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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+ ,7 + ,13 + ,17 + ,78 + ,47 + ,36 + ,34 + ,12 + ,6 + ,13 + ,11 + ,71 + ,44 + ,33 + ,32 + ,16 + ,9 + ,13 + ,13 + ,72 + ,45 + ,37 + ,33 + ,12 + ,10 + ,12 + ,17 + ,68 + ,44 + ,34 + ,33 + ,14 + ,11 + ,12 + ,15 + ,67 + ,43 + ,35 + ,37 + ,16 + ,12 + ,9 + ,21 + ,75 + ,43 + ,31 + ,32 + ,14 + ,8 + ,9 + ,18 + ,62 + ,40 + ,37 + ,34 + ,13 + ,11 + ,15 + ,15 + ,67 + ,41 + ,35 + ,30 + ,4 + ,3 + ,10 + ,8 + ,83 + ,52 + ,27 + ,30 + ,15 + ,11 + ,14 + ,12 + ,64 + ,38 + ,34 + ,38 + ,11 + ,12 + ,15 + ,12 + ,68 + ,41 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(8 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(8,264),dimnames=list(c('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 = '3' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Learning Connected Separate Software Happiness Depression Belonging 1 13 41 38 12 14 12.0 53 2 16 39 32 11 18 11.0 83 3 19 30 35 15 11 14.0 66 4 15 31 33 6 12 12.0 67 5 14 34 37 13 16 21.0 76 6 13 35 29 10 18 12.0 78 7 19 39 31 12 14 22.0 53 8 15 34 36 14 14 11.0 80 9 14 36 35 12 15 10.0 74 10 15 37 38 9 15 13.0 76 11 16 38 31 10 17 10.0 79 12 16 36 34 12 19 8.0 54 13 16 38 35 12 10 15.0 67 14 16 39 38 11 16 14.0 54 15 17 33 37 15 18 10.0 87 16 15 32 33 12 14 14.0 58 17 15 36 32 10 14 14.0 75 18 20 38 38 12 17 11.0 88 19 18 39 38 11 14 10.0 64 20 16 32 32 12 16 13.0 57 21 16 32 33 11 18 9.5 66 22 16 31 31 12 11 14.0 68 23 19 39 38 13 14 12.0 54 24 16 37 39 11 12 14.0 56 25 17 39 32 12 17 11.0 86 26 17 41 32 13 9 9.0 80 27 16 36 35 10 16 11.0 76 28 15 33 37 14 14 15.0 69 29 16 33 33 12 15 14.0 78 30 14 34 33 10 11 13.0 67 31 15 31 31 12 16 9.0 80 32 12 27 32 8 13 15.0 54 33 14 37 31 10 17 10.0 71 34 16 34 37 12 15 11.0 84 35 14 34 30 12 14 13.0 74 36 10 32 33 7 16 8.0 71 37 10 29 31 9 9 20.0 63 38 14 36 33 12 15 12.0 71 39 16 29 31 10 17 10.0 76 40 16 35 33 10 13 10.0 69 41 16 37 32 10 15 9.0 74 42 14 34 33 12 16 14.0 75 43 20 38 32 15 16 8.0 54 44 14 35 33 10 12 14.0 52 45 14 38 28 10 15 11.0 69 46 11 37 35 12 11 13.0 68 47 14 38 39 13 15 9.0 65 48 15 33 34 11 15 11.0 75 49 16 36 38 11 17 15.0 74 50 14 38 32 12 13 11.0 75 51 16 32 38 14 16 10.0 72 52 14 32 30 10 14 14.0 67 53 12 32 33 12 11 18.0 63 54 16 34 38 13 12 14.0 62 55 9 32 32 5 12 11.0 63 56 14 37 35 6 15 14.5 76 57 16 39 34 12 16 13.0 74 58 16 29 34 12 15 9.0 67 59 15 37 36 11 12 10.0 73 60 16 35 34 10 12 15.0 70 61 12 30 28 7 8 20.0 53 62 16 38 34 12 13 12.0 77 63 16 34 35 14 11 12.0 80 64 14 31 35 11 14 14.0 52 65 16 34 31 12 15 13.0 54 66 17 35 37 13 10 11.0 80 67 18 36 35 14 11 17.0 66 68 18 30 27 11 12 12.0 73 69 12 39 40 12 15 13.0 63 70 16 35 37 12 15 14.0 69 71 10 38 36 8 14 13.0 67 72 14 31 38 11 16 15.0 54 73 18 34 39 14 15 13.0 81 74 18 38 41 14 15 10.0 69 75 16 34 27 12 13 11.0 84 76 17 39 30 9 12 19.0 80 77 16 37 37 13 17 13.0 70 78 16 34 31 11 13 17.0 69 79 13 28 31 12 15 13.0 77 80 16 37 27 12 13 9.0 54 81 16 33 36 12 15 11.0 79 82 16 35 37 12 15 9.0 71 83 15 37 33 12 16 12.0 73 84 15 32 34 11 15 12.0 72 85 16 33 31 10 14 13.0 77 86 14 38 39 9 15 13.0 75 87 16 33 34 12 14 12.0 69 88 16 29 32 12 13 15.0 54 89 15 33 33 12 7 22.0 70 90 12 31 36 9 17 13.0 73 91 17 36 32 15 13 15.0 54 92 16 35 41 12 15 13.0 77 93 15 32 28 12 14 15.0 82 94 13 29 30 12 13 12.5 80 95 16 39 36 10 16 11.0 80 96 16 37 35 13 12 16.0 69 97 16 35 31 9 14 11.0 78 98 16 37 34 12 17 11.0 81 99 14 32 36 10 15 10.0 76 100 16 38 36 14 17 10.0 76 101 16 37 35 11 12 16.0 73 102 20 36 37 15 16 12.0 85 103 15 32 28 11 11 11.0 66 104 16 33 39 11 15 16.0 79 105 13 40 32 12 9 19.0 68 106 17 38 35 12 16 11.0 76 107 16 41 39 12 15 16.0 71 108 16 36 35 11 10 15.0 54 109 12 43 42 7 10 24.0 46 110 16 30 34 12 15 14.0 85 111 16 31 33 14 11 15.0 74 112 17 32 41 11 13 11.0 88 113 13 32 33 11 14 15.0 38 114 12 37 34 10 18 12.0 76 115 18 37 32 13 16 10.0 86 116 14 33 40 13 14 14.0 54 117 14 34 40 8 14 13.0 67 118 13 33 35 11 14 9.0 69 119 16 38 36 12 14 15.0 90 120 13 33 37 11 12 15.0 54 121 16 31 27 13 14 14.0 76 122 13 38 39 12 15 11.0 89 123 16 37 38 14 15 8.0 76 124 15 36 31 13 15 11.0 73 125 16 31 33 15 13 11.0 79 126 15 39 32 10 17 8.0 90 127 17 44 39 11 17 10.0 74 128 15 33 36 9 19 11.0 81 129 12 35 33 11 15 13.0 72 130 16 32 33 10 13 11.0 71 131 10 28 32 11 9 20.0 66 132 16 40 37 8 15 10.0 77 133 12 27 30 11 15 15.0 65 134 14 37 38 12 15 12.0 74 135 15 32 29 12 16 14.0 85 136 13 28 22 9 11 23.0 54 137 15 34 35 11 14 14.0 63 138 11 30 35 10 11 16.0 54 139 12 35 34 8 15 11.0 64 140 11 31 35 9 13 12.0 69 141 16 32 34 8 15 10.0 54 142 15 30 37 9 16 14.0 84 143 17 30 35 15 14 12.0 86 144 16 31 23 11 15 12.0 77 145 10 40 31 8 16 11.0 89 146 18 32 27 13 16 12.0 76 147 13 36 36 12 11 13.0 60 148 16 32 31 12 12 11.0 75 149 13 35 32 9 9 19.0 73 150 10 38 39 7 16 12.0 85 151 15 42 37 13 13 17.0 79 152 16 34 38 9 16 9.0 71 153 16 35 39 6 12 12.0 72 154 14 38 34 8 9 19.0 69 155 10 33 31 8 13 18.0 78 156 17 36 32 15 13 15.0 54 157 13 32 37 6 14 14.0 69 158 15 33 36 9 19 11.0 81 159 16 34 32 11 13 9.0 84 160 12 32 38 8 12 18.0 84 161 13 34 36 8 13 16.0 69 162 13 27 26 10 10 24.0 66 163 12 31 26 8 14 14.0 81 164 17 38 33 14 16 20.0 82 165 15 34 39 10 10 18.0 72 166 10 24 30 8 11 23.0 54 167 14 30 33 11 14 12.0 78 168 11 26 25 12 12 14.0 74 169 13 34 38 12 9 16.0 82 170 16 27 37 12 9 18.0 73 171 12 37 31 5 11 20.0 55 172 16 36 37 12 16 12.0 72 173 12 41 35 10 9 12.0 78 174 9 29 25 7 13 17.0 59 175 12 36 28 12 16 13.0 72 176 15 32 35 11 13 9.0 78 177 12 37 33 8 9 16.0 68 178 12 30 30 9 12 18.0 69 179 14 31 31 10 16 10.0 67 180 12 38 37 9 11 14.0 74 181 16 36 36 12 14 11.0 54 182 11 35 30 6 13 9.0 67 183 19 31 36 15 15 11.0 70 184 15 38 32 12 14 10.0 80 185 8 22 28 12 16 11.0 89 186 16 32 36 12 13 19.0 76 187 17 36 34 11 14 14.0 74 188 12 39 31 7 15 12.0 87 189 11 28 28 7 13 14.0 54 190 11 32 36 5 11 21.0 61 191 14 32 36 12 11 13.0 38 192 16 38 40 12 14 10.0 75 193 12 32 33 3 15 15.0 69 194 16 35 37 11 11 16.0 62 195 13 32 32 10 15 14.0 72 196 15 37 38 12 12 12.0 70 197 16 34 31 9 14 19.0 79 198 16 33 37 12 14 15.0 87 199 14 33 33 9 8 19.0 62 200 16 26 32 12 13 13.0 77 201 16 30 30 12 9 17.0 69 202 14 24 30 10 15 12.0 69 203 11 34 31 9 17 11.0 75 204 12 34 32 12 13 14.0 54 205 15 33 34 8 15 11.0 72 206 15 34 36 11 15 13.0 74 207 16 35 37 11 14 12.0 85 208 16 35 36 12 16 15.0 52 209 11 36 33 10 13 14.0 70 210 15 34 33 10 16 12.0 84 211 12 34 33 12 9 17.0 64 212 12 41 44 12 16 11.0 84 213 15 32 39 11 11 18.0 87 214 15 30 32 8 10 13.0 79 215 16 35 35 12 11 17.0 67 216 14 28 25 10 15 13.0 65 217 17 33 35 11 17 11.0 85 218 14 39 34 10 14 12.0 83 219 13 36 35 8 8 22.0 61 220 15 36 39 12 15 14.0 82 221 13 35 33 12 11 12.0 76 222 14 38 36 10 16 12.0 58 223 15 33 32 12 10 17.0 72 224 12 31 32 9 15 9.0 72 225 13 34 36 9 9 21.0 38 226 8 32 36 6 16 10.0 78 227 14 31 32 10 19 11.0 54 228 14 33 34 9 12 12.0 63 229 11 34 33 9 8 23.0 66 230 12 34 35 9 11 13.0 70 231 13 34 30 6 14 12.0 71 232 10 33 38 10 9 16.0 67 233 16 32 34 6 15 9.0 58 234 18 41 33 14 13 17.0 72 235 13 34 32 10 16 9.0 72 236 11 36 31 10 11 14.0 70 237 4 37 30 6 12 17.0 76 238 13 36 27 12 13 13.0 50 239 16 29 31 12 10 11.0 72 240 10 37 30 7 11 12.0 72 241 12 27 32 8 12 10.0 88 242 12 35 35 11 8 19.0 53 243 10 28 28 3 12 16.0 58 244 13 35 33 6 12 16.0 66 245 15 37 31 10 15 14.0 82 246 12 29 35 8 11 20.0 69 247 14 32 35 9 13 15.0 68 248 10 36 32 9 14 23.0 44 249 12 19 21 8 10 20.0 56 250 12 21 20 9 12 16.0 53 251 11 31 34 7 15 14.0 70 252 10 33 32 7 13 17.0 78 253 12 36 34 6 13 11.0 71 254 16 33 32 9 13 13.0 72 255 12 37 33 10 12 17.0 68 256 14 34 33 11 12 15.0 67 257 16 35 37 12 9 21.0 75 258 14 31 32 8 9 18.0 62 259 13 37 34 11 15 15.0 67 260 4 35 30 3 10 8.0 83 261 15 27 30 11 14 12.0 64 262 11 34 38 12 15 12.0 68 263 11 40 36 7 7 22.0 62 264 14 29 32 9 14 12.0 72 Belonging_Final t 1 32 1 2 51 2 3 42 3 4 41 4 5 46 5 6 47 6 7 37 7 8 49 8 9 45 9 10 47 10 11 49 11 12 33 12 13 42 13 14 33 14 15 53 15 16 36 16 17 45 17 18 54 18 19 41 19 20 36 20 21 41 21 22 44 22 23 33 23 24 37 24 25 52 25 26 47 26 27 43 27 28 44 28 29 45 29 30 44 30 31 49 31 32 33 32 33 43 33 34 54 34 35 42 35 36 44 36 37 37 37 38 43 38 39 46 39 40 42 40 41 45 41 42 44 42 43 33 43 44 31 44 45 42 45 46 40 46 47 43 47 48 46 48 49 42 49 50 45 50 51 44 51 52 40 52 53 37 53 54 46 54 55 36 55 56 47 56 57 45 57 58 42 58 59 43 59 60 43 60 61 32 61 62 45 62 63 48 63 64 31 64 65 33 65 66 49 66 67 42 67 68 41 68 69 38 69 70 42 70 71 44 71 72 33 72 73 48 73 74 40 74 75 50 75 76 49 76 77 43 77 78 44 78 79 47 79 80 33 80 81 46 81 82 45 82 83 43 83 84 44 84 85 47 85 86 45 86 87 42 87 88 33 88 89 43 89 90 46 90 91 33 91 92 46 92 93 48 93 94 47 94 95 47 95 96 43 96 97 46 97 98 48 98 99 46 99 100 45 100 101 45 101 102 52 102 103 42 103 104 47 104 105 41 105 106 47 106 107 43 107 108 33 108 109 30 109 110 52 110 111 44 111 112 55 112 113 11 113 114 47 114 115 53 115 116 33 116 117 44 117 118 42 118 119 55 119 120 33 120 121 46 121 122 54 122 123 47 123 124 45 124 125 47 125 126 55 126 127 44 127 128 53 128 129 44 129 130 42 130 131 40 131 132 46 132 133 40 133 134 46 134 135 53 135 136 33 136 137 42 137 138 35 138 139 40 139 140 41 140 141 33 141 142 51 142 143 53 143 144 46 144 145 55 145 146 47 146 147 38 147 148 46 148 149 46 149 150 53 150 151 47 151 152 41 152 153 44 153 154 43 154 155 51 155 156 33 156 157 43 157 158 53 158 159 51 159 160 50 160 161 46 161 162 43 162 163 47 163 164 50 164 165 43 165 166 33 166 167 48 167 168 44 168 169 50 169 170 41 170 171 34 171 172 44 172 173 47 173 174 35 174 175 44 175 176 44 176 177 43 177 178 41 178 179 41 179 180 42 180 181 33 181 182 41 182 183 44 183 184 48 184 185 55 185 186 44 186 187 43 187 188 52 188 189 30 189 190 39 190 191 11 191 192 44 192 193 42 193 194 41 194 195 44 195 196 44 196 197 48 197 198 53 198 199 37 199 200 44 200 201 44 201 202 40 202 203 42 203 204 35 204 205 43 205 206 45 206 207 55 207 208 31 208 209 44 209 210 50 210 211 40 211 212 53 212 213 54 213 214 49 214 215 40 215 216 41 216 217 52 217 218 52 218 219 36 219 220 52 220 221 46 221 222 31 222 223 44 223 224 44 224 225 11 225 226 46 226 227 33 227 228 34 228 229 42 229 230 43 230 231 43 231 232 44 232 233 36 233 234 46 234 235 44 235 236 43 236 237 50 237 238 33 238 239 43 239 240 44 240 241 53 241 242 34 242 243 35 243 244 40 244 245 53 245 246 42 246 247 43 247 248 29 248 249 36 249 250 30 250 251 42 251 252 47 252 253 44 253 254 45 254 255 44 255 256 43 256 257 43 257 258 40 258 259 41 259 260 52 260 261 38 261 262 41 262 263 39 263 264 43 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software 5.334627 0.033432 0.042910 0.557382 Happiness Depression Belonging Belonging_Final 0.070714 -0.028988 0.023564 -0.025260 t -0.004938 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9166 -1.0426 0.2822 1.2093 4.6857 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.334627 1.962792 2.718 0.00702 ** Connected 0.033432 0.034590 0.967 0.33471 Separate 0.042910 0.035132 1.221 0.22307 Software 0.557382 0.053864 10.348 < 2e-16 *** Happiness 0.070714 0.057909 1.221 0.22317 Depression -0.028988 0.042089 -0.689 0.49162 Belonging 0.023564 0.037386 0.630 0.52906 Belonging_Final -0.025260 0.055660 -0.454 0.65035 t -0.004938 0.001684 -2.933 0.00367 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.857 on 255 degrees of freedom Multiple R-squared: 0.4456, Adjusted R-squared: 0.4282 F-statistic: 25.62 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,] 0.094906934 0.189813867 0.9050931 [2,] 0.168821052 0.337642105 0.8311789 [3,] 0.178863828 0.357727656 0.8211362 [4,] 0.102108222 0.204216444 0.8978918 [5,] 0.078428197 0.156856394 0.9215718 [6,] 0.087023766 0.174047532 0.9129762 [7,] 0.213223440 0.426446880 0.7867766 [8,] 0.175810464 0.351620929 0.8241895 [9,] 0.139406042 0.278812084 0.8605940 [10,] 0.107616873 0.215233746 0.8923831 [11,] 0.131400316 0.262800633 0.8685997 [12,] 0.229848580 0.459697160 0.7701514 [13,] 0.345400678 0.690801356 0.6545993 [14,] 0.282158757 0.564317513 0.7178412 [15,] 0.237323312 0.474646624 0.7626767 [16,] 0.227896246 0.455792491 0.7721038 [17,] 0.338485052 0.676970104 0.6615149 [18,] 0.289325481 0.578650962 0.7106745 [19,] 0.397882860 0.795765720 0.6021171 [20,] 0.356890257 0.713780515 0.6431097 [21,] 0.329416582 0.658833164 0.6705834 [22,] 0.314121460 0.628242920 0.6858785 [23,] 0.278262895 0.556525790 0.7217371 [24,] 0.236820684 0.473641369 0.7631793 [25,] 0.366904764 0.733809527 0.6330952 [26,] 0.395621729 0.791243457 0.6043783 [27,] 0.371576673 0.743153345 0.6284233 [28,] 0.416207603 0.832415205 0.5837924 [29,] 0.396546651 0.793093301 0.6034533 [30,] 0.357799571 0.715599141 0.6422004 [31,] 0.319486474 0.638972948 0.6805135 [32,] 0.338409103 0.676818206 0.6615909 [33,] 0.291402618 0.582805236 0.7085974 [34,] 0.264493102 0.528986203 0.7355069 [35,] 0.441612475 0.883224950 0.5583875 [36,] 0.532267003 0.935465995 0.4677330 [37,] 0.489201696 0.978403393 0.5107983 [38,] 0.513642655 0.972714690 0.4863573 [39,] 0.486621948 0.973243896 0.5133781 [40,] 0.441557259 0.883114518 0.5584427 [41,] 0.400425011 0.800850022 0.5995750 [42,] 0.399864147 0.799728295 0.6001359 [43,] 0.358080564 0.716161128 0.6419194 [44,] 0.346095997 0.692191993 0.6539040 [45,] 0.343301127 0.686602254 0.6566989 [46,] 0.304239889 0.608479778 0.6957601 [47,] 0.285720086 0.571440172 0.7142799 [48,] 0.255885143 0.511770286 0.7441149 [49,] 0.276307802 0.552615603 0.7236922 [50,] 0.252992904 0.505985808 0.7470071 [51,] 0.228275085 0.456550171 0.7717249 [52,] 0.199677702 0.399355404 0.8003223 [53,] 0.172011899 0.344023798 0.8279881 [54,] 0.151781538 0.303563076 0.8482185 [55,] 0.143071387 0.286142774 0.8569286 [56,] 0.139047554 0.278095107 0.8609524 [57,] 0.269203231 0.538406462 0.7307968 [58,] 0.364495176 0.728990352 0.6355048 [59,] 0.334000902 0.668001805 0.6659991 [60,] 0.443101781 0.886203562 0.5568982 [61,] 0.408390199 0.816780398 0.5916098 [62,] 0.408893218 0.817786435 0.5911068 [63,] 0.397135724 0.794271447 0.6028643 [64,] 0.360034057 0.720068113 0.6399659 [65,] 0.425793182 0.851586363 0.5742068 [66,] 0.388042441 0.776084882 0.6119576 [67,] 0.362094759 0.724189517 0.6379052 [68,] 0.376793250 0.753586499 0.6232068 [69,] 0.343116850 0.686233701 0.6568831 [70,] 0.314522821 0.629045643 0.6854772 [71,] 0.280862484 0.561724969 0.7191375 [72,] 0.253282202 0.506564405 0.7467178 [73,] 0.223423532 0.446847064 0.7765765 [74,] 0.217294113 0.434588226 0.7827059 [75,] 0.189821596 0.379643192 0.8101784 [76,] 0.167487050 0.334974100 0.8325129 [77,] 0.155126911 0.310253823 0.8448731 [78,] 0.133636740 0.267273480 0.8663633 [79,] 0.132269927 0.264539853 0.8677301 [80,] 0.113156521 0.226313043 0.8868435 [81,] 0.098884736 0.197769472 0.9011153 [82,] 0.084107031 0.168214063 0.9158930 [83,] 0.087387903 0.174775806 0.9126121 [84,] 0.079055976 0.158111952 0.9209440 [85,] 0.066156816 0.132313631 0.9338432 [86,] 0.070875730 0.141751461 0.9291243 [87,] 0.058872468 0.117744936 0.9411275 [88,] 0.048848841 0.097697682 0.9511512 [89,] 0.042921172 0.085842344 0.9570788 [90,] 0.037574430 0.075148859 0.9624256 [91,] 0.043465022 0.086930044 0.9565350 [92,] 0.036635802 0.073271604 0.9633642 [93,] 0.032852846 0.065705693 0.9671472 [94,] 0.039139202 0.078278404 0.9608608 [95,] 0.034051571 0.068103142 0.9659484 [96,] 0.027651645 0.055303290 0.9723484 [97,] 0.026780271 0.053560541 0.9732197 [98,] 0.021754995 0.043509990 0.9782450 [99,] 0.017726901 0.035453801 0.9822731 [100,] 0.014054539 0.028109078 0.9859455 [101,] 0.014195181 0.028390362 0.9858048 [102,] 0.012888492 0.025776984 0.9871115 [103,] 0.018860640 0.037721279 0.9811394 [104,] 0.017729977 0.035459954 0.9822700 [105,] 0.017234496 0.034468992 0.9827655 [106,] 0.014162764 0.028325528 0.9858372 [107,] 0.014253226 0.028506453 0.9857468 [108,] 0.011488413 0.022976825 0.9885116 [109,] 0.010285054 0.020570109 0.9897149 [110,] 0.008261910 0.016523819 0.9917381 [111,] 0.012518578 0.025037157 0.9874814 [112,] 0.010447201 0.020894403 0.9895528 [113,] 0.008973745 0.017947491 0.9910263 [114,] 0.007295192 0.014590384 0.9927048 [115,] 0.005773670 0.011547340 0.9942263 [116,] 0.005258363 0.010516726 0.9947416 [117,] 0.004250609 0.008501218 0.9957494 [118,] 0.006006641 0.012013283 0.9939934 [119,] 0.006603666 0.013207332 0.9933963 [120,] 0.013738602 0.027477203 0.9862614 [121,] 0.016419826 0.032839652 0.9835802 [122,] 0.017602040 0.035204079 0.9823980 [123,] 0.016695082 0.033390165 0.9833049 [124,] 0.013344148 0.026688296 0.9866559 [125,] 0.011222517 0.022445035 0.9887775 [126,] 0.008916657 0.017833315 0.9910833 [127,] 0.011150542 0.022301084 0.9888495 [128,] 0.009557228 0.019114455 0.9904428 [129,] 0.011312387 0.022624774 0.9886876 [130,] 0.017959078 0.035918156 0.9820409 [131,] 0.016548886 0.033097772 0.9834511 [132,] 0.013305854 0.026611707 0.9866941 [133,] 0.013698861 0.027397722 0.9863011 [134,] 0.024579466 0.049158932 0.9754205 [135,] 0.029835940 0.059671880 0.9701641 [136,] 0.031317522 0.062635044 0.9686825 [137,] 0.027711794 0.055423588 0.9722882 [138,] 0.022552407 0.045104814 0.9774476 [139,] 0.031496616 0.062993232 0.9685034 [140,] 0.026599897 0.053199794 0.9734001 [141,] 0.028945975 0.057891950 0.9710540 [142,] 0.058537603 0.117075206 0.9414624 [143,] 0.056404986 0.112809972 0.9435950 [144,] 0.064608244 0.129216487 0.9353918 [145,] 0.054937053 0.109874106 0.9450629 [146,] 0.049018871 0.098037742 0.9509811 [147,] 0.042689542 0.085379085 0.9573105 [148,] 0.041329549 0.082659097 0.9586705 [149,] 0.035466990 0.070933980 0.9645330 [150,] 0.028956466 0.057912931 0.9710435 [151,] 0.023635708 0.047271416 0.9763643 [152,] 0.019309639 0.038619278 0.9806904 [153,] 0.016329110 0.032658220 0.9836709 [154,] 0.014450083 0.028900167 0.9855499 [155,] 0.014948612 0.029897224 0.9850514 [156,] 0.011884217 0.023768435 0.9881158 [157,] 0.017513278 0.035026557 0.9824867 [158,] 0.017634008 0.035268016 0.9823660 [159,] 0.016735388 0.033470777 0.9832646 [160,] 0.015456662 0.030913323 0.9845433 [161,] 0.012472373 0.024944746 0.9875276 [162,] 0.012088212 0.024176424 0.9879118 [163,] 0.013774829 0.027549657 0.9862252 [164,] 0.017933506 0.035867012 0.9820665 [165,] 0.014378215 0.028756431 0.9856218 [166,] 0.011352380 0.022704760 0.9886476 [167,] 0.009545563 0.019091125 0.9904544 [168,] 0.007389758 0.014779516 0.9926102 [169,] 0.006509052 0.013018104 0.9934909 [170,] 0.005310520 0.010621041 0.9946895 [171,] 0.004086687 0.008173374 0.9959133 [172,] 0.004424693 0.008849386 0.9955753 [173,] 0.003352023 0.006704046 0.9966480 [174,] 0.091632702 0.183265405 0.9083673 [175,] 0.081927934 0.163855867 0.9180721 [176,] 0.090420002 0.180840005 0.9095800 [177,] 0.075900513 0.151801026 0.9240995 [178,] 0.067746923 0.135493845 0.9322531 [179,] 0.056589712 0.113179424 0.9434103 [180,] 0.052365080 0.104730160 0.9476349 [181,] 0.043257361 0.086514722 0.9567426 [182,] 0.044375219 0.088750438 0.9556248 [183,] 0.044859205 0.089718410 0.9551408 [184,] 0.038791360 0.077582720 0.9612086 [185,] 0.031214253 0.062428506 0.9687857 [186,] 0.039414893 0.078829787 0.9605851 [187,] 0.032183611 0.064367223 0.9678164 [188,] 0.028964272 0.057928543 0.9710357 [189,] 0.024856920 0.049713841 0.9751431 [190,] 0.023246267 0.046492534 0.9767537 [191,] 0.019650123 0.039300245 0.9803499 [192,] 0.025806160 0.051612320 0.9741938 [193,] 0.030055872 0.060111744 0.9699441 [194,] 0.031477354 0.062954707 0.9685226 [195,] 0.024673454 0.049346908 0.9753265 [196,] 0.023619571 0.047239143 0.9763804 [197,] 0.019309718 0.038619436 0.9806903 [198,] 0.021531713 0.043063425 0.9784683 [199,] 0.017159994 0.034319988 0.9828400 [200,] 0.019720061 0.039440122 0.9802799 [201,] 0.031986014 0.063972029 0.9680140 [202,] 0.024900999 0.049801998 0.9750990 [203,] 0.032199633 0.064399267 0.9678004 [204,] 0.027323206 0.054646411 0.9726768 [205,] 0.021137575 0.042275150 0.9788624 [206,] 0.023220552 0.046441103 0.9767794 [207,] 0.019579733 0.039159465 0.9804203 [208,] 0.018191434 0.036382867 0.9818086 [209,] 0.013910465 0.027820931 0.9860895 [210,] 0.011584636 0.023169271 0.9884154 [211,] 0.008318636 0.016637273 0.9916814 [212,] 0.006380204 0.012760408 0.9936198 [213,] 0.004809066 0.009618132 0.9951909 [214,] 0.004093750 0.008187501 0.9959062 [215,] 0.009466810 0.018933620 0.9905332 [216,] 0.007417951 0.014835902 0.9925820 [217,] 0.005488961 0.010977923 0.9945110 [218,] 0.003925897 0.007851793 0.9960741 [219,] 0.002890463 0.005780927 0.9971095 [220,] 0.002606182 0.005212364 0.9973938 [221,] 0.006137343 0.012274686 0.9938627 [222,] 0.024764967 0.049529935 0.9752350 [223,] 0.037747758 0.075495516 0.9622522 [224,] 0.027231099 0.054462198 0.9727689 [225,] 0.022825449 0.045650898 0.9771746 [226,] 0.197370722 0.394741444 0.8026293 [227,] 0.155667522 0.311335043 0.8443325 [228,] 0.129607141 0.259214282 0.8703929 [229,] 0.100544198 0.201088396 0.8994558 [230,] 0.088408181 0.176816362 0.9115918 [231,] 0.137938494 0.275876989 0.8620615 [232,] 0.112959828 0.225919657 0.8870402 [233,] 0.118293731 0.236587461 0.8817063 [234,] 0.096819834 0.193639669 0.9031802 [235,] 0.082174010 0.164348020 0.9178260 [236,] 0.053056083 0.106112166 0.9469439 [237,] 0.045202873 0.090405746 0.9547971 [238,] 0.040451904 0.080903807 0.9595481 [239,] 0.115496462 0.230992924 0.8845035 [240,] 0.094703841 0.189407682 0.9052962 [241,] 0.431439000 0.862878000 0.5685610 > postscript(file="/var/wessaorg/rcomp/tmp/1fsje1352115409.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2n8qy1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3e22a1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/44wxh1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/54n6w1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 264 Frequency = 1 1 2 3 4 5 6 -3.102298221 0.245508554 1.948287423 2.844541916 -2.431877832 -1.869129571 7 8 9 10 11 12 3.710736971 -2.098463663 -2.062068866 0.543207803 1.009137890 -0.177005218 13 14 15 16 17 18 0.478496420 0.504384283 -1.006512051 -0.471600438 0.384029318 3.571776300 19 20 21 22 23 24 2.520984906 0.444210606 0.634956735 0.855854122 2.517513085 0.914481509 25 26 27 28 29 30 0.826979440 0.730492812 1.002205332 -1.760323978 0.244499178 -0.181416578 31 32 33 34 35 36 -0.754683476 -0.834815524 -0.811839163 0.063104612 -1.570370764 -3.005541434 37 38 39 40 41 42 -3.074708695 -1.754899768 1.443200553 1.508493443 1.277020449 -1.750019986 43 44 45 46 47 48 2.535021239 -0.162354026 -0.465001909 -4.527891846 -2.537742048 -0.138218462 49 50 51 52 53 54 0.491833471 -1.650896199 -1.013287553 -0.161379564 -3.053366612 0.177010959 55 56 57 58 59 60 -2.397791027 1.614711519 0.133816130 0.517006043 -0.148945246 1.781689396 61 62 63 64 65 66 0.433922695 0.304398347 -0.568097897 -0.714497575 0.708091983 0.951831948 67 68 69 70 71 72 1.708070000 3.523165161 -3.911285460 0.344750275 -3.338794504 -0.912773043 73 74 75 76 77 78 1.032210414 0.811332375 0.735049423 3.487857269 -0.413649675 1.511437148 79 80 81 82 83 84 -2.210529169 0.878982388 0.287274495 0.287717529 -0.683966261 0.122140390 85 86 87 88 89 90 1.837417717 -0.184805355 0.637027251 1.145316210 0.476380270 -1.871340301 91 92 93 94 95 96 0.253960372 0.165281862 -0.110267757 -2.070740981 1.201561147 0.230086471 97 98 99 100 101 102 2.280392742 0.185275621 -0.433941180 -1.025810198 1.325803102 2.544064759 103 104 105 106 107 108 0.818154279 0.999697148 -1.867506176 1.311712397 0.277149621 1.650845580 109 110 111 112 113 114 -0.275456138 0.713725048 -0.017651315 1.973299951 -1.566454216 -2.570117240 115 116 117 118 119 120 1.847860891 -1.850512307 0.850437998 -1.782387174 0.462550890 -1.416850975 121 122 123 124 125 126 0.608832531 -2.839724798 -0.830654743 -0.827396197 -0.805322704 0.335034965 127 128 129 130 131 132 1.472209186 1.038338342 -2.684050075 2.035062790 -3.729699165 2.509846847 133 134 135 136 137 138 -2.146227277 -1.523753324 -0.060594176 0.890344539 0.564325197 -2.434249991 139 140 141 142 143 144 -0.975933494 -2.359707165 3.164075491 1.342747174 0.176053779 1.856556734 145 146 147 148 149 150 -3.265660580 2.524706201 -1.900643556 1.172495986 0.197545859 -2.887279570 151 152 153 154 155 156 -0.927635686 2.124290496 4.147065404 1.611980680 -2.409036113 0.574926510 157 158 159 160 161 162 1.314913659 1.186476560 1.459952990 -0.747215610 0.400414580 0.392666584 163 164 165 166 167 168 -0.446513743 0.764450432 1.300316767 -1.613687684 -0.327870372 -3.210686251 169 170 171 172 173 174 -1.797873078 1.526714914 1.520357883 0.666125952 -1.866121650 -2.352065885 175 176 177 178 179 180 -2.903882575 0.446600156 -0.261503599 -0.679444147 0.224142391 -1.375191100 181 182 183 184 185 186 1.012217824 -0.439178441 2.347219736 -0.130955841 -6.567169685 1.232691977 187 188 189 190 191 192 2.553322055 -0.391463876 -0.468734491 0.580687856 -0.713241554 0.582050572 193 194 195 196 197 198 2.269481008 1.994956854 -0.628570597 0.038277629 3.066474539 0.997070790 199 200 201 202 203 204 1.570980621 1.476564003 2.020913293 0.670948723 -2.405240124 -2.427510351 205 206 207 208 209 210 2.304099234 0.579004290 1.542715447 1.150106666 -2.547521027 1.075822786 211 212 213 214 215 216 -2.175381646 -3.688307789 0.900497361 2.932807324 1.512998471 0.969406554 217 218 219 220 221 222 2.427871407 0.120767734 1.126273919 -0.087549794 -1.577012700 0.005359026 223 224 225 226 227 228 0.802026355 -1.039494366 0.433260160 -3.695135417 0.339358634 1.086160183 229 230 231 232 233 234 -1.166321626 -0.818218205 1.808724003 -3.136680595 4.685664825 2.269592673 235 236 237 238 239 240 -0.713568415 -2.212208868 -6.916582701 -1.097183889 1.858485315 -1.590676066 241 242 243 244 245 246 -0.173002681 -1.347837056 1.188173325 2.010175644 1.485771091 0.186548364 247 248 249 250 251 252 1.296266786 -2.330697678 1.361911326 0.447271006 -0.735677772 -1.545609222 253 254 255 256 257 258 0.745840576 3.324417558 -1.149002649 0.339177797 1.779214818 2.505552324 259 260 261 262 263 264 -1.051873737 -5.297891218 1.442625011 -3.776313919 -0.152783633 1.357303915 > postscript(file="/var/wessaorg/rcomp/tmp/6dbob1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.102298221 NA 1 0.245508554 -3.102298221 2 1.948287423 0.245508554 3 2.844541916 1.948287423 4 -2.431877832 2.844541916 5 -1.869129571 -2.431877832 6 3.710736971 -1.869129571 7 -2.098463663 3.710736971 8 -2.062068866 -2.098463663 9 0.543207803 -2.062068866 10 1.009137890 0.543207803 11 -0.177005218 1.009137890 12 0.478496420 -0.177005218 13 0.504384283 0.478496420 14 -1.006512051 0.504384283 15 -0.471600438 -1.006512051 16 0.384029318 -0.471600438 17 3.571776300 0.384029318 18 2.520984906 3.571776300 19 0.444210606 2.520984906 20 0.634956735 0.444210606 21 0.855854122 0.634956735 22 2.517513085 0.855854122 23 0.914481509 2.517513085 24 0.826979440 0.914481509 25 0.730492812 0.826979440 26 1.002205332 0.730492812 27 -1.760323978 1.002205332 28 0.244499178 -1.760323978 29 -0.181416578 0.244499178 30 -0.754683476 -0.181416578 31 -0.834815524 -0.754683476 32 -0.811839163 -0.834815524 33 0.063104612 -0.811839163 34 -1.570370764 0.063104612 35 -3.005541434 -1.570370764 36 -3.074708695 -3.005541434 37 -1.754899768 -3.074708695 38 1.443200553 -1.754899768 39 1.508493443 1.443200553 40 1.277020449 1.508493443 41 -1.750019986 1.277020449 42 2.535021239 -1.750019986 43 -0.162354026 2.535021239 44 -0.465001909 -0.162354026 45 -4.527891846 -0.465001909 46 -2.537742048 -4.527891846 47 -0.138218462 -2.537742048 48 0.491833471 -0.138218462 49 -1.650896199 0.491833471 50 -1.013287553 -1.650896199 51 -0.161379564 -1.013287553 52 -3.053366612 -0.161379564 53 0.177010959 -3.053366612 54 -2.397791027 0.177010959 55 1.614711519 -2.397791027 56 0.133816130 1.614711519 57 0.517006043 0.133816130 58 -0.148945246 0.517006043 59 1.781689396 -0.148945246 60 0.433922695 1.781689396 61 0.304398347 0.433922695 62 -0.568097897 0.304398347 63 -0.714497575 -0.568097897 64 0.708091983 -0.714497575 65 0.951831948 0.708091983 66 1.708070000 0.951831948 67 3.523165161 1.708070000 68 -3.911285460 3.523165161 69 0.344750275 -3.911285460 70 -3.338794504 0.344750275 71 -0.912773043 -3.338794504 72 1.032210414 -0.912773043 73 0.811332375 1.032210414 74 0.735049423 0.811332375 75 3.487857269 0.735049423 76 -0.413649675 3.487857269 77 1.511437148 -0.413649675 78 -2.210529169 1.511437148 79 0.878982388 -2.210529169 80 0.287274495 0.878982388 81 0.287717529 0.287274495 82 -0.683966261 0.287717529 83 0.122140390 -0.683966261 84 1.837417717 0.122140390 85 -0.184805355 1.837417717 86 0.637027251 -0.184805355 87 1.145316210 0.637027251 88 0.476380270 1.145316210 89 -1.871340301 0.476380270 90 0.253960372 -1.871340301 91 0.165281862 0.253960372 92 -0.110267757 0.165281862 93 -2.070740981 -0.110267757 94 1.201561147 -2.070740981 95 0.230086471 1.201561147 96 2.280392742 0.230086471 97 0.185275621 2.280392742 98 -0.433941180 0.185275621 99 -1.025810198 -0.433941180 100 1.325803102 -1.025810198 101 2.544064759 1.325803102 102 0.818154279 2.544064759 103 0.999697148 0.818154279 104 -1.867506176 0.999697148 105 1.311712397 -1.867506176 106 0.277149621 1.311712397 107 1.650845580 0.277149621 108 -0.275456138 1.650845580 109 0.713725048 -0.275456138 110 -0.017651315 0.713725048 111 1.973299951 -0.017651315 112 -1.566454216 1.973299951 113 -2.570117240 -1.566454216 114 1.847860891 -2.570117240 115 -1.850512307 1.847860891 116 0.850437998 -1.850512307 117 -1.782387174 0.850437998 118 0.462550890 -1.782387174 119 -1.416850975 0.462550890 120 0.608832531 -1.416850975 121 -2.839724798 0.608832531 122 -0.830654743 -2.839724798 123 -0.827396197 -0.830654743 124 -0.805322704 -0.827396197 125 0.335034965 -0.805322704 126 1.472209186 0.335034965 127 1.038338342 1.472209186 128 -2.684050075 1.038338342 129 2.035062790 -2.684050075 130 -3.729699165 2.035062790 131 2.509846847 -3.729699165 132 -2.146227277 2.509846847 133 -1.523753324 -2.146227277 134 -0.060594176 -1.523753324 135 0.890344539 -0.060594176 136 0.564325197 0.890344539 137 -2.434249991 0.564325197 138 -0.975933494 -2.434249991 139 -2.359707165 -0.975933494 140 3.164075491 -2.359707165 141 1.342747174 3.164075491 142 0.176053779 1.342747174 143 1.856556734 0.176053779 144 -3.265660580 1.856556734 145 2.524706201 -3.265660580 146 -1.900643556 2.524706201 147 1.172495986 -1.900643556 148 0.197545859 1.172495986 149 -2.887279570 0.197545859 150 -0.927635686 -2.887279570 151 2.124290496 -0.927635686 152 4.147065404 2.124290496 153 1.611980680 4.147065404 154 -2.409036113 1.611980680 155 0.574926510 -2.409036113 156 1.314913659 0.574926510 157 1.186476560 1.314913659 158 1.459952990 1.186476560 159 -0.747215610 1.459952990 160 0.400414580 -0.747215610 161 0.392666584 0.400414580 162 -0.446513743 0.392666584 163 0.764450432 -0.446513743 164 1.300316767 0.764450432 165 -1.613687684 1.300316767 166 -0.327870372 -1.613687684 167 -3.210686251 -0.327870372 168 -1.797873078 -3.210686251 169 1.526714914 -1.797873078 170 1.520357883 1.526714914 171 0.666125952 1.520357883 172 -1.866121650 0.666125952 173 -2.352065885 -1.866121650 174 -2.903882575 -2.352065885 175 0.446600156 -2.903882575 176 -0.261503599 0.446600156 177 -0.679444147 -0.261503599 178 0.224142391 -0.679444147 179 -1.375191100 0.224142391 180 1.012217824 -1.375191100 181 -0.439178441 1.012217824 182 2.347219736 -0.439178441 183 -0.130955841 2.347219736 184 -6.567169685 -0.130955841 185 1.232691977 -6.567169685 186 2.553322055 1.232691977 187 -0.391463876 2.553322055 188 -0.468734491 -0.391463876 189 0.580687856 -0.468734491 190 -0.713241554 0.580687856 191 0.582050572 -0.713241554 192 2.269481008 0.582050572 193 1.994956854 2.269481008 194 -0.628570597 1.994956854 195 0.038277629 -0.628570597 196 3.066474539 0.038277629 197 0.997070790 3.066474539 198 1.570980621 0.997070790 199 1.476564003 1.570980621 200 2.020913293 1.476564003 201 0.670948723 2.020913293 202 -2.405240124 0.670948723 203 -2.427510351 -2.405240124 204 2.304099234 -2.427510351 205 0.579004290 2.304099234 206 1.542715447 0.579004290 207 1.150106666 1.542715447 208 -2.547521027 1.150106666 209 1.075822786 -2.547521027 210 -2.175381646 1.075822786 211 -3.688307789 -2.175381646 212 0.900497361 -3.688307789 213 2.932807324 0.900497361 214 1.512998471 2.932807324 215 0.969406554 1.512998471 216 2.427871407 0.969406554 217 0.120767734 2.427871407 218 1.126273919 0.120767734 219 -0.087549794 1.126273919 220 -1.577012700 -0.087549794 221 0.005359026 -1.577012700 222 0.802026355 0.005359026 223 -1.039494366 0.802026355 224 0.433260160 -1.039494366 225 -3.695135417 0.433260160 226 0.339358634 -3.695135417 227 1.086160183 0.339358634 228 -1.166321626 1.086160183 229 -0.818218205 -1.166321626 230 1.808724003 -0.818218205 231 -3.136680595 1.808724003 232 4.685664825 -3.136680595 233 2.269592673 4.685664825 234 -0.713568415 2.269592673 235 -2.212208868 -0.713568415 236 -6.916582701 -2.212208868 237 -1.097183889 -6.916582701 238 1.858485315 -1.097183889 239 -1.590676066 1.858485315 240 -0.173002681 -1.590676066 241 -1.347837056 -0.173002681 242 1.188173325 -1.347837056 243 2.010175644 1.188173325 244 1.485771091 2.010175644 245 0.186548364 1.485771091 246 1.296266786 0.186548364 247 -2.330697678 1.296266786 248 1.361911326 -2.330697678 249 0.447271006 1.361911326 250 -0.735677772 0.447271006 251 -1.545609222 -0.735677772 252 0.745840576 -1.545609222 253 3.324417558 0.745840576 254 -1.149002649 3.324417558 255 0.339177797 -1.149002649 256 1.779214818 0.339177797 257 2.505552324 1.779214818 258 -1.051873737 2.505552324 259 -5.297891218 -1.051873737 260 1.442625011 -5.297891218 261 -3.776313919 1.442625011 262 -0.152783633 -3.776313919 263 1.357303915 -0.152783633 264 NA 1.357303915 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.245508554 -3.102298221 [2,] 1.948287423 0.245508554 [3,] 2.844541916 1.948287423 [4,] -2.431877832 2.844541916 [5,] -1.869129571 -2.431877832 [6,] 3.710736971 -1.869129571 [7,] -2.098463663 3.710736971 [8,] -2.062068866 -2.098463663 [9,] 0.543207803 -2.062068866 [10,] 1.009137890 0.543207803 [11,] -0.177005218 1.009137890 [12,] 0.478496420 -0.177005218 [13,] 0.504384283 0.478496420 [14,] -1.006512051 0.504384283 [15,] -0.471600438 -1.006512051 [16,] 0.384029318 -0.471600438 [17,] 3.571776300 0.384029318 [18,] 2.520984906 3.571776300 [19,] 0.444210606 2.520984906 [20,] 0.634956735 0.444210606 [21,] 0.855854122 0.634956735 [22,] 2.517513085 0.855854122 [23,] 0.914481509 2.517513085 [24,] 0.826979440 0.914481509 [25,] 0.730492812 0.826979440 [26,] 1.002205332 0.730492812 [27,] -1.760323978 1.002205332 [28,] 0.244499178 -1.760323978 [29,] -0.181416578 0.244499178 [30,] -0.754683476 -0.181416578 [31,] -0.834815524 -0.754683476 [32,] -0.811839163 -0.834815524 [33,] 0.063104612 -0.811839163 [34,] -1.570370764 0.063104612 [35,] -3.005541434 -1.570370764 [36,] -3.074708695 -3.005541434 [37,] -1.754899768 -3.074708695 [38,] 1.443200553 -1.754899768 [39,] 1.508493443 1.443200553 [40,] 1.277020449 1.508493443 [41,] -1.750019986 1.277020449 [42,] 2.535021239 -1.750019986 [43,] -0.162354026 2.535021239 [44,] -0.465001909 -0.162354026 [45,] -4.527891846 -0.465001909 [46,] -2.537742048 -4.527891846 [47,] -0.138218462 -2.537742048 [48,] 0.491833471 -0.138218462 [49,] -1.650896199 0.491833471 [50,] -1.013287553 -1.650896199 [51,] -0.161379564 -1.013287553 [52,] -3.053366612 -0.161379564 [53,] 0.177010959 -3.053366612 [54,] -2.397791027 0.177010959 [55,] 1.614711519 -2.397791027 [56,] 0.133816130 1.614711519 [57,] 0.517006043 0.133816130 [58,] -0.148945246 0.517006043 [59,] 1.781689396 -0.148945246 [60,] 0.433922695 1.781689396 [61,] 0.304398347 0.433922695 [62,] -0.568097897 0.304398347 [63,] -0.714497575 -0.568097897 [64,] 0.708091983 -0.714497575 [65,] 0.951831948 0.708091983 [66,] 1.708070000 0.951831948 [67,] 3.523165161 1.708070000 [68,] -3.911285460 3.523165161 [69,] 0.344750275 -3.911285460 [70,] -3.338794504 0.344750275 [71,] -0.912773043 -3.338794504 [72,] 1.032210414 -0.912773043 [73,] 0.811332375 1.032210414 [74,] 0.735049423 0.811332375 [75,] 3.487857269 0.735049423 [76,] -0.413649675 3.487857269 [77,] 1.511437148 -0.413649675 [78,] -2.210529169 1.511437148 [79,] 0.878982388 -2.210529169 [80,] 0.287274495 0.878982388 [81,] 0.287717529 0.287274495 [82,] -0.683966261 0.287717529 [83,] 0.122140390 -0.683966261 [84,] 1.837417717 0.122140390 [85,] -0.184805355 1.837417717 [86,] 0.637027251 -0.184805355 [87,] 1.145316210 0.637027251 [88,] 0.476380270 1.145316210 [89,] -1.871340301 0.476380270 [90,] 0.253960372 -1.871340301 [91,] 0.165281862 0.253960372 [92,] -0.110267757 0.165281862 [93,] -2.070740981 -0.110267757 [94,] 1.201561147 -2.070740981 [95,] 0.230086471 1.201561147 [96,] 2.280392742 0.230086471 [97,] 0.185275621 2.280392742 [98,] -0.433941180 0.185275621 [99,] -1.025810198 -0.433941180 [100,] 1.325803102 -1.025810198 [101,] 2.544064759 1.325803102 [102,] 0.818154279 2.544064759 [103,] 0.999697148 0.818154279 [104,] -1.867506176 0.999697148 [105,] 1.311712397 -1.867506176 [106,] 0.277149621 1.311712397 [107,] 1.650845580 0.277149621 [108,] -0.275456138 1.650845580 [109,] 0.713725048 -0.275456138 [110,] -0.017651315 0.713725048 [111,] 1.973299951 -0.017651315 [112,] -1.566454216 1.973299951 [113,] -2.570117240 -1.566454216 [114,] 1.847860891 -2.570117240 [115,] -1.850512307 1.847860891 [116,] 0.850437998 -1.850512307 [117,] -1.782387174 0.850437998 [118,] 0.462550890 -1.782387174 [119,] -1.416850975 0.462550890 [120,] 0.608832531 -1.416850975 [121,] -2.839724798 0.608832531 [122,] -0.830654743 -2.839724798 [123,] -0.827396197 -0.830654743 [124,] -0.805322704 -0.827396197 [125,] 0.335034965 -0.805322704 [126,] 1.472209186 0.335034965 [127,] 1.038338342 1.472209186 [128,] -2.684050075 1.038338342 [129,] 2.035062790 -2.684050075 [130,] -3.729699165 2.035062790 [131,] 2.509846847 -3.729699165 [132,] -2.146227277 2.509846847 [133,] -1.523753324 -2.146227277 [134,] -0.060594176 -1.523753324 [135,] 0.890344539 -0.060594176 [136,] 0.564325197 0.890344539 [137,] -2.434249991 0.564325197 [138,] -0.975933494 -2.434249991 [139,] -2.359707165 -0.975933494 [140,] 3.164075491 -2.359707165 [141,] 1.342747174 3.164075491 [142,] 0.176053779 1.342747174 [143,] 1.856556734 0.176053779 [144,] -3.265660580 1.856556734 [145,] 2.524706201 -3.265660580 [146,] -1.900643556 2.524706201 [147,] 1.172495986 -1.900643556 [148,] 0.197545859 1.172495986 [149,] -2.887279570 0.197545859 [150,] -0.927635686 -2.887279570 [151,] 2.124290496 -0.927635686 [152,] 4.147065404 2.124290496 [153,] 1.611980680 4.147065404 [154,] -2.409036113 1.611980680 [155,] 0.574926510 -2.409036113 [156,] 1.314913659 0.574926510 [157,] 1.186476560 1.314913659 [158,] 1.459952990 1.186476560 [159,] -0.747215610 1.459952990 [160,] 0.400414580 -0.747215610 [161,] 0.392666584 0.400414580 [162,] -0.446513743 0.392666584 [163,] 0.764450432 -0.446513743 [164,] 1.300316767 0.764450432 [165,] -1.613687684 1.300316767 [166,] -0.327870372 -1.613687684 [167,] -3.210686251 -0.327870372 [168,] -1.797873078 -3.210686251 [169,] 1.526714914 -1.797873078 [170,] 1.520357883 1.526714914 [171,] 0.666125952 1.520357883 [172,] -1.866121650 0.666125952 [173,] -2.352065885 -1.866121650 [174,] -2.903882575 -2.352065885 [175,] 0.446600156 -2.903882575 [176,] -0.261503599 0.446600156 [177,] -0.679444147 -0.261503599 [178,] 0.224142391 -0.679444147 [179,] -1.375191100 0.224142391 [180,] 1.012217824 -1.375191100 [181,] -0.439178441 1.012217824 [182,] 2.347219736 -0.439178441 [183,] -0.130955841 2.347219736 [184,] -6.567169685 -0.130955841 [185,] 1.232691977 -6.567169685 [186,] 2.553322055 1.232691977 [187,] -0.391463876 2.553322055 [188,] -0.468734491 -0.391463876 [189,] 0.580687856 -0.468734491 [190,] -0.713241554 0.580687856 [191,] 0.582050572 -0.713241554 [192,] 2.269481008 0.582050572 [193,] 1.994956854 2.269481008 [194,] -0.628570597 1.994956854 [195,] 0.038277629 -0.628570597 [196,] 3.066474539 0.038277629 [197,] 0.997070790 3.066474539 [198,] 1.570980621 0.997070790 [199,] 1.476564003 1.570980621 [200,] 2.020913293 1.476564003 [201,] 0.670948723 2.020913293 [202,] -2.405240124 0.670948723 [203,] -2.427510351 -2.405240124 [204,] 2.304099234 -2.427510351 [205,] 0.579004290 2.304099234 [206,] 1.542715447 0.579004290 [207,] 1.150106666 1.542715447 [208,] -2.547521027 1.150106666 [209,] 1.075822786 -2.547521027 [210,] -2.175381646 1.075822786 [211,] -3.688307789 -2.175381646 [212,] 0.900497361 -3.688307789 [213,] 2.932807324 0.900497361 [214,] 1.512998471 2.932807324 [215,] 0.969406554 1.512998471 [216,] 2.427871407 0.969406554 [217,] 0.120767734 2.427871407 [218,] 1.126273919 0.120767734 [219,] -0.087549794 1.126273919 [220,] -1.577012700 -0.087549794 [221,] 0.005359026 -1.577012700 [222,] 0.802026355 0.005359026 [223,] -1.039494366 0.802026355 [224,] 0.433260160 -1.039494366 [225,] -3.695135417 0.433260160 [226,] 0.339358634 -3.695135417 [227,] 1.086160183 0.339358634 [228,] -1.166321626 1.086160183 [229,] -0.818218205 -1.166321626 [230,] 1.808724003 -0.818218205 [231,] -3.136680595 1.808724003 [232,] 4.685664825 -3.136680595 [233,] 2.269592673 4.685664825 [234,] -0.713568415 2.269592673 [235,] -2.212208868 -0.713568415 [236,] -6.916582701 -2.212208868 [237,] -1.097183889 -6.916582701 [238,] 1.858485315 -1.097183889 [239,] -1.590676066 1.858485315 [240,] -0.173002681 -1.590676066 [241,] -1.347837056 -0.173002681 [242,] 1.188173325 -1.347837056 [243,] 2.010175644 1.188173325 [244,] 1.485771091 2.010175644 [245,] 0.186548364 1.485771091 [246,] 1.296266786 0.186548364 [247,] -2.330697678 1.296266786 [248,] 1.361911326 -2.330697678 [249,] 0.447271006 1.361911326 [250,] -0.735677772 0.447271006 [251,] -1.545609222 -0.735677772 [252,] 0.745840576 -1.545609222 [253,] 3.324417558 0.745840576 [254,] -1.149002649 3.324417558 [255,] 0.339177797 -1.149002649 [256,] 1.779214818 0.339177797 [257,] 2.505552324 1.779214818 [258,] -1.051873737 2.505552324 [259,] -5.297891218 -1.051873737 [260,] 1.442625011 -5.297891218 [261,] -3.776313919 1.442625011 [262,] -0.152783633 -3.776313919 [263,] 1.357303915 -0.152783633 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.245508554 -3.102298221 2 1.948287423 0.245508554 3 2.844541916 1.948287423 4 -2.431877832 2.844541916 5 -1.869129571 -2.431877832 6 3.710736971 -1.869129571 7 -2.098463663 3.710736971 8 -2.062068866 -2.098463663 9 0.543207803 -2.062068866 10 1.009137890 0.543207803 11 -0.177005218 1.009137890 12 0.478496420 -0.177005218 13 0.504384283 0.478496420 14 -1.006512051 0.504384283 15 -0.471600438 -1.006512051 16 0.384029318 -0.471600438 17 3.571776300 0.384029318 18 2.520984906 3.571776300 19 0.444210606 2.520984906 20 0.634956735 0.444210606 21 0.855854122 0.634956735 22 2.517513085 0.855854122 23 0.914481509 2.517513085 24 0.826979440 0.914481509 25 0.730492812 0.826979440 26 1.002205332 0.730492812 27 -1.760323978 1.002205332 28 0.244499178 -1.760323978 29 -0.181416578 0.244499178 30 -0.754683476 -0.181416578 31 -0.834815524 -0.754683476 32 -0.811839163 -0.834815524 33 0.063104612 -0.811839163 34 -1.570370764 0.063104612 35 -3.005541434 -1.570370764 36 -3.074708695 -3.005541434 37 -1.754899768 -3.074708695 38 1.443200553 -1.754899768 39 1.508493443 1.443200553 40 1.277020449 1.508493443 41 -1.750019986 1.277020449 42 2.535021239 -1.750019986 43 -0.162354026 2.535021239 44 -0.465001909 -0.162354026 45 -4.527891846 -0.465001909 46 -2.537742048 -4.527891846 47 -0.138218462 -2.537742048 48 0.491833471 -0.138218462 49 -1.650896199 0.491833471 50 -1.013287553 -1.650896199 51 -0.161379564 -1.013287553 52 -3.053366612 -0.161379564 53 0.177010959 -3.053366612 54 -2.397791027 0.177010959 55 1.614711519 -2.397791027 56 0.133816130 1.614711519 57 0.517006043 0.133816130 58 -0.148945246 0.517006043 59 1.781689396 -0.148945246 60 0.433922695 1.781689396 61 0.304398347 0.433922695 62 -0.568097897 0.304398347 63 -0.714497575 -0.568097897 64 0.708091983 -0.714497575 65 0.951831948 0.708091983 66 1.708070000 0.951831948 67 3.523165161 1.708070000 68 -3.911285460 3.523165161 69 0.344750275 -3.911285460 70 -3.338794504 0.344750275 71 -0.912773043 -3.338794504 72 1.032210414 -0.912773043 73 0.811332375 1.032210414 74 0.735049423 0.811332375 75 3.487857269 0.735049423 76 -0.413649675 3.487857269 77 1.511437148 -0.413649675 78 -2.210529169 1.511437148 79 0.878982388 -2.210529169 80 0.287274495 0.878982388 81 0.287717529 0.287274495 82 -0.683966261 0.287717529 83 0.122140390 -0.683966261 84 1.837417717 0.122140390 85 -0.184805355 1.837417717 86 0.637027251 -0.184805355 87 1.145316210 0.637027251 88 0.476380270 1.145316210 89 -1.871340301 0.476380270 90 0.253960372 -1.871340301 91 0.165281862 0.253960372 92 -0.110267757 0.165281862 93 -2.070740981 -0.110267757 94 1.201561147 -2.070740981 95 0.230086471 1.201561147 96 2.280392742 0.230086471 97 0.185275621 2.280392742 98 -0.433941180 0.185275621 99 -1.025810198 -0.433941180 100 1.325803102 -1.025810198 101 2.544064759 1.325803102 102 0.818154279 2.544064759 103 0.999697148 0.818154279 104 -1.867506176 0.999697148 105 1.311712397 -1.867506176 106 0.277149621 1.311712397 107 1.650845580 0.277149621 108 -0.275456138 1.650845580 109 0.713725048 -0.275456138 110 -0.017651315 0.713725048 111 1.973299951 -0.017651315 112 -1.566454216 1.973299951 113 -2.570117240 -1.566454216 114 1.847860891 -2.570117240 115 -1.850512307 1.847860891 116 0.850437998 -1.850512307 117 -1.782387174 0.850437998 118 0.462550890 -1.782387174 119 -1.416850975 0.462550890 120 0.608832531 -1.416850975 121 -2.839724798 0.608832531 122 -0.830654743 -2.839724798 123 -0.827396197 -0.830654743 124 -0.805322704 -0.827396197 125 0.335034965 -0.805322704 126 1.472209186 0.335034965 127 1.038338342 1.472209186 128 -2.684050075 1.038338342 129 2.035062790 -2.684050075 130 -3.729699165 2.035062790 131 2.509846847 -3.729699165 132 -2.146227277 2.509846847 133 -1.523753324 -2.146227277 134 -0.060594176 -1.523753324 135 0.890344539 -0.060594176 136 0.564325197 0.890344539 137 -2.434249991 0.564325197 138 -0.975933494 -2.434249991 139 -2.359707165 -0.975933494 140 3.164075491 -2.359707165 141 1.342747174 3.164075491 142 0.176053779 1.342747174 143 1.856556734 0.176053779 144 -3.265660580 1.856556734 145 2.524706201 -3.265660580 146 -1.900643556 2.524706201 147 1.172495986 -1.900643556 148 0.197545859 1.172495986 149 -2.887279570 0.197545859 150 -0.927635686 -2.887279570 151 2.124290496 -0.927635686 152 4.147065404 2.124290496 153 1.611980680 4.147065404 154 -2.409036113 1.611980680 155 0.574926510 -2.409036113 156 1.314913659 0.574926510 157 1.186476560 1.314913659 158 1.459952990 1.186476560 159 -0.747215610 1.459952990 160 0.400414580 -0.747215610 161 0.392666584 0.400414580 162 -0.446513743 0.392666584 163 0.764450432 -0.446513743 164 1.300316767 0.764450432 165 -1.613687684 1.300316767 166 -0.327870372 -1.613687684 167 -3.210686251 -0.327870372 168 -1.797873078 -3.210686251 169 1.526714914 -1.797873078 170 1.520357883 1.526714914 171 0.666125952 1.520357883 172 -1.866121650 0.666125952 173 -2.352065885 -1.866121650 174 -2.903882575 -2.352065885 175 0.446600156 -2.903882575 176 -0.261503599 0.446600156 177 -0.679444147 -0.261503599 178 0.224142391 -0.679444147 179 -1.375191100 0.224142391 180 1.012217824 -1.375191100 181 -0.439178441 1.012217824 182 2.347219736 -0.439178441 183 -0.130955841 2.347219736 184 -6.567169685 -0.130955841 185 1.232691977 -6.567169685 186 2.553322055 1.232691977 187 -0.391463876 2.553322055 188 -0.468734491 -0.391463876 189 0.580687856 -0.468734491 190 -0.713241554 0.580687856 191 0.582050572 -0.713241554 192 2.269481008 0.582050572 193 1.994956854 2.269481008 194 -0.628570597 1.994956854 195 0.038277629 -0.628570597 196 3.066474539 0.038277629 197 0.997070790 3.066474539 198 1.570980621 0.997070790 199 1.476564003 1.570980621 200 2.020913293 1.476564003 201 0.670948723 2.020913293 202 -2.405240124 0.670948723 203 -2.427510351 -2.405240124 204 2.304099234 -2.427510351 205 0.579004290 2.304099234 206 1.542715447 0.579004290 207 1.150106666 1.542715447 208 -2.547521027 1.150106666 209 1.075822786 -2.547521027 210 -2.175381646 1.075822786 211 -3.688307789 -2.175381646 212 0.900497361 -3.688307789 213 2.932807324 0.900497361 214 1.512998471 2.932807324 215 0.969406554 1.512998471 216 2.427871407 0.969406554 217 0.120767734 2.427871407 218 1.126273919 0.120767734 219 -0.087549794 1.126273919 220 -1.577012700 -0.087549794 221 0.005359026 -1.577012700 222 0.802026355 0.005359026 223 -1.039494366 0.802026355 224 0.433260160 -1.039494366 225 -3.695135417 0.433260160 226 0.339358634 -3.695135417 227 1.086160183 0.339358634 228 -1.166321626 1.086160183 229 -0.818218205 -1.166321626 230 1.808724003 -0.818218205 231 -3.136680595 1.808724003 232 4.685664825 -3.136680595 233 2.269592673 4.685664825 234 -0.713568415 2.269592673 235 -2.212208868 -0.713568415 236 -6.916582701 -2.212208868 237 -1.097183889 -6.916582701 238 1.858485315 -1.097183889 239 -1.590676066 1.858485315 240 -0.173002681 -1.590676066 241 -1.347837056 -0.173002681 242 1.188173325 -1.347837056 243 2.010175644 1.188173325 244 1.485771091 2.010175644 245 0.186548364 1.485771091 246 1.296266786 0.186548364 247 -2.330697678 1.296266786 248 1.361911326 -2.330697678 249 0.447271006 1.361911326 250 -0.735677772 0.447271006 251 -1.545609222 -0.735677772 252 0.745840576 -1.545609222 253 3.324417558 0.745840576 254 -1.149002649 3.324417558 255 0.339177797 -1.149002649 256 1.779214818 0.339177797 257 2.505552324 1.779214818 258 -1.051873737 2.505552324 259 -5.297891218 -1.051873737 260 1.442625011 -5.297891218 261 -3.776313919 1.442625011 262 -0.152783633 -3.776313919 263 1.357303915 -0.152783633 > 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/7se8o1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8pi2i1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9n4pl1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10wbqx1352115409.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11xo4n1352115409.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/123etl1352115409.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/13c28b1352115409.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/146jlp1352115409.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/15dxwn1352115409.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/16awnb1352115409.tab") + } > > try(system("convert tmp/1fsje1352115409.ps tmp/1fsje1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/2n8qy1352115409.ps tmp/2n8qy1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/3e22a1352115409.ps tmp/3e22a1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/44wxh1352115409.ps tmp/44wxh1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/54n6w1352115409.ps tmp/54n6w1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/6dbob1352115409.ps tmp/6dbob1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/7se8o1352115409.ps tmp/7se8o1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/8pi2i1352115409.ps tmp/8pi2i1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/9n4pl1352115409.ps tmp/9n4pl1352115409.png",intern=TRUE)) character(0) > try(system("convert tmp/10wbqx1352115409.ps tmp/10wbqx1352115409.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.071 1.224 12.336