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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+ ,15 + ,68 + ,43 + ,3 + ,36 + ,32 + ,10 + ,9 + ,14 + ,23 + ,44 + ,29 + ,3 + ,19 + ,21 + ,12 + ,8 + ,10 + ,20 + ,56 + ,36 + ,3 + ,21 + ,20 + ,12 + ,9 + ,12 + ,16 + ,53 + ,30 + ,3 + ,31 + ,34 + ,11 + ,7 + ,15 + ,14 + ,70 + ,42 + ,3 + ,33 + ,32 + ,10 + ,7 + ,13 + ,17 + ,78 + ,47 + ,3 + ,36 + ,34 + ,12 + ,6 + ,13 + ,11 + ,71 + ,44 + ,3 + ,33 + ,32 + ,16 + ,9 + ,13 + ,13 + ,72 + ,45 + ,3 + ,37 + ,33 + ,12 + ,10 + ,12 + ,17 + ,68 + ,44 + ,3 + ,34 + ,33 + ,14 + ,11 + ,12 + ,15 + ,67 + ,43 + ,3 + ,35 + ,37 + ,16 + ,12 + ,9 + ,21 + ,75 + ,43 + ,3 + ,31 + ,32 + ,14 + ,8 + ,9 + ,18 + ,62 + ,40 + ,3 + ,37 + ,34 + ,13 + ,11 + ,15 + ,15 + ,67 + ,41 + ,3 + ,35 + ,30 + ,4 + ,3 + ,10 + ,8 + ,83 + ,52 + ,3 + ,27 + ,30 + ,15 + ,11 + ,14 + ,12 + ,64 + ,38 + ,3 + ,34 + ,38 + ,11 + ,12 + ,15 + ,12 + ,68 + ,41 + ,3 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,3 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(9 + ,264) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Learning month Connected Separate Software Happiness Depression Belonging 1 13 1 41 38 12 14 12.0 53 2 16 1 39 32 11 18 11.0 83 3 19 1 30 35 15 11 14.0 66 4 15 1 31 33 6 12 12.0 67 5 14 1 34 37 13 16 21.0 76 6 13 1 35 29 10 18 12.0 78 7 19 1 39 31 12 14 22.0 53 8 15 1 34 36 14 14 11.0 80 9 14 1 36 35 12 15 10.0 74 10 15 1 37 38 9 15 13.0 76 11 16 1 38 31 10 17 10.0 79 12 16 1 36 34 12 19 8.0 54 13 16 1 38 35 12 10 15.0 67 14 16 1 39 38 11 16 14.0 54 15 17 1 33 37 15 18 10.0 87 16 15 1 32 33 12 14 14.0 58 17 15 1 36 32 10 14 14.0 75 18 20 1 38 38 12 17 11.0 88 19 18 1 39 38 11 14 10.0 64 20 16 1 32 32 12 16 13.0 57 21 16 1 32 33 11 18 9.5 66 22 16 1 31 31 12 11 14.0 68 23 19 1 39 38 13 14 12.0 54 24 16 1 37 39 11 12 14.0 56 25 17 1 39 32 12 17 11.0 86 26 17 1 41 32 13 9 9.0 80 27 16 1 36 35 10 16 11.0 76 28 15 1 33 37 14 14 15.0 69 29 16 1 33 33 12 15 14.0 78 30 14 1 34 33 10 11 13.0 67 31 15 1 31 31 12 16 9.0 80 32 12 1 27 32 8 13 15.0 54 33 14 1 37 31 10 17 10.0 71 34 16 1 34 37 12 15 11.0 84 35 14 1 34 30 12 14 13.0 74 36 10 1 32 33 7 16 8.0 71 37 10 1 29 31 9 9 20.0 63 38 14 1 36 33 12 15 12.0 71 39 16 1 29 31 10 17 10.0 76 40 16 1 35 33 10 13 10.0 69 41 16 1 37 32 10 15 9.0 74 42 14 1 34 33 12 16 14.0 75 43 20 1 38 32 15 16 8.0 54 44 14 1 35 33 10 12 14.0 52 45 14 1 38 28 10 15 11.0 69 46 11 1 37 35 12 11 13.0 68 47 14 1 38 39 13 15 9.0 65 48 15 1 33 34 11 15 11.0 75 49 16 1 36 38 11 17 15.0 74 50 14 1 38 32 12 13 11.0 75 51 16 1 32 38 14 16 10.0 72 52 14 1 32 30 10 14 14.0 67 53 12 1 32 33 12 11 18.0 63 54 16 1 34 38 13 12 14.0 62 55 9 1 32 32 5 12 11.0 63 56 14 1 37 35 6 15 14.5 76 57 16 1 39 34 12 16 13.0 74 58 16 1 29 34 12 15 9.0 67 59 15 1 37 36 11 12 10.0 73 60 16 1 35 34 10 12 15.0 70 61 12 1 30 28 7 8 20.0 53 62 16 1 38 34 12 13 12.0 77 63 16 1 34 35 14 11 12.0 80 64 14 1 31 35 11 14 14.0 52 65 16 1 34 31 12 15 13.0 54 66 17 2 35 37 13 10 11.0 80 67 18 2 36 35 14 11 17.0 66 68 18 2 30 27 11 12 12.0 73 69 12 2 39 40 12 15 13.0 63 70 16 2 35 37 12 15 14.0 69 71 10 2 38 36 8 14 13.0 67 72 14 2 31 38 11 16 15.0 54 73 18 2 34 39 14 15 13.0 81 74 18 2 38 41 14 15 10.0 69 75 16 2 34 27 12 13 11.0 84 76 17 2 39 30 9 12 19.0 80 77 16 2 37 37 13 17 13.0 70 78 16 2 34 31 11 13 17.0 69 79 13 2 28 31 12 15 13.0 77 80 16 2 37 27 12 13 9.0 54 81 16 2 33 36 12 15 11.0 79 82 16 2 35 37 12 15 9.0 71 83 15 2 37 33 12 16 12.0 73 84 15 2 32 34 11 15 12.0 72 85 16 2 33 31 10 14 13.0 77 86 14 2 38 39 9 15 13.0 75 87 16 2 33 34 12 14 12.0 69 88 16 2 29 32 12 13 15.0 54 89 15 2 33 33 12 7 22.0 70 90 12 2 31 36 9 17 13.0 73 91 17 2 36 32 15 13 15.0 54 92 16 2 35 41 12 15 13.0 77 93 15 2 32 28 12 14 15.0 82 94 13 2 29 30 12 13 12.5 80 95 16 2 39 36 10 16 11.0 80 96 16 2 37 35 13 12 16.0 69 97 16 2 35 31 9 14 11.0 78 98 16 2 37 34 12 17 11.0 81 99 14 2 32 36 10 15 10.0 76 100 16 2 38 36 14 17 10.0 76 101 16 2 37 35 11 12 16.0 73 102 20 2 36 37 15 16 12.0 85 103 15 2 32 28 11 11 11.0 66 104 16 2 33 39 11 15 16.0 79 105 13 2 40 32 12 9 19.0 68 106 17 2 38 35 12 16 11.0 76 107 16 2 41 39 12 15 16.0 71 108 16 2 36 35 11 10 15.0 54 109 12 2 43 42 7 10 24.0 46 110 16 2 30 34 12 15 14.0 85 111 16 2 31 33 14 11 15.0 74 112 17 2 32 41 11 13 11.0 88 113 13 2 32 33 11 14 15.0 38 114 12 2 37 34 10 18 12.0 76 115 18 2 37 32 13 16 10.0 86 116 14 2 33 40 13 14 14.0 54 117 14 2 34 40 8 14 13.0 67 118 13 2 33 35 11 14 9.0 69 119 16 2 38 36 12 14 15.0 90 120 13 2 33 37 11 12 15.0 54 121 16 2 31 27 13 14 14.0 76 122 13 2 38 39 12 15 11.0 89 123 16 2 37 38 14 15 8.0 76 124 15 2 36 31 13 15 11.0 73 125 16 2 31 33 15 13 11.0 79 126 15 2 39 32 10 17 8.0 90 127 17 2 44 39 11 17 10.0 74 128 15 2 33 36 9 19 11.0 81 129 12 2 35 33 11 15 13.0 72 130 16 2 32 33 10 13 11.0 71 131 10 2 28 32 11 9 20.0 66 132 16 2 40 37 8 15 10.0 77 133 12 2 27 30 11 15 15.0 65 134 14 2 37 38 12 15 12.0 74 135 15 2 32 29 12 16 14.0 85 136 13 2 28 22 9 11 23.0 54 137 15 2 34 35 11 14 14.0 63 138 11 2 30 35 10 11 16.0 54 139 12 2 35 34 8 15 11.0 64 140 11 2 31 35 9 13 12.0 69 141 16 2 32 34 8 15 10.0 54 142 15 2 30 37 9 16 14.0 84 143 17 2 30 35 15 14 12.0 86 144 16 2 31 23 11 15 12.0 77 145 10 2 40 31 8 16 11.0 89 146 18 2 32 27 13 16 12.0 76 147 13 2 36 36 12 11 13.0 60 148 16 2 32 31 12 12 11.0 75 149 13 2 35 32 9 9 19.0 73 150 10 2 38 39 7 16 12.0 85 151 15 2 42 37 13 13 17.0 79 152 16 2 34 38 9 16 9.0 71 153 16 2 35 39 6 12 12.0 72 154 14 1 38 34 8 9 19.0 69 155 10 2 33 31 8 13 18.0 78 156 17 2 36 32 15 13 15.0 54 157 13 2 32 37 6 14 14.0 69 158 15 2 33 36 9 19 11.0 81 159 16 2 34 32 11 13 9.0 84 160 12 2 32 38 8 12 18.0 84 161 13 2 34 36 8 13 16.0 69 162 13 3 27 26 10 10 24.0 66 163 12 3 31 26 8 14 14.0 81 164 17 3 38 33 14 16 20.0 82 165 15 3 34 39 10 10 18.0 72 166 10 3 24 30 8 11 23.0 54 167 14 3 30 33 11 14 12.0 78 168 11 3 26 25 12 12 14.0 74 169 13 3 34 38 12 9 16.0 82 170 16 3 27 37 12 9 18.0 73 171 12 3 37 31 5 11 20.0 55 172 16 3 36 37 12 16 12.0 72 173 12 3 41 35 10 9 12.0 78 174 9 3 29 25 7 13 17.0 59 175 12 3 36 28 12 16 13.0 72 176 15 3 32 35 11 13 9.0 78 177 12 3 37 33 8 9 16.0 68 178 12 3 30 30 9 12 18.0 69 179 14 3 31 31 10 16 10.0 67 180 12 3 38 37 9 11 14.0 74 181 16 3 36 36 12 14 11.0 54 182 11 3 35 30 6 13 9.0 67 183 19 3 31 36 15 15 11.0 70 184 15 3 38 32 12 14 10.0 80 185 8 3 22 28 12 16 11.0 89 186 16 3 32 36 12 13 19.0 76 187 17 3 36 34 11 14 14.0 74 188 12 3 39 31 7 15 12.0 87 189 11 3 28 28 7 13 14.0 54 190 11 3 32 36 5 11 21.0 61 191 14 3 32 36 12 11 13.0 38 192 16 3 38 40 12 14 10.0 75 193 12 3 32 33 3 15 15.0 69 194 16 3 35 37 11 11 16.0 62 195 13 3 32 32 10 15 14.0 72 196 15 3 37 38 12 12 12.0 70 197 16 3 34 31 9 14 19.0 79 198 16 3 33 37 12 14 15.0 87 199 14 3 33 33 9 8 19.0 62 200 16 3 26 32 12 13 13.0 77 201 16 3 30 30 12 9 17.0 69 202 14 3 24 30 10 15 12.0 69 203 11 3 34 31 9 17 11.0 75 204 12 3 34 32 12 13 14.0 54 205 15 3 33 34 8 15 11.0 72 206 15 3 34 36 11 15 13.0 74 207 16 3 35 37 11 14 12.0 85 208 16 3 35 36 12 16 15.0 52 209 11 3 36 33 10 13 14.0 70 210 15 3 34 33 10 16 12.0 84 211 12 3 34 33 12 9 17.0 64 212 12 3 41 44 12 16 11.0 84 213 15 3 32 39 11 11 18.0 87 214 15 3 30 32 8 10 13.0 79 215 16 3 35 35 12 11 17.0 67 216 14 3 28 25 10 15 13.0 65 217 17 3 33 35 11 17 11.0 85 218 14 3 39 34 10 14 12.0 83 219 13 3 36 35 8 8 22.0 61 220 15 3 36 39 12 15 14.0 82 221 13 3 35 33 12 11 12.0 76 222 14 3 38 36 10 16 12.0 58 223 15 3 33 32 12 10 17.0 72 224 12 3 31 32 9 15 9.0 72 225 13 3 34 36 9 9 21.0 38 226 8 3 32 36 6 16 10.0 78 227 14 3 31 32 10 19 11.0 54 228 14 3 33 34 9 12 12.0 63 229 11 3 34 33 9 8 23.0 66 230 12 3 34 35 9 11 13.0 70 231 13 3 34 30 6 14 12.0 71 232 10 3 33 38 10 9 16.0 67 233 16 3 32 34 6 15 9.0 58 234 18 3 41 33 14 13 17.0 72 235 13 3 34 32 10 16 9.0 72 236 11 3 36 31 10 11 14.0 70 237 4 3 37 30 6 12 17.0 76 238 13 3 36 27 12 13 13.0 50 239 16 3 29 31 12 10 11.0 72 240 10 3 37 30 7 11 12.0 72 241 12 3 27 32 8 12 10.0 88 242 12 3 35 35 11 8 19.0 53 243 10 3 28 28 3 12 16.0 58 244 13 3 35 33 6 12 16.0 66 245 15 3 37 31 10 15 14.0 82 246 12 3 29 35 8 11 20.0 69 247 14 3 32 35 9 13 15.0 68 248 10 3 36 32 9 14 23.0 44 249 12 3 19 21 8 10 20.0 56 250 12 3 21 20 9 12 16.0 53 251 11 3 31 34 7 15 14.0 70 252 10 3 33 32 7 13 17.0 78 253 12 3 36 34 6 13 11.0 71 254 16 3 33 32 9 13 13.0 72 255 12 3 37 33 10 12 17.0 68 256 14 3 34 33 11 12 15.0 67 257 16 3 35 37 12 9 21.0 75 258 14 3 31 32 8 9 18.0 62 259 13 3 37 34 11 15 15.0 67 260 4 3 35 30 3 10 8.0 83 261 15 3 27 30 11 14 12.0 64 262 11 3 34 38 12 15 12.0 68 263 11 3 40 36 7 7 22.0 62 264 14 3 29 32 9 14 12.0 72 Belonging_Final 1 32 2 51 3 42 4 41 5 46 6 47 7 37 8 49 9 45 10 47 11 49 12 33 13 42 14 33 15 53 16 36 17 45 18 54 19 41 20 36 21 41 22 44 23 33 24 37 25 52 26 47 27 43 28 44 29 45 30 44 31 49 32 33 33 43 34 54 35 42 36 44 37 37 38 43 39 46 40 42 41 45 42 44 43 33 44 31 45 42 46 40 47 43 48 46 49 42 50 45 51 44 52 40 53 37 54 46 55 36 56 47 57 45 58 42 59 43 60 43 61 32 62 45 63 48 64 31 65 33 66 49 67 42 68 41 69 38 70 42 71 44 72 33 73 48 74 40 75 50 76 49 77 43 78 44 79 47 80 33 81 46 82 45 83 43 84 44 85 47 86 45 87 42 88 33 89 43 90 46 91 33 92 46 93 48 94 47 95 47 96 43 97 46 98 48 99 46 100 45 101 45 102 52 103 42 104 47 105 41 106 47 107 43 108 33 109 30 110 52 111 44 112 55 113 11 114 47 115 53 116 33 117 44 118 42 119 55 120 33 121 46 122 54 123 47 124 45 125 47 126 55 127 44 128 53 129 44 130 42 131 40 132 46 133 40 134 46 135 53 136 33 137 42 138 35 139 40 140 41 141 33 142 51 143 53 144 46 145 55 146 47 147 38 148 46 149 46 150 53 151 47 152 41 153 44 154 43 155 51 156 33 157 43 158 53 159 51 160 50 161 46 162 43 163 47 164 50 165 43 166 33 167 48 168 44 169 50 170 41 171 34 172 44 173 47 174 35 175 44 176 44 177 43 178 41 179 41 180 42 181 33 182 41 183 44 184 48 185 55 186 44 187 43 188 52 189 30 190 39 191 11 192 44 193 42 194 41 195 44 196 44 197 48 198 53 199 37 200 44 201 44 202 40 203 42 204 35 205 43 206 45 207 55 208 31 209 44 210 50 211 40 212 53 213 54 214 49 215 40 216 41 217 52 218 52 219 36 220 52 221 46 222 31 223 44 224 44 225 11 226 46 227 33 228 34 229 42 230 43 231 43 232 44 233 36 234 46 235 44 236 43 237 50 238 33 239 43 240 44 241 53 242 34 243 35 244 40 245 53 246 42 247 43 248 29 249 36 250 30 251 42 252 47 253 44 254 45 255 44 256 43 257 43 258 40 259 41 260 52 261 38 262 41 263 39 264 43 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Connected Separate 5.11368 -0.40460 0.03478 0.04334 Software Happiness Depression Belonging 0.57606 0.07962 -0.02610 0.02829 Belonging_Final -0.03265 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9727 -1.1695 0.2867 1.1592 4.6323 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.11368 1.96917 2.597 0.00995 ** month -0.40460 0.15952 -2.536 0.01180 * Connected 0.03478 0.03476 1.001 0.31788 Separate 0.04334 0.03528 1.228 0.22040 Software 0.57606 0.05277 10.916 < 2e-16 *** Happiness 0.07962 0.05784 1.376 0.16988 Depression -0.02610 0.04244 -0.615 0.53905 Belonging 0.02829 0.03777 0.749 0.45450 Belonging_Final -0.03265 0.05610 -0.582 0.56105 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.865 on 255 degrees of freedom Multiple R-squared: 0.441, Adjusted R-squared: 0.4235 F-statistic: 25.15 on 8 and 255 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,] 0.249272273 0.498544547 0.7507277 [2,] 0.168954109 0.337908219 0.8310459 [3,] 0.188652474 0.377304949 0.8113475 [4,] 0.113822200 0.227644399 0.8861778 [5,] 0.077358179 0.154716357 0.9226418 [6,] 0.111089922 0.222179843 0.8889101 [7,] 0.350297244 0.700594488 0.6497028 [8,] 0.273076148 0.546152296 0.7269239 [9,] 0.200364780 0.400729560 0.7996352 [10,] 0.147329038 0.294658076 0.8526710 [11,] 0.142429822 0.284859644 0.8575702 [12,] 0.329613611 0.659227221 0.6703864 [13,] 0.396108111 0.792216222 0.6038919 [14,] 0.364643783 0.729287565 0.6353562 [15,] 0.343013785 0.686027571 0.6569862 [16,] 0.419522217 0.839044434 0.5804778 [17,] 0.431039270 0.862078539 0.5689607 [18,] 0.411178258 0.822356515 0.5888217 [19,] 0.444562864 0.889125729 0.5554371 [20,] 0.389182053 0.778364107 0.6108179 [21,] 0.346620753 0.693241507 0.6533792 [22,] 0.316677131 0.633354262 0.6833229 [23,] 0.275674898 0.551349797 0.7243251 [24,] 0.233139525 0.466279050 0.7668605 [25,] 0.363623900 0.727247799 0.6363761 [26,] 0.400480018 0.800960036 0.5995200 [27,] 0.392518457 0.785036914 0.6074815 [28,] 0.420646898 0.841293796 0.5793531 [29,] 0.394036669 0.788073338 0.6059633 [30,] 0.352608511 0.705217021 0.6473915 [31,] 0.317815561 0.635631122 0.6821844 [32,] 0.330602998 0.661205997 0.6693970 [33,] 0.284193040 0.568386080 0.7158070 [34,] 0.259307354 0.518614708 0.7406926 [35,] 0.457373137 0.914746273 0.5426269 [36,] 0.613015043 0.773969915 0.3869850 [37,] 0.564790965 0.870418070 0.4352090 [38,] 0.550115831 0.899768338 0.4498842 [39,] 0.538886872 0.922226256 0.4611131 [40,] 0.496029188 0.992058376 0.5039708 [41,] 0.448976552 0.897953103 0.5510234 [42,] 0.469409971 0.938819942 0.5305900 [43,] 0.449833836 0.899667672 0.5501662 [44,] 0.452066183 0.904132367 0.5479338 [45,] 0.420708590 0.841417180 0.5792914 [46,] 0.377122147 0.754244294 0.6228779 [47,] 0.343504288 0.687008576 0.6564957 [48,] 0.305381737 0.610763474 0.6946183 [49,] 0.304715665 0.609431330 0.6952843 [50,] 0.274885364 0.549770729 0.7251146 [51,] 0.243391513 0.486783027 0.7566085 [52,] 0.213205556 0.426411111 0.7867944 [53,] 0.184850255 0.369700511 0.8151497 [54,] 0.159612090 0.319224181 0.8403879 [55,] 0.135701683 0.271403365 0.8642983 [56,] 0.118078555 0.236157110 0.8819214 [57,] 0.147491712 0.294983424 0.8525083 [58,] 0.347255539 0.694511077 0.6527445 [59,] 0.309383521 0.618767042 0.6906165 [60,] 0.466018474 0.932036948 0.5339815 [61,] 0.427694883 0.855389767 0.5723051 [62,] 0.415756314 0.831512629 0.5842437 [63,] 0.394545849 0.789091698 0.6054542 [64,] 0.359444377 0.718888754 0.6405556 [65,] 0.407073803 0.814147605 0.5929262 [66,] 0.374300585 0.748601169 0.6256994 [67,] 0.346591345 0.693182689 0.6534087 [68,] 0.375124097 0.750248194 0.6248759 [69,] 0.344523837 0.689047674 0.6554762 [70,] 0.311892770 0.623785539 0.6881072 [71,] 0.277800972 0.555601945 0.7221990 [72,] 0.254136867 0.508273734 0.7458631 [73,] 0.223381796 0.446763591 0.7766182 [74,] 0.213624304 0.427248607 0.7863757 [75,] 0.186095184 0.372190367 0.8139048 [76,] 0.162784483 0.325568967 0.8372155 [77,] 0.147969314 0.295938628 0.8520307 [78,] 0.127536796 0.255073591 0.8724632 [79,] 0.129326333 0.258652666 0.8706737 [80,] 0.111535332 0.223070664 0.8884647 [81,] 0.096128398 0.192256796 0.9038716 [82,] 0.082550296 0.165100591 0.9174497 [83,] 0.087742539 0.175485078 0.9122575 [84,] 0.078087986 0.156175972 0.9219120 [85,] 0.065829406 0.131658811 0.9341706 [86,] 0.068906807 0.137813615 0.9310932 [87,] 0.057357952 0.114715903 0.9426420 [88,] 0.047590636 0.095181272 0.9524094 [89,] 0.042543858 0.085087717 0.9574561 [90,] 0.036985931 0.073971862 0.9630141 [91,] 0.042000243 0.084000487 0.9579998 [92,] 0.035665390 0.071330780 0.9643346 [93,] 0.031666520 0.063333040 0.9683335 [94,] 0.038809498 0.077618995 0.9611905 [95,] 0.033580103 0.067160206 0.9664199 [96,] 0.027290386 0.054580772 0.9727096 [97,] 0.026013415 0.052026830 0.9739866 [98,] 0.021222130 0.042444260 0.9787779 [99,] 0.017203818 0.034407637 0.9827962 [100,] 0.013632186 0.027264372 0.9863678 [101,] 0.013622757 0.027245513 0.9863772 [102,] 0.012487296 0.024974592 0.9875127 [103,] 0.019146176 0.038292352 0.9808538 [104,] 0.017761714 0.035523429 0.9822383 [105,] 0.017603646 0.035207292 0.9823964 [106,] 0.014365082 0.028730163 0.9856349 [107,] 0.014933316 0.029866632 0.9850667 [108,] 0.012069116 0.024138232 0.9879309 [109,] 0.011017215 0.022034430 0.9889828 [110,] 0.008849280 0.017698560 0.9911507 [111,] 0.014052017 0.028104034 0.9859480 [112,] 0.012043124 0.024086249 0.9879569 [113,] 0.010823044 0.021646088 0.9891770 [114,] 0.009037311 0.018074622 0.9909627 [115,] 0.007284510 0.014569021 0.9927155 [116,] 0.006399142 0.012798283 0.9936009 [117,] 0.005106591 0.010213182 0.9948934 [118,] 0.007833751 0.015667501 0.9921662 [119,] 0.008079630 0.016159261 0.9919204 [120,] 0.018094933 0.036189866 0.9819051 [121,] 0.020368964 0.040737929 0.9796310 [122,] 0.023261761 0.046523522 0.9767382 [123,] 0.023256224 0.046512448 0.9767438 [124,] 0.019036038 0.038072076 0.9809640 [125,] 0.015637249 0.031274498 0.9843628 [126,] 0.012443014 0.024886029 0.9875570 [127,] 0.016577269 0.033154538 0.9834227 [128,] 0.014924063 0.029848125 0.9850759 [129,] 0.019031155 0.038062311 0.9809688 [130,] 0.026112930 0.052225859 0.9738871 [131,] 0.023296615 0.046593229 0.9767034 [132,] 0.019013388 0.038026776 0.9809866 [133,] 0.018077456 0.036154912 0.9819225 [134,] 0.035502616 0.071005232 0.9644974 [135,] 0.038422197 0.076844393 0.9615778 [136,] 0.043198752 0.086397504 0.9568012 [137,] 0.036964585 0.073929170 0.9630354 [138,] 0.030326830 0.060653660 0.9696732 [139,] 0.045729337 0.091458674 0.9542707 [140,] 0.041381104 0.082762209 0.9586189 [141,] 0.041087092 0.082174183 0.9589129 [142,] 0.071708180 0.143416360 0.9282918 [143,] 0.063159712 0.126319425 0.9368403 [144,] 0.077112812 0.154225624 0.9228872 [145,] 0.064814840 0.129629680 0.9351852 [146,] 0.056421290 0.112842581 0.9435787 [147,] 0.047850295 0.095700591 0.9521497 [148,] 0.045457012 0.090914024 0.9545430 [149,] 0.039035534 0.078071068 0.9609645 [150,] 0.031868811 0.063737623 0.9681312 [151,] 0.026187933 0.052375865 0.9738121 [152,] 0.021581342 0.043162684 0.9784187 [153,] 0.018252422 0.036504845 0.9817476 [154,] 0.016138330 0.032276661 0.9838617 [155,] 0.016251666 0.032503332 0.9837483 [156,] 0.012956801 0.025913601 0.9870432 [157,] 0.018479417 0.036958835 0.9815206 [158,] 0.017894260 0.035788519 0.9821057 [159,] 0.017018918 0.034037836 0.9829811 [160,] 0.016561667 0.033123335 0.9834383 [161,] 0.013436048 0.026872095 0.9865640 [162,] 0.012958356 0.025916711 0.9870416 [163,] 0.013877447 0.027754894 0.9861226 [164,] 0.017240763 0.034481526 0.9827592 [165,] 0.013895174 0.027790348 0.9861048 [166,] 0.011051999 0.022103998 0.9889480 [167,] 0.008870743 0.017741487 0.9911293 [168,] 0.006883216 0.013766432 0.9931168 [169,] 0.005774436 0.011548871 0.9942256 [170,] 0.004853936 0.009707871 0.9951461 [171,] 0.003788799 0.007577598 0.9962112 [172,] 0.004241499 0.008482999 0.9957585 [173,] 0.003270547 0.006541094 0.9967295 [174,] 0.072840711 0.145681421 0.9271593 [175,] 0.063836939 0.127673878 0.9361631 [176,] 0.075551377 0.151102755 0.9244486 [177,] 0.064037139 0.128074277 0.9359629 [178,] 0.053654156 0.107308313 0.9463458 [179,] 0.044308331 0.088616662 0.9556917 [180,] 0.038733534 0.077467069 0.9612665 [181,] 0.032480446 0.064960891 0.9675196 [182,] 0.038777019 0.077554038 0.9612230 [183,] 0.042580372 0.085160744 0.9574196 [184,] 0.035541480 0.071082959 0.9644585 [185,] 0.029143015 0.058286030 0.9708570 [186,] 0.039343221 0.078686442 0.9606568 [187,] 0.032175175 0.064350349 0.9678248 [188,] 0.029969169 0.059938339 0.9700308 [189,] 0.025468536 0.050937072 0.9745315 [190,] 0.024209926 0.048419852 0.9757901 [191,] 0.020183007 0.040366014 0.9798170 [192,] 0.025527130 0.051054260 0.9744729 [193,] 0.028059869 0.056119738 0.9719401 [194,] 0.031222434 0.062444868 0.9687776 [195,] 0.024571621 0.049143242 0.9754284 [196,] 0.024269913 0.048539826 0.9757301 [197,] 0.019969288 0.039938576 0.9800307 [198,] 0.021672832 0.043345664 0.9783272 [199,] 0.017456347 0.034912694 0.9825437 [200,] 0.018929280 0.037858560 0.9810707 [201,] 0.029270319 0.058540638 0.9707297 [202,] 0.022869354 0.045738709 0.9771306 [203,] 0.033024021 0.066048042 0.9669760 [204,] 0.028456855 0.056913710 0.9715431 [205,] 0.022084984 0.044169968 0.9779150 [206,] 0.024359112 0.048718225 0.9756409 [207,] 0.020489589 0.040979178 0.9795104 [208,] 0.018320551 0.036641101 0.9816794 [209,] 0.013754798 0.027509597 0.9862452 [210,] 0.011927236 0.023854471 0.9880728 [211,] 0.008581221 0.017162442 0.9914188 [212,] 0.006346574 0.012693148 0.9936534 [213,] 0.004964717 0.009929434 0.9950353 [214,] 0.004294646 0.008589292 0.9957054 [215,] 0.009946292 0.019892583 0.9900537 [216,] 0.007534384 0.015068767 0.9924656 [217,] 0.005373386 0.010746773 0.9946266 [218,] 0.003807178 0.007614357 0.9961928 [219,] 0.002664712 0.005329424 0.9973353 [220,] 0.002671582 0.005343163 0.9973284 [221,] 0.004943364 0.009886728 0.9950566 [222,] 0.025946735 0.051893471 0.9740533 [223,] 0.038457941 0.076915881 0.9615421 [224,] 0.027718788 0.055437577 0.9722812 [225,] 0.023961814 0.047923628 0.9760382 [226,] 0.187771175 0.375542351 0.8122288 [227,] 0.147044861 0.294089722 0.8529551 [228,] 0.130216166 0.260432332 0.8697838 [229,] 0.098868434 0.197736868 0.9011316 [230,] 0.076600142 0.153200283 0.9233999 [231,] 0.061463418 0.122926836 0.9385366 [232,] 0.056209776 0.112419552 0.9437902 [233,] 0.103562676 0.207125352 0.8964373 [234,] 0.090783773 0.181567545 0.9092162 [235,] 0.069638293 0.139276586 0.9303617 [236,] 0.046529644 0.093059287 0.9534704 [237,] 0.037806000 0.075612000 0.9621940 [238,] 0.034074745 0.068149490 0.9659253 [239,] 0.041448869 0.082897738 0.9585511 [240,] 0.021822448 0.043644896 0.9781776 [241,] 0.054742676 0.109485353 0.9452573 > postscript(file="/var/wessaorg/rcomp/tmp/13e0s1353952026.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/2tqcr1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3s9r91353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4p9xx1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5rmxg1353952026.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -2.950927427 0.381817133 2.083335628 3.127025193 -2.358051988 -1.736016272 7 8 9 10 11 12 3.846299991 -2.007756051 -1.948445497 0.701963529 1.137382762 -0.101807701 13 14 15 16 17 18 0.610689034 0.592021392 -1.004395063 -0.379815924 0.489441182 3.616619183 19 20 21 22 23 24 2.625165134 0.506467834 0.697230535 0.958823175 2.546938147 1.010762105 25 26 27 28 29 30 0.833157851 0.778791226 1.028275393 -1.763968014 0.233833016 -0.077895767 31 32 33 34 35 36 -0.746027284 -0.737377122 -0.797418619 0.071499880 -1.602200306 -2.921926401 37 38 39 40 41 42 -3.014672990 -1.789991690 1.437350999 1.527886673 1.272815400 -1.828353335 43 44 45 46 47 48 2.425987887 -0.166310878 -0.492903544 -4.579946776 -2.604228986 -0.194229434 49 50 51 52 53 54 0.370906087 -1.730933528 -1.147151290 -0.221677409 -3.145340373 0.130447599 55 56 57 58 59 60 -2.364552178 1.599325081 -0.010771712 0.412355641 -0.248651715 1.699039550 61 62 63 64 65 66 0.431959429 0.151906615 -0.732096822 -0.849165795 0.546781501 1.213271705 67 68 69 70 71 72 1.933602545 3.776399158 -3.703958865 0.552161863 -3.029195508 -0.699009795 73 74 75 76 77 78 1.178461283 0.952614163 0.938146133 3.731342200 -0.274453484 1.725904951 79 80 81 82 83 84 -2.033478554 1.075230233 0.434465248 0.463024793 -0.556378965 0.290823812 85 86 87 88 89 90 2.024351240 -0.008573324 0.779169251 1.293406531 0.645248973 -1.705080737 91 92 93 94 95 96 0.321735886 0.256979711 -0.019563985 -1.963598029 1.302627336 0.316937043 97 98 99 100 101 102 2.417701080 0.231489430 -0.319858082 -1.024703834 1.421202664 2.531232510 103 104 105 106 107 108 0.947695374 1.043666722 -1.801167422 1.341788163 0.285054577 1.734830472 109 110 111 112 113 114 -0.144502540 0.729968270 -0.019022897 2.027100876 -1.623526260 -2.561106515 115 116 117 118 119 120 1.817433595 -1.874239203 0.936575639 -1.766415230 0.427246196 -1.406745685 121 122 123 124 125 126 0.560835235 -2.891167271 -0.904258187 -0.892153489 -0.902236171 0.296367937 127 128 129 130 131 132 1.388689572 1.016139203 -2.744083838 2.006357737 -3.757672592 2.482367886 133 134 135 136 137 138 -2.216163118 -1.623796371 -0.169866756 0.857844295 0.499053080 -2.468624925 139 140 141 142 143 144 -1.015724374 -2.419446534 3.116868495 1.244149429 -0.009779507 1.726205342 145 146 147 148 149 150 -3.356731563 2.347253794 -2.022884772 1.037980818 0.122740838 -2.983866911 151 152 153 154 155 156 -1.149480124 1.972426862 4.088948985 1.118379703 -2.511077298 0.321735886 157 158 159 160 161 162 1.225159115 1.016139203 1.277952243 -0.902448709 0.276588644 0.640555249 163 164 165 166 167 168 -0.219714058 0.844074307 1.507288989 -1.369085649 -0.151183747 -3.047381403 169 170 171 172 173 174 -1.628420757 1.671359890 1.789630485 0.770582649 -1.678958916 -2.142243663 175 176 177 178 179 180 -2.813249493 0.563279913 -0.044313330 -0.527125898 0.347961979 -1.242363629 181 182 183 184 185 186 1.097128568 -0.230816591 2.369755033 -0.070957371 -6.500257228 1.261477725 187 188 189 190 191 192 2.598884232 -0.276935001 -0.337592691 0.766460632 -0.738347363 0.593164124 193 194 195 196 197 198 2.445135861 2.068898527 -0.589629361 0.067529462 3.102911981 0.981990529 199 200 201 202 203 204 1.650521446 1.458638605 2.055406796 0.677381539 -2.427517795 -2.436710987 205 206 207 208 209 210 2.330073087 0.541347575 1.532062754 1.068353080 -2.556278024 1.022060198 211 212 213 214 215 216 -2.202902206 -3.778443865 0.855979821 2.969296732 1.431517041 0.926865366 217 218 219 220 221 222 2.325387625 0.057640177 1.109497163 -0.205965436 -1.671017171 -0.131920566 223 224 225 226 227 228 0.699881753 -1.109293774 0.288333593 -3.747213134 0.198419826 0.979732572 229 230 231 232 233 234 -1.229759183 -0.896838278 1.754791518 -3.213065762 4.632282599 2.052583830 235 236 237 238 239 240 -0.869328864 -2.343002739 -6.972696730 -1.267817848 1.693092913 -1.682392341 241 242 243 244 245 246 -0.287911980 -1.501192112 1.148577467 1.897137210 1.290751900 0.031494268 247 248 249 250 251 252 1.122268014 -2.535800023 1.237586914 0.260609056 -0.922062430 -1.730463173 253 254 255 256 257 258 0.598033801 3.117443737 -1.376552283 0.095170915 1.480120220 2.331720115 259 260 261 262 263 264 -1.356690157 -5.383345806 1.152740672 -4.108360954 -0.347629194 1.085550529 > postscript(file="/var/wessaorg/rcomp/tmp/6w8ck1353952026.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 -2.950927427 NA 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 264 NA 1.085550529 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.381817133 -2.950927427 [2,] 2.083335628 0.381817133 [3,] 3.127025193 2.083335628 [4,] -2.358051988 3.127025193 [5,] -1.736016272 -2.358051988 [6,] 3.846299991 -1.736016272 [7,] -2.007756051 3.846299991 [8,] -1.948445497 -2.007756051 [9,] 0.701963529 -1.948445497 [10,] 1.137382762 0.701963529 [11,] -0.101807701 1.137382762 [12,] 0.610689034 -0.101807701 [13,] 0.592021392 0.610689034 [14,] -1.004395063 0.592021392 [15,] -0.379815924 -1.004395063 [16,] 0.489441182 -0.379815924 [17,] 3.616619183 0.489441182 [18,] 2.625165134 3.616619183 [19,] 0.506467834 2.625165134 [20,] 0.697230535 0.506467834 [21,] 0.958823175 0.697230535 [22,] 2.546938147 0.958823175 [23,] 1.010762105 2.546938147 [24,] 0.833157851 1.010762105 [25,] 0.778791226 0.833157851 [26,] 1.028275393 0.778791226 [27,] -1.763968014 1.028275393 [28,] 0.233833016 -1.763968014 [29,] -0.077895767 0.233833016 [30,] -0.746027284 -0.077895767 [31,] -0.737377122 -0.746027284 [32,] -0.797418619 -0.737377122 [33,] 0.071499880 -0.797418619 [34,] -1.602200306 0.071499880 [35,] -2.921926401 -1.602200306 [36,] -3.014672990 -2.921926401 [37,] -1.789991690 -3.014672990 [38,] 1.437350999 -1.789991690 [39,] 1.527886673 1.437350999 [40,] 1.272815400 1.527886673 [41,] -1.828353335 1.272815400 [42,] 2.425987887 -1.828353335 [43,] -0.166310878 2.425987887 [44,] -0.492903544 -0.166310878 [45,] -4.579946776 -0.492903544 [46,] -2.604228986 -4.579946776 [47,] -0.194229434 -2.604228986 [48,] 0.370906087 -0.194229434 [49,] -1.730933528 0.370906087 [50,] -1.147151290 -1.730933528 [51,] -0.221677409 -1.147151290 [52,] -3.145340373 -0.221677409 [53,] 0.130447599 -3.145340373 [54,] -2.364552178 0.130447599 [55,] 1.599325081 -2.364552178 [56,] -0.010771712 1.599325081 [57,] 0.412355641 -0.010771712 [58,] -0.248651715 0.412355641 [59,] 1.699039550 -0.248651715 [60,] 0.431959429 1.699039550 [61,] 0.151906615 0.431959429 [62,] -0.732096822 0.151906615 [63,] -0.849165795 -0.732096822 [64,] 0.546781501 -0.849165795 [65,] 1.213271705 0.546781501 [66,] 1.933602545 1.213271705 [67,] 3.776399158 1.933602545 [68,] -3.703958865 3.776399158 [69,] 0.552161863 -3.703958865 [70,] -3.029195508 0.552161863 [71,] -0.699009795 -3.029195508 [72,] 1.178461283 -0.699009795 [73,] 0.952614163 1.178461283 [74,] 0.938146133 0.952614163 [75,] 3.731342200 0.938146133 [76,] -0.274453484 3.731342200 [77,] 1.725904951 -0.274453484 [78,] -2.033478554 1.725904951 [79,] 1.075230233 -2.033478554 [80,] 0.434465248 1.075230233 [81,] 0.463024793 0.434465248 [82,] -0.556378965 0.463024793 [83,] 0.290823812 -0.556378965 [84,] 2.024351240 0.290823812 [85,] -0.008573324 2.024351240 [86,] 0.779169251 -0.008573324 [87,] 1.293406531 0.779169251 [88,] 0.645248973 1.293406531 [89,] -1.705080737 0.645248973 [90,] 0.321735886 -1.705080737 [91,] 0.256979711 0.321735886 [92,] -0.019563985 0.256979711 [93,] -1.963598029 -0.019563985 [94,] 1.302627336 -1.963598029 [95,] 0.316937043 1.302627336 [96,] 2.417701080 0.316937043 [97,] 0.231489430 2.417701080 [98,] -0.319858082 0.231489430 [99,] -1.024703834 -0.319858082 [100,] 1.421202664 -1.024703834 [101,] 2.531232510 1.421202664 [102,] 0.947695374 2.531232510 [103,] 1.043666722 0.947695374 [104,] -1.801167422 1.043666722 [105,] 1.341788163 -1.801167422 [106,] 0.285054577 1.341788163 [107,] 1.734830472 0.285054577 [108,] -0.144502540 1.734830472 [109,] 0.729968270 -0.144502540 [110,] -0.019022897 0.729968270 [111,] 2.027100876 -0.019022897 [112,] -1.623526260 2.027100876 [113,] -2.561106515 -1.623526260 [114,] 1.817433595 -2.561106515 [115,] -1.874239203 1.817433595 [116,] 0.936575639 -1.874239203 [117,] -1.766415230 0.936575639 [118,] 0.427246196 -1.766415230 [119,] -1.406745685 0.427246196 [120,] 0.560835235 -1.406745685 [121,] -2.891167271 0.560835235 [122,] -0.904258187 -2.891167271 [123,] -0.892153489 -0.904258187 [124,] -0.902236171 -0.892153489 [125,] 0.296367937 -0.902236171 [126,] 1.388689572 0.296367937 [127,] 1.016139203 1.388689572 [128,] -2.744083838 1.016139203 [129,] 2.006357737 -2.744083838 [130,] -3.757672592 2.006357737 [131,] 2.482367886 -3.757672592 [132,] -2.216163118 2.482367886 [133,] -1.623796371 -2.216163118 [134,] -0.169866756 -1.623796371 [135,] 0.857844295 -0.169866756 [136,] 0.499053080 0.857844295 [137,] -2.468624925 0.499053080 [138,] -1.015724374 -2.468624925 [139,] -2.419446534 -1.015724374 [140,] 3.116868495 -2.419446534 [141,] 1.244149429 3.116868495 [142,] -0.009779507 1.244149429 [143,] 1.726205342 -0.009779507 [144,] -3.356731563 1.726205342 [145,] 2.347253794 -3.356731563 [146,] -2.022884772 2.347253794 [147,] 1.037980818 -2.022884772 [148,] 0.122740838 1.037980818 [149,] -2.983866911 0.122740838 [150,] -1.149480124 -2.983866911 [151,] 1.972426862 -1.149480124 [152,] 4.088948985 1.972426862 [153,] 1.118379703 4.088948985 [154,] -2.511077298 1.118379703 [155,] 0.321735886 -2.511077298 [156,] 1.225159115 0.321735886 [157,] 1.016139203 1.225159115 [158,] 1.277952243 1.016139203 [159,] -0.902448709 1.277952243 [160,] 0.276588644 -0.902448709 [161,] 0.640555249 0.276588644 [162,] -0.219714058 0.640555249 [163,] 0.844074307 -0.219714058 [164,] 1.507288989 0.844074307 [165,] -1.369085649 1.507288989 [166,] -0.151183747 -1.369085649 [167,] -3.047381403 -0.151183747 [168,] -1.628420757 -3.047381403 [169,] 1.671359890 -1.628420757 [170,] 1.789630485 1.671359890 [171,] 0.770582649 1.789630485 [172,] -1.678958916 0.770582649 [173,] -2.142243663 -1.678958916 [174,] -2.813249493 -2.142243663 [175,] 0.563279913 -2.813249493 [176,] -0.044313330 0.563279913 [177,] -0.527125898 -0.044313330 [178,] 0.347961979 -0.527125898 [179,] -1.242363629 0.347961979 [180,] 1.097128568 -1.242363629 [181,] -0.230816591 1.097128568 [182,] 2.369755033 -0.230816591 [183,] -0.070957371 2.369755033 [184,] -6.500257228 -0.070957371 [185,] 1.261477725 -6.500257228 [186,] 2.598884232 1.261477725 [187,] -0.276935001 2.598884232 [188,] -0.337592691 -0.276935001 [189,] 0.766460632 -0.337592691 [190,] -0.738347363 0.766460632 [191,] 0.593164124 -0.738347363 [192,] 2.445135861 0.593164124 [193,] 2.068898527 2.445135861 [194,] -0.589629361 2.068898527 [195,] 0.067529462 -0.589629361 [196,] 3.102911981 0.067529462 [197,] 0.981990529 3.102911981 [198,] 1.650521446 0.981990529 [199,] 1.458638605 1.650521446 [200,] 2.055406796 1.458638605 [201,] 0.677381539 2.055406796 [202,] -2.427517795 0.677381539 [203,] -2.436710987 -2.427517795 [204,] 2.330073087 -2.436710987 [205,] 0.541347575 2.330073087 [206,] 1.532062754 0.541347575 [207,] 1.068353080 1.532062754 [208,] -2.556278024 1.068353080 [209,] 1.022060198 -2.556278024 [210,] -2.202902206 1.022060198 [211,] -3.778443865 -2.202902206 [212,] 0.855979821 -3.778443865 [213,] 2.969296732 0.855979821 [214,] 1.431517041 2.969296732 [215,] 0.926865366 1.431517041 [216,] 2.325387625 0.926865366 [217,] 0.057640177 2.325387625 [218,] 1.109497163 0.057640177 [219,] -0.205965436 1.109497163 [220,] -1.671017171 -0.205965436 [221,] -0.131920566 -1.671017171 [222,] 0.699881753 -0.131920566 [223,] -1.109293774 0.699881753 [224,] 0.288333593 -1.109293774 [225,] -3.747213134 0.288333593 [226,] 0.198419826 -3.747213134 [227,] 0.979732572 0.198419826 [228,] -1.229759183 0.979732572 [229,] -0.896838278 -1.229759183 [230,] 1.754791518 -0.896838278 [231,] -3.213065762 1.754791518 [232,] 4.632282599 -3.213065762 [233,] 2.052583830 4.632282599 [234,] -0.869328864 2.052583830 [235,] -2.343002739 -0.869328864 [236,] -6.972696730 -2.343002739 [237,] -1.267817848 -6.972696730 [238,] 1.693092913 -1.267817848 [239,] -1.682392341 1.693092913 [240,] -0.287911980 -1.682392341 [241,] -1.501192112 -0.287911980 [242,] 1.148577467 -1.501192112 [243,] 1.897137210 1.148577467 [244,] 1.290751900 1.897137210 [245,] 0.031494268 1.290751900 [246,] 1.122268014 0.031494268 [247,] -2.535800023 1.122268014 [248,] 1.237586914 -2.535800023 [249,] 0.260609056 1.237586914 [250,] -0.922062430 0.260609056 [251,] -1.730463173 -0.922062430 [252,] 0.598033801 -1.730463173 [253,] 3.117443737 0.598033801 [254,] -1.376552283 3.117443737 [255,] 0.095170915 -1.376552283 [256,] 1.480120220 0.095170915 [257,] 2.331720115 1.480120220 [258,] -1.356690157 2.331720115 [259,] -5.383345806 -1.356690157 [260,] 1.152740672 -5.383345806 [261,] -4.108360954 1.152740672 [262,] -0.347629194 -4.108360954 [263,] 1.085550529 -0.347629194 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 > 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/7mt7s1353952026.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/8dys71353952026.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/9ntm41353952026.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/10vtd41353952026.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11l0jg1353952026.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12obpe1353952026.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/136shz1353952026.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14nukf1353952026.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/152od41353952026.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/1663361353952026.tab") + } > > try(system("convert tmp/13e0s1353952026.ps tmp/13e0s1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/2tqcr1353952026.ps tmp/2tqcr1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/3s9r91353952026.ps tmp/3s9r91353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/4p9xx1353952026.ps tmp/4p9xx1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/5rmxg1353952026.ps tmp/5rmxg1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/6w8ck1353952026.ps tmp/6w8ck1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/7mt7s1353952026.ps tmp/7mt7s1353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/8dys71353952026.ps tmp/8dys71353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/9ntm41353952026.ps tmp/9ntm41353952026.png",intern=TRUE)) character(0) > try(system("convert tmp/10vtd41353952026.ps tmp/10vtd41353952026.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 13.842 0.991 14.904