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]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > 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 Learning month Connected Separate Software Happiness Depression Belonging 1 13 9 41 38 12 14 12.0 53 2 16 9 39 32 11 18 11.0 83 3 19 9 30 35 15 11 14.0 66 4 15 9 31 33 6 12 12.0 67 5 14 9 34 37 13 16 21.0 76 6 13 9 35 29 10 18 12.0 78 7 19 9 39 31 12 14 22.0 53 8 15 9 34 36 14 14 11.0 80 9 14 9 36 35 12 15 10.0 74 10 15 9 37 38 9 15 13.0 76 11 16 9 38 31 10 17 10.0 79 12 16 9 36 34 12 19 8.0 54 13 16 9 38 35 12 10 15.0 67 14 16 9 39 38 11 16 14.0 54 15 17 9 33 37 15 18 10.0 87 16 15 9 32 33 12 14 14.0 58 17 15 9 36 32 10 14 14.0 75 18 20 9 38 38 12 17 11.0 88 19 18 9 39 38 11 14 10.0 64 20 16 9 32 32 12 16 13.0 57 21 16 9 32 33 11 18 9.5 66 22 16 9 31 31 12 11 14.0 68 23 19 9 39 38 13 14 12.0 54 24 16 9 37 39 11 12 14.0 56 25 17 9 39 32 12 17 11.0 86 26 17 9 41 32 13 9 9.0 80 27 16 9 36 35 10 16 11.0 76 28 15 9 33 37 14 14 15.0 69 29 16 9 33 33 12 15 14.0 78 30 14 9 34 33 10 11 13.0 67 31 15 9 31 31 12 16 9.0 80 32 12 9 27 32 8 13 15.0 54 33 14 9 37 31 10 17 10.0 71 34 16 9 34 37 12 15 11.0 84 35 14 9 34 30 12 14 13.0 74 36 10 9 32 33 7 16 8.0 71 37 10 9 29 31 9 9 20.0 63 38 14 9 36 33 12 15 12.0 71 39 16 9 29 31 10 17 10.0 76 40 16 9 35 33 10 13 10.0 69 41 16 9 37 32 10 15 9.0 74 42 14 9 34 33 12 16 14.0 75 43 20 9 38 32 15 16 8.0 54 44 14 9 35 33 10 12 14.0 52 45 14 9 38 28 10 15 11.0 69 46 11 9 37 35 12 11 13.0 68 47 14 9 38 39 13 15 9.0 65 48 15 9 33 34 11 15 11.0 75 49 16 9 36 38 11 17 15.0 74 50 14 9 38 32 12 13 11.0 75 51 16 9 32 38 14 16 10.0 72 52 14 9 32 30 10 14 14.0 67 53 12 9 32 33 12 11 18.0 63 54 16 9 34 38 13 12 14.0 62 55 9 9 32 32 5 12 11.0 63 56 14 9 37 35 6 15 14.5 76 57 16 9 39 34 12 16 13.0 74 58 16 9 29 34 12 15 9.0 67 59 15 9 37 36 11 12 10.0 73 60 16 9 35 34 10 12 15.0 70 61 12 9 30 28 7 8 20.0 53 62 16 9 38 34 12 13 12.0 77 63 16 9 34 35 14 11 12.0 80 64 14 9 31 35 11 14 14.0 52 65 16 9 34 31 12 15 13.0 54 66 17 10 35 37 13 10 11.0 80 67 18 10 36 35 14 11 17.0 66 68 18 10 30 27 11 12 12.0 73 69 12 10 39 40 12 15 13.0 63 70 16 10 35 37 12 15 14.0 69 71 10 10 38 36 8 14 13.0 67 72 14 10 31 38 11 16 15.0 54 73 18 10 34 39 14 15 13.0 81 74 18 10 38 41 14 15 10.0 69 75 16 10 34 27 12 13 11.0 84 76 17 10 39 30 9 12 19.0 80 77 16 10 37 37 13 17 13.0 70 78 16 10 34 31 11 13 17.0 69 79 13 10 28 31 12 15 13.0 77 80 16 10 37 27 12 13 9.0 54 81 16 10 33 36 12 15 11.0 79 82 16 10 35 37 12 15 9.0 71 83 15 10 37 33 12 16 12.0 73 84 15 10 32 34 11 15 12.0 72 85 16 10 33 31 10 14 13.0 77 86 14 10 38 39 9 15 13.0 75 87 16 10 33 34 12 14 12.0 69 88 16 10 29 32 12 13 15.0 54 89 15 10 33 33 12 7 22.0 70 90 12 10 31 36 9 17 13.0 73 91 17 10 36 32 15 13 15.0 54 92 16 10 35 41 12 15 13.0 77 93 15 10 32 28 12 14 15.0 82 94 13 10 29 30 12 13 12.5 80 95 16 10 39 36 10 16 11.0 80 96 16 10 37 35 13 12 16.0 69 97 16 10 35 31 9 14 11.0 78 98 16 10 37 34 12 17 11.0 81 99 14 10 32 36 10 15 10.0 76 100 16 10 38 36 14 17 10.0 76 101 16 10 37 35 11 12 16.0 73 102 20 10 36 37 15 16 12.0 85 103 15 10 32 28 11 11 11.0 66 104 16 10 33 39 11 15 16.0 79 105 13 10 40 32 12 9 19.0 68 106 17 10 38 35 12 16 11.0 76 107 16 10 41 39 12 15 16.0 71 108 16 10 36 35 11 10 15.0 54 109 12 10 43 42 7 10 24.0 46 110 16 10 30 34 12 15 14.0 85 111 16 10 31 33 14 11 15.0 74 112 17 10 32 41 11 13 11.0 88 113 13 10 32 33 11 14 15.0 38 114 12 10 37 34 10 18 12.0 76 115 18 10 37 32 13 16 10.0 86 116 14 10 33 40 13 14 14.0 54 117 14 10 34 40 8 14 13.0 67 118 13 10 33 35 11 14 9.0 69 119 16 10 38 36 12 14 15.0 90 120 13 10 33 37 11 12 15.0 54 121 16 10 31 27 13 14 14.0 76 122 13 10 38 39 12 15 11.0 89 123 16 10 37 38 14 15 8.0 76 124 15 10 36 31 13 15 11.0 73 125 16 10 31 33 15 13 11.0 79 126 15 10 39 32 10 17 8.0 90 127 17 10 44 39 11 17 10.0 74 128 15 10 33 36 9 19 11.0 81 129 12 10 35 33 11 15 13.0 72 130 16 10 32 33 10 13 11.0 71 131 10 10 28 32 11 9 20.0 66 132 16 10 40 37 8 15 10.0 77 133 12 10 27 30 11 15 15.0 65 134 14 10 37 38 12 15 12.0 74 135 15 10 32 29 12 16 14.0 85 136 13 10 28 22 9 11 23.0 54 137 15 10 34 35 11 14 14.0 63 138 11 10 30 35 10 11 16.0 54 139 12 10 35 34 8 15 11.0 64 140 11 10 31 35 9 13 12.0 69 141 16 10 32 34 8 15 10.0 54 142 15 10 30 37 9 16 14.0 84 143 17 10 30 35 15 14 12.0 86 144 16 10 31 23 11 15 12.0 77 145 10 10 40 31 8 16 11.0 89 146 18 10 32 27 13 16 12.0 76 147 13 10 36 36 12 11 13.0 60 148 16 10 32 31 12 12 11.0 75 149 13 10 35 32 9 9 19.0 73 150 10 10 38 39 7 16 12.0 85 151 15 10 42 37 13 13 17.0 79 152 16 10 34 38 9 16 9.0 71 153 16 10 35 39 6 12 12.0 72 154 14 9 38 34 8 9 19.0 69 155 10 10 33 31 8 13 18.0 78 156 17 10 36 32 15 13 15.0 54 157 13 10 32 37 6 14 14.0 69 158 15 10 33 36 9 19 11.0 81 159 16 10 34 32 11 13 9.0 84 160 12 10 32 38 8 12 18.0 84 161 13 10 34 36 8 13 16.0 69 162 13 11 27 26 10 10 24.0 66 163 12 11 31 26 8 14 14.0 81 164 17 11 38 33 14 16 20.0 82 165 15 11 34 39 10 10 18.0 72 166 10 11 24 30 8 11 23.0 54 167 14 11 30 33 11 14 12.0 78 168 11 11 26 25 12 12 14.0 74 169 13 11 34 38 12 9 16.0 82 170 16 11 27 37 12 9 18.0 73 171 12 11 37 31 5 11 20.0 55 172 16 11 36 37 12 16 12.0 72 173 12 11 41 35 10 9 12.0 78 174 9 11 29 25 7 13 17.0 59 175 12 11 36 28 12 16 13.0 72 176 15 11 32 35 11 13 9.0 78 177 12 11 37 33 8 9 16.0 68 178 12 11 30 30 9 12 18.0 69 179 14 11 31 31 10 16 10.0 67 180 12 11 38 37 9 11 14.0 74 181 16 11 36 36 12 14 11.0 54 182 11 11 35 30 6 13 9.0 67 183 19 11 31 36 15 15 11.0 70 184 15 11 38 32 12 14 10.0 80 185 8 11 22 28 12 16 11.0 89 186 16 11 32 36 12 13 19.0 76 187 17 11 36 34 11 14 14.0 74 188 12 11 39 31 7 15 12.0 87 189 11 11 28 28 7 13 14.0 54 190 11 11 32 36 5 11 21.0 61 191 14 11 32 36 12 11 13.0 38 192 16 11 38 40 12 14 10.0 75 193 12 11 32 33 3 15 15.0 69 194 16 11 35 37 11 11 16.0 62 195 13 11 32 32 10 15 14.0 72 196 15 11 37 38 12 12 12.0 70 197 16 11 34 31 9 14 19.0 79 198 16 11 33 37 12 14 15.0 87 199 14 11 33 33 9 8 19.0 62 200 16 11 26 32 12 13 13.0 77 201 16 11 30 30 12 9 17.0 69 202 14 11 24 30 10 15 12.0 69 203 11 11 34 31 9 17 11.0 75 204 12 11 34 32 12 13 14.0 54 205 15 11 33 34 8 15 11.0 72 206 15 11 34 36 11 15 13.0 74 207 16 11 35 37 11 14 12.0 85 208 16 11 35 36 12 16 15.0 52 209 11 11 36 33 10 13 14.0 70 210 15 11 34 33 10 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 15 11 32 39 11 11 18.0 87 214 15 11 30 32 8 10 13.0 79 215 16 11 35 35 12 11 17.0 67 216 14 11 28 25 10 15 13.0 65 217 17 11 33 35 11 17 11.0 85 218 14 11 39 34 10 14 12.0 83 219 13 11 36 35 8 8 22.0 61 220 15 11 36 39 12 15 14.0 82 221 13 11 35 33 12 11 12.0 76 222 14 11 38 36 10 16 12.0 58 223 15 11 33 32 12 10 17.0 72 224 12 11 31 32 9 15 9.0 72 225 13 11 34 36 9 9 21.0 38 226 8 11 32 36 6 16 10.0 78 227 14 11 31 32 10 19 11.0 54 228 14 11 33 34 9 12 12.0 63 229 11 11 34 33 9 8 23.0 66 230 12 11 34 35 9 11 13.0 70 231 13 11 34 30 6 14 12.0 71 232 10 11 33 38 10 9 16.0 67 233 16 11 32 34 6 15 9.0 58 234 18 11 41 33 14 13 17.0 72 235 13 11 34 32 10 16 9.0 72 236 11 11 36 31 10 11 14.0 70 237 4 11 37 30 6 12 17.0 76 238 13 11 36 27 12 13 13.0 50 239 16 11 29 31 12 10 11.0 72 240 10 11 37 30 7 11 12.0 72 241 12 11 27 32 8 12 10.0 88 242 12 11 35 35 11 8 19.0 53 243 10 11 28 28 3 12 16.0 58 244 13 11 35 33 6 12 16.0 66 245 15 11 37 31 10 15 14.0 82 246 12 11 29 35 8 11 20.0 69 247 14 11 32 35 9 13 15.0 68 248 10 11 36 32 9 14 23.0 44 249 12 11 19 21 8 10 20.0 56 250 12 11 21 20 9 12 16.0 53 251 11 11 31 34 7 15 14.0 70 252 10 11 33 32 7 13 17.0 78 253 12 11 36 34 6 13 11.0 71 254 16 11 33 32 9 13 13.0 72 255 12 11 37 33 10 12 17.0 68 256 14 11 34 33 11 12 15.0 67 257 16 11 35 37 12 9 21.0 75 258 14 11 31 32 8 9 18.0 62 259 13 11 37 34 11 15 15.0 67 260 4 11 35 30 3 10 8.0 83 261 15 11 27 30 11 14 12.0 64 262 11 11 34 38 12 15 12.0 68 263 11 11 40 36 7 7 22.0 62 264 14 11 29 32 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) month Connected Separate 4.004454 0.161768 0.033972 0.042665 Software Happiness Depression Belonging 0.553914 0.069384 -0.030972 0.021059 Belonging_Final t -0.021837 -0.006517 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9144 -1.0411 0.2563 1.2191 4.6761 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.004454 3.893744 1.028 0.305 month 0.161768 0.408738 0.396 0.693 Connected 0.033972 0.034675 0.980 0.328 Separate 0.042665 0.035196 1.212 0.227 Software 0.553914 0.054661 10.134 <2e-16 *** Happiness 0.069384 0.058102 1.194 0.234 Depression -0.030972 0.042456 -0.730 0.466 Belonging 0.021059 0.037979 0.554 0.580 Belonging_Final -0.021837 0.056419 -0.387 0.699 t -0.006517 0.004332 -1.504 0.134 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.86 on 254 degrees of freedom Multiple R-squared: 0.446, Adjusted R-squared: 0.4263 F-statistic: 22.72 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,] 0.309807656 0.619615313 0.6901923 [2,] 0.298245890 0.596491779 0.7017541 [3,] 0.180880646 0.361761291 0.8191194 [4,] 0.137959528 0.275919056 0.8620405 [5,] 0.143729596 0.287459191 0.8562704 [6,] 0.295672612 0.591345224 0.7043274 [7,] 0.245953052 0.491906104 0.7540469 [8,] 0.197531583 0.395063165 0.8024684 [9,] 0.154638598 0.309277197 0.8453614 [10,] 0.181179794 0.362359588 0.8188202 [11,] 0.292370722 0.584741444 0.7076293 [12,] 0.413563053 0.827126106 0.5864369 [13,] 0.343871514 0.687743029 0.6561285 [14,] 0.292745396 0.585490791 0.7072546 [15,] 0.280275008 0.560550017 0.7197250 [16,] 0.396379430 0.792758860 0.6036206 [17,] 0.342705495 0.685410990 0.6572945 [18,] 0.453590892 0.907181785 0.5464091 [19,] 0.409820304 0.819640608 0.5901797 [20,] 0.379700408 0.759400816 0.6202996 [21,] 0.362199175 0.724398350 0.6378008 [22,] 0.323061419 0.646122839 0.6769386 [23,] 0.277419371 0.554838742 0.7225806 [24,] 0.412599374 0.825198748 0.5874006 [25,] 0.440486741 0.880973482 0.5595133 [26,] 0.414751241 0.829502482 0.5852488 [27,] 0.458996582 0.917993164 0.5410034 [28,] 0.438077281 0.876154562 0.5619227 [29,] 0.397606632 0.795213265 0.6023934 [30,] 0.357011021 0.714022041 0.6429890 [31,] 0.375803324 0.751606648 0.6241967 [32,] 0.326219196 0.652438392 0.6737808 [33,] 0.297228598 0.594457195 0.7027714 [34,] 0.477767577 0.955535153 0.5222324 [35,] 0.566983009 0.866033981 0.4330170 [36,] 0.523694346 0.952611309 0.4763057 [37,] 0.547090628 0.905818743 0.4529094 [38,] 0.519795954 0.960408092 0.4802040 [39,] 0.474361893 0.948723787 0.5256381 [40,] 0.432325414 0.864650829 0.5676746 [41,] 0.432585257 0.865170513 0.5674147 [42,] 0.389368675 0.778737351 0.6106313 [43,] 0.377808514 0.755617028 0.6221915 [44,] 0.373594208 0.747188416 0.6264058 [45,] 0.332852275 0.665704551 0.6671477 [46,] 0.312980768 0.625961536 0.6870192 [47,] 0.281726108 0.563452217 0.7182739 [48,] 0.301554968 0.603109936 0.6984450 [49,] 0.276786835 0.553573670 0.7232132 [50,] 0.250508190 0.501016379 0.7494918 [51,] 0.220683000 0.441366000 0.7793170 [52,] 0.191708281 0.383416563 0.8082917 [53,] 0.169368196 0.338736391 0.8306318 [54,] 0.143783932 0.287567864 0.8562161 [55,] 0.124528037 0.249056075 0.8754720 [56,] 0.150714127 0.301428254 0.8492859 [57,] 0.343888669 0.687777338 0.6561113 [58,] 0.305643257 0.611286514 0.6943567 [59,] 0.456877586 0.913755171 0.5431224 [60,] 0.419928698 0.839857396 0.5800713 [61,] 0.410250268 0.820500535 0.5897497 [62,] 0.389475788 0.778951576 0.6105242 [63,] 0.353067153 0.706134307 0.6469328 [64,] 0.402478662 0.804957323 0.5975213 [65,] 0.367580301 0.735160603 0.6324197 [66,] 0.338946912 0.677893824 0.6610531 [67,] 0.366387167 0.732774334 0.6336128 [68,] 0.334440607 0.668881215 0.6655594 [69,] 0.302660280 0.605320560 0.6973397 [70,] 0.268712181 0.537424363 0.7312878 [71,] 0.243465165 0.486930330 0.7565348 [72,] 0.213395170 0.426790340 0.7866048 [73,] 0.204473723 0.408947447 0.7955263 [74,] 0.177589056 0.355178111 0.8224109 [75,] 0.155174073 0.310348147 0.8448259 [76,] 0.141134380 0.282268761 0.8588656 [77,] 0.120988634 0.241977269 0.8790114 [78,] 0.120255159 0.240510318 0.8797448 [79,] 0.102479154 0.204958308 0.8975208 [80,] 0.089146102 0.178292204 0.9108539 [81,] 0.075510787 0.151021574 0.9244892 [82,] 0.079532704 0.159065408 0.9204673 [83,] 0.071832778 0.143665556 0.9281672 [84,] 0.059771881 0.119543762 0.9402281 [85,] 0.063651308 0.127302617 0.9363487 [86,] 0.052572923 0.105145845 0.9474271 [87,] 0.043348528 0.086697056 0.9566515 [88,] 0.037539241 0.075078482 0.9624608 [89,] 0.032990411 0.065980822 0.9670096 [90,] 0.039839380 0.079678759 0.9601606 [91,] 0.033549079 0.067098158 0.9664509 [92,] 0.030520175 0.061040349 0.9694798 [93,] 0.035705145 0.071410289 0.9642949 [94,] 0.031265039 0.062530078 0.9687350 [95,] 0.025309677 0.050619354 0.9746903 [96,] 0.024604968 0.049209935 0.9753950 [97,] 0.019840648 0.039681296 0.9801594 [98,] 0.016240071 0.032480143 0.9837599 [99,] 0.012811324 0.025622647 0.9871887 [100,] 0.013225903 0.026451806 0.9867741 [101,] 0.012122277 0.024244555 0.9878777 [102,] 0.016980419 0.033960838 0.9830196 [103,] 0.016192752 0.032385504 0.9838072 [104,] 0.015486735 0.030973470 0.9845133 [105,] 0.012793425 0.025586850 0.9872066 [106,] 0.012693487 0.025386975 0.9873065 [107,] 0.010127424 0.020254849 0.9898726 [108,] 0.008949416 0.017898831 0.9910506 [109,] 0.007135535 0.014271070 0.9928645 [110,] 0.010300917 0.020601834 0.9896991 [111,] 0.008418226 0.016836451 0.9915818 [112,] 0.007014959 0.014029917 0.9929850 [113,] 0.005612308 0.011224616 0.9943877 [114,] 0.004373092 0.008746184 0.9956269 [115,] 0.004114882 0.008229765 0.9958851 [116,] 0.003371336 0.006742672 0.9966287 [117,] 0.004595192 0.009190385 0.9954048 [118,] 0.005208333 0.010416667 0.9947917 [119,] 0.010657165 0.021314331 0.9893428 [120,] 0.013163784 0.026327567 0.9868362 [121,] 0.014059861 0.028119723 0.9859401 [122,] 0.013026635 0.026053270 0.9869734 [123,] 0.010268300 0.020536601 0.9897317 [124,] 0.008693013 0.017386027 0.9913070 [125,] 0.006919306 0.013838613 0.9930807 [126,] 0.008731244 0.017462488 0.9912688 [127,] 0.007445631 0.014891263 0.9925544 [128,] 0.009115022 0.018230043 0.9908850 [129,] 0.014924498 0.029848996 0.9850755 [130,] 0.014288027 0.028576054 0.9857120 [131,] 0.011787519 0.023575038 0.9882125 [132,] 0.011742474 0.023484947 0.9882575 [133,] 0.020854508 0.041709016 0.9791455 [134,] 0.024896668 0.049793336 0.9751033 [135,] 0.026423582 0.052847165 0.9735764 [136,] 0.023088558 0.046177116 0.9769114 [137,] 0.018575377 0.037150755 0.9814246 [138,] 0.026582641 0.053165283 0.9734174 [139,] 0.022685178 0.045370356 0.9773148 [140,] 0.025275197 0.050550393 0.9747248 [141,] 0.051014385 0.102028770 0.9489856 [142,] 0.049997334 0.099994669 0.9500027 [143,] 0.059056258 0.118112516 0.9409437 [144,] 0.050052945 0.100105889 0.9499471 [145,] 0.044237858 0.088475716 0.9557621 [146,] 0.037977723 0.075955445 0.9620223 [147,] 0.036754538 0.073509076 0.9632455 [148,] 0.031059385 0.062118770 0.9689406 [149,] 0.025175038 0.050350076 0.9748250 [150,] 0.020331532 0.040663065 0.9796685 [151,] 0.016886847 0.033773695 0.9831132 [152,] 0.014085069 0.028170138 0.9859149 [153,] 0.012037770 0.024075539 0.9879622 [154,] 0.012719040 0.025438080 0.9872810 [155,] 0.010156626 0.020313252 0.9898434 [156,] 0.015725394 0.031450788 0.9842746 [157,] 0.016088701 0.032177402 0.9839113 [158,] 0.015000827 0.030001654 0.9849992 [159,] 0.013589503 0.027179005 0.9864105 [160,] 0.010839421 0.021678842 0.9891606 [161,] 0.010844163 0.021688325 0.9891558 [162,] 0.012596460 0.025192919 0.9874035 [163,] 0.016631120 0.033262241 0.9833689 [164,] 0.013248075 0.026496149 0.9867519 [165,] 0.010452921 0.020905842 0.9895471 [166,] 0.008768598 0.017537197 0.9912314 [167,] 0.006752181 0.013504362 0.9932478 [168,] 0.005952276 0.011904552 0.9940477 [169,] 0.004820213 0.009640425 0.9951798 [170,] 0.003708346 0.007416693 0.9962917 [171,] 0.003986924 0.007973848 0.9960131 [172,] 0.003009610 0.006019220 0.9969904 [173,] 0.085341477 0.170682954 0.9146585 [174,] 0.076021534 0.152043067 0.9239785 [175,] 0.083741392 0.167482785 0.9162586 [176,] 0.070040319 0.140080639 0.9299597 [177,] 0.062287597 0.124575194 0.9377124 [178,] 0.051744810 0.103489620 0.9482552 [179,] 0.047731499 0.095462999 0.9522685 [180,] 0.039225752 0.078451504 0.9607742 [181,] 0.040024352 0.080048704 0.9599756 [182,] 0.040304708 0.080609416 0.9596953 [183,] 0.034700608 0.069401216 0.9652994 [184,] 0.027766152 0.055532304 0.9722338 [185,] 0.035105201 0.070210403 0.9648948 [186,] 0.028501418 0.057002837 0.9714986 [187,] 0.025531444 0.051062889 0.9744686 [188,] 0.021819610 0.043639220 0.9781804 [189,] 0.020326946 0.040653891 0.9796731 [190,] 0.017089958 0.034179916 0.9829100 [191,] 0.022464385 0.044928770 0.9775356 [192,] 0.026154888 0.052309776 0.9738451 [193,] 0.027321996 0.054643991 0.9726780 [194,] 0.021257012 0.042514024 0.9787430 [195,] 0.020265311 0.040530621 0.9797347 [196,] 0.016468826 0.032937652 0.9835312 [197,] 0.018327292 0.036654583 0.9816727 [198,] 0.014501004 0.029002008 0.9854990 [199,] 0.016618287 0.033236573 0.9833817 [200,] 0.027090095 0.054180191 0.9729099 [201,] 0.020913116 0.041826232 0.9790869 [202,] 0.027113933 0.054227867 0.9728861 [203,] 0.022872549 0.045745099 0.9771275 [204,] 0.017531978 0.035063957 0.9824680 [205,] 0.019255959 0.038511919 0.9807440 [206,] 0.016098071 0.032196143 0.9839019 [207,] 0.014873793 0.029747587 0.9851262 [208,] 0.011245228 0.022490457 0.9887548 [209,] 0.009261827 0.018523655 0.9907382 [210,] 0.006565628 0.013131255 0.9934344 [211,] 0.004982036 0.009964073 0.9950180 [212,] 0.003705866 0.007411732 0.9962941 [213,] 0.003129106 0.006258211 0.9968709 [214,] 0.007308024 0.014616048 0.9926920 [215,] 0.005656587 0.011313174 0.9943434 [216,] 0.004130036 0.008260072 0.9958700 [217,] 0.002900956 0.005801913 0.9970990 [218,] 0.002100065 0.004200131 0.9978999 [219,] 0.001874350 0.003748700 0.9981256 [220,] 0.004452684 0.008905369 0.9955473 [221,] 0.018651361 0.037302721 0.9813486 [222,] 0.028705624 0.057411247 0.9712944 [223,] 0.020240816 0.040481631 0.9797592 [224,] 0.016638518 0.033277036 0.9833615 [225,] 0.158811527 0.317623054 0.8411885 [226,] 0.122053660 0.244107319 0.8779463 [227,] 0.099604870 0.199209739 0.9003951 [228,] 0.075085772 0.150171543 0.9249142 [229,] 0.064642642 0.129285284 0.9353574 [230,] 0.102490656 0.204981311 0.8975093 [231,] 0.081491721 0.162983442 0.9185083 [232,] 0.084268263 0.168536525 0.9157317 [233,] 0.066587119 0.133174239 0.9334129 [234,] 0.054435684 0.108871368 0.9455643 [235,] 0.032990754 0.065981509 0.9670092 [236,] 0.026639164 0.053278328 0.9733608 [237,] 0.022491358 0.044982715 0.9775086 [238,] 0.066447339 0.132894678 0.9335527 [239,] 0.048697248 0.097394497 0.9513028 > postscript(file="/var/fisher/rcomp/tmp/1i2d01352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2yzmw1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3fnqt1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4tras1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5mhzh1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -3.131999295 0.226986679 1.935679119 2.804558516 -2.418032225 -1.880226893 7 8 9 10 11 12 3.692611150 -2.099410970 -2.071694532 0.529072337 0.995167912 -0.189823032 13 14 15 16 17 18 0.470117024 0.498538318 -0.984972301 -0.471169368 0.388482697 3.584932386 19 20 21 22 23 24 2.510108792 0.448882448 0.639130463 0.859494434 2.526189315 0.911757507 25 26 27 28 29 30 0.851015089 0.745973842 1.029242723 -1.731403298 0.285551942 -0.177697671 31 32 33 34 35 36 -0.727157091 -0.819627423 -0.790028236 0.090766983 -1.524184275 -2.994926432 37 38 39 40 41 42 -3.036017733 -1.715914366 1.481067760 1.536025997 1.307738561 -1.691739352 43 44 45 46 47 48 2.576009592 -0.126833311 -0.427775531 -4.476901087 -2.501670057 -0.087273356 49 50 51 52 53 54 0.565501318 -1.595751557 -0.947005562 -0.102905664 -2.981443297 0.214206701 55 56 57 58 59 60 -2.356371538 1.665065486 0.205418568 0.579053002 -0.083012473 1.848732075 61 62 63 64 65 66 0.493038981 0.385981197 -0.481004602 -0.638611608 0.783933502 0.871640504 67 68 69 70 71 72 1.634018971 3.453934790 -3.985955793 0.276410320 -3.436462873 -0.982476413 73 74 75 76 77 78 0.984116988 0.754511906 0.674279402 3.424247981 -0.468790299 1.447784167 79 80 81 82 83 84 -2.261400258 0.803547438 0.242550333 0.223148307 -0.729877341 0.070029770 85 86 87 88 89 90 1.785056577 -0.236630957 0.590582925 1.099972796 0.442475612 -1.919576969 91 92 93 94 95 96 0.219977319 0.137035181 -0.130179843 -2.094841811 1.169182749 0.201266765 97 98 99 100 101 102 2.244394121 0.165573237 -0.466132834 -1.039710817 1.301119982 2.539345072 103 104 105 106 107 108 0.779093109 0.995067536 -1.881628561 1.293917079 0.270053027 1.626587838 109 110 111 112 113 114 -0.305982558 0.716382937 -0.010771667 1.964918736 -1.540611240 -2.577275020 115 116 117 118 119 120 1.850084192 -1.849020562 0.828562586 -1.789048013 0.478505290 -1.417386693 121 122 123 124 125 126 0.626735160 -2.823989421 -0.820672763 -0.815194361 -0.775886466 0.343682304 127 128 129 130 131 132 1.486454155 1.043809413 -2.664969621 2.051587473 -3.699345786 2.521267351 133 134 135 136 137 138 -2.117119104 -1.496982583 -0.022851451 0.921716463 0.582024794 -2.414884325 139 140 141 142 143 144 -0.961538813 -2.329429108 3.180171327 1.388523482 0.235263838 1.902733519 145 146 147 148 149 150 -3.232606357 2.576819178 -1.864314906 1.218895990 0.240626106 -2.847945621 151 152 153 154 155 156 -0.857124586 2.175679631 4.182209460 1.820374733 -2.360367445 0.643603147 157 158 159 160 161 162 1.360040724 1.239329026 1.522216200 -0.671268683 0.449841607 0.304814334 163 164 165 166 167 168 -0.532526582 0.705563450 1.219730503 -1.696048432 -0.409813871 -3.282401493 169 170 171 172 173 174 -1.869656451 1.472274377 1.421840303 0.594604288 -1.950732603 -2.432755979 175 176 177 178 179 180 -2.970887501 0.384688185 -0.348484733 -0.741023545 0.151743067 -1.436382572 181 182 183 184 185 186 0.942577449 -0.529090078 2.297606534 -0.186108035 -6.609847763 1.205124572 187 188 189 190 191 192 2.511032191 -0.449278889 -0.525825323 0.516008059 -0.729729103 0.542659139 193 194 195 196 197 198 2.205050020 1.941762393 -0.667126774 -0.005961548 3.038724351 0.978304049 199 200 201 202 203 204 1.534504831 1.463965120 1.989822678 0.649485355 -2.424889464 -2.462945348 205 206 207 208 209 210 2.271819267 0.560792297 1.515804166 1.136082517 -2.573550732 1.067008366 211 212 213 214 215 216 -2.190939360 -3.700365776 0.901527852 2.910196045 1.513882632 0.955208844 217 218 219 220 221 222 2.429615296 0.110123039 1.123659284 -0.082459821 -1.575055682 0.013966772 223 224 225 226 227 228 0.813396739 -1.045100363 0.472207287 -3.696045992 0.343800671 1.099928300 229 230 231 232 233 234 -1.155109630 -0.814198702 1.807201525 -3.132401339 4.676127579 2.298337625 235 236 237 238 239 240 -0.698624739 -2.195321322 -6.914416877 -1.086386543 1.888555398 -1.581042084 241 242 243 244 245 246 -0.145789506 -1.322333028 1.198044918 2.032402960 1.517485572 0.229882402 247 248 249 250 251 252 1.329833795 -2.293447889 1.398592634 0.495413938 -0.693327081 -1.497025470 253 254 255 256 257 258 0.772227707 3.366970537 -1.103306445 0.388489873 1.861977961 2.548703480 259 260 261 262 263 264 -0.988365848 -5.278553518 1.509182186 -3.705447878 -0.100381546 1.424001562 > postscript(file="/var/fisher/rcomp/tmp/6wyam1352156889.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 -3.131999295 NA 1 0.226986679 -3.131999295 2 1.935679119 0.226986679 3 2.804558516 1.935679119 4 -2.418032225 2.804558516 5 -1.880226893 -2.418032225 6 3.692611150 -1.880226893 7 -2.099410970 3.692611150 8 -2.071694532 -2.099410970 9 0.529072337 -2.071694532 10 0.995167912 0.529072337 11 -0.189823032 0.995167912 12 0.470117024 -0.189823032 13 0.498538318 0.470117024 14 -0.984972301 0.498538318 15 -0.471169368 -0.984972301 16 0.388482697 -0.471169368 17 3.584932386 0.388482697 18 2.510108792 3.584932386 19 0.448882448 2.510108792 20 0.639130463 0.448882448 21 0.859494434 0.639130463 22 2.526189315 0.859494434 23 0.911757507 2.526189315 24 0.851015089 0.911757507 25 0.745973842 0.851015089 26 1.029242723 0.745973842 27 -1.731403298 1.029242723 28 0.285551942 -1.731403298 29 -0.177697671 0.285551942 30 -0.727157091 -0.177697671 31 -0.819627423 -0.727157091 32 -0.790028236 -0.819627423 33 0.090766983 -0.790028236 34 -1.524184275 0.090766983 35 -2.994926432 -1.524184275 36 -3.036017733 -2.994926432 37 -1.715914366 -3.036017733 38 1.481067760 -1.715914366 39 1.536025997 1.481067760 40 1.307738561 1.536025997 41 -1.691739352 1.307738561 42 2.576009592 -1.691739352 43 -0.126833311 2.576009592 44 -0.427775531 -0.126833311 45 -4.476901087 -0.427775531 46 -2.501670057 -4.476901087 47 -0.087273356 -2.501670057 48 0.565501318 -0.087273356 49 -1.595751557 0.565501318 50 -0.947005562 -1.595751557 51 -0.102905664 -0.947005562 52 -2.981443297 -0.102905664 53 0.214206701 -2.981443297 54 -2.356371538 0.214206701 55 1.665065486 -2.356371538 56 0.205418568 1.665065486 57 0.579053002 0.205418568 58 -0.083012473 0.579053002 59 1.848732075 -0.083012473 60 0.493038981 1.848732075 61 0.385981197 0.493038981 62 -0.481004602 0.385981197 63 -0.638611608 -0.481004602 64 0.783933502 -0.638611608 65 0.871640504 0.783933502 66 1.634018971 0.871640504 67 3.453934790 1.634018971 68 -3.985955793 3.453934790 69 0.276410320 -3.985955793 70 -3.436462873 0.276410320 71 -0.982476413 -3.436462873 72 0.984116988 -0.982476413 73 0.754511906 0.984116988 74 0.674279402 0.754511906 75 3.424247981 0.674279402 76 -0.468790299 3.424247981 77 1.447784167 -0.468790299 78 -2.261400258 1.447784167 79 0.803547438 -2.261400258 80 0.242550333 0.803547438 81 0.223148307 0.242550333 82 -0.729877341 0.223148307 83 0.070029770 -0.729877341 84 1.785056577 0.070029770 85 -0.236630957 1.785056577 86 0.590582925 -0.236630957 87 1.099972796 0.590582925 88 0.442475612 1.099972796 89 -1.919576969 0.442475612 90 0.219977319 -1.919576969 91 0.137035181 0.219977319 92 -0.130179843 0.137035181 93 -2.094841811 -0.130179843 94 1.169182749 -2.094841811 95 0.201266765 1.169182749 96 2.244394121 0.201266765 97 0.165573237 2.244394121 98 -0.466132834 0.165573237 99 -1.039710817 -0.466132834 100 1.301119982 -1.039710817 101 2.539345072 1.301119982 102 0.779093109 2.539345072 103 0.995067536 0.779093109 104 -1.881628561 0.995067536 105 1.293917079 -1.881628561 106 0.270053027 1.293917079 107 1.626587838 0.270053027 108 -0.305982558 1.626587838 109 0.716382937 -0.305982558 110 -0.010771667 0.716382937 111 1.964918736 -0.010771667 112 -1.540611240 1.964918736 113 -2.577275020 -1.540611240 114 1.850084192 -2.577275020 115 -1.849020562 1.850084192 116 0.828562586 -1.849020562 117 -1.789048013 0.828562586 118 0.478505290 -1.789048013 119 -1.417386693 0.478505290 120 0.626735160 -1.417386693 121 -2.823989421 0.626735160 122 -0.820672763 -2.823989421 123 -0.815194361 -0.820672763 124 -0.775886466 -0.815194361 125 0.343682304 -0.775886466 126 1.486454155 0.343682304 127 1.043809413 1.486454155 128 -2.664969621 1.043809413 129 2.051587473 -2.664969621 130 -3.699345786 2.051587473 131 2.521267351 -3.699345786 132 -2.117119104 2.521267351 133 -1.496982583 -2.117119104 134 -0.022851451 -1.496982583 135 0.921716463 -0.022851451 136 0.582024794 0.921716463 137 -2.414884325 0.582024794 138 -0.961538813 -2.414884325 139 -2.329429108 -0.961538813 140 3.180171327 -2.329429108 141 1.388523482 3.180171327 142 0.235263838 1.388523482 143 1.902733519 0.235263838 144 -3.232606357 1.902733519 145 2.576819178 -3.232606357 146 -1.864314906 2.576819178 147 1.218895990 -1.864314906 148 0.240626106 1.218895990 149 -2.847945621 0.240626106 150 -0.857124586 -2.847945621 151 2.175679631 -0.857124586 152 4.182209460 2.175679631 153 1.820374733 4.182209460 154 -2.360367445 1.820374733 155 0.643603147 -2.360367445 156 1.360040724 0.643603147 157 1.239329026 1.360040724 158 1.522216200 1.239329026 159 -0.671268683 1.522216200 160 0.449841607 -0.671268683 161 0.304814334 0.449841607 162 -0.532526582 0.304814334 163 0.705563450 -0.532526582 164 1.219730503 0.705563450 165 -1.696048432 1.219730503 166 -0.409813871 -1.696048432 167 -3.282401493 -0.409813871 168 -1.869656451 -3.282401493 169 1.472274377 -1.869656451 170 1.421840303 1.472274377 171 0.594604288 1.421840303 172 -1.950732603 0.594604288 173 -2.432755979 -1.950732603 174 -2.970887501 -2.432755979 175 0.384688185 -2.970887501 176 -0.348484733 0.384688185 177 -0.741023545 -0.348484733 178 0.151743067 -0.741023545 179 -1.436382572 0.151743067 180 0.942577449 -1.436382572 181 -0.529090078 0.942577449 182 2.297606534 -0.529090078 183 -0.186108035 2.297606534 184 -6.609847763 -0.186108035 185 1.205124572 -6.609847763 186 2.511032191 1.205124572 187 -0.449278889 2.511032191 188 -0.525825323 -0.449278889 189 0.516008059 -0.525825323 190 -0.729729103 0.516008059 191 0.542659139 -0.729729103 192 2.205050020 0.542659139 193 1.941762393 2.205050020 194 -0.667126774 1.941762393 195 -0.005961548 -0.667126774 196 3.038724351 -0.005961548 197 0.978304049 3.038724351 198 1.534504831 0.978304049 199 1.463965120 1.534504831 200 1.989822678 1.463965120 201 0.649485355 1.989822678 202 -2.424889464 0.649485355 203 -2.462945348 -2.424889464 204 2.271819267 -2.462945348 205 0.560792297 2.271819267 206 1.515804166 0.560792297 207 1.136082517 1.515804166 208 -2.573550732 1.136082517 209 1.067008366 -2.573550732 210 -2.190939360 1.067008366 211 -3.700365776 -2.190939360 212 0.901527852 -3.700365776 213 2.910196045 0.901527852 214 1.513882632 2.910196045 215 0.955208844 1.513882632 216 2.429615296 0.955208844 217 0.110123039 2.429615296 218 1.123659284 0.110123039 219 -0.082459821 1.123659284 220 -1.575055682 -0.082459821 221 0.013966772 -1.575055682 222 0.813396739 0.013966772 223 -1.045100363 0.813396739 224 0.472207287 -1.045100363 225 -3.696045992 0.472207287 226 0.343800671 -3.696045992 227 1.099928300 0.343800671 228 -1.155109630 1.099928300 229 -0.814198702 -1.155109630 230 1.807201525 -0.814198702 231 -3.132401339 1.807201525 232 4.676127579 -3.132401339 233 2.298337625 4.676127579 234 -0.698624739 2.298337625 235 -2.195321322 -0.698624739 236 -6.914416877 -2.195321322 237 -1.086386543 -6.914416877 238 1.888555398 -1.086386543 239 -1.581042084 1.888555398 240 -0.145789506 -1.581042084 241 -1.322333028 -0.145789506 242 1.198044918 -1.322333028 243 2.032402960 1.198044918 244 1.517485572 2.032402960 245 0.229882402 1.517485572 246 1.329833795 0.229882402 247 -2.293447889 1.329833795 248 1.398592634 -2.293447889 249 0.495413938 1.398592634 250 -0.693327081 0.495413938 251 -1.497025470 -0.693327081 252 0.772227707 -1.497025470 253 3.366970537 0.772227707 254 -1.103306445 3.366970537 255 0.388489873 -1.103306445 256 1.861977961 0.388489873 257 2.548703480 1.861977961 258 -0.988365848 2.548703480 259 -5.278553518 -0.988365848 260 1.509182186 -5.278553518 261 -3.705447878 1.509182186 262 -0.100381546 -3.705447878 263 1.424001562 -0.100381546 264 NA 1.424001562 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.226986679 -3.131999295 [2,] 1.935679119 0.226986679 [3,] 2.804558516 1.935679119 [4,] -2.418032225 2.804558516 [5,] -1.880226893 -2.418032225 [6,] 3.692611150 -1.880226893 [7,] -2.099410970 3.692611150 [8,] -2.071694532 -2.099410970 [9,] 0.529072337 -2.071694532 [10,] 0.995167912 0.529072337 [11,] -0.189823032 0.995167912 [12,] 0.470117024 -0.189823032 [13,] 0.498538318 0.470117024 [14,] -0.984972301 0.498538318 [15,] -0.471169368 -0.984972301 [16,] 0.388482697 -0.471169368 [17,] 3.584932386 0.388482697 [18,] 2.510108792 3.584932386 [19,] 0.448882448 2.510108792 [20,] 0.639130463 0.448882448 [21,] 0.859494434 0.639130463 [22,] 2.526189315 0.859494434 [23,] 0.911757507 2.526189315 [24,] 0.851015089 0.911757507 [25,] 0.745973842 0.851015089 [26,] 1.029242723 0.745973842 [27,] -1.731403298 1.029242723 [28,] 0.285551942 -1.731403298 [29,] -0.177697671 0.285551942 [30,] -0.727157091 -0.177697671 [31,] -0.819627423 -0.727157091 [32,] -0.790028236 -0.819627423 [33,] 0.090766983 -0.790028236 [34,] -1.524184275 0.090766983 [35,] -2.994926432 -1.524184275 [36,] -3.036017733 -2.994926432 [37,] -1.715914366 -3.036017733 [38,] 1.481067760 -1.715914366 [39,] 1.536025997 1.481067760 [40,] 1.307738561 1.536025997 [41,] -1.691739352 1.307738561 [42,] 2.576009592 -1.691739352 [43,] -0.126833311 2.576009592 [44,] -0.427775531 -0.126833311 [45,] -4.476901087 -0.427775531 [46,] -2.501670057 -4.476901087 [47,] -0.087273356 -2.501670057 [48,] 0.565501318 -0.087273356 [49,] -1.595751557 0.565501318 [50,] -0.947005562 -1.595751557 [51,] -0.102905664 -0.947005562 [52,] -2.981443297 -0.102905664 [53,] 0.214206701 -2.981443297 [54,] -2.356371538 0.214206701 [55,] 1.665065486 -2.356371538 [56,] 0.205418568 1.665065486 [57,] 0.579053002 0.205418568 [58,] -0.083012473 0.579053002 [59,] 1.848732075 -0.083012473 [60,] 0.493038981 1.848732075 [61,] 0.385981197 0.493038981 [62,] -0.481004602 0.385981197 [63,] -0.638611608 -0.481004602 [64,] 0.783933502 -0.638611608 [65,] 0.871640504 0.783933502 [66,] 1.634018971 0.871640504 [67,] 3.453934790 1.634018971 [68,] -3.985955793 3.453934790 [69,] 0.276410320 -3.985955793 [70,] -3.436462873 0.276410320 [71,] -0.982476413 -3.436462873 [72,] 0.984116988 -0.982476413 [73,] 0.754511906 0.984116988 [74,] 0.674279402 0.754511906 [75,] 3.424247981 0.674279402 [76,] -0.468790299 3.424247981 [77,] 1.447784167 -0.468790299 [78,] -2.261400258 1.447784167 [79,] 0.803547438 -2.261400258 [80,] 0.242550333 0.803547438 [81,] 0.223148307 0.242550333 [82,] -0.729877341 0.223148307 [83,] 0.070029770 -0.729877341 [84,] 1.785056577 0.070029770 [85,] -0.236630957 1.785056577 [86,] 0.590582925 -0.236630957 [87,] 1.099972796 0.590582925 [88,] 0.442475612 1.099972796 [89,] -1.919576969 0.442475612 [90,] 0.219977319 -1.919576969 [91,] 0.137035181 0.219977319 [92,] -0.130179843 0.137035181 [93,] -2.094841811 -0.130179843 [94,] 1.169182749 -2.094841811 [95,] 0.201266765 1.169182749 [96,] 2.244394121 0.201266765 [97,] 0.165573237 2.244394121 [98,] -0.466132834 0.165573237 [99,] -1.039710817 -0.466132834 [100,] 1.301119982 -1.039710817 [101,] 2.539345072 1.301119982 [102,] 0.779093109 2.539345072 [103,] 0.995067536 0.779093109 [104,] -1.881628561 0.995067536 [105,] 1.293917079 -1.881628561 [106,] 0.270053027 1.293917079 [107,] 1.626587838 0.270053027 [108,] -0.305982558 1.626587838 [109,] 0.716382937 -0.305982558 [110,] -0.010771667 0.716382937 [111,] 1.964918736 -0.010771667 [112,] -1.540611240 1.964918736 [113,] -2.577275020 -1.540611240 [114,] 1.850084192 -2.577275020 [115,] -1.849020562 1.850084192 [116,] 0.828562586 -1.849020562 [117,] -1.789048013 0.828562586 [118,] 0.478505290 -1.789048013 [119,] -1.417386693 0.478505290 [120,] 0.626735160 -1.417386693 [121,] -2.823989421 0.626735160 [122,] -0.820672763 -2.823989421 [123,] -0.815194361 -0.820672763 [124,] -0.775886466 -0.815194361 [125,] 0.343682304 -0.775886466 [126,] 1.486454155 0.343682304 [127,] 1.043809413 1.486454155 [128,] -2.664969621 1.043809413 [129,] 2.051587473 -2.664969621 [130,] -3.699345786 2.051587473 [131,] 2.521267351 -3.699345786 [132,] -2.117119104 2.521267351 [133,] -1.496982583 -2.117119104 [134,] -0.022851451 -1.496982583 [135,] 0.921716463 -0.022851451 [136,] 0.582024794 0.921716463 [137,] -2.414884325 0.582024794 [138,] -0.961538813 -2.414884325 [139,] -2.329429108 -0.961538813 [140,] 3.180171327 -2.329429108 [141,] 1.388523482 3.180171327 [142,] 0.235263838 1.388523482 [143,] 1.902733519 0.235263838 [144,] -3.232606357 1.902733519 [145,] 2.576819178 -3.232606357 [146,] -1.864314906 2.576819178 [147,] 1.218895990 -1.864314906 [148,] 0.240626106 1.218895990 [149,] -2.847945621 0.240626106 [150,] -0.857124586 -2.847945621 [151,] 2.175679631 -0.857124586 [152,] 4.182209460 2.175679631 [153,] 1.820374733 4.182209460 [154,] -2.360367445 1.820374733 [155,] 0.643603147 -2.360367445 [156,] 1.360040724 0.643603147 [157,] 1.239329026 1.360040724 [158,] 1.522216200 1.239329026 [159,] -0.671268683 1.522216200 [160,] 0.449841607 -0.671268683 [161,] 0.304814334 0.449841607 [162,] -0.532526582 0.304814334 [163,] 0.705563450 -0.532526582 [164,] 1.219730503 0.705563450 [165,] -1.696048432 1.219730503 [166,] -0.409813871 -1.696048432 [167,] -3.282401493 -0.409813871 [168,] -1.869656451 -3.282401493 [169,] 1.472274377 -1.869656451 [170,] 1.421840303 1.472274377 [171,] 0.594604288 1.421840303 [172,] -1.950732603 0.594604288 [173,] -2.432755979 -1.950732603 [174,] -2.970887501 -2.432755979 [175,] 0.384688185 -2.970887501 [176,] -0.348484733 0.384688185 [177,] -0.741023545 -0.348484733 [178,] 0.151743067 -0.741023545 [179,] -1.436382572 0.151743067 [180,] 0.942577449 -1.436382572 [181,] -0.529090078 0.942577449 [182,] 2.297606534 -0.529090078 [183,] -0.186108035 2.297606534 [184,] -6.609847763 -0.186108035 [185,] 1.205124572 -6.609847763 [186,] 2.511032191 1.205124572 [187,] -0.449278889 2.511032191 [188,] -0.525825323 -0.449278889 [189,] 0.516008059 -0.525825323 [190,] -0.729729103 0.516008059 [191,] 0.542659139 -0.729729103 [192,] 2.205050020 0.542659139 [193,] 1.941762393 2.205050020 [194,] -0.667126774 1.941762393 [195,] -0.005961548 -0.667126774 [196,] 3.038724351 -0.005961548 [197,] 0.978304049 3.038724351 [198,] 1.534504831 0.978304049 [199,] 1.463965120 1.534504831 [200,] 1.989822678 1.463965120 [201,] 0.649485355 1.989822678 [202,] -2.424889464 0.649485355 [203,] -2.462945348 -2.424889464 [204,] 2.271819267 -2.462945348 [205,] 0.560792297 2.271819267 [206,] 1.515804166 0.560792297 [207,] 1.136082517 1.515804166 [208,] -2.573550732 1.136082517 [209,] 1.067008366 -2.573550732 [210,] -2.190939360 1.067008366 [211,] -3.700365776 -2.190939360 [212,] 0.901527852 -3.700365776 [213,] 2.910196045 0.901527852 [214,] 1.513882632 2.910196045 [215,] 0.955208844 1.513882632 [216,] 2.429615296 0.955208844 [217,] 0.110123039 2.429615296 [218,] 1.123659284 0.110123039 [219,] -0.082459821 1.123659284 [220,] -1.575055682 -0.082459821 [221,] 0.013966772 -1.575055682 [222,] 0.813396739 0.013966772 [223,] -1.045100363 0.813396739 [224,] 0.472207287 -1.045100363 [225,] -3.696045992 0.472207287 [226,] 0.343800671 -3.696045992 [227,] 1.099928300 0.343800671 [228,] -1.155109630 1.099928300 [229,] -0.814198702 -1.155109630 [230,] 1.807201525 -0.814198702 [231,] -3.132401339 1.807201525 [232,] 4.676127579 -3.132401339 [233,] 2.298337625 4.676127579 [234,] -0.698624739 2.298337625 [235,] -2.195321322 -0.698624739 [236,] -6.914416877 -2.195321322 [237,] -1.086386543 -6.914416877 [238,] 1.888555398 -1.086386543 [239,] -1.581042084 1.888555398 [240,] -0.145789506 -1.581042084 [241,] -1.322333028 -0.145789506 [242,] 1.198044918 -1.322333028 [243,] 2.032402960 1.198044918 [244,] 1.517485572 2.032402960 [245,] 0.229882402 1.517485572 [246,] 1.329833795 0.229882402 [247,] -2.293447889 1.329833795 [248,] 1.398592634 -2.293447889 [249,] 0.495413938 1.398592634 [250,] -0.693327081 0.495413938 [251,] -1.497025470 -0.693327081 [252,] 0.772227707 -1.497025470 [253,] 3.366970537 0.772227707 [254,] -1.103306445 3.366970537 [255,] 0.388489873 -1.103306445 [256,] 1.861977961 0.388489873 [257,] 2.548703480 1.861977961 [258,] -0.988365848 2.548703480 [259,] -5.278553518 -0.988365848 [260,] 1.509182186 -5.278553518 [261,] -3.705447878 1.509182186 [262,] -0.100381546 -3.705447878 [263,] 1.424001562 -0.100381546 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.226986679 -3.131999295 2 1.935679119 0.226986679 3 2.804558516 1.935679119 4 -2.418032225 2.804558516 5 -1.880226893 -2.418032225 6 3.692611150 -1.880226893 7 -2.099410970 3.692611150 8 -2.071694532 -2.099410970 9 0.529072337 -2.071694532 10 0.995167912 0.529072337 11 -0.189823032 0.995167912 12 0.470117024 -0.189823032 13 0.498538318 0.470117024 14 -0.984972301 0.498538318 15 -0.471169368 -0.984972301 16 0.388482697 -0.471169368 17 3.584932386 0.388482697 18 2.510108792 3.584932386 19 0.448882448 2.510108792 20 0.639130463 0.448882448 21 0.859494434 0.639130463 22 2.526189315 0.859494434 23 0.911757507 2.526189315 24 0.851015089 0.911757507 25 0.745973842 0.851015089 26 1.029242723 0.745973842 27 -1.731403298 1.029242723 28 0.285551942 -1.731403298 29 -0.177697671 0.285551942 30 -0.727157091 -0.177697671 31 -0.819627423 -0.727157091 32 -0.790028236 -0.819627423 33 0.090766983 -0.790028236 34 -1.524184275 0.090766983 35 -2.994926432 -1.524184275 36 -3.036017733 -2.994926432 37 -1.715914366 -3.036017733 38 1.481067760 -1.715914366 39 1.536025997 1.481067760 40 1.307738561 1.536025997 41 -1.691739352 1.307738561 42 2.576009592 -1.691739352 43 -0.126833311 2.576009592 44 -0.427775531 -0.126833311 45 -4.476901087 -0.427775531 46 -2.501670057 -4.476901087 47 -0.087273356 -2.501670057 48 0.565501318 -0.087273356 49 -1.595751557 0.565501318 50 -0.947005562 -1.595751557 51 -0.102905664 -0.947005562 52 -2.981443297 -0.102905664 53 0.214206701 -2.981443297 54 -2.356371538 0.214206701 55 1.665065486 -2.356371538 56 0.205418568 1.665065486 57 0.579053002 0.205418568 58 -0.083012473 0.579053002 59 1.848732075 -0.083012473 60 0.493038981 1.848732075 61 0.385981197 0.493038981 62 -0.481004602 0.385981197 63 -0.638611608 -0.481004602 64 0.783933502 -0.638611608 65 0.871640504 0.783933502 66 1.634018971 0.871640504 67 3.453934790 1.634018971 68 -3.985955793 3.453934790 69 0.276410320 -3.985955793 70 -3.436462873 0.276410320 71 -0.982476413 -3.436462873 72 0.984116988 -0.982476413 73 0.754511906 0.984116988 74 0.674279402 0.754511906 75 3.424247981 0.674279402 76 -0.468790299 3.424247981 77 1.447784167 -0.468790299 78 -2.261400258 1.447784167 79 0.803547438 -2.261400258 80 0.242550333 0.803547438 81 0.223148307 0.242550333 82 -0.729877341 0.223148307 83 0.070029770 -0.729877341 84 1.785056577 0.070029770 85 -0.236630957 1.785056577 86 0.590582925 -0.236630957 87 1.099972796 0.590582925 88 0.442475612 1.099972796 89 -1.919576969 0.442475612 90 0.219977319 -1.919576969 91 0.137035181 0.219977319 92 -0.130179843 0.137035181 93 -2.094841811 -0.130179843 94 1.169182749 -2.094841811 95 0.201266765 1.169182749 96 2.244394121 0.201266765 97 0.165573237 2.244394121 98 -0.466132834 0.165573237 99 -1.039710817 -0.466132834 100 1.301119982 -1.039710817 101 2.539345072 1.301119982 102 0.779093109 2.539345072 103 0.995067536 0.779093109 104 -1.881628561 0.995067536 105 1.293917079 -1.881628561 106 0.270053027 1.293917079 107 1.626587838 0.270053027 108 -0.305982558 1.626587838 109 0.716382937 -0.305982558 110 -0.010771667 0.716382937 111 1.964918736 -0.010771667 112 -1.540611240 1.964918736 113 -2.577275020 -1.540611240 114 1.850084192 -2.577275020 115 -1.849020562 1.850084192 116 0.828562586 -1.849020562 117 -1.789048013 0.828562586 118 0.478505290 -1.789048013 119 -1.417386693 0.478505290 120 0.626735160 -1.417386693 121 -2.823989421 0.626735160 122 -0.820672763 -2.823989421 123 -0.815194361 -0.820672763 124 -0.775886466 -0.815194361 125 0.343682304 -0.775886466 126 1.486454155 0.343682304 127 1.043809413 1.486454155 128 -2.664969621 1.043809413 129 2.051587473 -2.664969621 130 -3.699345786 2.051587473 131 2.521267351 -3.699345786 132 -2.117119104 2.521267351 133 -1.496982583 -2.117119104 134 -0.022851451 -1.496982583 135 0.921716463 -0.022851451 136 0.582024794 0.921716463 137 -2.414884325 0.582024794 138 -0.961538813 -2.414884325 139 -2.329429108 -0.961538813 140 3.180171327 -2.329429108 141 1.388523482 3.180171327 142 0.235263838 1.388523482 143 1.902733519 0.235263838 144 -3.232606357 1.902733519 145 2.576819178 -3.232606357 146 -1.864314906 2.576819178 147 1.218895990 -1.864314906 148 0.240626106 1.218895990 149 -2.847945621 0.240626106 150 -0.857124586 -2.847945621 151 2.175679631 -0.857124586 152 4.182209460 2.175679631 153 1.820374733 4.182209460 154 -2.360367445 1.820374733 155 0.643603147 -2.360367445 156 1.360040724 0.643603147 157 1.239329026 1.360040724 158 1.522216200 1.239329026 159 -0.671268683 1.522216200 160 0.449841607 -0.671268683 161 0.304814334 0.449841607 162 -0.532526582 0.304814334 163 0.705563450 -0.532526582 164 1.219730503 0.705563450 165 -1.696048432 1.219730503 166 -0.409813871 -1.696048432 167 -3.282401493 -0.409813871 168 -1.869656451 -3.282401493 169 1.472274377 -1.869656451 170 1.421840303 1.472274377 171 0.594604288 1.421840303 172 -1.950732603 0.594604288 173 -2.432755979 -1.950732603 174 -2.970887501 -2.432755979 175 0.384688185 -2.970887501 176 -0.348484733 0.384688185 177 -0.741023545 -0.348484733 178 0.151743067 -0.741023545 179 -1.436382572 0.151743067 180 0.942577449 -1.436382572 181 -0.529090078 0.942577449 182 2.297606534 -0.529090078 183 -0.186108035 2.297606534 184 -6.609847763 -0.186108035 185 1.205124572 -6.609847763 186 2.511032191 1.205124572 187 -0.449278889 2.511032191 188 -0.525825323 -0.449278889 189 0.516008059 -0.525825323 190 -0.729729103 0.516008059 191 0.542659139 -0.729729103 192 2.205050020 0.542659139 193 1.941762393 2.205050020 194 -0.667126774 1.941762393 195 -0.005961548 -0.667126774 196 3.038724351 -0.005961548 197 0.978304049 3.038724351 198 1.534504831 0.978304049 199 1.463965120 1.534504831 200 1.989822678 1.463965120 201 0.649485355 1.989822678 202 -2.424889464 0.649485355 203 -2.462945348 -2.424889464 204 2.271819267 -2.462945348 205 0.560792297 2.271819267 206 1.515804166 0.560792297 207 1.136082517 1.515804166 208 -2.573550732 1.136082517 209 1.067008366 -2.573550732 210 -2.190939360 1.067008366 211 -3.700365776 -2.190939360 212 0.901527852 -3.700365776 213 2.910196045 0.901527852 214 1.513882632 2.910196045 215 0.955208844 1.513882632 216 2.429615296 0.955208844 217 0.110123039 2.429615296 218 1.123659284 0.110123039 219 -0.082459821 1.123659284 220 -1.575055682 -0.082459821 221 0.013966772 -1.575055682 222 0.813396739 0.013966772 223 -1.045100363 0.813396739 224 0.472207287 -1.045100363 225 -3.696045992 0.472207287 226 0.343800671 -3.696045992 227 1.099928300 0.343800671 228 -1.155109630 1.099928300 229 -0.814198702 -1.155109630 230 1.807201525 -0.814198702 231 -3.132401339 1.807201525 232 4.676127579 -3.132401339 233 2.298337625 4.676127579 234 -0.698624739 2.298337625 235 -2.195321322 -0.698624739 236 -6.914416877 -2.195321322 237 -1.086386543 -6.914416877 238 1.888555398 -1.086386543 239 -1.581042084 1.888555398 240 -0.145789506 -1.581042084 241 -1.322333028 -0.145789506 242 1.198044918 -1.322333028 243 2.032402960 1.198044918 244 1.517485572 2.032402960 245 0.229882402 1.517485572 246 1.329833795 0.229882402 247 -2.293447889 1.329833795 248 1.398592634 -2.293447889 249 0.495413938 1.398592634 250 -0.693327081 0.495413938 251 -1.497025470 -0.693327081 252 0.772227707 -1.497025470 253 3.366970537 0.772227707 254 -1.103306445 3.366970537 255 0.388489873 -1.103306445 256 1.861977961 0.388489873 257 2.548703480 1.861977961 258 -0.988365848 2.548703480 259 -5.278553518 -0.988365848 260 1.509182186 -5.278553518 261 -3.705447878 1.509182186 262 -0.100381546 -3.705447878 263 1.424001562 -0.100381546 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/76sgd1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8z6eu1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9l0k11352156889.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/fisher/rcomp/tmp/10cl0y1352156889.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11coez1352156889.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12vg8q1352156889.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13f19z1352156889.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14xn861352156889.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/fisher/rcomp/tmp/15a0sa1352156889.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/fisher/rcomp/tmp/16qiqa1352156889.tab") + } > > try(system("convert tmp/1i2d01352156889.ps tmp/1i2d01352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/2yzmw1352156889.ps tmp/2yzmw1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/3fnqt1352156889.ps tmp/3fnqt1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/4tras1352156889.ps tmp/4tras1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/5mhzh1352156889.ps tmp/5mhzh1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/6wyam1352156889.ps tmp/6wyam1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/76sgd1352156889.ps tmp/76sgd1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/8z6eu1352156889.ps tmp/8z6eu1352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/9l0k11352156889.ps tmp/9l0k11352156889.png",intern=TRUE)) character(0) > try(system("convert tmp/10cl0y1352156889.ps tmp/10cl0y1352156889.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.893 1.186 13.080