R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43 + ,11) + ,dim=c(9 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Sport1' + ,'Sport2' + ,'Month') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '5' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '5' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Happiness Connected Separate Learning Software Depression Sport1 Sport2 1 14 41 38 13 12 12.0 53 32 2 18 39 32 16 11 11.0 83 51 3 11 30 35 19 15 14.0 66 42 4 12 31 33 15 6 12.0 67 41 5 16 34 37 14 13 21.0 76 46 6 18 35 29 13 10 12.0 78 47 7 14 39 31 19 12 22.0 53 37 8 14 34 36 15 14 11.0 80 49 9 15 36 35 14 12 10.0 74 45 10 15 37 38 15 9 13.0 76 47 11 17 38 31 16 10 10.0 79 49 12 19 36 34 16 12 8.0 54 33 13 10 38 35 16 12 15.0 67 42 14 16 39 38 16 11 14.0 54 33 15 18 33 37 17 15 10.0 87 53 16 14 32 33 15 12 14.0 58 36 17 14 36 32 15 10 14.0 75 45 18 17 38 38 20 12 11.0 88 54 19 14 39 38 18 11 10.0 64 41 20 16 32 32 16 12 13.0 57 36 21 18 32 33 16 11 9.5 66 41 22 11 31 31 16 12 14.0 68 44 23 14 39 38 19 13 12.0 54 33 24 12 37 39 16 11 14.0 56 37 25 17 39 32 17 12 11.0 86 52 26 9 41 32 17 13 9.0 80 47 27 16 36 35 16 10 11.0 76 43 28 14 33 37 15 14 15.0 69 44 29 15 33 33 16 12 14.0 78 45 30 11 34 33 14 10 13.0 67 44 31 16 31 31 15 12 9.0 80 49 32 13 27 32 12 8 15.0 54 33 33 17 37 31 14 10 10.0 71 43 34 15 34 37 16 12 11.0 84 54 35 14 34 30 14 12 13.0 74 42 36 16 32 33 10 7 8.0 71 44 37 9 29 31 10 9 20.0 63 37 38 15 36 33 14 12 12.0 71 43 39 17 29 31 16 10 10.0 76 46 40 13 35 33 16 10 10.0 69 42 41 15 37 32 16 10 9.0 74 45 42 16 34 33 14 12 14.0 75 44 43 16 38 32 20 15 8.0 54 33 44 12 35 33 14 10 14.0 52 31 45 15 38 28 14 10 11.0 69 42 46 11 37 35 11 12 13.0 68 40 47 15 38 39 14 13 9.0 65 43 48 15 33 34 15 11 11.0 75 46 49 17 36 38 16 11 15.0 74 42 50 13 38 32 14 12 11.0 75 45 51 16 32 38 16 14 10.0 72 44 52 14 32 30 14 10 14.0 67 40 53 11 32 33 12 12 18.0 63 37 54 12 34 38 16 13 14.0 62 46 55 12 32 32 9 5 11.0 63 36 56 15 37 35 14 6 14.5 76 47 57 16 39 34 16 12 13.0 74 45 58 15 29 34 16 12 9.0 67 42 59 12 37 36 15 11 10.0 73 43 60 12 35 34 16 10 15.0 70 43 61 8 30 28 12 7 20.0 53 32 62 13 38 34 16 12 12.0 77 45 63 11 34 35 16 14 12.0 80 48 64 14 31 35 14 11 14.0 52 31 65 15 34 31 16 12 13.0 54 33 66 10 35 37 17 13 11.0 80 49 67 11 36 35 18 14 17.0 66 42 68 12 30 27 18 11 12.0 73 41 69 15 39 40 12 12 13.0 63 38 70 15 35 37 16 12 14.0 69 42 71 14 38 36 10 8 13.0 67 44 72 16 31 38 14 11 15.0 54 33 73 15 34 39 18 14 13.0 81 48 74 15 38 41 18 14 10.0 69 40 75 13 34 27 16 12 11.0 84 50 76 12 39 30 17 9 19.0 80 49 77 17 37 37 16 13 13.0 70 43 78 13 34 31 16 11 17.0 69 44 79 15 28 31 13 12 13.0 77 47 80 13 37 27 16 12 9.0 54 33 81 15 33 36 16 12 11.0 79 46 82 15 35 37 16 12 9.0 71 45 83 16 37 33 15 12 12.0 73 43 84 15 32 34 15 11 12.0 72 44 85 14 33 31 16 10 13.0 77 47 86 15 38 39 14 9 13.0 75 45 87 14 33 34 16 12 12.0 69 42 88 13 29 32 16 12 15.0 54 33 89 7 33 33 15 12 22.0 70 43 90 17 31 36 12 9 13.0 73 46 91 13 36 32 17 15 15.0 54 33 92 15 35 41 16 12 13.0 77 46 93 14 32 28 15 12 15.0 82 48 94 13 29 30 13 12 12.5 80 47 95 16 39 36 16 10 11.0 80 47 96 12 37 35 16 13 16.0 69 43 97 14 35 31 16 9 11.0 78 46 98 17 37 34 16 12 11.0 81 48 99 15 32 36 14 10 10.0 76 46 100 17 38 36 16 14 10.0 76 45 101 12 37 35 16 11 16.0 73 45 102 16 36 37 20 15 12.0 85 52 103 11 32 28 15 11 11.0 66 42 104 15 33 39 16 11 16.0 79 47 105 9 40 32 13 12 19.0 68 41 106 16 38 35 17 12 11.0 76 47 107 15 41 39 16 12 16.0 71 43 108 10 36 35 16 11 15.0 54 33 109 10 43 42 12 7 24.0 46 30 110 15 30 34 16 12 14.0 85 52 111 11 31 33 16 14 15.0 74 44 112 13 32 41 17 11 11.0 88 55 113 14 32 33 13 11 15.0 38 11 114 18 37 34 12 10 12.0 76 47 115 16 37 32 18 13 10.0 86 53 116 14 33 40 14 13 14.0 54 33 117 14 34 40 14 8 13.0 67 44 118 14 33 35 13 11 9.0 69 42 119 14 38 36 16 12 15.0 90 55 120 12 33 37 13 11 15.0 54 33 121 14 31 27 16 13 14.0 76 46 122 15 38 39 13 12 11.0 89 54 123 15 37 38 16 14 8.0 76 47 124 15 36 31 15 13 11.0 73 45 125 13 31 33 16 15 11.0 79 47 126 17 39 32 15 10 8.0 90 55 127 17 44 39 17 11 10.0 74 44 128 19 33 36 15 9 11.0 81 53 129 15 35 33 12 11 13.0 72 44 130 13 32 33 16 10 11.0 71 42 131 9 28 32 10 11 20.0 66 40 132 15 40 37 16 8 10.0 77 46 133 15 27 30 12 11 15.0 65 40 134 15 37 38 14 12 12.0 74 46 135 16 32 29 15 12 14.0 85 53 136 11 28 22 13 9 23.0 54 33 137 14 34 35 15 11 14.0 63 42 138 11 30 35 11 10 16.0 54 35 139 15 35 34 12 8 11.0 64 40 140 13 31 35 11 9 12.0 69 41 141 15 32 34 16 8 10.0 54 33 142 16 30 37 15 9 14.0 84 51 143 14 30 35 17 15 12.0 86 53 144 15 31 23 16 11 12.0 77 46 145 16 40 31 10 8 11.0 89 55 146 16 32 27 18 13 12.0 76 47 147 11 36 36 13 12 13.0 60 38 148 12 32 31 16 12 11.0 75 46 149 9 35 32 13 9 19.0 73 46 150 16 38 39 10 7 12.0 85 53 151 13 42 37 15 13 17.0 79 47 152 16 34 38 16 9 9.0 71 41 153 12 35 39 16 6 12.0 72 44 154 9 38 34 14 8 19.0 69 43 155 13 33 31 10 8 18.0 78 51 156 13 36 32 17 15 15.0 54 33 157 14 32 37 13 6 14.0 69 43 158 19 33 36 15 9 11.0 81 53 159 13 34 32 16 11 9.0 84 51 160 12 32 38 12 8 18.0 84 50 161 13 34 36 13 8 16.0 69 46 162 10 27 26 13 10 24.0 66 43 163 14 31 26 12 8 14.0 81 47 164 16 38 33 17 14 20.0 82 50 165 10 34 39 15 10 18.0 72 43 166 11 24 30 10 8 23.0 54 33 167 14 30 33 14 11 12.0 78 48 168 12 26 25 11 12 14.0 74 44 169 9 34 38 13 12 16.0 82 50 170 9 27 37 16 12 18.0 73 41 171 11 37 31 12 5 20.0 55 34 172 16 36 37 16 12 12.0 72 44 173 9 41 35 12 10 12.0 78 47 174 13 29 25 9 7 17.0 59 35 175 16 36 28 12 12 13.0 72 44 176 13 32 35 15 11 9.0 78 44 177 9 37 33 12 8 16.0 68 43 178 12 30 30 12 9 18.0 69 41 179 16 31 31 14 10 10.0 67 41 180 11 38 37 12 9 14.0 74 42 181 14 36 36 16 12 11.0 54 33 182 13 35 30 11 6 9.0 67 41 183 15 31 36 19 15 11.0 70 44 184 14 38 32 15 12 10.0 80 48 185 16 22 28 8 12 11.0 89 55 186 13 32 36 16 12 19.0 76 44 187 14 36 34 17 11 14.0 74 43 188 15 39 31 12 7 12.0 87 52 189 13 28 28 11 7 14.0 54 30 190 11 32 36 11 5 21.0 61 39 191 11 32 36 14 12 13.0 38 11 192 14 38 40 16 12 10.0 75 44 193 15 32 33 12 3 15.0 69 42 194 11 35 37 16 11 16.0 62 41 195 15 32 32 13 10 14.0 72 44 196 12 37 38 15 12 12.0 70 44 197 14 34 31 16 9 19.0 79 48 198 14 33 37 16 12 15.0 87 53 199 8 33 33 14 9 19.0 62 37 200 13 26 32 16 12 13.0 77 44 201 9 30 30 16 12 17.0 69 44 202 15 24 30 14 10 12.0 69 40 203 17 34 31 11 9 11.0 75 42 204 13 34 32 12 12 14.0 54 35 205 15 33 34 15 8 11.0 72 43 206 15 34 36 15 11 13.0 74 45 207 14 35 37 16 11 12.0 85 55 208 16 35 36 16 12 15.0 52 31 209 13 36 33 11 10 14.0 70 44 210 16 34 33 15 10 12.0 84 50 211 9 34 33 12 12 17.0 64 40 212 16 41 44 12 12 11.0 84 53 213 11 32 39 15 11 18.0 87 54 214 10 30 32 15 8 13.0 79 49 215 11 35 35 16 12 17.0 67 40 216 15 28 25 14 10 13.0 65 41 217 17 33 35 17 11 11.0 85 52 218 14 39 34 14 10 12.0 83 52 219 8 36 35 13 8 22.0 61 36 220 15 36 39 15 12 14.0 82 52 221 11 35 33 13 12 12.0 76 46 222 16 38 36 14 10 12.0 58 31 223 10 33 32 15 12 17.0 72 44 224 15 31 32 12 9 9.0 72 44 225 9 34 36 13 9 21.0 38 11 226 16 32 36 8 6 10.0 78 46 227 19 31 32 14 10 11.0 54 33 228 12 33 34 14 9 12.0 63 34 229 8 34 33 11 9 23.0 66 42 230 11 34 35 12 9 13.0 70 43 231 14 34 30 13 6 12.0 71 43 232 9 33 38 10 10 16.0 67 44 233 15 32 34 16 6 9.0 58 36 234 13 41 33 18 14 17.0 72 46 235 16 34 32 13 10 9.0 72 44 236 11 36 31 11 10 14.0 70 43 237 12 37 30 4 6 17.0 76 50 238 13 36 27 13 12 13.0 50 33 239 10 29 31 16 12 11.0 72 43 240 11 37 30 10 7 12.0 72 44 241 12 27 32 12 8 10.0 88 53 242 8 35 35 12 11 19.0 53 34 243 12 28 28 10 3 16.0 58 35 244 12 35 33 13 6 16.0 66 40 245 15 37 31 15 10 14.0 82 53 246 11 29 35 12 8 20.0 69 42 247 13 32 35 14 9 15.0 68 43 248 14 36 32 10 9 23.0 44 29 249 10 19 21 12 8 20.0 56 36 250 12 21 20 12 9 16.0 53 30 251 15 31 34 11 7 14.0 70 42 252 13 33 32 10 7 17.0 78 47 253 13 36 34 12 6 11.0 71 44 254 13 33 32 16 9 13.0 72 45 255 12 37 33 12 10 17.0 68 44 256 12 34 33 14 11 15.0 67 43 257 9 35 37 16 12 21.0 75 43 258 9 31 32 14 8 18.0 62 40 259 15 37 34 13 11 15.0 67 41 260 10 35 30 4 3 8.0 83 52 261 14 27 30 15 11 12.0 64 38 262 15 34 38 11 12 12.0 68 41 263 7 40 36 11 7 22.0 62 39 264 14 29 32 14 9 12.0 72 43 Month t 1 9 1 2 9 2 3 9 3 4 9 4 5 9 5 6 9 6 7 9 7 8 9 8 9 9 9 10 9 10 11 9 11 12 9 12 13 9 13 14 9 14 15 9 15 16 9 16 17 9 17 18 9 18 19 9 19 20 9 20 21 9 21 22 9 22 23 9 23 24 9 24 25 9 25 26 9 26 27 9 27 28 9 28 29 9 29 30 9 30 31 9 31 32 9 32 33 9 33 34 9 34 35 9 35 36 9 36 37 9 37 38 9 38 39 9 39 40 9 40 41 9 41 42 9 42 43 9 43 44 9 44 45 9 45 46 9 46 47 9 47 48 9 48 49 9 49 50 9 50 51 9 51 52 9 52 53 9 53 54 9 54 55 9 55 56 9 56 57 9 57 58 9 58 59 9 59 60 9 60 61 9 61 62 9 62 63 9 63 64 9 64 65 9 65 66 10 66 67 10 67 68 10 68 69 10 69 70 10 70 71 10 71 72 10 72 73 10 73 74 10 74 75 10 75 76 10 76 77 10 77 78 10 78 79 10 79 80 10 80 81 10 81 82 10 82 83 10 83 84 10 84 85 10 85 86 10 86 87 10 87 88 10 88 89 10 89 90 10 90 91 10 91 92 10 92 93 10 93 94 10 94 95 10 95 96 10 96 97 10 97 98 10 98 99 10 99 100 10 100 101 10 101 102 10 102 103 10 103 104 10 104 105 10 105 106 10 106 107 10 107 108 10 108 109 10 109 110 10 110 111 10 111 112 10 112 113 10 113 114 10 114 115 10 115 116 10 116 117 10 117 118 10 118 119 10 119 120 10 120 121 10 121 122 10 122 123 10 123 124 10 124 125 10 125 126 10 126 127 10 127 128 10 128 129 10 129 130 10 130 131 10 131 132 10 132 133 10 133 134 10 134 135 10 135 136 10 136 137 10 137 138 10 138 139 10 139 140 10 140 141 10 141 142 10 142 143 10 143 144 10 144 145 10 145 146 10 146 147 10 147 148 10 148 149 10 149 150 10 150 151 10 151 152 10 152 153 10 153 154 9 154 155 10 155 156 10 156 157 10 157 158 10 158 159 10 159 160 10 160 161 10 161 162 11 162 163 11 163 164 11 164 165 11 165 166 11 166 167 11 167 168 11 168 169 11 169 170 11 170 171 11 171 172 11 172 173 11 173 174 11 174 175 11 175 176 11 176 177 11 177 178 11 178 179 11 179 180 11 180 181 11 181 182 11 182 183 11 183 184 11 184 185 11 185 186 11 186 187 11 187 188 11 188 189 11 189 190 11 190 191 11 191 192 11 192 193 11 193 194 11 194 195 11 195 196 11 196 197 11 197 198 11 198 199 11 199 200 11 200 201 11 201 202 11 202 203 11 203 204 11 204 205 11 205 206 11 206 207 11 207 208 11 208 209 11 209 210 11 210 211 11 211 212 11 212 213 11 213 214 11 214 215 11 215 216 11 216 217 11 217 218 11 218 219 11 219 220 11 220 221 11 221 222 11 222 223 11 223 224 11 224 225 11 225 226 11 226 227 11 227 228 11 228 229 11 229 230 11 230 231 11 231 232 11 232 233 11 233 234 11 234 235 11 235 236 11 236 237 11 237 238 11 238 239 11 239 240 11 240 241 11 241 242 11 242 243 11 243 244 11 244 245 11 245 246 11 246 247 11 247 248 11 248 249 11 249 250 11 250 251 11 251 252 11 252 253 11 253 254 11 254 255 11 255 256 11 256 257 11 257 258 11 258 259 11 259 260 11 260 261 11 261 262 11 262 263 11 263 264 11 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Learning Software Depression 12.665640 0.003321 0.011327 0.080466 -0.041045 -0.365596 Sport1 Sport2 Month t 0.007602 0.027717 0.391360 -0.008161 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9291 -1.3971 0.3185 1.3248 5.4065 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 12.665640 4.126056 3.070 0.00238 ** Connected 0.003321 0.037411 0.089 0.92934 Separate 0.011327 0.038005 0.298 0.76591 Learning 0.080466 0.067382 1.194 0.23352 Software -0.041045 0.069708 -0.589 0.55651 Depression -0.365596 0.039605 -9.231 < 2e-16 *** Sport1 0.007602 0.040921 0.186 0.85278 Sport2 0.027717 0.060751 0.456 0.64861 Month 0.391360 0.439621 0.890 0.37419 t -0.008161 0.004658 -1.752 0.08099 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.003 on 254 degrees of freedom Multiple R-squared: 0.3792, Adjusted R-squared: 0.3572 F-statistic: 17.24 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,] 0.99209183 0.015816344 0.0079081719 [2,] 0.98197176 0.036056474 0.0180282370 [3,] 0.97758703 0.044825935 0.0224129677 [4,] 0.95842531 0.083149387 0.0415746937 [5,] 0.98087628 0.038247441 0.0191237207 [6,] 0.96706799 0.065864027 0.0329320135 [7,] 0.94716402 0.105671957 0.0528359784 [8,] 0.93836501 0.123269977 0.0616349884 [9,] 0.94348240 0.113035209 0.0565176043 [10,] 0.95233210 0.095335806 0.0476679030 [11,] 0.95467969 0.090640613 0.0453203066 [12,] 0.93550768 0.128984639 0.0644923196 [13,] 0.92030407 0.159391859 0.0796959297 [14,] 0.99937598 0.001248048 0.0006240241 [15,] 0.99904075 0.001918509 0.0009592547 [16,] 0.99848295 0.003034098 0.0015170489 [17,] 0.99761366 0.004772684 0.0023863419 [18,] 0.99748124 0.005037520 0.0025187599 [19,] 0.99647620 0.007047608 0.0035238041 [20,] 0.99459521 0.010809575 0.0054047877 [21,] 0.99461680 0.010766395 0.0053831977 [22,] 0.99254078 0.014918447 0.0074592233 [23,] 0.98963401 0.020731981 0.0103659905 [24,] 0.98582769 0.028344624 0.0141723119 [25,] 0.98796816 0.024063671 0.0120318356 [26,] 0.98422678 0.031546440 0.0157732200 [27,] 0.98360938 0.032781243 0.0163906214 [28,] 0.98112804 0.037743920 0.0188719600 [29,] 0.97442688 0.051146244 0.0255731219 [30,] 0.97562839 0.048743220 0.0243716101 [31,] 0.96919309 0.061613811 0.0308069053 [32,] 0.96098107 0.078037854 0.0390189269 [33,] 0.95009379 0.099812410 0.0499062050 [34,] 0.95116547 0.097669063 0.0488345314 [35,] 0.94231409 0.115371819 0.0576859094 [36,] 0.92869375 0.142612494 0.0713062470 [37,] 0.95067061 0.098658789 0.0493293945 [38,] 0.94353424 0.112931514 0.0564657571 [39,] 0.93324007 0.133519867 0.0667599337 [40,] 0.91827080 0.163458399 0.0817291995 [41,] 0.90209788 0.195804240 0.0979021202 [42,] 0.88463192 0.230736157 0.1153680783 [43,] 0.87479739 0.250405228 0.1252026139 [44,] 0.86405470 0.271890601 0.1359453007 [45,] 0.85993295 0.280134108 0.1400670542 [46,] 0.83403450 0.331931006 0.1659655030 [47,] 0.85864275 0.282714498 0.1413572492 [48,] 0.84267547 0.314649065 0.1573245326 [49,] 0.85158318 0.296833648 0.1484168239 [50,] 0.83623932 0.327521365 0.1637606823 [51,] 0.87100056 0.257998887 0.1289994436 [52,] 0.86485384 0.270292315 0.1351461577 [53,] 0.86550208 0.268995832 0.1344979162 [54,] 0.88357701 0.232845982 0.1164229908 [55,] 0.88626372 0.227472553 0.1137362765 [56,] 0.87663670 0.246726603 0.1233633014 [57,] 0.91489227 0.170215465 0.0851077324 [58,] 0.92211653 0.155766936 0.0778834678 [59,] 0.91543245 0.169135110 0.0845675549 [60,] 0.93999150 0.120017009 0.0600085046 [61,] 0.92854549 0.142909015 0.0714545075 [62,] 0.91396007 0.172079860 0.0860399300 [63,] 0.90556889 0.188862223 0.0944311113 [64,] 0.88860733 0.222785346 0.1113926732 [65,] 0.91238259 0.175234811 0.0876174053 [66,] 0.89841165 0.203176710 0.1015883548 [67,] 0.88946921 0.221061589 0.1105307944 [68,] 0.88302772 0.233944556 0.1169722782 [69,] 0.86275735 0.274485305 0.1372426526 [70,] 0.84192610 0.316147799 0.1580738993 [71,] 0.83708758 0.325824848 0.1629124241 [72,] 0.81634606 0.367307882 0.1836539408 [73,] 0.79075656 0.418486881 0.2092434403 [74,] 0.76498612 0.470027761 0.2350138806 [75,] 0.73531850 0.529362995 0.2646814973 [76,] 0.70388750 0.592224994 0.2961124969 [77,] 0.77468080 0.450638408 0.2253192039 [78,] 0.81219448 0.375611037 0.1878055187 [79,] 0.78875152 0.422496959 0.2112484796 [80,] 0.76260103 0.474797939 0.2373989694 [81,] 0.74034583 0.519308331 0.2596541655 [82,] 0.71582047 0.568359064 0.2841795320 [83,] 0.69080516 0.618389674 0.3091948372 [84,] 0.66190333 0.676193343 0.3380966714 [85,] 0.63389058 0.732218831 0.3661094156 [86,] 0.64046797 0.719064051 0.3595320257 [87,] 0.60497022 0.790059551 0.3950297753 [88,] 0.60113420 0.797731591 0.3988657957 [89,] 0.57299876 0.854002475 0.4270012373 [90,] 0.54900004 0.901999921 0.4509999603 [91,] 0.60077194 0.798456122 0.3992280612 [92,] 0.58875116 0.822497679 0.4112488396 [93,] 0.60186809 0.796263827 0.3981319133 [94,] 0.58144214 0.837115727 0.4185578633 [95,] 0.57655641 0.846887174 0.4234435872 [96,] 0.61373659 0.772526820 0.3862634100 [97,] 0.57906519 0.841869625 0.4209348124 [98,] 0.55654406 0.886911888 0.4434559438 [99,] 0.55779148 0.884417041 0.4422085205 [100,] 0.57257839 0.854843212 0.4274216060 [101,] 0.55949279 0.881014421 0.4405072107 [102,] 0.66560200 0.668796003 0.3343980015 [103,] 0.64141822 0.717163552 0.3585817761 [104,] 0.61475161 0.770496778 0.3852483891 [105,] 0.57956598 0.840868048 0.4204340241 [106,] 0.55333216 0.893335679 0.4466678397 [107,] 0.51823889 0.963522225 0.4817611127 [108,] 0.48453513 0.969070259 0.5154648707 [109,] 0.46396811 0.927936218 0.5360318910 [110,] 0.42872874 0.857457472 0.5712712642 [111,] 0.39886178 0.797723556 0.6011382221 [112,] 0.37308550 0.746171000 0.6269144999 [113,] 0.35949863 0.718997253 0.6405013736 [114,] 0.33557153 0.671143058 0.6644284711 [115,] 0.32312962 0.646259243 0.6768703783 [116,] 0.43621483 0.872429663 0.5637851683 [117,] 0.41935495 0.838709903 0.5806450485 [118,] 0.40619474 0.812389470 0.5938052649 [119,] 0.39165074 0.783301481 0.6083492593 [120,] 0.35921730 0.718434602 0.6407826989 [121,] 0.38405340 0.768106799 0.6159466003 [122,] 0.35500865 0.710017296 0.6449913521 [123,] 0.37191137 0.743822741 0.6280886297 [124,] 0.36473043 0.729460862 0.6352695691 [125,] 0.33531363 0.670627266 0.6646863669 [126,] 0.31125654 0.622513083 0.6887434586 [127,] 0.28373125 0.567462499 0.7162687503 [128,] 0.25911084 0.518221679 0.7408891606 [129,] 0.23192913 0.463858252 0.7680708738 [130,] 0.23722096 0.474441928 0.7627790361 [131,] 0.21105803 0.422116069 0.7889419657 [132,] 0.19189056 0.383781115 0.8081094424 [133,] 0.17905397 0.358107935 0.8209460324 [134,] 0.17369391 0.347387825 0.8263060877 [135,] 0.18111580 0.362231594 0.8188842032 [136,] 0.19633521 0.392670415 0.8036647924 [137,] 0.21315456 0.426309116 0.7868454422 [138,] 0.20848249 0.416964979 0.7915175104 [139,] 0.18667592 0.373351832 0.8133240842 [140,] 0.16661060 0.333221192 0.8333894041 [141,] 0.17669491 0.353389826 0.8233050871 [142,] 0.18871940 0.377438801 0.8112805995 [143,] 0.17032741 0.340654818 0.8296725910 [144,] 0.15417663 0.308353257 0.8458233717 [145,] 0.13418218 0.268364351 0.8658178244 [146,] 0.20651947 0.413038930 0.7934805349 [147,] 0.22351473 0.447029456 0.7764852718 [148,] 0.19790707 0.395814146 0.8020929270 [149,] 0.17341669 0.346833381 0.8265833096 [150,] 0.15101247 0.302024932 0.8489875342 [151,] 0.13122856 0.262457119 0.8687714405 [152,] 0.21949485 0.438989693 0.7805051534 [153,] 0.22169136 0.443382724 0.7783086379 [154,] 0.21195747 0.423914934 0.7880425328 [155,] 0.18695992 0.373919830 0.8130400849 [156,] 0.16892823 0.337856463 0.8310717687 [157,] 0.22624517 0.452490338 0.7737548311 [158,] 0.25335473 0.506709462 0.7466452690 [159,] 0.22498316 0.449966327 0.7750168364 [160,] 0.21767521 0.435350421 0.7823247893 [161,] 0.38293531 0.765870617 0.6170646914 [162,] 0.36255574 0.725111473 0.6374442635 [163,] 0.37940777 0.758815534 0.6205922332 [164,] 0.38900136 0.778002711 0.6109986444 [165,] 0.45821403 0.916428053 0.5417859734 [166,] 0.42118952 0.842379048 0.5788104761 [167,] 0.39633357 0.792667139 0.6036664304 [168,] 0.40058675 0.801173496 0.5994132520 [169,] 0.36608348 0.732166955 0.6339165224 [170,] 0.37249647 0.744992949 0.6275035257 [171,] 0.33676136 0.673522718 0.6632386410 [172,] 0.31446133 0.628922651 0.6855386746 [173,] 0.31299668 0.625993351 0.6870033246 [174,] 0.30078326 0.601566521 0.6992167397 [175,] 0.26794578 0.535891553 0.7320542237 [176,] 0.23895104 0.477902074 0.7610489632 [177,] 0.20977675 0.419553496 0.7902232518 [178,] 0.18488325 0.369766500 0.8151167501 [179,] 0.19155688 0.383113753 0.8084431236 [180,] 0.17621950 0.352438991 0.8237805046 [181,] 0.18186478 0.363729559 0.8181352205 [182,] 0.17242475 0.344849505 0.8275752476 [183,] 0.16847627 0.336952532 0.8315237341 [184,] 0.17979038 0.359580755 0.8202096223 [185,] 0.21446746 0.428934928 0.7855325358 [186,] 0.19705965 0.394119305 0.8029403474 [187,] 0.22877917 0.457558345 0.7712208273 [188,] 0.19903942 0.398078841 0.8009605793 [189,] 0.23812738 0.476254751 0.7618726245 [190,] 0.21497666 0.429953319 0.7850233406 [191,] 0.26184698 0.523693952 0.7381530238 [192,] 0.23332158 0.466643163 0.7666784183 [193,] 0.20223929 0.404478590 0.7977607050 [194,] 0.18281926 0.365638518 0.8171807411 [195,] 0.15532322 0.310646433 0.8446767835 [196,] 0.17441034 0.348820687 0.8255896563 [197,] 0.14685418 0.293708359 0.8531458203 [198,] 0.16112669 0.322253370 0.8388733148 [199,] 0.18204884 0.364097671 0.8179511645 [200,] 0.16836601 0.336732027 0.8316339863 [201,] 0.14385356 0.287707126 0.8561464371 [202,] 0.18468084 0.369361689 0.8153191554 [203,] 0.16241749 0.324834971 0.8375825144 [204,] 0.14886162 0.297723250 0.8511383752 [205,] 0.17472521 0.349450423 0.8252747885 [206,] 0.14873260 0.297465205 0.8512673975 [207,] 0.13554605 0.271092099 0.8644539507 [208,] 0.14281103 0.285622059 0.8571889706 [209,] 0.14401958 0.288039156 0.8559804219 [210,] 0.14785717 0.295714339 0.8521428307 [211,] 0.13213685 0.264273706 0.8678631472 [212,] 0.10719372 0.214387432 0.8928062841 [213,] 0.09519162 0.190383232 0.9048083842 [214,] 0.11798668 0.235973357 0.8820133217 [215,] 0.30974325 0.619486500 0.6902567500 [216,] 0.27201141 0.544022815 0.7279885927 [217,] 0.23630216 0.472604319 0.7636978406 [218,] 0.21342632 0.426852643 0.7865736787 [219,] 0.18017494 0.360349889 0.8198250557 [220,] 0.20288537 0.405770744 0.7971146280 [221,] 0.16574721 0.331494419 0.8342527903 [222,] 0.13444890 0.268897803 0.8655510984 [223,] 0.13920766 0.278415317 0.8607923417 [224,] 0.11483420 0.229668409 0.8851657957 [225,] 0.09518069 0.190361387 0.9048193066 [226,] 0.06970211 0.139404222 0.9302978888 [227,] 0.14484569 0.289691374 0.8551543128 [228,] 0.16083286 0.321665714 0.8391671428 [229,] 0.16070255 0.321405104 0.8392974482 [230,] 0.76363081 0.472738376 0.2363691879 [231,] 0.68954324 0.620913527 0.3104567634 [232,] 0.63743438 0.725131249 0.3625656246 [233,] 0.58876410 0.822471806 0.4112359031 [234,] 0.50321074 0.993578529 0.4967892646 [235,] 0.47588071 0.951761420 0.5241192901 [236,] 0.45828296 0.916565924 0.5417170379 [237,] 0.33884519 0.677690377 0.6611548117 [238,] 0.35927509 0.718550184 0.6407249080 [239,] 0.24407618 0.488152366 0.7559238171 > postscript(file="/var/wessaorg/rcomp/tmp/1pxzh1384794171.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2cch61384794171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/33sl11384794171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/41kn51384794171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5tf3j1384794171.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 -0.20248767 2.47756427 -3.12011640 -2.85124406 4.55279887 3.27229796 7 8 9 10 11 12 2.96699365 -1.22033404 -0.41823015 0.37517833 1.24486027 3.21009300 13 14 15 16 17 18 -3.58881394 2.32367803 2.17922393 0.42783381 -0.02673256 1.14152049 19 20 21 22 23 24 -1.55658778 2.03334437 2.50254492 -2.87544399 -0.49337250 -1.72546932 25 26 27 28 29 30 1.57532604 -6.92910625 0.89147358 0.61946965 1.04865472 -3.12192556 31 32 33 34 35 36 0.22068411 0.14271118 1.81577163 -0.35102288 0.03717542 0.27402060 37 38 39 40 41 42 -1.96112056 0.65052692 1.60921113 -2.26112591 -0.73503469 2.36288173 43 44 45 46 47 48 0.28037142 -1.17104800 0.35288220 -2.59720901 -0.36076253 0.13010618 49 50 51 52 53 54 3.58338685 -1.69829153 0.86791400 0.57470552 -0.63214843 -1.67232183 55 56 57 58 59 60 -2.18187764 1.29029281 1.91072346 -0.37393316 -3.08330079 -1.31656834 61 62 63 64 65 66 -2.76301534 -1.43355197 -3.44730120 1.02384635 1.51123410 -5.35497852 67 68 69 70 71 72 -1.87288278 -2.73079337 1.14883388 1.09151329 0.01382629 2.95875172 73 74 75 76 77 78 0.39470300 -0.41690579 -2.18363927 -0.44676958 2.78212974 0.22839533 79 80 81 82 83 84 0.93257300 -2.18474929 -0.08442079 -0.73689118 1.52742419 0.47969986 85 86 87 88 89 90 -0.35855235 0.73291208 -0.46031899 0.04404654 -3.73155750 2.97119992 91 92 93 94 95 96 0.08795610 0.68846973 0.57206305 -1.14260758 0.89250090 -0.93576473 97 98 99 100 101 102 -1.01938373 1.99305180 -0.19815192 1.82105273 -1.06289104 1.02062888 103 104 105 106 107 108 -3.56182943 1.82852164 -2.47811744 1.02895158 2.03916706 -2.89100348 109 110 111 112 113 114 0.60663778 1.06974291 -2.16104724 -2.32412381 2.15853929 3.79472326 115 116 117 118 119 120 0.49236742 1.00503691 0.03534424 -1.11509056 0.33841047 -0.56436565 121 122 123 124 125 126 0.51124718 0.14324267 -0.79720421 0.50801705 -1.58929244 0.87672578 127 128 129 130 131 132 1.82681411 4.04725509 1.45530720 -1.55763648 -1.61721392 -0.21735337 133 134 135 136 137 138 2.44377037 0.87671474 2.37651303 1.59540340 0.74930897 -0.95480186 139 140 141 142 143 144 0.84294253 -0.72555856 0.45180196 2.28956333 -0.39611090 0.92337481 145 146 147 148 149 150 1.46442249 1.79211086 -2.21699455 -2.44726716 -2.40215833 1.83164285 151 152 153 154 155 156 0.73301385 0.81410817 -2.30947849 -2.01056241 1.32293859 0.61842458 157 158 159 160 161 162 0.77889471 4.29208669 -2.35470247 0.10894953 0.54635744 0.41249337 163 164 165 166 167 168 0.52488978 4.37728504 -2.13364179 1.57189158 -0.29233694 -1.02536450 169 170 171 172 173 174 -3.84787649 -2.99748046 0.14202571 1.71982771 -5.15494766 1.42961257 175 176 177 178 179 180 2.53371624 -2.31456988 -3.51918712 0.36627073 1.33032873 -2.25137335 181 182 183 184 185 186 -0.11927472 -1.93552353 0.36887448 -0.95465306 1.81836988 1.38746103 187 188 189 190 191 192 0.49842070 0.68928170 0.44023958 0.51891961 -1.40085411 -0.91157179 193 194 195 196 197 198 2.07728439 -1.51680063 1.86795032 -2.00328564 2.27041783 0.67528800 199 200 201 202 203 204 -3.13754621 -0.63423248 -3.09350054 1.29631112 2.99365138 0.48359860 205 206 207 208 209 210 0.61149067 1.37736856 -0.43596970 3.63741776 0.13373071 1.82275079 211 212 213 214 215 216 -2.58841346 1.56597037 -1.11313722 -3.77076479 -0.92641294 1.82220541 217 218 219 220 221 222 2.31202540 -0.10725758 -1.83541254 1.60280963 -2.67609398 2.67815281 223 224 225 226 227 228 -1.88891232 0.31937950 -0.24791787 1.75035233 5.39688651 -1.39582846 229 230 231 232 233 234 -1.36124342 -2.17029124 0.31770551 -2.89077572 0.25001325 0.94822617 235 236 237 238 239 240 1.35976886 -1.59554925 0.67685832 0.45084760 -3.98005375 -2.37168209 241 242 243 244 245 246 -2.57512915 -2.42132784 0.35942148 -0.02995763 1.78432511 0.53033721 247 248 249 250 251 252 0.56055203 5.40652670 0.01173077 0.79234509 2.41406853 1.41609983 253 254 255 256 257 258 -0.86754901 -0.32962511 0.53734529 -0.26029269 -1.28788137 -2.12786910 259 260 261 262 263 264 2.87880183 -4.65094656 0.82187332 1.96552414 -2.47180223 0.61603746 > postscript(file="/var/wessaorg/rcomp/tmp/6cucn1384794171.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 -0.20248767 NA 1 2.47756427 -0.20248767 2 -3.12011640 2.47756427 3 -2.85124406 -3.12011640 4 4.55279887 -2.85124406 5 3.27229796 4.55279887 6 2.96699365 3.27229796 7 -1.22033404 2.96699365 8 -0.41823015 -1.22033404 9 0.37517833 -0.41823015 10 1.24486027 0.37517833 11 3.21009300 1.24486027 12 -3.58881394 3.21009300 13 2.32367803 -3.58881394 14 2.17922393 2.32367803 15 0.42783381 2.17922393 16 -0.02673256 0.42783381 17 1.14152049 -0.02673256 18 -1.55658778 1.14152049 19 2.03334437 -1.55658778 20 2.50254492 2.03334437 21 -2.87544399 2.50254492 22 -0.49337250 -2.87544399 23 -1.72546932 -0.49337250 24 1.57532604 -1.72546932 25 -6.92910625 1.57532604 26 0.89147358 -6.92910625 27 0.61946965 0.89147358 28 1.04865472 0.61946965 29 -3.12192556 1.04865472 30 0.22068411 -3.12192556 31 0.14271118 0.22068411 32 1.81577163 0.14271118 33 -0.35102288 1.81577163 34 0.03717542 -0.35102288 35 0.27402060 0.03717542 36 -1.96112056 0.27402060 37 0.65052692 -1.96112056 38 1.60921113 0.65052692 39 -2.26112591 1.60921113 40 -0.73503469 -2.26112591 41 2.36288173 -0.73503469 42 0.28037142 2.36288173 43 -1.17104800 0.28037142 44 0.35288220 -1.17104800 45 -2.59720901 0.35288220 46 -0.36076253 -2.59720901 47 0.13010618 -0.36076253 48 3.58338685 0.13010618 49 -1.69829153 3.58338685 50 0.86791400 -1.69829153 51 0.57470552 0.86791400 52 -0.63214843 0.57470552 53 -1.67232183 -0.63214843 54 -2.18187764 -1.67232183 55 1.29029281 -2.18187764 56 1.91072346 1.29029281 57 -0.37393316 1.91072346 58 -3.08330079 -0.37393316 59 -1.31656834 -3.08330079 60 -2.76301534 -1.31656834 61 -1.43355197 -2.76301534 62 -3.44730120 -1.43355197 63 1.02384635 -3.44730120 64 1.51123410 1.02384635 65 -5.35497852 1.51123410 66 -1.87288278 -5.35497852 67 -2.73079337 -1.87288278 68 1.14883388 -2.73079337 69 1.09151329 1.14883388 70 0.01382629 1.09151329 71 2.95875172 0.01382629 72 0.39470300 2.95875172 73 -0.41690579 0.39470300 74 -2.18363927 -0.41690579 75 -0.44676958 -2.18363927 76 2.78212974 -0.44676958 77 0.22839533 2.78212974 78 0.93257300 0.22839533 79 -2.18474929 0.93257300 80 -0.08442079 -2.18474929 81 -0.73689118 -0.08442079 82 1.52742419 -0.73689118 83 0.47969986 1.52742419 84 -0.35855235 0.47969986 85 0.73291208 -0.35855235 86 -0.46031899 0.73291208 87 0.04404654 -0.46031899 88 -3.73155750 0.04404654 89 2.97119992 -3.73155750 90 0.08795610 2.97119992 91 0.68846973 0.08795610 92 0.57206305 0.68846973 93 -1.14260758 0.57206305 94 0.89250090 -1.14260758 95 -0.93576473 0.89250090 96 -1.01938373 -0.93576473 97 1.99305180 -1.01938373 98 -0.19815192 1.99305180 99 1.82105273 -0.19815192 100 -1.06289104 1.82105273 101 1.02062888 -1.06289104 102 -3.56182943 1.02062888 103 1.82852164 -3.56182943 104 -2.47811744 1.82852164 105 1.02895158 -2.47811744 106 2.03916706 1.02895158 107 -2.89100348 2.03916706 108 0.60663778 -2.89100348 109 1.06974291 0.60663778 110 -2.16104724 1.06974291 111 -2.32412381 -2.16104724 112 2.15853929 -2.32412381 113 3.79472326 2.15853929 114 0.49236742 3.79472326 115 1.00503691 0.49236742 116 0.03534424 1.00503691 117 -1.11509056 0.03534424 118 0.33841047 -1.11509056 119 -0.56436565 0.33841047 120 0.51124718 -0.56436565 121 0.14324267 0.51124718 122 -0.79720421 0.14324267 123 0.50801705 -0.79720421 124 -1.58929244 0.50801705 125 0.87672578 -1.58929244 126 1.82681411 0.87672578 127 4.04725509 1.82681411 128 1.45530720 4.04725509 129 -1.55763648 1.45530720 130 -1.61721392 -1.55763648 131 -0.21735337 -1.61721392 132 2.44377037 -0.21735337 133 0.87671474 2.44377037 134 2.37651303 0.87671474 135 1.59540340 2.37651303 136 0.74930897 1.59540340 137 -0.95480186 0.74930897 138 0.84294253 -0.95480186 139 -0.72555856 0.84294253 140 0.45180196 -0.72555856 141 2.28956333 0.45180196 142 -0.39611090 2.28956333 143 0.92337481 -0.39611090 144 1.46442249 0.92337481 145 1.79211086 1.46442249 146 -2.21699455 1.79211086 147 -2.44726716 -2.21699455 148 -2.40215833 -2.44726716 149 1.83164285 -2.40215833 150 0.73301385 1.83164285 151 0.81410817 0.73301385 152 -2.30947849 0.81410817 153 -2.01056241 -2.30947849 154 1.32293859 -2.01056241 155 0.61842458 1.32293859 156 0.77889471 0.61842458 157 4.29208669 0.77889471 158 -2.35470247 4.29208669 159 0.10894953 -2.35470247 160 0.54635744 0.10894953 161 0.41249337 0.54635744 162 0.52488978 0.41249337 163 4.37728504 0.52488978 164 -2.13364179 4.37728504 165 1.57189158 -2.13364179 166 -0.29233694 1.57189158 167 -1.02536450 -0.29233694 168 -3.84787649 -1.02536450 169 -2.99748046 -3.84787649 170 0.14202571 -2.99748046 171 1.71982771 0.14202571 172 -5.15494766 1.71982771 173 1.42961257 -5.15494766 174 2.53371624 1.42961257 175 -2.31456988 2.53371624 176 -3.51918712 -2.31456988 177 0.36627073 -3.51918712 178 1.33032873 0.36627073 179 -2.25137335 1.33032873 180 -0.11927472 -2.25137335 181 -1.93552353 -0.11927472 182 0.36887448 -1.93552353 183 -0.95465306 0.36887448 184 1.81836988 -0.95465306 185 1.38746103 1.81836988 186 0.49842070 1.38746103 187 0.68928170 0.49842070 188 0.44023958 0.68928170 189 0.51891961 0.44023958 190 -1.40085411 0.51891961 191 -0.91157179 -1.40085411 192 2.07728439 -0.91157179 193 -1.51680063 2.07728439 194 1.86795032 -1.51680063 195 -2.00328564 1.86795032 196 2.27041783 -2.00328564 197 0.67528800 2.27041783 198 -3.13754621 0.67528800 199 -0.63423248 -3.13754621 200 -3.09350054 -0.63423248 201 1.29631112 -3.09350054 202 2.99365138 1.29631112 203 0.48359860 2.99365138 204 0.61149067 0.48359860 205 1.37736856 0.61149067 206 -0.43596970 1.37736856 207 3.63741776 -0.43596970 208 0.13373071 3.63741776 209 1.82275079 0.13373071 210 -2.58841346 1.82275079 211 1.56597037 -2.58841346 212 -1.11313722 1.56597037 213 -3.77076479 -1.11313722 214 -0.92641294 -3.77076479 215 1.82220541 -0.92641294 216 2.31202540 1.82220541 217 -0.10725758 2.31202540 218 -1.83541254 -0.10725758 219 1.60280963 -1.83541254 220 -2.67609398 1.60280963 221 2.67815281 -2.67609398 222 -1.88891232 2.67815281 223 0.31937950 -1.88891232 224 -0.24791787 0.31937950 225 1.75035233 -0.24791787 226 5.39688651 1.75035233 227 -1.39582846 5.39688651 228 -1.36124342 -1.39582846 229 -2.17029124 -1.36124342 230 0.31770551 -2.17029124 231 -2.89077572 0.31770551 232 0.25001325 -2.89077572 233 0.94822617 0.25001325 234 1.35976886 0.94822617 235 -1.59554925 1.35976886 236 0.67685832 -1.59554925 237 0.45084760 0.67685832 238 -3.98005375 0.45084760 239 -2.37168209 -3.98005375 240 -2.57512915 -2.37168209 241 -2.42132784 -2.57512915 242 0.35942148 -2.42132784 243 -0.02995763 0.35942148 244 1.78432511 -0.02995763 245 0.53033721 1.78432511 246 0.56055203 0.53033721 247 5.40652670 0.56055203 248 0.01173077 5.40652670 249 0.79234509 0.01173077 250 2.41406853 0.79234509 251 1.41609983 2.41406853 252 -0.86754901 1.41609983 253 -0.32962511 -0.86754901 254 0.53734529 -0.32962511 255 -0.26029269 0.53734529 256 -1.28788137 -0.26029269 257 -2.12786910 -1.28788137 258 2.87880183 -2.12786910 259 -4.65094656 2.87880183 260 0.82187332 -4.65094656 261 1.96552414 0.82187332 262 -2.47180223 1.96552414 263 0.61603746 -2.47180223 264 NA 0.61603746 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.47756427 -0.20248767 [2,] -3.12011640 2.47756427 [3,] -2.85124406 -3.12011640 [4,] 4.55279887 -2.85124406 [5,] 3.27229796 4.55279887 [6,] 2.96699365 3.27229796 [7,] -1.22033404 2.96699365 [8,] -0.41823015 -1.22033404 [9,] 0.37517833 -0.41823015 [10,] 1.24486027 0.37517833 [11,] 3.21009300 1.24486027 [12,] -3.58881394 3.21009300 [13,] 2.32367803 -3.58881394 [14,] 2.17922393 2.32367803 [15,] 0.42783381 2.17922393 [16,] -0.02673256 0.42783381 [17,] 1.14152049 -0.02673256 [18,] -1.55658778 1.14152049 [19,] 2.03334437 -1.55658778 [20,] 2.50254492 2.03334437 [21,] -2.87544399 2.50254492 [22,] -0.49337250 -2.87544399 [23,] -1.72546932 -0.49337250 [24,] 1.57532604 -1.72546932 [25,] -6.92910625 1.57532604 [26,] 0.89147358 -6.92910625 [27,] 0.61946965 0.89147358 [28,] 1.04865472 0.61946965 [29,] -3.12192556 1.04865472 [30,] 0.22068411 -3.12192556 [31,] 0.14271118 0.22068411 [32,] 1.81577163 0.14271118 [33,] -0.35102288 1.81577163 [34,] 0.03717542 -0.35102288 [35,] 0.27402060 0.03717542 [36,] -1.96112056 0.27402060 [37,] 0.65052692 -1.96112056 [38,] 1.60921113 0.65052692 [39,] -2.26112591 1.60921113 [40,] -0.73503469 -2.26112591 [41,] 2.36288173 -0.73503469 [42,] 0.28037142 2.36288173 [43,] -1.17104800 0.28037142 [44,] 0.35288220 -1.17104800 [45,] -2.59720901 0.35288220 [46,] -0.36076253 -2.59720901 [47,] 0.13010618 -0.36076253 [48,] 3.58338685 0.13010618 [49,] -1.69829153 3.58338685 [50,] 0.86791400 -1.69829153 [51,] 0.57470552 0.86791400 [52,] -0.63214843 0.57470552 [53,] -1.67232183 -0.63214843 [54,] -2.18187764 -1.67232183 [55,] 1.29029281 -2.18187764 [56,] 1.91072346 1.29029281 [57,] -0.37393316 1.91072346 [58,] -3.08330079 -0.37393316 [59,] -1.31656834 -3.08330079 [60,] -2.76301534 -1.31656834 [61,] -1.43355197 -2.76301534 [62,] -3.44730120 -1.43355197 [63,] 1.02384635 -3.44730120 [64,] 1.51123410 1.02384635 [65,] -5.35497852 1.51123410 [66,] -1.87288278 -5.35497852 [67,] -2.73079337 -1.87288278 [68,] 1.14883388 -2.73079337 [69,] 1.09151329 1.14883388 [70,] 0.01382629 1.09151329 [71,] 2.95875172 0.01382629 [72,] 0.39470300 2.95875172 [73,] -0.41690579 0.39470300 [74,] -2.18363927 -0.41690579 [75,] -0.44676958 -2.18363927 [76,] 2.78212974 -0.44676958 [77,] 0.22839533 2.78212974 [78,] 0.93257300 0.22839533 [79,] -2.18474929 0.93257300 [80,] -0.08442079 -2.18474929 [81,] -0.73689118 -0.08442079 [82,] 1.52742419 -0.73689118 [83,] 0.47969986 1.52742419 [84,] -0.35855235 0.47969986 [85,] 0.73291208 -0.35855235 [86,] -0.46031899 0.73291208 [87,] 0.04404654 -0.46031899 [88,] -3.73155750 0.04404654 [89,] 2.97119992 -3.73155750 [90,] 0.08795610 2.97119992 [91,] 0.68846973 0.08795610 [92,] 0.57206305 0.68846973 [93,] -1.14260758 0.57206305 [94,] 0.89250090 -1.14260758 [95,] -0.93576473 0.89250090 [96,] -1.01938373 -0.93576473 [97,] 1.99305180 -1.01938373 [98,] -0.19815192 1.99305180 [99,] 1.82105273 -0.19815192 [100,] -1.06289104 1.82105273 [101,] 1.02062888 -1.06289104 [102,] -3.56182943 1.02062888 [103,] 1.82852164 -3.56182943 [104,] -2.47811744 1.82852164 [105,] 1.02895158 -2.47811744 [106,] 2.03916706 1.02895158 [107,] -2.89100348 2.03916706 [108,] 0.60663778 -2.89100348 [109,] 1.06974291 0.60663778 [110,] -2.16104724 1.06974291 [111,] -2.32412381 -2.16104724 [112,] 2.15853929 -2.32412381 [113,] 3.79472326 2.15853929 [114,] 0.49236742 3.79472326 [115,] 1.00503691 0.49236742 [116,] 0.03534424 1.00503691 [117,] -1.11509056 0.03534424 [118,] 0.33841047 -1.11509056 [119,] -0.56436565 0.33841047 [120,] 0.51124718 -0.56436565 [121,] 0.14324267 0.51124718 [122,] -0.79720421 0.14324267 [123,] 0.50801705 -0.79720421 [124,] -1.58929244 0.50801705 [125,] 0.87672578 -1.58929244 [126,] 1.82681411 0.87672578 [127,] 4.04725509 1.82681411 [128,] 1.45530720 4.04725509 [129,] -1.55763648 1.45530720 [130,] -1.61721392 -1.55763648 [131,] -0.21735337 -1.61721392 [132,] 2.44377037 -0.21735337 [133,] 0.87671474 2.44377037 [134,] 2.37651303 0.87671474 [135,] 1.59540340 2.37651303 [136,] 0.74930897 1.59540340 [137,] -0.95480186 0.74930897 [138,] 0.84294253 -0.95480186 [139,] -0.72555856 0.84294253 [140,] 0.45180196 -0.72555856 [141,] 2.28956333 0.45180196 [142,] -0.39611090 2.28956333 [143,] 0.92337481 -0.39611090 [144,] 1.46442249 0.92337481 [145,] 1.79211086 1.46442249 [146,] -2.21699455 1.79211086 [147,] -2.44726716 -2.21699455 [148,] -2.40215833 -2.44726716 [149,] 1.83164285 -2.40215833 [150,] 0.73301385 1.83164285 [151,] 0.81410817 0.73301385 [152,] -2.30947849 0.81410817 [153,] -2.01056241 -2.30947849 [154,] 1.32293859 -2.01056241 [155,] 0.61842458 1.32293859 [156,] 0.77889471 0.61842458 [157,] 4.29208669 0.77889471 [158,] -2.35470247 4.29208669 [159,] 0.10894953 -2.35470247 [160,] 0.54635744 0.10894953 [161,] 0.41249337 0.54635744 [162,] 0.52488978 0.41249337 [163,] 4.37728504 0.52488978 [164,] -2.13364179 4.37728504 [165,] 1.57189158 -2.13364179 [166,] -0.29233694 1.57189158 [167,] -1.02536450 -0.29233694 [168,] -3.84787649 -1.02536450 [169,] -2.99748046 -3.84787649 [170,] 0.14202571 -2.99748046 [171,] 1.71982771 0.14202571 [172,] -5.15494766 1.71982771 [173,] 1.42961257 -5.15494766 [174,] 2.53371624 1.42961257 [175,] -2.31456988 2.53371624 [176,] -3.51918712 -2.31456988 [177,] 0.36627073 -3.51918712 [178,] 1.33032873 0.36627073 [179,] -2.25137335 1.33032873 [180,] -0.11927472 -2.25137335 [181,] -1.93552353 -0.11927472 [182,] 0.36887448 -1.93552353 [183,] -0.95465306 0.36887448 [184,] 1.81836988 -0.95465306 [185,] 1.38746103 1.81836988 [186,] 0.49842070 1.38746103 [187,] 0.68928170 0.49842070 [188,] 0.44023958 0.68928170 [189,] 0.51891961 0.44023958 [190,] -1.40085411 0.51891961 [191,] -0.91157179 -1.40085411 [192,] 2.07728439 -0.91157179 [193,] -1.51680063 2.07728439 [194,] 1.86795032 -1.51680063 [195,] -2.00328564 1.86795032 [196,] 2.27041783 -2.00328564 [197,] 0.67528800 2.27041783 [198,] -3.13754621 0.67528800 [199,] -0.63423248 -3.13754621 [200,] -3.09350054 -0.63423248 [201,] 1.29631112 -3.09350054 [202,] 2.99365138 1.29631112 [203,] 0.48359860 2.99365138 [204,] 0.61149067 0.48359860 [205,] 1.37736856 0.61149067 [206,] -0.43596970 1.37736856 [207,] 3.63741776 -0.43596970 [208,] 0.13373071 3.63741776 [209,] 1.82275079 0.13373071 [210,] -2.58841346 1.82275079 [211,] 1.56597037 -2.58841346 [212,] -1.11313722 1.56597037 [213,] -3.77076479 -1.11313722 [214,] -0.92641294 -3.77076479 [215,] 1.82220541 -0.92641294 [216,] 2.31202540 1.82220541 [217,] -0.10725758 2.31202540 [218,] -1.83541254 -0.10725758 [219,] 1.60280963 -1.83541254 [220,] -2.67609398 1.60280963 [221,] 2.67815281 -2.67609398 [222,] -1.88891232 2.67815281 [223,] 0.31937950 -1.88891232 [224,] -0.24791787 0.31937950 [225,] 1.75035233 -0.24791787 [226,] 5.39688651 1.75035233 [227,] -1.39582846 5.39688651 [228,] -1.36124342 -1.39582846 [229,] -2.17029124 -1.36124342 [230,] 0.31770551 -2.17029124 [231,] -2.89077572 0.31770551 [232,] 0.25001325 -2.89077572 [233,] 0.94822617 0.25001325 [234,] 1.35976886 0.94822617 [235,] -1.59554925 1.35976886 [236,] 0.67685832 -1.59554925 [237,] 0.45084760 0.67685832 [238,] -3.98005375 0.45084760 [239,] -2.37168209 -3.98005375 [240,] -2.57512915 -2.37168209 [241,] -2.42132784 -2.57512915 [242,] 0.35942148 -2.42132784 [243,] -0.02995763 0.35942148 [244,] 1.78432511 -0.02995763 [245,] 0.53033721 1.78432511 [246,] 0.56055203 0.53033721 [247,] 5.40652670 0.56055203 [248,] 0.01173077 5.40652670 [249,] 0.79234509 0.01173077 [250,] 2.41406853 0.79234509 [251,] 1.41609983 2.41406853 [252,] -0.86754901 1.41609983 [253,] -0.32962511 -0.86754901 [254,] 0.53734529 -0.32962511 [255,] -0.26029269 0.53734529 [256,] -1.28788137 -0.26029269 [257,] -2.12786910 -1.28788137 [258,] 2.87880183 -2.12786910 [259,] -4.65094656 2.87880183 [260,] 0.82187332 -4.65094656 [261,] 1.96552414 0.82187332 [262,] -2.47180223 1.96552414 [263,] 0.61603746 -2.47180223 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.47756427 -0.20248767 2 -3.12011640 2.47756427 3 -2.85124406 -3.12011640 4 4.55279887 -2.85124406 5 3.27229796 4.55279887 6 2.96699365 3.27229796 7 -1.22033404 2.96699365 8 -0.41823015 -1.22033404 9 0.37517833 -0.41823015 10 1.24486027 0.37517833 11 3.21009300 1.24486027 12 -3.58881394 3.21009300 13 2.32367803 -3.58881394 14 2.17922393 2.32367803 15 0.42783381 2.17922393 16 -0.02673256 0.42783381 17 1.14152049 -0.02673256 18 -1.55658778 1.14152049 19 2.03334437 -1.55658778 20 2.50254492 2.03334437 21 -2.87544399 2.50254492 22 -0.49337250 -2.87544399 23 -1.72546932 -0.49337250 24 1.57532604 -1.72546932 25 -6.92910625 1.57532604 26 0.89147358 -6.92910625 27 0.61946965 0.89147358 28 1.04865472 0.61946965 29 -3.12192556 1.04865472 30 0.22068411 -3.12192556 31 0.14271118 0.22068411 32 1.81577163 0.14271118 33 -0.35102288 1.81577163 34 0.03717542 -0.35102288 35 0.27402060 0.03717542 36 -1.96112056 0.27402060 37 0.65052692 -1.96112056 38 1.60921113 0.65052692 39 -2.26112591 1.60921113 40 -0.73503469 -2.26112591 41 2.36288173 -0.73503469 42 0.28037142 2.36288173 43 -1.17104800 0.28037142 44 0.35288220 -1.17104800 45 -2.59720901 0.35288220 46 -0.36076253 -2.59720901 47 0.13010618 -0.36076253 48 3.58338685 0.13010618 49 -1.69829153 3.58338685 50 0.86791400 -1.69829153 51 0.57470552 0.86791400 52 -0.63214843 0.57470552 53 -1.67232183 -0.63214843 54 -2.18187764 -1.67232183 55 1.29029281 -2.18187764 56 1.91072346 1.29029281 57 -0.37393316 1.91072346 58 -3.08330079 -0.37393316 59 -1.31656834 -3.08330079 60 -2.76301534 -1.31656834 61 -1.43355197 -2.76301534 62 -3.44730120 -1.43355197 63 1.02384635 -3.44730120 64 1.51123410 1.02384635 65 -5.35497852 1.51123410 66 -1.87288278 -5.35497852 67 -2.73079337 -1.87288278 68 1.14883388 -2.73079337 69 1.09151329 1.14883388 70 0.01382629 1.09151329 71 2.95875172 0.01382629 72 0.39470300 2.95875172 73 -0.41690579 0.39470300 74 -2.18363927 -0.41690579 75 -0.44676958 -2.18363927 76 2.78212974 -0.44676958 77 0.22839533 2.78212974 78 0.93257300 0.22839533 79 -2.18474929 0.93257300 80 -0.08442079 -2.18474929 81 -0.73689118 -0.08442079 82 1.52742419 -0.73689118 83 0.47969986 1.52742419 84 -0.35855235 0.47969986 85 0.73291208 -0.35855235 86 -0.46031899 0.73291208 87 0.04404654 -0.46031899 88 -3.73155750 0.04404654 89 2.97119992 -3.73155750 90 0.08795610 2.97119992 91 0.68846973 0.08795610 92 0.57206305 0.68846973 93 -1.14260758 0.57206305 94 0.89250090 -1.14260758 95 -0.93576473 0.89250090 96 -1.01938373 -0.93576473 97 1.99305180 -1.01938373 98 -0.19815192 1.99305180 99 1.82105273 -0.19815192 100 -1.06289104 1.82105273 101 1.02062888 -1.06289104 102 -3.56182943 1.02062888 103 1.82852164 -3.56182943 104 -2.47811744 1.82852164 105 1.02895158 -2.47811744 106 2.03916706 1.02895158 107 -2.89100348 2.03916706 108 0.60663778 -2.89100348 109 1.06974291 0.60663778 110 -2.16104724 1.06974291 111 -2.32412381 -2.16104724 112 2.15853929 -2.32412381 113 3.79472326 2.15853929 114 0.49236742 3.79472326 115 1.00503691 0.49236742 116 0.03534424 1.00503691 117 -1.11509056 0.03534424 118 0.33841047 -1.11509056 119 -0.56436565 0.33841047 120 0.51124718 -0.56436565 121 0.14324267 0.51124718 122 -0.79720421 0.14324267 123 0.50801705 -0.79720421 124 -1.58929244 0.50801705 125 0.87672578 -1.58929244 126 1.82681411 0.87672578 127 4.04725509 1.82681411 128 1.45530720 4.04725509 129 -1.55763648 1.45530720 130 -1.61721392 -1.55763648 131 -0.21735337 -1.61721392 132 2.44377037 -0.21735337 133 0.87671474 2.44377037 134 2.37651303 0.87671474 135 1.59540340 2.37651303 136 0.74930897 1.59540340 137 -0.95480186 0.74930897 138 0.84294253 -0.95480186 139 -0.72555856 0.84294253 140 0.45180196 -0.72555856 141 2.28956333 0.45180196 142 -0.39611090 2.28956333 143 0.92337481 -0.39611090 144 1.46442249 0.92337481 145 1.79211086 1.46442249 146 -2.21699455 1.79211086 147 -2.44726716 -2.21699455 148 -2.40215833 -2.44726716 149 1.83164285 -2.40215833 150 0.73301385 1.83164285 151 0.81410817 0.73301385 152 -2.30947849 0.81410817 153 -2.01056241 -2.30947849 154 1.32293859 -2.01056241 155 0.61842458 1.32293859 156 0.77889471 0.61842458 157 4.29208669 0.77889471 158 -2.35470247 4.29208669 159 0.10894953 -2.35470247 160 0.54635744 0.10894953 161 0.41249337 0.54635744 162 0.52488978 0.41249337 163 4.37728504 0.52488978 164 -2.13364179 4.37728504 165 1.57189158 -2.13364179 166 -0.29233694 1.57189158 167 -1.02536450 -0.29233694 168 -3.84787649 -1.02536450 169 -2.99748046 -3.84787649 170 0.14202571 -2.99748046 171 1.71982771 0.14202571 172 -5.15494766 1.71982771 173 1.42961257 -5.15494766 174 2.53371624 1.42961257 175 -2.31456988 2.53371624 176 -3.51918712 -2.31456988 177 0.36627073 -3.51918712 178 1.33032873 0.36627073 179 -2.25137335 1.33032873 180 -0.11927472 -2.25137335 181 -1.93552353 -0.11927472 182 0.36887448 -1.93552353 183 -0.95465306 0.36887448 184 1.81836988 -0.95465306 185 1.38746103 1.81836988 186 0.49842070 1.38746103 187 0.68928170 0.49842070 188 0.44023958 0.68928170 189 0.51891961 0.44023958 190 -1.40085411 0.51891961 191 -0.91157179 -1.40085411 192 2.07728439 -0.91157179 193 -1.51680063 2.07728439 194 1.86795032 -1.51680063 195 -2.00328564 1.86795032 196 2.27041783 -2.00328564 197 0.67528800 2.27041783 198 -3.13754621 0.67528800 199 -0.63423248 -3.13754621 200 -3.09350054 -0.63423248 201 1.29631112 -3.09350054 202 2.99365138 1.29631112 203 0.48359860 2.99365138 204 0.61149067 0.48359860 205 1.37736856 0.61149067 206 -0.43596970 1.37736856 207 3.63741776 -0.43596970 208 0.13373071 3.63741776 209 1.82275079 0.13373071 210 -2.58841346 1.82275079 211 1.56597037 -2.58841346 212 -1.11313722 1.56597037 213 -3.77076479 -1.11313722 214 -0.92641294 -3.77076479 215 1.82220541 -0.92641294 216 2.31202540 1.82220541 217 -0.10725758 2.31202540 218 -1.83541254 -0.10725758 219 1.60280963 -1.83541254 220 -2.67609398 1.60280963 221 2.67815281 -2.67609398 222 -1.88891232 2.67815281 223 0.31937950 -1.88891232 224 -0.24791787 0.31937950 225 1.75035233 -0.24791787 226 5.39688651 1.75035233 227 -1.39582846 5.39688651 228 -1.36124342 -1.39582846 229 -2.17029124 -1.36124342 230 0.31770551 -2.17029124 231 -2.89077572 0.31770551 232 0.25001325 -2.89077572 233 0.94822617 0.25001325 234 1.35976886 0.94822617 235 -1.59554925 1.35976886 236 0.67685832 -1.59554925 237 0.45084760 0.67685832 238 -3.98005375 0.45084760 239 -2.37168209 -3.98005375 240 -2.57512915 -2.37168209 241 -2.42132784 -2.57512915 242 0.35942148 -2.42132784 243 -0.02995763 0.35942148 244 1.78432511 -0.02995763 245 0.53033721 1.78432511 246 0.56055203 0.53033721 247 5.40652670 0.56055203 248 0.01173077 5.40652670 249 0.79234509 0.01173077 250 2.41406853 0.79234509 251 1.41609983 2.41406853 252 -0.86754901 1.41609983 253 -0.32962511 -0.86754901 254 0.53734529 -0.32962511 255 -0.26029269 0.53734529 256 -1.28788137 -0.26029269 257 -2.12786910 -1.28788137 258 2.87880183 -2.12786910 259 -4.65094656 2.87880183 260 0.82187332 -4.65094656 261 1.96552414 0.82187332 262 -2.47180223 1.96552414 263 0.61603746 -2.47180223 > 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/7cozq1384794172.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/83l3g1384794172.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9x5r61384794172.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/103yci1384794172.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$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/11ob1v1384794172.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$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/126iz31384794172.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, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/132rrl1384794172.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,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14fy431384794172.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,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15d37s1384794172.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,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + 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,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + 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,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + 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/16ue2v1384794172.tab") + } > > try(system("convert tmp/1pxzh1384794171.ps tmp/1pxzh1384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/2cch61384794171.ps tmp/2cch61384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/33sl11384794171.ps tmp/33sl11384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/41kn51384794171.ps tmp/41kn51384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/5tf3j1384794171.ps tmp/5tf3j1384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/6cucn1384794171.ps tmp/6cucn1384794171.png",intern=TRUE)) character(0) > try(system("convert tmp/7cozq1384794172.ps tmp/7cozq1384794172.png",intern=TRUE)) character(0) > try(system("convert tmp/83l3g1384794172.ps tmp/83l3g1384794172.png",intern=TRUE)) character(0) > try(system("convert tmp/9x5r61384794172.ps tmp/9x5r61384794172.png",intern=TRUE)) character(0) > try(system("convert tmp/103yci1384794172.ps tmp/103yci1384794172.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.824 2.944 19.760