753826 72 0.2933660046 0.4343618864 73 0.3481553464 0.2933660046 74 0.3728608697 0.3481553464 75 0.3799247156 0.3728608697 76 0.3424379963 0.3799247156 77 0.3877245545 0.3424379963 78 0.3228907957 0.3877245545 79 0.4629665181 0.3228907957 80 0.2753763122 0.4629665181 81 0.3980505810 0.2753763122 82 0.2864136935 0.3980505810 83 0.3216662169 0.2864136935 84 0.3166800050 0.3216662169 85 0.3210737228 0.3166800050 86 0.2846773463 0.3210737228 87 0.2759353837 0.2846773463 88 0.2077730717 0.2759353837 89 0.3053133883 0.2077730717 90 0.2091050638 0.3053133883 91 0.1739824524 0.2091050638 92 0.1235103847 0.1739824524 93 0.1568728728 0.1235103847 94 0.1955754306 0.1568728728 95 0.1772778231 0.1955754306 96 0.2138443950 0.1772778231 97 0.1210881086 0.2138443950 98 0.2006519518 0.1210881086 99 0.0898373979 0.2006519518 100 0.1475322916 0.0898373979 101 0.0194832841 0.1475322916 102 0.2383469332 0.0194832841 103 0.0248101804 0.2383469332 104 0.0944149710 0.0248101804 105 0.1050077401 0.0944149710 106 0.0428130400 0.1050077401 107 0.1436643928 0.0428130400 108 0.1897542081 0.1436643928 109 -0.0191493795 0.1897542081 110 -0.0424605840 -0.0191493795 111 0.0442921932 -0.0424605840 112 -0.1537853314 0.0442921932 113 0.0540387574 -0.1537853314 114 -0.0207837091 0.0540387574 115 -0.0021714480 -0.0207837091 116 0.1319869601 -0.0021714480 117 0.0479657356 0.1319869601 118 -0.1003873247 0.0479657356 119 0.0087095437 -0.1003873247 120 -0.1129879669 0.0087095437 121 -0.0864524275 -0.1129879669 122 -0.0585408624 -0.0864524275 123 -0.0722757257 -0.0585408624 124 -0.1788975010 -0.0722757257 125 -0.0547315584 -0.1788975010 126 -0.0936667440 -0.0547315584 127 -0.0377762964 -0.0936667440 128 -0.1077237397 -0.0377762964 129 -0.1099035758 -0.1077237397 130 -0.1734257237 -0.1099035758 131 -0.0801599609 -0.1734257237 132 -0.1717609415 -0.0801599609 133 -0.1596831648 -0.1717609415 134 -0.2330828590 -0.1596831648 135 -0.1973234340 -0.2330828590 136 -0.1115633240 -0.1973234340 137 -0.1104385109 -0.1115633240 138 -0.0852653101 -0.1104385109 139 -0.1781794893 -0.0852653101 140 -0.1115538752 -0.1781794893 141 -0.2880903879 -0.1115538752 142 -0.3666887794 -0.2880903879 143 -0.2925235177 -0.3666887794 144 -0.1918893879 -0.2925235177 145 -0.3317647035 -0.1918893879 146 -0.2173312972 -0.3317647035 147 -0.2912972633 -0.2173312972 148 -0.2670612101 -0.2912972633 149 -0.2321502418 -0.2670612101 150 -0.4323434566 -0.2321502418 151 -0.3257646429 -0.4323434566 152 -0.2330489880 -0.3257646429 153 -1.2943681119 -0.2330489880 154 -0.2916765561 -1.2943681119 155 -0.4267278228 -0.2916765561 156 -0.2839709345 -0.4267278228 157 -0.3312376287 -0.2839709345 158 -0.3904537857 -0.3312376287 159 -0.4666333986 -0.3904537857 160 -0.3070679060 -0.4666333986 161 0.5415791365 -0.3070679060 162 0.5334055751 0.5415791365 163 0.3611084982 0.5334055751 164 0.4932051262 0.3611084982 165 0.5152761682 0.4932051262 166 0.5077981774 0.5152761682 167 0.4555532103 0.5077981774 168 0.4505918561 0.4555532103 169 0.3307179039 0.4505918561 170 0.6032126029 0.3307179039 171 0.4395867478 0.6032126029 172 0.5301479508 0.4395867478 173 0.5077617733 0.5301479508 174 0.4252666566 0.5077617733 175 0.3810189775 0.4252666566 176 0.5386864224 0.3810189775 177 0.3832531035 0.5386864224 178 0.4466960265 0.3832531035 179 0.3835057129 0.4466960265 180 0.4266386698 0.3835057129 181 0.5574787733 0.4266386698 182 0.2977508784 0.5574787733 183 0.3414325120 0.2977508784 184 0.2888420411 0.3414325120 185 0.1657167803 0.2888420411 186 0.2516950852 0.1657167803 187 0.3588857080 0.2516950852 188 0.3547034359 0.3588857080 189 0.3976179638 0.3547034359 190 0.1046381614 0.3976179638 191 0.2412880171 0.1046381614 192 0.3896459565 0.2412880171 193 0.3212311498 0.3896459565 194 0.2407369391 0.3212311498 195 0.2733269147 0.2407369391 196 0.1598603621 0.2733269147 197 0.1122114925 0.1598603621 198 0.2194968176 0.1122114925 199 0.0722569551 0.2194968176 200 0.1844931971 0.0722569551 201 0.1301236500 0.1844931971 202 0.1306295240 0.1301236500 203 0.2282963623 0.1306295240 204 0.1907665213 0.2282963623 205 0.1103749578 0.1907665213 206 0.1604796950 0.1103749578 207 0.0823112398 0.1604796950 208 0.1706130012 0.0823112398 209 0.0503893322 0.1706130012 210 0.1044606564 0.0503893322 211 0.0885903652 0.1044606564 212 -0.0098008735 0.0885903652 213 0.1286024732 -0.0098008735 214 -0.0112298131 0.1286024732 215 0.0840725643 -0.0112298131 216 -0.0200298506 0.0840725643 217 0.0653420728 -0.0200298506 218 0.0118719915 0.0653420728 219 -0.0311310760 0.0118719915 220 -0.0060895894 -0.0311310760 221 -0.0532308077 -0.0060895894 222 -0.0733437063 -0.0532308077 223 0.0386147690 -0.0733437063 224 -0.2424095741 0.0386147690 225 0.0197065706 -0.2424095741 226 -0.0287521667 0.0197065706 227 -0.0892482323 -0.0287521667 228 -0.0648656113 -0.0892482323 229 -0.0217300705 -0.0648656113 230 0.0025206291 -0.0217300705 231 -0.0145029363 0.0025206291 232 0.0411048866 -0.0145029363 233 -0.1863389588 0.0411048866 234 -0.0896585844 -0.1863389588 235 -0.0959654804 -0.0896585844 236 0.0129622610 -0.0959654804 237 -0.0625657774 0.0129622610 238 -0.1929642743 -0.0625657774 239 -0.0534941219 -0.1929642743 240 -0.1675642273 -0.0534941219 241 -0.1525229438 -0.1675642273 242 -0.0655497896 -0.1525229438 243 -0.1450606562 -0.0655497896 244 -0.2017094671 -0.1450606562 245 -0.2685881715 -0.2017094671 246 -0.2124392492 -0.2685881715 247 -0.2213870752 -0.2124392492 248 -0.2319409581 -0.2213870752 249 -0.2993085406 -0.2319409581 250 -0.2589959400 -0.2993085406 251 -0.2944411046 -0.2589959400 252 -0.1659584789 -0.2944411046 253 -0.2757145163 -0.1659584789 254 -0.2781021774 -0.2757145163 255 -0.3061240182 -0.2781021774 256 -0.5183942755 -0.3061240182 257 -0.2762925601 -0.5183942755 258 -0.3885784735 -0.2762925601 259 -0.0993557668 -0.3885784735 260 -0.4169317168 -0.0993557668 261 -0.4236847285 -0.4169317168 262 -0.3233512312 -0.4236847285 263 -0.4174750184 -0.3233512312 > 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/7apnd1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/806ur1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9b0ya1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10tpzm1356106677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11euhb1356106677.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/12dhon1356106677.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/13moiq1356106677.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/14yovd1356106678.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/155eqr1356106678.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/16l3pa1356106678.tab") + } > > try(system("convert tmp/14ia01356106677.ps tmp/14ia01356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/281xq1356106677.ps tmp/281xq1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/34i4r1356106677.ps tmp/34i4r1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/476uj1356106677.ps tmp/476uj1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/5ab8v1356106677.ps tmp/5ab8v1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/6ythl1356106677.ps tmp/6ythl1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/7apnd1356106677.ps tmp/7apnd1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/806ur1356106677.ps tmp/806ur1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/9b0ya1356106677.ps tmp/9b0ya1356106677.png",intern=TRUE)) character(0) > try(system("convert tmp/10tpzm1356106677.ps tmp/10tpzm1356106677.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.842 1.642 17.409