R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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. Natural language support but running in an English locale 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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,'Belonging') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('Pop','month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '5' > #'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.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 Pop month Connected Separate Software Happiness Depression 1 13 1 9 41 38 12 14 12.0 2 16 1 9 39 32 11 18 11.0 3 19 1 9 30 35 15 11 14.0 4 15 1 9 31 33 6 12 12.0 5 14 1 9 34 37 13 16 21.0 6 13 1 9 35 29 10 18 12.0 7 19 1 9 39 31 12 14 22.0 8 15 1 9 34 36 14 14 11.0 9 14 1 9 36 35 12 15 10.0 10 15 1 9 37 38 9 15 13.0 11 16 1 9 38 31 10 17 10.0 12 16 1 9 36 34 12 19 8.0 13 16 1 9 38 35 12 10 15.0 14 16 1 9 39 38 11 16 14.0 15 17 1 9 33 37 15 18 10.0 16 15 1 9 32 33 12 14 14.0 17 15 1 9 36 32 10 14 14.0 18 20 1 9 38 38 12 17 11.0 19 18 1 9 39 38 11 14 10.0 20 16 1 9 32 32 12 16 13.0 21 16 1 9 32 33 11 18 9.5 22 16 1 9 31 31 12 11 14.0 23 19 1 9 39 38 13 14 12.0 24 16 1 9 37 39 11 12 14.0 25 17 1 9 39 32 12 17 11.0 26 17 1 9 41 32 13 9 9.0 27 16 1 9 36 35 10 16 11.0 28 15 1 9 33 37 14 14 15.0 29 16 1 9 33 33 12 15 14.0 30 14 1 9 34 33 10 11 13.0 31 15 1 9 31 31 12 16 9.0 32 12 1 9 27 32 8 13 15.0 33 14 1 9 37 31 10 17 10.0 34 16 1 9 34 37 12 15 11.0 35 14 1 9 34 30 12 14 13.0 36 10 1 9 32 33 7 16 8.0 37 10 1 9 29 31 9 9 20.0 38 14 1 9 36 33 12 15 12.0 39 16 1 9 29 31 10 17 10.0 40 16 1 9 35 33 10 13 10.0 41 16 1 9 37 32 10 15 9.0 42 14 1 9 34 33 12 16 14.0 43 20 1 9 38 32 15 16 8.0 44 14 1 9 35 33 10 12 14.0 45 14 1 9 38 28 10 15 11.0 46 11 1 9 37 35 12 11 13.0 47 14 1 9 38 39 13 15 9.0 48 15 1 9 33 34 11 15 11.0 49 16 1 9 36 38 11 17 15.0 50 14 1 9 38 32 12 13 11.0 51 16 1 9 32 38 14 16 10.0 52 14 1 9 32 30 10 14 14.0 53 12 1 9 32 33 12 11 18.0 54 16 1 9 34 38 13 12 14.0 55 9 1 9 32 32 5 12 11.0 56 14 1 9 37 35 6 15 14.5 57 16 1 9 39 34 12 16 13.0 58 16 1 9 29 34 12 15 9.0 59 15 1 9 37 36 11 12 10.0 60 16 1 9 35 34 10 12 15.0 61 12 1 9 30 28 7 8 20.0 62 16 1 9 38 34 12 13 12.0 63 16 1 9 34 35 14 11 12.0 64 14 1 9 31 35 11 14 14.0 65 16 1 9 34 31 12 15 13.0 66 17 1 10 35 37 13 10 11.0 67 18 1 10 36 35 14 11 17.0 68 18 1 10 30 27 11 12 12.0 69 12 1 10 39 40 12 15 13.0 70 16 1 10 35 37 12 15 14.0 71 10 1 10 38 36 8 14 13.0 72 14 1 10 31 38 11 16 15.0 73 18 1 10 34 39 14 15 13.0 74 18 1 10 38 41 14 15 10.0 75 16 1 10 34 27 12 13 11.0 76 17 1 10 39 30 9 12 19.0 77 16 1 10 37 37 13 17 13.0 78 16 1 10 34 31 11 13 17.0 79 13 1 10 28 31 12 15 13.0 80 16 1 10 37 27 12 13 9.0 81 16 1 10 33 36 12 15 11.0 82 16 1 10 35 37 12 15 9.0 83 15 1 10 37 33 12 16 12.0 84 15 1 10 32 34 11 15 12.0 85 16 1 10 33 31 10 14 13.0 86 14 1 10 38 39 9 15 13.0 87 16 1 10 33 34 12 14 12.0 88 16 1 10 29 32 12 13 15.0 89 15 1 10 33 33 12 7 22.0 90 12 1 10 31 36 9 17 13.0 91 17 1 10 36 32 15 13 15.0 92 16 1 10 35 41 12 15 13.0 93 15 1 10 32 28 12 14 15.0 94 13 1 10 29 30 12 13 12.5 95 16 1 10 39 36 10 16 11.0 96 16 1 10 37 35 13 12 16.0 97 16 1 10 35 31 9 14 11.0 98 16 1 10 37 34 12 17 11.0 99 14 1 10 32 36 10 15 10.0 100 16 1 10 38 36 14 17 10.0 101 16 1 10 37 35 11 12 16.0 102 20 1 10 36 37 15 16 12.0 103 15 1 10 32 28 11 11 11.0 104 16 1 10 33 39 11 15 16.0 105 13 1 10 40 32 12 9 19.0 106 17 1 10 38 35 12 16 11.0 107 16 1 10 41 39 12 15 16.0 108 16 1 10 36 35 11 10 15.0 109 12 1 10 43 42 7 10 24.0 110 16 1 10 30 34 12 15 14.0 111 16 1 10 31 33 14 11 15.0 112 17 1 10 32 41 11 13 11.0 113 13 1 10 32 33 11 14 15.0 114 12 1 10 37 34 10 18 12.0 115 18 1 10 37 32 13 16 10.0 116 14 1 10 33 40 13 14 14.0 117 14 1 10 34 40 8 14 13.0 118 13 1 10 33 35 11 14 9.0 119 16 1 10 38 36 12 14 15.0 120 13 1 10 33 37 11 12 15.0 121 16 1 10 31 27 13 14 14.0 122 13 1 10 38 39 12 15 11.0 123 16 1 10 37 38 14 15 8.0 124 15 1 10 36 31 13 15 11.0 125 16 1 10 31 33 15 13 11.0 126 15 1 10 39 32 10 17 8.0 127 17 1 10 44 39 11 17 10.0 128 15 1 10 33 36 9 19 11.0 129 12 1 10 35 33 11 15 13.0 130 16 1 10 32 33 10 13 11.0 131 10 1 10 28 32 11 9 20.0 132 16 1 10 40 37 8 15 10.0 133 12 1 10 27 30 11 15 15.0 134 14 1 10 37 38 12 15 12.0 135 15 1 10 32 29 12 16 14.0 136 13 1 10 28 22 9 11 23.0 137 15 1 10 34 35 11 14 14.0 138 11 1 10 30 35 10 11 16.0 139 12 1 10 35 34 8 15 11.0 140 11 1 10 31 35 9 13 12.0 141 16 1 10 32 34 8 15 10.0 142 15 1 10 30 37 9 16 14.0 143 17 1 10 30 35 15 14 12.0 144 16 1 10 31 23 11 15 12.0 145 10 1 10 40 31 8 16 11.0 146 18 1 10 32 27 13 16 12.0 147 13 1 10 36 36 12 11 13.0 148 16 1 10 32 31 12 12 11.0 149 13 1 10 35 32 9 9 19.0 150 10 1 10 38 39 7 16 12.0 151 15 1 10 42 37 13 13 17.0 152 16 1 10 34 38 9 16 9.0 153 16 1 10 35 39 6 12 12.0 154 14 1 9 38 34 8 9 19.0 155 10 1 10 33 31 8 13 18.0 156 17 1 10 36 32 15 13 15.0 157 13 1 10 32 37 6 14 14.0 158 15 1 10 33 36 9 19 11.0 159 16 1 10 34 32 11 13 9.0 160 12 1 10 32 38 8 12 18.0 161 13 1 10 34 36 8 13 16.0 162 13 0 11 27 26 10 10 24.0 163 12 0 11 31 26 8 14 14.0 164 17 0 11 38 33 14 16 20.0 165 15 0 11 34 39 10 10 18.0 166 10 0 11 24 30 8 11 23.0 167 14 0 11 30 33 11 14 12.0 168 11 0 11 26 25 12 12 14.0 169 13 0 11 34 38 12 9 16.0 170 16 0 11 27 37 12 9 18.0 171 12 0 11 37 31 5 11 20.0 172 16 0 11 36 37 12 16 12.0 173 12 0 11 41 35 10 9 12.0 174 9 0 11 29 25 7 13 17.0 175 12 0 11 36 28 12 16 13.0 176 15 0 11 32 35 11 13 9.0 177 12 0 11 37 33 8 9 16.0 178 12 0 11 30 30 9 12 18.0 179 14 0 11 31 31 10 16 10.0 180 12 0 11 38 37 9 11 14.0 181 16 0 11 36 36 12 14 11.0 182 11 0 11 35 30 6 13 9.0 183 19 0 11 31 36 15 15 11.0 184 15 0 11 38 32 12 14 10.0 185 8 0 11 22 28 12 16 11.0 186 16 0 11 32 36 12 13 19.0 187 17 0 11 36 34 11 14 14.0 188 12 0 11 39 31 7 15 12.0 189 11 0 11 28 28 7 13 14.0 190 11 0 11 32 36 5 11 21.0 191 14 0 11 32 36 12 11 13.0 192 16 0 11 38 40 12 14 10.0 193 12 0 11 32 33 3 15 15.0 194 16 0 11 35 37 11 11 16.0 195 13 0 11 32 32 10 15 14.0 196 15 0 11 37 38 12 12 12.0 197 16 0 11 34 31 9 14 19.0 198 16 0 11 33 37 12 14 15.0 199 14 0 11 33 33 9 8 19.0 200 16 0 11 26 32 12 13 13.0 201 16 0 11 30 30 12 9 17.0 202 14 0 11 24 30 10 15 12.0 203 11 0 11 34 31 9 17 11.0 204 12 0 11 34 32 12 13 14.0 205 15 0 11 33 34 8 15 11.0 206 15 0 11 34 36 11 15 13.0 207 16 0 11 35 37 11 14 12.0 208 16 0 11 35 36 12 16 15.0 209 11 0 11 36 33 10 13 14.0 210 15 0 11 34 33 10 16 12.0 211 12 0 11 34 33 12 9 17.0 212 12 0 11 41 44 12 16 11.0 213 15 0 11 32 39 11 11 18.0 214 15 0 11 30 32 8 10 13.0 215 16 0 11 35 35 12 11 17.0 216 14 0 11 28 25 10 15 13.0 217 17 0 11 33 35 11 17 11.0 218 14 0 11 39 34 10 14 12.0 219 13 0 11 36 35 8 8 22.0 220 15 0 11 36 39 12 15 14.0 221 13 0 11 35 33 12 11 12.0 222 14 0 11 38 36 10 16 12.0 223 15 0 11 33 32 12 10 17.0 224 12 0 11 31 32 9 15 9.0 225 13 0 11 34 36 9 9 21.0 226 8 0 11 32 36 6 16 10.0 227 14 0 11 31 32 10 19 11.0 228 14 0 11 33 34 9 12 12.0 229 11 0 11 34 33 9 8 23.0 230 12 0 11 34 35 9 11 13.0 231 13 0 11 34 30 6 14 12.0 232 10 0 11 33 38 10 9 16.0 233 16 0 11 32 34 6 15 9.0 234 18 0 11 41 33 14 13 17.0 235 13 0 11 34 32 10 16 9.0 236 11 0 11 36 31 10 11 14.0 237 4 0 11 37 30 6 12 17.0 238 13 0 11 36 27 12 13 13.0 239 16 0 11 29 31 12 10 11.0 240 10 0 11 37 30 7 11 12.0 241 12 0 11 27 32 8 12 10.0 242 12 0 11 35 35 11 8 19.0 243 10 0 11 28 28 3 12 16.0 244 13 0 11 35 33 6 12 16.0 245 15 0 11 37 31 10 15 14.0 246 12 0 11 29 35 8 11 20.0 247 14 0 11 32 35 9 13 15.0 248 10 0 11 36 32 9 14 23.0 249 12 0 11 19 21 8 10 20.0 250 12 0 11 21 20 9 12 16.0 251 11 0 11 31 34 7 15 14.0 252 10 0 11 33 32 7 13 17.0 253 12 0 11 36 34 6 13 11.0 254 16 0 11 33 32 9 13 13.0 255 12 0 11 37 33 10 12 17.0 256 14 0 11 34 33 11 12 15.0 257 16 0 11 35 37 12 9 21.0 258 14 0 11 31 32 8 9 18.0 259 13 0 11 37 34 11 15 15.0 260 4 0 11 35 30 3 10 8.0 261 15 0 11 27 30 11 14 12.0 262 11 0 11 34 38 12 15 12.0 263 11 0 11 40 36 7 7 22.0 264 14 0 11 29 32 9 14 12.0 Belonging t 1 53 1 2 83 2 3 66 3 4 67 4 5 76 5 6 78 6 7 53 7 8 80 8 9 74 9 10 76 10 11 79 11 12 54 12 13 67 13 14 54 14 15 87 15 16 58 16 17 75 17 18 88 18 19 64 19 20 57 20 21 66 21 22 68 22 23 54 23 24 56 24 25 86 25 26 80 26 27 76 27 28 69 28 29 78 29 30 67 30 31 80 31 32 54 32 33 71 33 34 84 34 35 74 35 36 71 36 37 63 37 38 71 38 39 76 39 40 69 40 41 74 41 42 75 42 43 54 43 44 52 44 45 69 45 46 68 46 47 65 47 48 75 48 49 74 49 50 75 50 51 72 51 52 67 52 53 63 53 54 62 54 55 63 55 56 76 56 57 74 57 58 67 58 59 73 59 60 70 60 61 53 61 62 77 62 63 80 63 64 52 64 65 54 65 66 80 66 67 66 67 68 73 68 69 63 69 70 69 70 71 67 71 72 54 72 73 81 73 74 69 74 75 84 75 76 80 76 77 70 77 78 69 78 79 77 79 80 54 80 81 79 81 82 71 82 83 73 83 84 72 84 85 77 85 86 75 86 87 69 87 88 54 88 89 70 89 90 73 90 91 54 91 92 77 92 93 82 93 94 80 94 95 80 95 96 69 96 97 78 97 98 81 98 99 76 99 100 76 100 101 73 101 102 85 102 103 66 103 104 79 104 105 68 105 106 76 106 107 71 107 108 54 108 109 46 109 110 85 110 111 74 111 112 88 112 113 38 113 114 76 114 115 86 115 116 54 116 117 67 117 118 69 118 119 90 119 120 54 120 121 76 121 122 89 122 123 76 123 124 73 124 125 79 125 126 90 126 127 74 127 128 81 128 129 72 129 130 71 130 131 66 131 132 77 132 133 65 133 134 74 134 135 85 135 136 54 136 137 63 137 138 54 138 139 64 139 140 69 140 141 54 141 142 84 142 143 86 143 144 77 144 145 89 145 146 76 146 147 60 147 148 75 148 149 73 149 150 85 150 151 79 151 152 71 152 153 72 153 154 69 154 155 78 155 156 54 156 157 69 157 158 81 158 159 84 159 160 84 160 161 69 161 162 66 162 163 81 163 164 82 164 165 72 165 166 54 166 167 78 167 168 74 168 169 82 169 170 73 170 171 55 171 172 72 172 173 78 173 174 59 174 175 72 175 176 78 176 177 68 177 178 69 178 179 67 179 180 74 180 181 54 181 182 67 182 183 70 183 184 80 184 185 89 185 186 76 186 187 74 187 188 87 188 189 54 189 190 61 190 191 38 191 192 75 192 193 69 193 194 62 194 195 72 195 196 70 196 197 79 197 198 87 198 199 62 199 200 77 200 201 69 201 202 69 202 203 75 203 204 54 204 205 72 205 206 74 206 207 85 207 208 52 208 209 70 209 210 84 210 211 64 211 212 84 212 213 87 213 214 79 214 215 67 215 216 65 216 217 85 217 218 83 218 219 61 219 220 82 220 221 76 221 222 58 222 223 72 223 224 72 224 225 38 225 226 78 226 227 54 227 228 63 228 229 66 229 230 70 230 231 71 231 232 67 232 233 58 233 234 72 234 235 72 235 236 70 236 237 76 237 238 50 238 239 72 239 240 72 240 241 88 241 242 53 242 243 58 243 244 66 244 245 82 245 246 69 246 247 68 247 248 44 248 249 56 249 250 53 250 251 70 251 252 78 252 253 71 253 254 72 254 255 68 255 256 67 256 257 75 257 258 62 258 259 67 259 260 83 260 261 64 261 262 68 262 263 62 263 264 72 264 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop month Connected Separate Software 2.710369 0.267704 0.298791 0.033134 0.040902 0.552244 Happiness Depression Belonging t 0.066924 -0.034004 0.006428 -0.006408 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9584 -1.0770 0.2715 1.2098 4.6609 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.710369 4.475042 0.606 0.545 Pop 0.267704 0.498621 0.537 0.592 month 0.298791 0.455163 0.656 0.512 Connected 0.033134 0.034572 0.958 0.339 Separate 0.040902 0.035311 1.158 0.248 Software 0.552244 0.054773 10.082 <2e-16 *** Happiness 0.066924 0.058168 1.151 0.251 Depression -0.034004 0.042131 -0.807 0.420 Belonging 0.006428 0.011998 0.536 0.593 t -0.006408 0.004338 -1.477 0.141 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.86 on 254 degrees of freedom Multiple R-squared: 0.4463, Adjusted R-squared: 0.4266 F-statistic: 22.74 on 9 and 254 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.917403150 0.165193700 0.08259685 [2,] 0.856823094 0.286353811 0.14317691 [3,] 0.806455661 0.387088678 0.19354434 [4,] 0.836164817 0.327670366 0.16383518 [5,] 0.774902414 0.450195172 0.22509759 [6,] 0.932137792 0.135724415 0.06786221 [7,] 0.913328021 0.173343958 0.08667198 [8,] 0.877506157 0.244987686 0.12249384 [9,] 0.828716671 0.342566658 0.17128333 [10,] 0.795546058 0.408907884 0.20445394 [11,] 0.762164165 0.475671671 0.23783584 [12,] 0.731894807 0.536210386 0.26810519 [13,] 0.673513862 0.652972275 0.32648614 [14,] 0.624787689 0.750424622 0.37521231 [15,] 0.564973144 0.870053712 0.43502686 [16,] 0.611400340 0.777199320 0.38859966 [17,] 0.550499936 0.899000129 0.44950006 [18,] 0.556628870 0.886742261 0.44337113 [19,] 0.504560110 0.990879781 0.49543989 [20,] 0.496822025 0.993644051 0.50317797 [21,] 0.474006984 0.948013967 0.52599302 [22,] 0.412571359 0.825142717 0.58742864 [23,] 0.396862823 0.793725647 0.60313718 [24,] 0.496170487 0.992340974 0.50382951 [25,] 0.572158070 0.855683861 0.42784193 [26,] 0.545605511 0.908788978 0.45439449 [27,] 0.590779185 0.818441630 0.40922082 [28,] 0.571323792 0.857352416 0.42867621 [29,] 0.535885603 0.928228795 0.46411440 [30,] 0.502633773 0.994732453 0.49736623 [31,] 0.520200882 0.959598235 0.47979912 [32,] 0.471050166 0.942100333 0.52894983 [33,] 0.429396411 0.858792823 0.57060359 [34,] 0.645357706 0.709284587 0.35464229 [35,] 0.658790113 0.682419774 0.34120989 [36,] 0.621062936 0.757874128 0.37893706 [37,] 0.608035135 0.783929731 0.39196487 [38,] 0.579420730 0.841158540 0.42057927 [39,] 0.534229518 0.931540964 0.46577048 [40,] 0.491438972 0.982877944 0.50856103 [41,] 0.497723623 0.995447246 0.50227638 [42,] 0.466751375 0.933502749 0.53324863 [43,] 0.466011255 0.932022511 0.53398874 [44,] 0.479803897 0.959607793 0.52019610 [45,] 0.437413942 0.874827885 0.56258606 [46,] 0.421304302 0.842608604 0.57869570 [47,] 0.380217388 0.760434776 0.61978261 [48,] 0.405372307 0.810744613 0.59462769 [49,] 0.377129995 0.754259990 0.62287000 [50,] 0.340275237 0.680550474 0.65972476 [51,] 0.304335903 0.608671806 0.69566410 [52,] 0.272223504 0.544447008 0.72777650 [53,] 0.245832773 0.491665545 0.75416723 [54,] 0.213374540 0.426749081 0.78662546 [55,] 0.187961703 0.375923406 0.81203830 [56,] 0.205203238 0.410406477 0.79479676 [57,] 0.434631299 0.869262597 0.56536870 [58,] 0.393373492 0.786746984 0.60662651 [59,] 0.501301217 0.997397566 0.49869878 [60,] 0.465185202 0.930370404 0.53481480 [61,] 0.448463787 0.896927574 0.55153621 [62,] 0.419194900 0.838389799 0.58080510 [63,] 0.381714616 0.763429233 0.61828538 [64,] 0.442938195 0.885876391 0.55706180 [65,] 0.406560645 0.813121290 0.59343936 [66,] 0.379393562 0.758787125 0.62060644 [67,] 0.402363301 0.804726603 0.59763670 [68,] 0.368263037 0.736526074 0.63173696 [69,] 0.333157446 0.666314892 0.66684255 [70,] 0.298449480 0.596898961 0.70155052 [71,] 0.273337000 0.546674000 0.72666300 [72,] 0.241558177 0.483116354 0.75844182 [73,] 0.234207814 0.468415628 0.76579219 [74,] 0.205001382 0.410002765 0.79499862 [75,] 0.180407099 0.360814198 0.81959290 [76,] 0.163874819 0.327749638 0.83612518 [77,] 0.141648302 0.283296604 0.85835170 [78,] 0.138202988 0.276405976 0.86179701 [79,] 0.118527462 0.237054924 0.88147254 [80,] 0.102804298 0.205608596 0.89719570 [81,] 0.087984501 0.175969001 0.91201550 [82,] 0.092907809 0.185815619 0.90709219 [83,] 0.083744946 0.167489893 0.91625505 [84,] 0.069993747 0.139987495 0.93000625 [85,] 0.074559624 0.149119247 0.92544038 [86,] 0.062059767 0.124119535 0.93794023 [87,] 0.051537343 0.103074686 0.94846266 [88,] 0.045040025 0.090080051 0.95495997 [89,] 0.040193335 0.080386670 0.95980667 [90,] 0.049543025 0.099086050 0.95045697 [91,] 0.041751054 0.083502108 0.95824895 [92,] 0.037677941 0.075355883 0.96232206 [93,] 0.043753188 0.087506375 0.95624681 [94,] 0.039027677 0.078055355 0.96097232 [95,] 0.031891538 0.063783076 0.96810846 [96,] 0.030636182 0.061272365 0.96936382 [97,] 0.024835614 0.049671228 0.97516439 [98,] 0.020757042 0.041514084 0.97924296 [99,] 0.016540053 0.033080105 0.98345995 [100,] 0.017853009 0.035706017 0.98214699 [101,] 0.015407131 0.030814262 0.98459287 [102,] 0.019734499 0.039468997 0.98026550 [103,] 0.019380945 0.038761890 0.98061906 [104,] 0.018654959 0.037309918 0.98134504 [105,] 0.015885058 0.031770116 0.98411494 [106,] 0.015580981 0.031161962 0.98441902 [107,] 0.012622084 0.025244167 0.98737792 [108,] 0.011214369 0.022428737 0.98878563 [109,] 0.009116782 0.018233564 0.99088322 [110,] 0.012695495 0.025390991 0.98730450 [111,] 0.010385021 0.020770041 0.98961498 [112,] 0.008511951 0.017023903 0.99148805 [113,] 0.006809267 0.013618534 0.99319073 [114,] 0.005375740 0.010751480 0.99462426 [115,] 0.005069355 0.010138710 0.99493065 [116,] 0.004382794 0.008765588 0.99561721 [117,] 0.005649477 0.011298955 0.99435052 [118,] 0.006370793 0.012741586 0.99362921 [119,] 0.012202200 0.024404399 0.98779780 [120,] 0.015208149 0.030416298 0.98479185 [121,] 0.015878984 0.031757968 0.98412102 [122,] 0.014540660 0.029081321 0.98545934 [123,] 0.011524687 0.023049373 0.98847531 [124,] 0.010013664 0.020027328 0.98998634 [125,] 0.008227319 0.016454638 0.99177268 [126,] 0.010349349 0.020698698 0.98965065 [127,] 0.008801965 0.017603930 0.99119803 [128,] 0.010654189 0.021308378 0.98934581 [129,] 0.017171396 0.034342792 0.98282860 [130,] 0.016346821 0.032693641 0.98365318 [131,] 0.013561945 0.027123890 0.98643805 [132,] 0.014080024 0.028160047 0.98591998 [133,] 0.022916051 0.045832101 0.97708395 [134,] 0.028638574 0.057277149 0.97136143 [135,] 0.029976156 0.059952313 0.97002384 [136,] 0.026586614 0.053173228 0.97341339 [137,] 0.021592136 0.043184272 0.97840786 [138,] 0.029618184 0.059236369 0.97038182 [139,] 0.024921851 0.049843702 0.97507815 [140,] 0.026801618 0.053603237 0.97319838 [141,] 0.054628126 0.109256252 0.94537187 [142,] 0.051676367 0.103352734 0.94832363 [143,] 0.059435748 0.118871495 0.94056425 [144,] 0.050405020 0.100810040 0.94959498 [145,] 0.044617037 0.089234074 0.95538296 [146,] 0.038731176 0.077462353 0.96126882 [147,] 0.037886525 0.075773050 0.96211347 [148,] 0.031653984 0.063307968 0.96834602 [149,] 0.025809077 0.051618153 0.97419092 [150,] 0.020780599 0.041561198 0.97921940 [151,] 0.017282240 0.034564479 0.98271776 [152,] 0.014736020 0.029472040 0.98526398 [153,] 0.012440611 0.024881223 0.98755939 [154,] 0.013492007 0.026984015 0.98650799 [155,] 0.010747907 0.021495814 0.98925209 [156,] 0.015663949 0.031327897 0.98433605 [157,] 0.015357170 0.030714340 0.98464283 [158,] 0.014671010 0.029342021 0.98532899 [159,] 0.013425810 0.026851619 0.98657419 [160,] 0.010788604 0.021577208 0.98921140 [161,] 0.010335700 0.020671401 0.98966430 [162,] 0.011576511 0.023153023 0.98842349 [163,] 0.014028703 0.028057406 0.98597130 [164,] 0.011342052 0.022684104 0.98865795 [165,] 0.008814624 0.017629247 0.99118538 [166,] 0.007311733 0.014623466 0.99268827 [167,] 0.005692231 0.011384462 0.99430777 [168,] 0.004828665 0.009657329 0.99517134 [169,] 0.004003999 0.008007998 0.99599600 [170,] 0.003022346 0.006044692 0.99697765 [171,] 0.003405048 0.006810095 0.99659495 [172,] 0.002556437 0.005112874 0.99744356 [173,] 0.072457472 0.144914944 0.92754253 [174,] 0.065408020 0.130816041 0.93459198 [175,] 0.075627019 0.151254038 0.92437298 [176,] 0.062836742 0.125673485 0.93716326 [177,] 0.055541308 0.111082616 0.94445869 [178,] 0.046751818 0.093503637 0.95324818 [179,] 0.040058920 0.080117840 0.95994108 [180,] 0.033148623 0.066297247 0.96685138 [181,] 0.033667268 0.067334536 0.96633273 [182,] 0.032163753 0.064327506 0.96783625 [183,] 0.027619225 0.055238451 0.97238077 [184,] 0.021712019 0.043424037 0.97828798 [185,] 0.029166797 0.058333594 0.97083320 [186,] 0.023728366 0.047456732 0.97627163 [187,] 0.021216190 0.042432379 0.97878381 [188,] 0.017959665 0.035919331 0.98204033 [189,] 0.016389197 0.032778394 0.98361080 [190,] 0.013575346 0.027150692 0.98642465 [191,] 0.015000268 0.030000535 0.98499973 [192,] 0.019846743 0.039693487 0.98015326 [193,] 0.020968591 0.041937182 0.97903141 [194,] 0.016173882 0.032347764 0.98382612 [195,] 0.014312305 0.028624611 0.98568769 [196,] 0.011304534 0.022609068 0.98869547 [197,] 0.013091882 0.026183763 0.98690812 [198,] 0.010712932 0.021425864 0.98928707 [199,] 0.013091017 0.026182034 0.98690898 [200,] 0.023087900 0.046175800 0.97691210 [201,] 0.017784611 0.035569222 0.98221539 [202,] 0.022264574 0.044529147 0.97773543 [203,] 0.018731492 0.037462984 0.98126851 [204,] 0.014263796 0.028527592 0.98573620 [205,] 0.015960692 0.031921383 0.98403931 [206,] 0.013296668 0.026593337 0.98670333 [207,] 0.012269659 0.024539318 0.98773034 [208,] 0.009004209 0.018008418 0.99099579 [209,] 0.007304319 0.014608638 0.99269568 [210,] 0.005193192 0.010386384 0.99480681 [211,] 0.003925994 0.007851988 0.99607401 [212,] 0.002931170 0.005862340 0.99706883 [213,] 0.001996579 0.003993159 0.99800342 [214,] 0.004162431 0.008324862 0.99583757 [215,] 0.003236732 0.006473464 0.99676327 [216,] 0.002297809 0.004595618 0.99770219 [217,] 0.001576222 0.003152444 0.99842378 [218,] 0.001077294 0.002154588 0.99892271 [219,] 0.001164090 0.002328180 0.99883591 [220,] 0.004142608 0.008285217 0.99585739 [221,] 0.016806438 0.033612876 0.98319356 [222,] 0.026260013 0.052520026 0.97373999 [223,] 0.018497415 0.036994830 0.98150258 [224,] 0.014556095 0.029112190 0.98544390 [225,] 0.157747989 0.315495978 0.84225201 [226,] 0.121209604 0.242419208 0.87879040 [227,] 0.098626713 0.197253426 0.90137329 [228,] 0.074645367 0.149290734 0.92535463 [229,] 0.064255554 0.128511108 0.93574445 [230,] 0.101261068 0.202522136 0.89873893 [231,] 0.081063808 0.162127616 0.91893619 [232,] 0.079191795 0.158383590 0.92080821 [233,] 0.065521865 0.131043730 0.93447813 [234,] 0.053667116 0.107334232 0.94633288 [235,] 0.032448679 0.064897359 0.96755132 [236,] 0.026231087 0.052462175 0.97376891 [237,] 0.017404806 0.034809611 0.98259519 [238,] 0.044724686 0.089449372 0.95527531 [239,] 0.029966433 0.059932865 0.97003357 > postscript(file="/var/www/html/freestat/rcomp/tmp/1pa8p1289986122.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2zk7s1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3zk7s1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4sbpd1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5sbpd1289986122.ps",horizontal=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 -3.070065590 0.305732424 1.958423048 2.842334252 -2.299489473 -1.795006041 7 8 9 10 11 12 3.661000818 -2.023506594 -2.000339503 0.596114350 1.048315105 -0.147362586 13 14 15 16 17 18 0.508657122 0.559480169 -0.885358037 -0.435359193 0.474627775 3.678522423 19 20 21 22 23 24 2.525075597 0.469749825 0.676788002 0.854521377 2.578504823 0.903767276 25 26 27 28 29 30 0.948510469 0.842359383 1.173713167 -1.696396655 0.419328514 -0.198511580 31 32 33 34 35 36 -0.669580919 -0.790645268 -0.726155176 0.114047769 -1.394021043 -2.967415164 37 38 39 40 41 42 -2.956351361 -1.645416905 1.545228794 1.583717511 1.364767287 -1.578144671 43 44 45 46 47 48 2.610857930 -0.078439814 -0.378981577 -4.388108329 -2.515114881 -0.030307975 49 50 51 52 53 54 0.721682340 -1.519755748 -0.879933484 -0.035332935 -2.893619851 0.093254409 55 56 57 58 59 60 -2.279146097 1.721318459 0.283823078 0.597479837 0.005461434 1.901487455 61 62 63 64 65 66 0.522698330 0.496482687 -0.395395746 -0.585640930 0.818944285 0.795262750 67 68 69 70 71 72 1.525183016 3.432407625 -4.045855529 0.211233072 -3.586109520 -1.068575461 73 74 75 76 77 78 0.966162690 0.733351238 0.620847518 3.360272711 -0.536704506 1.329145509 79 80 81 82 83 84 -2.339168936 0.678309761 0.222601671 0.105253630 -0.768768435 -0.011994467 85 86 87 88 89 90 1.705017552 -0.283287816 0.508058603 0.994160315 0.363854889 -2.024002129 91 92 93 94 95 96 0.124711566 0.103172148 -0.156498164 -2.137720119 1.144640239 0.109908939 97 98 99 100 101 102 2.193451574 0.134097285 -0.539155258 -1.074377509 1.220724473 2.488642952 103 104 105 106 107 108 0.627427009 0.969538426 -1.947662519 1.210386941 0.222864884 1.520686847 109 110 111 112 113 114 -0.424730635 0.653081226 -0.064824352 1.878112083 -1.397784527 -2.659671257 115 116 117 118 119 120 1.773372914 -1.939344916 0.677582041 -1.883966910 0.432661257 -1.518667941 121 122 123 124 125 126 0.549277806 -2.867332169 -0.909824919 -0.910430725 -0.829360038 0.273678809 127 128 129 130 131 132 1.446708633 0.899949535 -2.748138474 1.982185540 -3.784341609 2.464386039 133 134 135 136 137 138 -2.221723543 -1.585980928 -0.115405526 0.806500733 0.413232922 -2.568947454 139 140 141 142 143 144 -1.084815764 -2.403302443 3.057677592 1.331661413 0.159396819 1.823394586 145 146 147 148 149 150 -3.316953939 2.474484036 -1.996050511 1.116056771 0.124547851 -2.933903096 151 152 153 154 155 156 -0.882333493 2.135843204 4.088225929 1.852125602 -2.511430068 0.541222566 157 158 159 160 161 162 1.248506832 1.092185381 1.438834369 -0.704212630 0.279215515 0.283095536 163 164 165 166 167 168 -0.442697433 0.795738640 1.296063480 -1.674784652 -0.375700396 -3.234215486 169 170 171 172 173 174 -1.807251341 1.597857143 1.433896950 0.646398648 -1.896671252 -2.402449826 175 176 177 178 179 180 -2.932256265 0.498810810 -0.351918154 -0.682300099 0.170956952 -1.322103228 181 182 183 184 185 186 0.960516475 -0.525711401 2.312504595 -0.124047048 -6.581574775 1.322635739 187 188 189 190 191 192 2.606466478 -0.373341154 -0.465775377 0.512245649 -0.471242324 0.632138904 193 194 195 196 197 198 2.235521301 1.907663590 -0.629753808 0.006703564 3.101886627 1.051849121 199 200 201 202 203 204 1.576850907 1.564310908 1.975118850 0.713257009 -2.306757022 -2.493290634 205 206 207 208 209 210 2.321862823 0.611752892 1.506339279 1.181684784 -2.566779467 1.147128186 211 212 213 214 215 216 -2.183907421 -3.660410208 0.954324991 2.918375114 1.573653668 0.934653915 217 218 219 220 221 222 2.483714320 0.132093104 1.184483165 -0.057174280 -1.533963485 0.135901172 223 224 225 226 227 228 0.848676791 -1.028565875 0.742964784 -3.627248856 0.354424359 1.209626515 229 230 231 232 233 234 -1.153744299 -0.795660805 1.830783777 -3.169517413 4.660886528 2.307925333 235 236 237 238 239 240 -0.676650766 -2.178114678 -6.958444628 -1.145474846 1.920620582 -1.568847509 241 242 243 244 245 246 -0.102918790 -1.342321241 1.198444309 2.060247615 1.501591044 0.269234279 247 248 249 250 251 252 1.326556074 -2.317496234 1.342915834 0.521133297 -0.649996427 -1.443616347 253 254 255 256 257 258 0.774800495 3.367264160 -1.123361527 0.368626554 1.979420708 2.513401424 259 260 261 262 263 264 -0.953228086 -5.305242539 1.538736966 -3.658891436 -0.094270601 1.462952724 > postscript(file="/var/www/html/freestat/rcomp/tmp/6sbpd1289986122.ps",horizontal=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 -3.070065590 NA 1 0.305732424 -3.070065590 2 1.958423048 0.305732424 3 2.842334252 1.958423048 4 -2.299489473 2.842334252 5 -1.795006041 -2.299489473 6 3.661000818 -1.795006041 7 -2.023506594 3.661000818 8 -2.000339503 -2.023506594 9 0.596114350 -2.000339503 10 1.048315105 0.596114350 11 -0.147362586 1.048315105 12 0.508657122 -0.147362586 13 0.559480169 0.508657122 14 -0.885358037 0.559480169 15 -0.435359193 -0.885358037 16 0.474627775 -0.435359193 17 3.678522423 0.474627775 18 2.525075597 3.678522423 19 0.469749825 2.525075597 20 0.676788002 0.469749825 21 0.854521377 0.676788002 22 2.578504823 0.854521377 23 0.903767276 2.578504823 24 0.948510469 0.903767276 25 0.842359383 0.948510469 26 1.173713167 0.842359383 27 -1.696396655 1.173713167 28 0.419328514 -1.696396655 29 -0.198511580 0.419328514 30 -0.669580919 -0.198511580 31 -0.790645268 -0.669580919 32 -0.726155176 -0.790645268 33 0.114047769 -0.726155176 34 -1.394021043 0.114047769 35 -2.967415164 -1.394021043 36 -2.956351361 -2.967415164 37 -1.645416905 -2.956351361 38 1.545228794 -1.645416905 39 1.583717511 1.545228794 40 1.364767287 1.583717511 41 -1.578144671 1.364767287 42 2.610857930 -1.578144671 43 -0.078439814 2.610857930 44 -0.378981577 -0.078439814 45 -4.388108329 -0.378981577 46 -2.515114881 -4.388108329 47 -0.030307975 -2.515114881 48 0.721682340 -0.030307975 49 -1.519755748 0.721682340 50 -0.879933484 -1.519755748 51 -0.035332935 -0.879933484 52 -2.893619851 -0.035332935 53 0.093254409 -2.893619851 54 -2.279146097 0.093254409 55 1.721318459 -2.279146097 56 0.283823078 1.721318459 57 0.597479837 0.283823078 58 0.005461434 0.597479837 59 1.901487455 0.005461434 60 0.522698330 1.901487455 61 0.496482687 0.522698330 62 -0.395395746 0.496482687 63 -0.585640930 -0.395395746 64 0.818944285 -0.585640930 65 0.795262750 0.818944285 66 1.525183016 0.795262750 67 3.432407625 1.525183016 68 -4.045855529 3.432407625 69 0.211233072 -4.045855529 70 -3.586109520 0.211233072 71 -1.068575461 -3.586109520 72 0.966162690 -1.068575461 73 0.733351238 0.966162690 74 0.620847518 0.733351238 75 3.360272711 0.620847518 76 -0.536704506 3.360272711 77 1.329145509 -0.536704506 78 -2.339168936 1.329145509 79 0.678309761 -2.339168936 80 0.222601671 0.678309761 81 0.105253630 0.222601671 82 -0.768768435 0.105253630 83 -0.011994467 -0.768768435 84 1.705017552 -0.011994467 85 -0.283287816 1.705017552 86 0.508058603 -0.283287816 87 0.994160315 0.508058603 88 0.363854889 0.994160315 89 -2.024002129 0.363854889 90 0.124711566 -2.024002129 91 0.103172148 0.124711566 92 -0.156498164 0.103172148 93 -2.137720119 -0.156498164 94 1.144640239 -2.137720119 95 0.109908939 1.144640239 96 2.193451574 0.109908939 97 0.134097285 2.193451574 98 -0.539155258 0.134097285 99 -1.074377509 -0.539155258 100 1.220724473 -1.074377509 101 2.488642952 1.220724473 102 0.627427009 2.488642952 103 0.969538426 0.627427009 104 -1.947662519 0.969538426 105 1.210386941 -1.947662519 106 0.222864884 1.210386941 107 1.520686847 0.222864884 108 -0.424730635 1.520686847 109 0.653081226 -0.424730635 110 -0.064824352 0.653081226 111 1.878112083 -0.064824352 112 -1.397784527 1.878112083 113 -2.659671257 -1.397784527 114 1.773372914 -2.659671257 115 -1.939344916 1.773372914 116 0.677582041 -1.939344916 117 -1.883966910 0.677582041 118 0.432661257 -1.883966910 119 -1.518667941 0.432661257 120 0.549277806 -1.518667941 121 -2.867332169 0.549277806 122 -0.909824919 -2.867332169 123 -0.910430725 -0.909824919 124 -0.829360038 -0.910430725 125 0.273678809 -0.829360038 126 1.446708633 0.273678809 127 0.899949535 1.446708633 128 -2.748138474 0.899949535 129 1.982185540 -2.748138474 130 -3.784341609 1.982185540 131 2.464386039 -3.784341609 132 -2.221723543 2.464386039 133 -1.585980928 -2.221723543 134 -0.115405526 -1.585980928 135 0.806500733 -0.115405526 136 0.413232922 0.806500733 137 -2.568947454 0.413232922 138 -1.084815764 -2.568947454 139 -2.403302443 -1.084815764 140 3.057677592 -2.403302443 141 1.331661413 3.057677592 142 0.159396819 1.331661413 143 1.823394586 0.159396819 144 -3.316953939 1.823394586 145 2.474484036 -3.316953939 146 -1.996050511 2.474484036 147 1.116056771 -1.996050511 148 0.124547851 1.116056771 149 -2.933903096 0.124547851 150 -0.882333493 -2.933903096 151 2.135843204 -0.882333493 152 4.088225929 2.135843204 153 1.852125602 4.088225929 154 -2.511430068 1.852125602 155 0.541222566 -2.511430068 156 1.248506832 0.541222566 157 1.092185381 1.248506832 158 1.438834369 1.092185381 159 -0.704212630 1.438834369 160 0.279215515 -0.704212630 161 0.283095536 0.279215515 162 -0.442697433 0.283095536 163 0.795738640 -0.442697433 164 1.296063480 0.795738640 165 -1.674784652 1.296063480 166 -0.375700396 -1.674784652 167 -3.234215486 -0.375700396 168 -1.807251341 -3.234215486 169 1.597857143 -1.807251341 170 1.433896950 1.597857143 171 0.646398648 1.433896950 172 -1.896671252 0.646398648 173 -2.402449826 -1.896671252 174 -2.932256265 -2.402449826 175 0.498810810 -2.932256265 176 -0.351918154 0.498810810 177 -0.682300099 -0.351918154 178 0.170956952 -0.682300099 179 -1.322103228 0.170956952 180 0.960516475 -1.322103228 181 -0.525711401 0.960516475 182 2.312504595 -0.525711401 183 -0.124047048 2.312504595 184 -6.581574775 -0.124047048 185 1.322635739 -6.581574775 186 2.606466478 1.322635739 187 -0.373341154 2.606466478 188 -0.465775377 -0.373341154 189 0.512245649 -0.465775377 190 -0.471242324 0.512245649 191 0.632138904 -0.471242324 192 2.235521301 0.632138904 193 1.907663590 2.235521301 194 -0.629753808 1.907663590 195 0.006703564 -0.629753808 196 3.101886627 0.006703564 197 1.051849121 3.101886627 198 1.576850907 1.051849121 199 1.564310908 1.576850907 200 1.975118850 1.564310908 201 0.713257009 1.975118850 202 -2.306757022 0.713257009 203 -2.493290634 -2.306757022 204 2.321862823 -2.493290634 205 0.611752892 2.321862823 206 1.506339279 0.611752892 207 1.181684784 1.506339279 208 -2.566779467 1.181684784 209 1.147128186 -2.566779467 210 -2.183907421 1.147128186 211 -3.660410208 -2.183907421 212 0.954324991 -3.660410208 213 2.918375114 0.954324991 214 1.573653668 2.918375114 215 0.934653915 1.573653668 216 2.483714320 0.934653915 217 0.132093104 2.483714320 218 1.184483165 0.132093104 219 -0.057174280 1.184483165 220 -1.533963485 -0.057174280 221 0.135901172 -1.533963485 222 0.848676791 0.135901172 223 -1.028565875 0.848676791 224 0.742964784 -1.028565875 225 -3.627248856 0.742964784 226 0.354424359 -3.627248856 227 1.209626515 0.354424359 228 -1.153744299 1.209626515 229 -0.795660805 -1.153744299 230 1.830783777 -0.795660805 231 -3.169517413 1.830783777 232 4.660886528 -3.169517413 233 2.307925333 4.660886528 234 -0.676650766 2.307925333 235 -2.178114678 -0.676650766 236 -6.958444628 -2.178114678 237 -1.145474846 -6.958444628 238 1.920620582 -1.145474846 239 -1.568847509 1.920620582 240 -0.102918790 -1.568847509 241 -1.342321241 -0.102918790 242 1.198444309 -1.342321241 243 2.060247615 1.198444309 244 1.501591044 2.060247615 245 0.269234279 1.501591044 246 1.326556074 0.269234279 247 -2.317496234 1.326556074 248 1.342915834 -2.317496234 249 0.521133297 1.342915834 250 -0.649996427 0.521133297 251 -1.443616347 -0.649996427 252 0.774800495 -1.443616347 253 3.367264160 0.774800495 254 -1.123361527 3.367264160 255 0.368626554 -1.123361527 256 1.979420708 0.368626554 257 2.513401424 1.979420708 258 -0.953228086 2.513401424 259 -5.305242539 -0.953228086 260 1.538736966 -5.305242539 261 -3.658891436 1.538736966 262 -0.094270601 -3.658891436 263 1.462952724 -0.094270601 264 NA 1.462952724 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.305732424 -3.070065590 [2,] 1.958423048 0.305732424 [3,] 2.842334252 1.958423048 [4,] -2.299489473 2.842334252 [5,] -1.795006041 -2.299489473 [6,] 3.661000818 -1.795006041 [7,] -2.023506594 3.661000818 [8,] -2.000339503 -2.023506594 [9,] 0.596114350 -2.000339503 [10,] 1.048315105 0.596114350 [11,] -0.147362586 1.048315105 [12,] 0.508657122 -0.147362586 [13,] 0.559480169 0.508657122 [14,] -0.885358037 0.559480169 [15,] -0.435359193 -0.885358037 [16,] 0.474627775 -0.435359193 [17,] 3.678522423 0.474627775 [18,] 2.525075597 3.678522423 [19,] 0.469749825 2.525075597 [20,] 0.676788002 0.469749825 [21,] 0.854521377 0.676788002 [22,] 2.578504823 0.854521377 [23,] 0.903767276 2.578504823 [24,] 0.948510469 0.903767276 [25,] 0.842359383 0.948510469 [26,] 1.173713167 0.842359383 [27,] -1.696396655 1.173713167 [28,] 0.419328514 -1.696396655 [29,] -0.198511580 0.419328514 [30,] -0.669580919 -0.198511580 [31,] -0.790645268 -0.669580919 [32,] -0.726155176 -0.790645268 [33,] 0.114047769 -0.726155176 [34,] -1.394021043 0.114047769 [35,] -2.967415164 -1.394021043 [36,] -2.956351361 -2.967415164 [37,] -1.645416905 -2.956351361 [38,] 1.545228794 -1.645416905 [39,] 1.583717511 1.545228794 [40,] 1.364767287 1.583717511 [41,] -1.578144671 1.364767287 [42,] 2.610857930 -1.578144671 [43,] -0.078439814 2.610857930 [44,] -0.378981577 -0.078439814 [45,] -4.388108329 -0.378981577 [46,] -2.515114881 -4.388108329 [47,] -0.030307975 -2.515114881 [48,] 0.721682340 -0.030307975 [49,] -1.519755748 0.721682340 [50,] -0.879933484 -1.519755748 [51,] -0.035332935 -0.879933484 [52,] -2.893619851 -0.035332935 [53,] 0.093254409 -2.893619851 [54,] -2.279146097 0.093254409 [55,] 1.721318459 -2.279146097 [56,] 0.283823078 1.721318459 [57,] 0.597479837 0.283823078 [58,] 0.005461434 0.597479837 [59,] 1.901487455 0.005461434 [60,] 0.522698330 1.901487455 [61,] 0.496482687 0.522698330 [62,] -0.395395746 0.496482687 [63,] -0.585640930 -0.395395746 [64,] 0.818944285 -0.585640930 [65,] 0.795262750 0.818944285 [66,] 1.525183016 0.795262750 [67,] 3.432407625 1.525183016 [68,] -4.045855529 3.432407625 [69,] 0.211233072 -4.045855529 [70,] -3.586109520 0.211233072 [71,] -1.068575461 -3.586109520 [72,] 0.966162690 -1.068575461 [73,] 0.733351238 0.966162690 [74,] 0.620847518 0.733351238 [75,] 3.360272711 0.620847518 [76,] -0.536704506 3.360272711 [77,] 1.329145509 -0.536704506 [78,] -2.339168936 1.329145509 [79,] 0.678309761 -2.339168936 [80,] 0.222601671 0.678309761 [81,] 0.105253630 0.222601671 [82,] -0.768768435 0.105253630 [83,] -0.011994467 -0.768768435 [84,] 1.705017552 -0.011994467 [85,] -0.283287816 1.705017552 [86,] 0.508058603 -0.283287816 [87,] 0.994160315 0.508058603 [88,] 0.363854889 0.994160315 [89,] -2.024002129 0.363854889 [90,] 0.124711566 -2.024002129 [91,] 0.103172148 0.124711566 [92,] -0.156498164 0.103172148 [93,] -2.137720119 -0.156498164 [94,] 1.144640239 -2.137720119 [95,] 0.109908939 1.144640239 [96,] 2.193451574 0.109908939 [97,] 0.134097285 2.193451574 [98,] -0.539155258 0.134097285 [99,] -1.074377509 -0.539155258 [100,] 1.220724473 -1.074377509 [101,] 2.488642952 1.220724473 [102,] 0.627427009 2.488642952 [103,] 0.969538426 0.627427009 [104,] -1.947662519 0.969538426 [105,] 1.210386941 -1.947662519 [106,] 0.222864884 1.210386941 [107,] 1.520686847 0.222864884 [108,] -0.424730635 1.520686847 [109,] 0.653081226 -0.424730635 [110,] -0.064824352 0.653081226 [111,] 1.878112083 -0.064824352 [112,] -1.397784527 1.878112083 [113,] -2.659671257 -1.397784527 [114,] 1.773372914 -2.659671257 [115,] -1.939344916 1.773372914 [116,] 0.677582041 -1.939344916 [117,] -1.883966910 0.677582041 [118,] 0.432661257 -1.883966910 [119,] -1.518667941 0.432661257 [120,] 0.549277806 -1.518667941 [121,] -2.867332169 0.549277806 [122,] -0.909824919 -2.867332169 [123,] -0.910430725 -0.909824919 [124,] -0.829360038 -0.910430725 [125,] 0.273678809 -0.829360038 [126,] 1.446708633 0.273678809 [127,] 0.899949535 1.446708633 [128,] -2.748138474 0.899949535 [129,] 1.982185540 -2.748138474 [130,] -3.784341609 1.982185540 [131,] 2.464386039 -3.784341609 [132,] -2.221723543 2.464386039 [133,] -1.585980928 -2.221723543 [134,] -0.115405526 -1.585980928 [135,] 0.806500733 -0.115405526 [136,] 0.413232922 0.806500733 [137,] -2.568947454 0.413232922 [138,] -1.084815764 -2.568947454 [139,] -2.403302443 -1.084815764 [140,] 3.057677592 -2.403302443 [141,] 1.331661413 3.057677592 [142,] 0.159396819 1.331661413 [143,] 1.823394586 0.159396819 [144,] -3.316953939 1.823394586 [145,] 2.474484036 -3.316953939 [146,] -1.996050511 2.474484036 [147,] 1.116056771 -1.996050511 [148,] 0.124547851 1.116056771 [149,] -2.933903096 0.124547851 [150,] -0.882333493 -2.933903096 [151,] 2.135843204 -0.882333493 [152,] 4.088225929 2.135843204 [153,] 1.852125602 4.088225929 [154,] -2.511430068 1.852125602 [155,] 0.541222566 -2.511430068 [156,] 1.248506832 0.541222566 [157,] 1.092185381 1.248506832 [158,] 1.438834369 1.092185381 [159,] -0.704212630 1.438834369 [160,] 0.279215515 -0.704212630 [161,] 0.283095536 0.279215515 [162,] -0.442697433 0.283095536 [163,] 0.795738640 -0.442697433 [164,] 1.296063480 0.795738640 [165,] -1.674784652 1.296063480 [166,] -0.375700396 -1.674784652 [167,] -3.234215486 -0.375700396 [168,] -1.807251341 -3.234215486 [169,] 1.597857143 -1.807251341 [170,] 1.433896950 1.597857143 [171,] 0.646398648 1.433896950 [172,] -1.896671252 0.646398648 [173,] -2.402449826 -1.896671252 [174,] -2.932256265 -2.402449826 [175,] 0.498810810 -2.932256265 [176,] -0.351918154 0.498810810 [177,] -0.682300099 -0.351918154 [178,] 0.170956952 -0.682300099 [179,] -1.322103228 0.170956952 [180,] 0.960516475 -1.322103228 [181,] -0.525711401 0.960516475 [182,] 2.312504595 -0.525711401 [183,] -0.124047048 2.312504595 [184,] -6.581574775 -0.124047048 [185,] 1.322635739 -6.581574775 [186,] 2.606466478 1.322635739 [187,] -0.373341154 2.606466478 [188,] -0.465775377 -0.373341154 [189,] 0.512245649 -0.465775377 [190,] -0.471242324 0.512245649 [191,] 0.632138904 -0.471242324 [192,] 2.235521301 0.632138904 [193,] 1.907663590 2.235521301 [194,] -0.629753808 1.907663590 [195,] 0.006703564 -0.629753808 [196,] 3.101886627 0.006703564 [197,] 1.051849121 3.101886627 [198,] 1.576850907 1.051849121 [199,] 1.564310908 1.576850907 [200,] 1.975118850 1.564310908 [201,] 0.713257009 1.975118850 [202,] -2.306757022 0.713257009 [203,] -2.493290634 -2.306757022 [204,] 2.321862823 -2.493290634 [205,] 0.611752892 2.321862823 [206,] 1.506339279 0.611752892 [207,] 1.181684784 1.506339279 [208,] -2.566779467 1.181684784 [209,] 1.147128186 -2.566779467 [210,] -2.183907421 1.147128186 [211,] -3.660410208 -2.183907421 [212,] 0.954324991 -3.660410208 [213,] 2.918375114 0.954324991 [214,] 1.573653668 2.918375114 [215,] 0.934653915 1.573653668 [216,] 2.483714320 0.934653915 [217,] 0.132093104 2.483714320 [218,] 1.184483165 0.132093104 [219,] -0.057174280 1.184483165 [220,] -1.533963485 -0.057174280 [221,] 0.135901172 -1.533963485 [222,] 0.848676791 0.135901172 [223,] -1.028565875 0.848676791 [224,] 0.742964784 -1.028565875 [225,] -3.627248856 0.742964784 [226,] 0.354424359 -3.627248856 [227,] 1.209626515 0.354424359 [228,] -1.153744299 1.209626515 [229,] -0.795660805 -1.153744299 [230,] 1.830783777 -0.795660805 [231,] -3.169517413 1.830783777 [232,] 4.660886528 -3.169517413 [233,] 2.307925333 4.660886528 [234,] -0.676650766 2.307925333 [235,] -2.178114678 -0.676650766 [236,] -6.958444628 -2.178114678 [237,] -1.145474846 -6.958444628 [238,] 1.920620582 -1.145474846 [239,] -1.568847509 1.920620582 [240,] -0.102918790 -1.568847509 [241,] -1.342321241 -0.102918790 [242,] 1.198444309 -1.342321241 [243,] 2.060247615 1.198444309 [244,] 1.501591044 2.060247615 [245,] 0.269234279 1.501591044 [246,] 1.326556074 0.269234279 [247,] -2.317496234 1.326556074 [248,] 1.342915834 -2.317496234 [249,] 0.521133297 1.342915834 [250,] -0.649996427 0.521133297 [251,] -1.443616347 -0.649996427 [252,] 0.774800495 -1.443616347 [253,] 3.367264160 0.774800495 [254,] -1.123361527 3.367264160 [255,] 0.368626554 -1.123361527 [256,] 1.979420708 0.368626554 [257,] 2.513401424 1.979420708 [258,] -0.953228086 2.513401424 [259,] -5.305242539 -0.953228086 [260,] 1.538736966 -5.305242539 [261,] -3.658891436 1.538736966 [262,] -0.094270601 -3.658891436 [263,] 1.462952724 -0.094270601 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.305732424 -3.070065590 2 1.958423048 0.305732424 3 2.842334252 1.958423048 4 -2.299489473 2.842334252 5 -1.795006041 -2.299489473 6 3.661000818 -1.795006041 7 -2.023506594 3.661000818 8 -2.000339503 -2.023506594 9 0.596114350 -2.000339503 10 1.048315105 0.596114350 11 -0.147362586 1.048315105 12 0.508657122 -0.147362586 13 0.559480169 0.508657122 14 -0.885358037 0.559480169 15 -0.435359193 -0.885358037 16 0.474627775 -0.435359193 17 3.678522423 0.474627775 18 2.525075597 3.678522423 19 0.469749825 2.525075597 20 0.676788002 0.469749825 21 0.854521377 0.676788002 22 2.578504823 0.854521377 23 0.903767276 2.578504823 24 0.948510469 0.903767276 25 0.842359383 0.948510469 26 1.173713167 0.842359383 27 -1.696396655 1.173713167 28 0.419328514 -1.696396655 29 -0.198511580 0.419328514 30 -0.669580919 -0.198511580 31 -0.790645268 -0.669580919 32 -0.726155176 -0.790645268 33 0.114047769 -0.726155176 34 -1.394021043 0.114047769 35 -2.967415164 -1.394021043 36 -2.956351361 -2.967415164 37 -1.645416905 -2.956351361 38 1.545228794 -1.645416905 39 1.583717511 1.545228794 40 1.364767287 1.583717511 41 -1.578144671 1.364767287 42 2.610857930 -1.578144671 43 -0.078439814 2.610857930 44 -0.378981577 -0.078439814 45 -4.388108329 -0.378981577 46 -2.515114881 -4.388108329 47 -0.030307975 -2.515114881 48 0.721682340 -0.030307975 49 -1.519755748 0.721682340 50 -0.879933484 -1.519755748 51 -0.035332935 -0.879933484 52 -2.893619851 -0.035332935 53 0.093254409 -2.893619851 54 -2.279146097 0.093254409 55 1.721318459 -2.279146097 56 0.283823078 1.721318459 57 0.597479837 0.283823078 58 0.005461434 0.597479837 59 1.901487455 0.005461434 60 0.522698330 1.901487455 61 0.496482687 0.522698330 62 -0.395395746 0.496482687 63 -0.585640930 -0.395395746 64 0.818944285 -0.585640930 65 0.795262750 0.818944285 66 1.525183016 0.795262750 67 3.432407625 1.525183016 68 -4.045855529 3.432407625 69 0.211233072 -4.045855529 70 -3.586109520 0.211233072 71 -1.068575461 -3.586109520 72 0.966162690 -1.068575461 73 0.733351238 0.966162690 74 0.620847518 0.733351238 75 3.360272711 0.620847518 76 -0.536704506 3.360272711 77 1.329145509 -0.536704506 78 -2.339168936 1.329145509 79 0.678309761 -2.339168936 80 0.222601671 0.678309761 81 0.105253630 0.222601671 82 -0.768768435 0.105253630 83 -0.011994467 -0.768768435 84 1.705017552 -0.011994467 85 -0.283287816 1.705017552 86 0.508058603 -0.283287816 87 0.994160315 0.508058603 88 0.363854889 0.994160315 89 -2.024002129 0.363854889 90 0.124711566 -2.024002129 91 0.103172148 0.124711566 92 -0.156498164 0.103172148 93 -2.137720119 -0.156498164 94 1.144640239 -2.137720119 95 0.109908939 1.144640239 96 2.193451574 0.109908939 97 0.134097285 2.193451574 98 -0.539155258 0.134097285 99 -1.074377509 -0.539155258 100 1.220724473 -1.074377509 101 2.488642952 1.220724473 102 0.627427009 2.488642952 103 0.969538426 0.627427009 104 -1.947662519 0.969538426 105 1.210386941 -1.947662519 106 0.222864884 1.210386941 107 1.520686847 0.222864884 108 -0.424730635 1.520686847 109 0.653081226 -0.424730635 110 -0.064824352 0.653081226 111 1.878112083 -0.064824352 112 -1.397784527 1.878112083 113 -2.659671257 -1.397784527 114 1.773372914 -2.659671257 115 -1.939344916 1.773372914 116 0.677582041 -1.939344916 117 -1.883966910 0.677582041 118 0.432661257 -1.883966910 119 -1.518667941 0.432661257 120 0.549277806 -1.518667941 121 -2.867332169 0.549277806 122 -0.909824919 -2.867332169 123 -0.910430725 -0.909824919 124 -0.829360038 -0.910430725 125 0.273678809 -0.829360038 126 1.446708633 0.273678809 127 0.899949535 1.446708633 128 -2.748138474 0.899949535 129 1.982185540 -2.748138474 130 -3.784341609 1.982185540 131 2.464386039 -3.784341609 132 -2.221723543 2.464386039 133 -1.585980928 -2.221723543 134 -0.115405526 -1.585980928 135 0.806500733 -0.115405526 136 0.413232922 0.806500733 137 -2.568947454 0.413232922 138 -1.084815764 -2.568947454 139 -2.403302443 -1.084815764 140 3.057677592 -2.403302443 141 1.331661413 3.057677592 142 0.159396819 1.331661413 143 1.823394586 0.159396819 144 -3.316953939 1.823394586 145 2.474484036 -3.316953939 146 -1.996050511 2.474484036 147 1.116056771 -1.996050511 148 0.124547851 1.116056771 149 -2.933903096 0.124547851 150 -0.882333493 -2.933903096 151 2.135843204 -0.882333493 152 4.088225929 2.135843204 153 1.852125602 4.088225929 154 -2.511430068 1.852125602 155 0.541222566 -2.511430068 156 1.248506832 0.541222566 157 1.092185381 1.248506832 158 1.438834369 1.092185381 159 -0.704212630 1.438834369 160 0.279215515 -0.704212630 161 0.283095536 0.279215515 162 -0.442697433 0.283095536 163 0.795738640 -0.442697433 164 1.296063480 0.795738640 165 -1.674784652 1.296063480 166 -0.375700396 -1.674784652 167 -3.234215486 -0.375700396 168 -1.807251341 -3.234215486 169 1.597857143 -1.807251341 170 1.433896950 1.597857143 171 0.646398648 1.433896950 172 -1.896671252 0.646398648 173 -2.402449826 -1.896671252 174 -2.932256265 -2.402449826 175 0.498810810 -2.932256265 176 -0.351918154 0.498810810 177 -0.682300099 -0.351918154 178 0.170956952 -0.682300099 179 -1.322103228 0.170956952 180 0.960516475 -1.322103228 181 -0.525711401 0.960516475 182 2.312504595 -0.525711401 183 -0.124047048 2.312504595 184 -6.581574775 -0.124047048 185 1.322635739 -6.581574775 186 2.606466478 1.322635739 187 -0.373341154 2.606466478 188 -0.465775377 -0.373341154 189 0.512245649 -0.465775377 190 -0.471242324 0.512245649 191 0.632138904 -0.471242324 192 2.235521301 0.632138904 193 1.907663590 2.235521301 194 -0.629753808 1.907663590 195 0.006703564 -0.629753808 196 3.101886627 0.006703564 197 1.051849121 3.101886627 198 1.576850907 1.051849121 199 1.564310908 1.576850907 200 1.975118850 1.564310908 201 0.713257009 1.975118850 202 -2.306757022 0.713257009 203 -2.493290634 -2.306757022 204 2.321862823 -2.493290634 205 0.611752892 2.321862823 206 1.506339279 0.611752892 207 1.181684784 1.506339279 208 -2.566779467 1.181684784 209 1.147128186 -2.566779467 210 -2.183907421 1.147128186 211 -3.660410208 -2.183907421 212 0.954324991 -3.660410208 213 2.918375114 0.954324991 214 1.573653668 2.918375114 215 0.934653915 1.573653668 216 2.483714320 0.934653915 217 0.132093104 2.483714320 218 1.184483165 0.132093104 219 -0.057174280 1.184483165 220 -1.533963485 -0.057174280 221 0.135901172 -1.533963485 222 0.848676791 0.135901172 223 -1.028565875 0.848676791 224 0.742964784 -1.028565875 225 -3.627248856 0.742964784 226 0.354424359 -3.627248856 227 1.209626515 0.354424359 228 -1.153744299 1.209626515 229 -0.795660805 -1.153744299 230 1.830783777 -0.795660805 231 -3.169517413 1.830783777 232 4.660886528 -3.169517413 233 2.307925333 4.660886528 234 -0.676650766 2.307925333 235 -2.178114678 -0.676650766 236 -6.958444628 -2.178114678 237 -1.145474846 -6.958444628 238 1.920620582 -1.145474846 239 -1.568847509 1.920620582 240 -0.102918790 -1.568847509 241 -1.342321241 -0.102918790 242 1.198444309 -1.342321241 243 2.060247615 1.198444309 244 1.501591044 2.060247615 245 0.269234279 1.501591044 246 1.326556074 0.269234279 247 -2.317496234 1.326556074 248 1.342915834 -2.317496234 249 0.521133297 1.342915834 250 -0.649996427 0.521133297 251 -1.443616347 -0.649996427 252 0.774800495 -1.443616347 253 3.367264160 0.774800495 254 -1.123361527 3.367264160 255 0.368626554 -1.123361527 256 1.979420708 0.368626554 257 2.513401424 1.979420708 258 -0.953228086 2.513401424 259 -5.305242539 -0.953228086 260 1.538736966 -5.305242539 261 -3.658891436 1.538736966 262 -0.094270601 -3.658891436 263 1.462952724 -0.094270601 > 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/www/html/freestat/rcomp/tmp/73k6g1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/html/freestat/rcomp/tmp/11a3l91289986122.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/www/html/freestat/rcomp/tmp/122v2u1289986122.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/www/html/freestat/rcomp/tmp/13rdz61289986122.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/www/html/freestat/rcomp/tmp/14vegu1289986122.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/www/html/freestat/rcomp/tmp/15gfei1289986122.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/www/html/freestat/rcomp/tmp/161xu51289986122.tab") + } > > try(system("convert tmp/1pa8p1289986122.ps tmp/1pa8p1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/2zk7s1289986122.ps tmp/2zk7s1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/3zk7s1289986122.ps tmp/3zk7s1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/4sbpd1289986122.ps tmp/4sbpd1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/5sbpd1289986122.ps tmp/5sbpd1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/6sbpd1289986122.ps tmp/6sbpd1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/73k6g1289986122.ps tmp/73k6g1289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/8dtn01289986122.ps tmp/8dtn01289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/9dtn01289986122.ps tmp/9dtn01289986122.png",intern=TRUE)) character(0) > try(system("convert tmp/10dtn01289986122.ps tmp/10dtn01289986122.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.769 2.930 10.112