R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,41 + ,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(9 + ,264) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '6' > 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 Happiness month Connected Separate Learning Software Depression Belonging 1 14 9 41 38 13 12 12.0 53 2 18 9 39 32 16 11 11.0 83 3 11 9 30 35 19 15 14.0 66 4 12 9 31 33 15 6 12.0 67 5 16 9 34 37 14 13 21.0 76 6 18 9 35 29 13 10 12.0 78 7 14 9 39 31 19 12 22.0 53 8 14 9 34 36 15 14 11.0 80 9 15 9 36 35 14 12 10.0 74 10 15 9 37 38 15 9 13.0 76 11 17 9 38 31 16 10 10.0 79 12 19 9 36 34 16 12 8.0 54 13 10 9 38 35 16 12 15.0 67 14 16 9 39 38 16 11 14.0 54 15 18 9 33 37 17 15 10.0 87 16 14 9 32 33 15 12 14.0 58 17 14 9 36 32 15 10 14.0 75 18 17 9 38 38 20 12 11.0 88 19 14 9 39 38 18 11 10.0 64 20 16 9 32 32 16 12 13.0 57 21 18 9 32 33 16 11 9.5 66 22 11 9 31 31 16 12 14.0 68 23 14 9 39 38 19 13 12.0 54 24 12 9 37 39 16 11 14.0 56 25 17 9 39 32 17 12 11.0 86 26 9 9 41 32 17 13 9.0 80 27 16 9 36 35 16 10 11.0 76 28 14 9 33 37 15 14 15.0 69 29 15 9 33 33 16 12 14.0 78 30 11 9 34 33 14 10 13.0 67 31 16 9 31 31 15 12 9.0 80 32 13 9 27 32 12 8 15.0 54 33 17 9 37 31 14 10 10.0 71 34 15 9 34 37 16 12 11.0 84 35 14 9 34 30 14 12 13.0 74 36 16 9 32 33 10 7 8.0 71 37 9 9 29 31 10 9 20.0 63 38 15 9 36 33 14 12 12.0 71 39 17 9 29 31 16 10 10.0 76 40 13 9 35 33 16 10 10.0 69 41 15 9 37 32 16 10 9.0 74 42 16 9 34 33 14 12 14.0 75 43 16 9 38 32 20 15 8.0 54 44 12 9 35 33 14 10 14.0 52 45 15 9 38 28 14 10 11.0 69 46 11 9 37 35 11 12 13.0 68 47 15 9 38 39 14 13 9.0 65 48 15 9 33 34 15 11 11.0 75 49 17 9 36 38 16 11 15.0 74 50 13 9 38 32 14 12 11.0 75 51 16 9 32 38 16 14 10.0 72 52 14 9 32 30 14 10 14.0 67 53 11 9 32 33 12 12 18.0 63 54 12 9 34 38 16 13 14.0 62 55 12 9 32 32 9 5 11.0 63 56 15 9 37 35 14 6 14.5 76 57 16 9 39 34 16 12 13.0 74 58 15 9 29 34 16 12 9.0 67 59 12 9 37 36 15 11 10.0 73 60 12 9 35 34 16 10 15.0 70 61 8 9 30 28 12 7 20.0 53 62 13 9 38 34 16 12 12.0 77 63 11 9 34 35 16 14 12.0 80 64 14 9 31 35 14 11 14.0 52 65 15 9 34 31 16 12 13.0 54 66 10 10 35 37 17 13 11.0 80 67 11 10 36 35 18 14 17.0 66 68 12 10 30 27 18 11 12.0 73 69 15 10 39 40 12 12 13.0 63 70 15 10 35 37 16 12 14.0 69 71 14 10 38 36 10 8 13.0 67 72 16 10 31 38 14 11 15.0 54 73 15 10 34 39 18 14 13.0 81 74 15 10 38 41 18 14 10.0 69 75 13 10 34 27 16 12 11.0 84 76 12 10 39 30 17 9 19.0 80 77 17 10 37 37 16 13 13.0 70 78 13 10 34 31 16 11 17.0 69 79 15 10 28 31 13 12 13.0 77 80 13 10 37 27 16 12 9.0 54 81 15 10 33 36 16 12 11.0 79 82 15 10 35 37 16 12 9.0 71 83 16 10 37 33 15 12 12.0 73 84 15 10 32 34 15 11 12.0 72 85 14 10 33 31 16 10 13.0 77 86 15 10 38 39 14 9 13.0 75 87 14 10 33 34 16 12 12.0 69 88 13 10 29 32 16 12 15.0 54 89 7 10 33 33 15 12 22.0 70 90 17 10 31 36 12 9 13.0 73 91 13 10 36 32 17 15 15.0 54 92 15 10 35 41 16 12 13.0 77 93 14 10 32 28 15 12 15.0 82 94 13 10 29 30 13 12 12.5 80 95 16 10 39 36 16 10 11.0 80 96 12 10 37 35 16 13 16.0 69 97 14 10 35 31 16 9 11.0 78 98 17 10 37 34 16 12 11.0 81 99 15 10 32 36 14 10 10.0 76 100 17 10 38 36 16 14 10.0 76 101 12 10 37 35 16 11 16.0 73 102 16 10 36 37 20 15 12.0 85 103 11 10 32 28 15 11 11.0 66 104 15 10 33 39 16 11 16.0 79 105 9 10 40 32 13 12 19.0 68 106 16 10 38 35 17 12 11.0 76 107 15 10 41 39 16 12 16.0 71 108 10 10 36 35 16 11 15.0 54 109 10 10 43 42 12 7 24.0 46 110 15 10 30 34 16 12 14.0 85 111 11 10 31 33 16 14 15.0 74 112 13 10 32 41 17 11 11.0 88 113 14 10 32 33 13 11 15.0 38 114 18 10 37 34 12 10 12.0 76 115 16 10 37 32 18 13 10.0 86 116 14 10 33 40 14 13 14.0 54 117 14 10 34 40 14 8 13.0 67 118 14 10 33 35 13 11 9.0 69 119 14 10 38 36 16 12 15.0 90 120 12 10 33 37 13 11 15.0 54 121 14 10 31 27 16 13 14.0 76 122 15 10 38 39 13 12 11.0 89 123 15 10 37 38 16 14 8.0 76 124 15 10 36 31 15 13 11.0 73 125 13 10 31 33 16 15 11.0 79 126 17 10 39 32 15 10 8.0 90 127 17 10 44 39 17 11 10.0 74 128 19 10 33 36 15 9 11.0 81 129 15 10 35 33 12 11 13.0 72 130 13 10 32 33 16 10 11.0 71 131 9 10 28 32 10 11 20.0 66 132 15 10 40 37 16 8 10.0 77 133 15 10 27 30 12 11 15.0 65 134 15 10 37 38 14 12 12.0 74 135 16 10 32 29 15 12 14.0 85 136 11 10 28 22 13 9 23.0 54 137 14 10 34 35 15 11 14.0 63 138 11 10 30 35 11 10 16.0 54 139 15 10 35 34 12 8 11.0 64 140 13 10 31 35 11 9 12.0 69 141 15 10 32 34 16 8 10.0 54 142 16 10 30 37 15 9 14.0 84 143 14 10 30 35 17 15 12.0 86 144 15 10 31 23 16 11 12.0 77 145 16 10 40 31 10 8 11.0 89 146 16 10 32 27 18 13 12.0 76 147 11 10 36 36 13 12 13.0 60 148 12 10 32 31 16 12 11.0 75 149 9 10 35 32 13 9 19.0 73 150 16 10 38 39 10 7 12.0 85 151 13 10 42 37 15 13 17.0 79 152 16 10 34 38 16 9 9.0 71 153 12 10 35 39 16 6 12.0 72 154 9 9 38 34 14 8 19.0 69 155 13 10 33 31 10 8 18.0 78 156 13 10 36 32 17 15 15.0 54 157 14 10 32 37 13 6 14.0 69 158 19 10 33 36 15 9 11.0 81 159 13 10 34 32 16 11 9.0 84 160 12 10 32 38 12 8 18.0 84 161 13 10 34 36 13 8 16.0 69 162 10 11 27 26 13 10 24.0 66 163 14 11 31 26 12 8 14.0 81 164 16 11 38 33 17 14 20.0 82 165 10 11 34 39 15 10 18.0 72 166 11 11 24 30 10 8 23.0 54 167 14 11 30 33 14 11 12.0 78 168 12 11 26 25 11 12 14.0 74 169 9 11 34 38 13 12 16.0 82 170 9 11 27 37 16 12 18.0 73 171 11 11 37 31 12 5 20.0 55 172 16 11 36 37 16 12 12.0 72 173 9 11 41 35 12 10 12.0 78 174 13 11 29 25 9 7 17.0 59 175 16 11 36 28 12 12 13.0 72 176 13 11 32 35 15 11 9.0 78 177 9 11 37 33 12 8 16.0 68 178 12 11 30 30 12 9 18.0 69 179 16 11 31 31 14 10 10.0 67 180 11 11 38 37 12 9 14.0 74 181 14 11 36 36 16 12 11.0 54 182 13 11 35 30 11 6 9.0 67 183 15 11 31 36 19 15 11.0 70 184 14 11 38 32 15 12 10.0 80 185 16 11 22 28 8 12 11.0 89 186 13 11 32 36 16 12 19.0 76 187 14 11 36 34 17 11 14.0 74 188 15 11 39 31 12 7 12.0 87 189 13 11 28 28 11 7 14.0 54 190 11 11 32 36 11 5 21.0 61 191 11 11 32 36 14 12 13.0 38 192 14 11 38 40 16 12 10.0 75 193 15 11 32 33 12 3 15.0 69 194 11 11 35 37 16 11 16.0 62 195 15 11 32 32 13 10 14.0 72 196 12 11 37 38 15 12 12.0 70 197 14 11 34 31 16 9 19.0 79 198 14 11 33 37 16 12 15.0 87 199 8 11 33 33 14 9 19.0 62 200 13 11 26 32 16 12 13.0 77 201 9 11 30 30 16 12 17.0 69 202 15 11 24 30 14 10 12.0 69 203 17 11 34 31 11 9 11.0 75 204 13 11 34 32 12 12 14.0 54 205 15 11 33 34 15 8 11.0 72 206 15 11 34 36 15 11 13.0 74 207 14 11 35 37 16 11 12.0 85 208 16 11 35 36 16 12 15.0 52 209 13 11 36 33 11 10 14.0 70 210 16 11 34 33 15 10 12.0 84 211 9 11 34 33 12 12 17.0 64 212 16 11 41 44 12 12 11.0 84 213 11 11 32 39 15 11 18.0 87 214 10 11 30 32 15 8 13.0 79 215 11 11 35 35 16 12 17.0 67 216 15 11 28 25 14 10 13.0 65 217 17 11 33 35 17 11 11.0 85 218 14 11 39 34 14 10 12.0 83 219 8 11 36 35 13 8 22.0 61 220 15 11 36 39 15 12 14.0 82 221 11 11 35 33 13 12 12.0 76 222 16 11 38 36 14 10 12.0 58 223 10 11 33 32 15 12 17.0 72 224 15 11 31 32 12 9 9.0 72 225 9 11 34 36 13 9 21.0 38 226 16 11 32 36 8 6 10.0 78 227 19 11 31 32 14 10 11.0 54 228 12 11 33 34 14 9 12.0 63 229 8 11 34 33 11 9 23.0 66 230 11 11 34 35 12 9 13.0 70 231 14 11 34 30 13 6 12.0 71 232 9 11 33 38 10 10 16.0 67 233 15 11 32 34 16 6 9.0 58 234 13 11 41 33 18 14 17.0 72 235 16 11 34 32 13 10 9.0 72 236 11 11 36 31 11 10 14.0 70 237 12 11 37 30 4 6 17.0 76 238 13 11 36 27 13 12 13.0 50 239 10 11 29 31 16 12 11.0 72 240 11 11 37 30 10 7 12.0 72 241 12 11 27 32 12 8 10.0 88 242 8 11 35 35 12 11 19.0 53 243 12 11 28 28 10 3 16.0 58 244 12 11 35 33 13 6 16.0 66 245 15 11 37 31 15 10 14.0 82 246 11 11 29 35 12 8 20.0 69 247 13 11 32 35 14 9 15.0 68 248 14 11 36 32 10 9 23.0 44 249 10 11 19 21 12 8 20.0 56 250 12 11 21 20 12 9 16.0 53 251 15 11 31 34 11 7 14.0 70 252 13 11 33 32 10 7 17.0 78 253 13 11 36 34 12 6 11.0 71 254 13 11 33 32 16 9 13.0 72 255 12 11 37 33 12 10 17.0 68 256 12 11 34 33 14 11 15.0 67 257 9 11 35 37 16 12 21.0 75 258 9 11 31 32 14 8 18.0 62 259 15 11 37 34 13 11 15.0 67 260 10 11 35 30 4 3 8.0 83 261 14 11 27 30 15 11 12.0 64 262 15 11 34 38 11 12 12.0 68 263 7 11 40 36 11 7 22.0 62 264 14 11 29 32 14 9 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 12.665640 0.391360 0.003321 0.011327 Learning Software Depression Belonging 0.080466 -0.041045 -0.365596 0.007602 Belonging_Final t 0.027717 -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 ** month 0.391360 0.439621 0.890 0.37419 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 *** Belonging 0.007602 0.040921 0.186 0.85278 Belonging_Final 0.027717 0.060751 0.456 0.64861 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/1msd51356194935.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/2nlvy1356194935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3tt2h1356194935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ecqn1356194935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5asn31356194935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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/60xj11356194935.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/7e3d21356194935.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/8qpww1356194935.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/9f12o1356194935.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/10ny041356194935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11tffq1356194936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12338g1356194936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/132c9c1356194936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/142lpy1356194936.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/153eyn1356194936.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16cgz21356194936.tab") + } > > try(system("convert tmp/1msd51356194935.ps tmp/1msd51356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/2nlvy1356194935.ps tmp/2nlvy1356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/3tt2h1356194935.ps tmp/3tt2h1356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/4ecqn1356194935.ps tmp/4ecqn1356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/5asn31356194935.ps tmp/5asn31356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/60xj11356194935.ps tmp/60xj11356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/7e3d21356194935.ps tmp/7e3d21356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/8qpww1356194935.ps tmp/8qpww1356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/9f12o1356194935.ps tmp/9f12o1356194935.png",intern=TRUE)) character(0) > try(system("convert tmp/10ny041356194935.ps tmp/10ny041356194935.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.616 1.504 16.428