R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43 + ,11) + ,dim=c(9 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Sport1' + ,'Sport2' + ,'Month') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Connected Separate Learning Software Happiness Depression Sport1 Sport2 1 41 38 13 12 14 12.0 53 32 2 39 32 16 11 18 11.0 83 51 3 30 35 19 15 11 14.0 66 42 4 31 33 15 6 12 12.0 67 41 5 34 37 14 13 16 21.0 76 46 6 35 29 13 10 18 12.0 78 47 7 39 31 19 12 14 22.0 53 37 8 34 36 15 14 14 11.0 80 49 9 36 35 14 12 15 10.0 74 45 10 37 38 15 9 15 13.0 76 47 11 38 31 16 10 17 10.0 79 49 12 36 34 16 12 19 8.0 54 33 13 38 35 16 12 10 15.0 67 42 14 39 38 16 11 16 14.0 54 33 15 33 37 17 15 18 10.0 87 53 16 32 33 15 12 14 14.0 58 36 17 36 32 15 10 14 14.0 75 45 18 38 38 20 12 17 11.0 88 54 19 39 38 18 11 14 10.0 64 41 20 32 32 16 12 16 13.0 57 36 21 32 33 16 11 18 9.5 66 41 22 31 31 16 12 11 14.0 68 44 23 39 38 19 13 14 12.0 54 33 24 37 39 16 11 12 14.0 56 37 25 39 32 17 12 17 11.0 86 52 26 41 32 17 13 9 9.0 80 47 27 36 35 16 10 16 11.0 76 43 28 33 37 15 14 14 15.0 69 44 29 33 33 16 12 15 14.0 78 45 30 34 33 14 10 11 13.0 67 44 31 31 31 15 12 16 9.0 80 49 32 27 32 12 8 13 15.0 54 33 33 37 31 14 10 17 10.0 71 43 34 34 37 16 12 15 11.0 84 54 35 34 30 14 12 14 13.0 74 42 36 32 33 10 7 16 8.0 71 44 37 29 31 10 9 9 20.0 63 37 38 36 33 14 12 15 12.0 71 43 39 29 31 16 10 17 10.0 76 46 40 35 33 16 10 13 10.0 69 42 41 37 32 16 10 15 9.0 74 45 42 34 33 14 12 16 14.0 75 44 43 38 32 20 15 16 8.0 54 33 44 35 33 14 10 12 14.0 52 31 45 38 28 14 10 15 11.0 69 42 46 37 35 11 12 11 13.0 68 40 47 38 39 14 13 15 9.0 65 43 48 33 34 15 11 15 11.0 75 46 49 36 38 16 11 17 15.0 74 42 50 38 32 14 12 13 11.0 75 45 51 32 38 16 14 16 10.0 72 44 52 32 30 14 10 14 14.0 67 40 53 32 33 12 12 11 18.0 63 37 54 34 38 16 13 12 14.0 62 46 55 32 32 9 5 12 11.0 63 36 56 37 35 14 6 15 14.5 76 47 57 39 34 16 12 16 13.0 74 45 58 29 34 16 12 15 9.0 67 42 59 37 36 15 11 12 10.0 73 43 60 35 34 16 10 12 15.0 70 43 61 30 28 12 7 8 20.0 53 32 62 38 34 16 12 13 12.0 77 45 63 34 35 16 14 11 12.0 80 48 64 31 35 14 11 14 14.0 52 31 65 34 31 16 12 15 13.0 54 33 66 35 37 17 13 10 11.0 80 49 67 36 35 18 14 11 17.0 66 42 68 30 27 18 11 12 12.0 73 41 69 39 40 12 12 15 13.0 63 38 70 35 37 16 12 15 14.0 69 42 71 38 36 10 8 14 13.0 67 44 72 31 38 14 11 16 15.0 54 33 73 34 39 18 14 15 13.0 81 48 74 38 41 18 14 15 10.0 69 40 75 34 27 16 12 13 11.0 84 50 76 39 30 17 9 12 19.0 80 49 77 37 37 16 13 17 13.0 70 43 78 34 31 16 11 13 17.0 69 44 79 28 31 13 12 15 13.0 77 47 80 37 27 16 12 13 9.0 54 33 81 33 36 16 12 15 11.0 79 46 82 35 37 16 12 15 9.0 71 45 83 37 33 15 12 16 12.0 73 43 84 32 34 15 11 15 12.0 72 44 85 33 31 16 10 14 13.0 77 47 86 38 39 14 9 15 13.0 75 45 87 33 34 16 12 14 12.0 69 42 88 29 32 16 12 13 15.0 54 33 89 33 33 15 12 7 22.0 70 43 90 31 36 12 9 17 13.0 73 46 91 36 32 17 15 13 15.0 54 33 92 35 41 16 12 15 13.0 77 46 93 32 28 15 12 14 15.0 82 48 94 29 30 13 12 13 12.5 80 47 95 39 36 16 10 16 11.0 80 47 96 37 35 16 13 12 16.0 69 43 97 35 31 16 9 14 11.0 78 46 98 37 34 16 12 17 11.0 81 48 99 32 36 14 10 15 10.0 76 46 100 38 36 16 14 17 10.0 76 45 101 37 35 16 11 12 16.0 73 45 102 36 37 20 15 16 12.0 85 52 103 32 28 15 11 11 11.0 66 42 104 33 39 16 11 15 16.0 79 47 105 40 32 13 12 9 19.0 68 41 106 38 35 17 12 16 11.0 76 47 107 41 39 16 12 15 16.0 71 43 108 36 35 16 11 10 15.0 54 33 109 43 42 12 7 10 24.0 46 30 110 30 34 16 12 15 14.0 85 52 111 31 33 16 14 11 15.0 74 44 112 32 41 17 11 13 11.0 88 55 113 32 33 13 11 14 15.0 38 11 114 37 34 12 10 18 12.0 76 47 115 37 32 18 13 16 10.0 86 53 116 33 40 14 13 14 14.0 54 33 117 34 40 14 8 14 13.0 67 44 118 33 35 13 11 14 9.0 69 42 119 38 36 16 12 14 15.0 90 55 120 33 37 13 11 12 15.0 54 33 121 31 27 16 13 14 14.0 76 46 122 38 39 13 12 15 11.0 89 54 123 37 38 16 14 15 8.0 76 47 124 36 31 15 13 15 11.0 73 45 125 31 33 16 15 13 11.0 79 47 126 39 32 15 10 17 8.0 90 55 127 44 39 17 11 17 10.0 74 44 128 33 36 15 9 19 11.0 81 53 129 35 33 12 11 15 13.0 72 44 130 32 33 16 10 13 11.0 71 42 131 28 32 10 11 9 20.0 66 40 132 40 37 16 8 15 10.0 77 46 133 27 30 12 11 15 15.0 65 40 134 37 38 14 12 15 12.0 74 46 135 32 29 15 12 16 14.0 85 53 136 28 22 13 9 11 23.0 54 33 137 34 35 15 11 14 14.0 63 42 138 30 35 11 10 11 16.0 54 35 139 35 34 12 8 15 11.0 64 40 140 31 35 11 9 13 12.0 69 41 141 32 34 16 8 15 10.0 54 33 142 30 37 15 9 16 14.0 84 51 143 30 35 17 15 14 12.0 86 53 144 31 23 16 11 15 12.0 77 46 145 40 31 10 8 16 11.0 89 55 146 32 27 18 13 16 12.0 76 47 147 36 36 13 12 11 13.0 60 38 148 32 31 16 12 12 11.0 75 46 149 35 32 13 9 9 19.0 73 46 150 38 39 10 7 16 12.0 85 53 151 42 37 15 13 13 17.0 79 47 152 34 38 16 9 16 9.0 71 41 153 35 39 16 6 12 12.0 72 44 154 38 34 14 8 9 19.0 69 43 155 33 31 10 8 13 18.0 78 51 156 36 32 17 15 13 15.0 54 33 157 32 37 13 6 14 14.0 69 43 158 33 36 15 9 19 11.0 81 53 159 34 32 16 11 13 9.0 84 51 160 32 38 12 8 12 18.0 84 50 161 34 36 13 8 13 16.0 69 46 162 27 26 13 10 10 24.0 66 43 163 31 26 12 8 14 14.0 81 47 164 38 33 17 14 16 20.0 82 50 165 34 39 15 10 10 18.0 72 43 166 24 30 10 8 11 23.0 54 33 167 30 33 14 11 14 12.0 78 48 168 26 25 11 12 12 14.0 74 44 169 34 38 13 12 9 16.0 82 50 170 27 37 16 12 9 18.0 73 41 171 37 31 12 5 11 20.0 55 34 172 36 37 16 12 16 12.0 72 44 173 41 35 12 10 9 12.0 78 47 174 29 25 9 7 13 17.0 59 35 175 36 28 12 12 16 13.0 72 44 176 32 35 15 11 13 9.0 78 44 177 37 33 12 8 9 16.0 68 43 178 30 30 12 9 12 18.0 69 41 179 31 31 14 10 16 10.0 67 41 180 38 37 12 9 11 14.0 74 42 181 36 36 16 12 14 11.0 54 33 182 35 30 11 6 13 9.0 67 41 183 31 36 19 15 15 11.0 70 44 184 38 32 15 12 14 10.0 80 48 185 22 28 8 12 16 11.0 89 55 186 32 36 16 12 13 19.0 76 44 187 36 34 17 11 14 14.0 74 43 188 39 31 12 7 15 12.0 87 52 189 28 28 11 7 13 14.0 54 30 190 32 36 11 5 11 21.0 61 39 191 32 36 14 12 11 13.0 38 11 192 38 40 16 12 14 10.0 75 44 193 32 33 12 3 15 15.0 69 42 194 35 37 16 11 11 16.0 62 41 195 32 32 13 10 15 14.0 72 44 196 37 38 15 12 12 12.0 70 44 197 34 31 16 9 14 19.0 79 48 198 33 37 16 12 14 15.0 87 53 199 33 33 14 9 8 19.0 62 37 200 26 32 16 12 13 13.0 77 44 201 30 30 16 12 9 17.0 69 44 202 24 30 14 10 15 12.0 69 40 203 34 31 11 9 17 11.0 75 42 204 34 32 12 12 13 14.0 54 35 205 33 34 15 8 15 11.0 72 43 206 34 36 15 11 15 13.0 74 45 207 35 37 16 11 14 12.0 85 55 208 35 36 16 12 16 15.0 52 31 209 36 33 11 10 13 14.0 70 44 210 34 33 15 10 16 12.0 84 50 211 34 33 12 12 9 17.0 64 40 212 41 44 12 12 16 11.0 84 53 213 32 39 15 11 11 18.0 87 54 214 30 32 15 8 10 13.0 79 49 215 35 35 16 12 11 17.0 67 40 216 28 25 14 10 15 13.0 65 41 217 33 35 17 11 17 11.0 85 52 218 39 34 14 10 14 12.0 83 52 219 36 35 13 8 8 22.0 61 36 220 36 39 15 12 15 14.0 82 52 221 35 33 13 12 11 12.0 76 46 222 38 36 14 10 16 12.0 58 31 223 33 32 15 12 10 17.0 72 44 224 31 32 12 9 15 9.0 72 44 225 34 36 13 9 9 21.0 38 11 226 32 36 8 6 16 10.0 78 46 227 31 32 14 10 19 11.0 54 33 228 33 34 14 9 12 12.0 63 34 229 34 33 11 9 8 23.0 66 42 230 34 35 12 9 11 13.0 70 43 231 34 30 13 6 14 12.0 71 43 232 33 38 10 10 9 16.0 67 44 233 32 34 16 6 15 9.0 58 36 234 41 33 18 14 13 17.0 72 46 235 34 32 13 10 16 9.0 72 44 236 36 31 11 10 11 14.0 70 43 237 37 30 4 6 12 17.0 76 50 238 36 27 13 12 13 13.0 50 33 239 29 31 16 12 10 11.0 72 43 240 37 30 10 7 11 12.0 72 44 241 27 32 12 8 12 10.0 88 53 242 35 35 12 11 8 19.0 53 34 243 28 28 10 3 12 16.0 58 35 244 35 33 13 6 12 16.0 66 40 245 37 31 15 10 15 14.0 82 53 246 29 35 12 8 11 20.0 69 42 247 32 35 14 9 13 15.0 68 43 248 36 32 10 9 14 23.0 44 29 249 19 21 12 8 10 20.0 56 36 250 21 20 12 9 12 16.0 53 30 251 31 34 11 7 15 14.0 70 42 252 33 32 10 7 13 17.0 78 47 253 36 34 12 6 13 11.0 71 44 254 33 32 16 9 13 13.0 72 45 255 37 33 12 10 12 17.0 68 44 256 34 33 14 11 12 15.0 67 43 257 35 37 16 12 9 21.0 75 43 258 31 32 14 8 9 18.0 62 40 259 37 34 13 11 15 15.0 67 41 260 35 30 4 3 10 8.0 83 52 261 27 30 15 11 14 12.0 64 38 262 34 38 11 12 15 12.0 68 41 263 40 36 11 7 7 22.0 62 39 264 29 32 14 9 14 12.0 72 43 Month 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 9 10 9 11 9 12 9 13 9 14 9 15 9 16 9 17 9 18 9 19 9 20 9 21 9 22 9 23 9 24 9 25 9 26 9 27 9 28 9 29 9 30 9 31 9 32 9 33 9 34 9 35 9 36 9 37 9 38 9 39 9 40 9 41 9 42 9 43 9 44 9 45 9 46 9 47 9 48 9 49 9 50 9 51 9 52 9 53 9 54 9 55 9 56 9 57 9 58 9 59 9 60 9 61 9 62 9 63 9 64 9 65 9 66 10 67 10 68 10 69 10 70 10 71 10 72 10 73 10 74 10 75 10 76 10 77 10 78 10 79 10 80 10 81 10 82 10 83 10 84 10 85 10 86 10 87 10 88 10 89 10 90 10 91 10 92 10 93 10 94 10 95 10 96 10 97 10 98 10 99 10 100 10 101 10 102 10 103 10 104 10 105 10 106 10 107 10 108 10 109 10 110 10 111 10 112 10 113 10 114 10 115 10 116 10 117 10 118 10 119 10 120 10 121 10 122 10 123 10 124 10 125 10 126 10 127 10 128 10 129 10 130 10 131 10 132 10 133 10 134 10 135 10 136 10 137 10 138 10 139 10 140 10 141 10 142 10 143 10 144 10 145 10 146 10 147 10 148 10 149 10 150 10 151 10 152 10 153 10 154 9 155 10 156 10 157 10 158 10 159 10 160 10 161 10 162 11 163 11 164 11 165 11 166 11 167 11 168 11 169 11 170 11 171 11 172 11 173 11 174 11 175 11 176 11 177 11 178 11 179 11 180 11 181 11 182 11 183 11 184 11 185 11 186 11 187 11 188 11 189 11 190 11 191 11 192 11 193 11 194 11 195 11 196 11 197 11 198 11 199 11 200 11 201 11 202 11 203 11 204 11 205 11 206 11 207 11 208 11 209 11 210 11 211 11 212 11 213 11 214 11 215 11 216 11 217 11 218 11 219 11 220 11 221 11 222 11 223 11 224 11 225 11 226 11 227 11 228 11 229 11 230 11 231 11 232 11 233 11 234 11 235 11 236 11 237 11 238 11 239 11 240 11 241 11 242 11 243 11 244 11 245 11 246 11 247 11 248 11 249 11 250 11 251 11 252 11 253 11 254 11 255 11 256 11 257 11 258 11 259 11 260 11 261 11 262 11 263 11 264 11 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Separate Learning Software Happiness Depression 23.55456 0.42900 0.11248 -0.05921 0.01112 -0.04134 Sport1 Sport2 Month -0.05306 0.11966 -0.58511 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.9020 -2.2434 0.0338 2.3256 7.4029 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 23.55456 4.56667 5.158 5.02e-07 *** Separate 0.42900 0.05768 7.438 1.56e-12 *** Learning 0.11248 0.11239 1.001 0.3179 Software -0.05921 0.11489 -0.515 0.6068 Happiness 0.01112 0.10440 0.106 0.9153 Depression -0.04134 0.07632 -0.542 0.5886 Sport1 -0.05306 0.06791 -0.781 0.4353 Sport2 0.11966 0.10067 1.189 0.2357 Month -0.58511 0.28812 -2.031 0.0433 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.353 on 255 degrees of freedom Multiple R-squared: 0.2436, Adjusted R-squared: 0.2198 F-statistic: 10.26 on 8 and 255 DF, p-value: 1.966e-12 > 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,] 0.55993628 0.88012745 0.4400637 [2,] 0.69621503 0.60756994 0.3037850 [3,] 0.67988303 0.64023394 0.3201170 [4,] 0.57816869 0.84366262 0.4218313 [5,] 0.60400012 0.79199977 0.3959999 [6,] 0.66030103 0.67939795 0.3396990 [7,] 0.65610835 0.68778331 0.3438917 [8,] 0.56790111 0.86419778 0.4320989 [9,] 0.59470367 0.81059266 0.4052963 [10,] 0.65114668 0.69770664 0.3488533 [11,] 0.62061315 0.75877370 0.3793868 [12,] 0.60621854 0.78756291 0.3937815 [13,] 0.53904660 0.92190681 0.4609534 [14,] 0.58798652 0.82402696 0.4120135 [15,] 0.74641696 0.50716607 0.2535830 [16,] 0.71945525 0.56108949 0.2805447 [17,] 0.67633608 0.64732785 0.3236639 [18,] 0.66639308 0.66721385 0.3336069 [19,] 0.60551233 0.78897534 0.3944877 [20,] 0.59626052 0.80747895 0.4037395 [21,] 0.73630630 0.52738739 0.2636937 [22,] 0.72362824 0.55274351 0.2763718 [23,] 0.68610134 0.62779732 0.3138987 [24,] 0.63309563 0.73380874 0.3669044 [25,] 0.58120806 0.83758388 0.4187919 [26,] 0.54166692 0.91666615 0.4583331 [27,] 0.50496992 0.99006017 0.4950301 [28,] 0.62225321 0.75549359 0.3777468 [29,] 0.56941584 0.86116832 0.4305842 [30,] 0.53313360 0.93373280 0.4668664 [31,] 0.47963242 0.95926484 0.5203676 [32,] 0.43779817 0.87559633 0.5622018 [33,] 0.38984039 0.77968077 0.6101596 [34,] 0.45776034 0.91552068 0.5422397 [35,] 0.46453043 0.92906086 0.5354696 [36,] 0.43399798 0.86799595 0.5660020 [37,] 0.40391689 0.80783379 0.5960831 [38,] 0.35779675 0.71559349 0.6422033 [39,] 0.36657861 0.73315722 0.6334214 [40,] 0.41423795 0.82847590 0.5857621 [41,] 0.37793255 0.75586509 0.6220675 [42,] 0.33788193 0.67576387 0.6621181 [43,] 0.30442806 0.60885613 0.6955719 [44,] 0.26673661 0.53347322 0.7332634 [45,] 0.25080419 0.50160838 0.7491958 [46,] 0.26212082 0.52424164 0.7378792 [47,] 0.37875937 0.75751875 0.6212406 [48,] 0.34031487 0.68062975 0.6596851 [49,] 0.29992433 0.59984867 0.7000757 [50,] 0.27030011 0.54060021 0.7296999 [51,] 0.25266099 0.50532198 0.7473390 [52,] 0.22646763 0.45293526 0.7735324 [53,] 0.23444168 0.46888335 0.7655583 [54,] 0.20237667 0.40475335 0.7976233 [55,] 0.17342286 0.34684572 0.8265771 [56,] 0.15185424 0.30370848 0.8481458 [57,] 0.15292720 0.30585440 0.8470728 [58,] 0.16878564 0.33757129 0.8312144 [59,] 0.14426836 0.28853673 0.8557316 [60,] 0.15377605 0.30755210 0.8462240 [61,] 0.17937644 0.35875287 0.8206236 [62,] 0.17098853 0.34197707 0.8290115 [63,] 0.14650596 0.29301193 0.8534940 [64,] 0.12962382 0.25924764 0.8703762 [65,] 0.17235889 0.34471778 0.8276411 [66,] 0.14976244 0.29952488 0.8502376 [67,] 0.12762038 0.25524075 0.8723796 [68,] 0.16143669 0.32287339 0.8385633 [69,] 0.18687960 0.37375920 0.8131204 [70,] 0.17770193 0.35540387 0.8222981 [71,] 0.15534252 0.31068505 0.8446575 [72,] 0.14835970 0.29671940 0.8516403 [73,] 0.14096498 0.28192996 0.8590350 [74,] 0.12259982 0.24519965 0.8774002 [75,] 0.10997179 0.21994358 0.8900282 [76,] 0.09719154 0.19438309 0.9028085 [77,] 0.11249070 0.22498141 0.8875093 [78,] 0.09494323 0.18988645 0.9050568 [79,] 0.09836087 0.19672174 0.9016391 [80,] 0.09254939 0.18509877 0.9074506 [81,] 0.08256182 0.16512364 0.9174382 [82,] 0.06933455 0.13866910 0.9306654 [83,] 0.06939318 0.13878636 0.9306068 [84,] 0.07057106 0.14114213 0.9294289 [85,] 0.06584173 0.13168346 0.9341583 [86,] 0.05633772 0.11267544 0.9436623 [87,] 0.05074540 0.10149081 0.9492546 [88,] 0.04974530 0.09949059 0.9502547 [89,] 0.04669151 0.09338302 0.9533085 [90,] 0.04204354 0.08408709 0.9579565 [91,] 0.03484677 0.06969355 0.9651532 [92,] 0.02839298 0.05678596 0.9716070 [93,] 0.02828834 0.05657668 0.9717117 [94,] 0.06559614 0.13119227 0.9344039 [95,] 0.06173343 0.12346686 0.9382666 [96,] 0.07556485 0.15112969 0.9244352 [97,] 0.06535353 0.13070706 0.9346465 [98,] 0.10367531 0.20735062 0.8963247 [99,] 0.12055945 0.24111890 0.8794406 [100,] 0.11571835 0.23143670 0.8842817 [101,] 0.15213615 0.30427230 0.8478639 [102,] 0.14068677 0.28137354 0.8593132 [103,] 0.13657597 0.27315194 0.8634240 [104,] 0.13016442 0.26032884 0.8698356 [105,] 0.12767424 0.25534848 0.8723258 [106,] 0.12452234 0.24904468 0.8754777 [107,] 0.10977539 0.21955078 0.8902246 [108,] 0.10358455 0.20716910 0.8964154 [109,] 0.09300709 0.18601418 0.9069929 [110,] 0.08114922 0.16229843 0.9188508 [111,] 0.07415336 0.14830672 0.9258466 [112,] 0.06344727 0.12689455 0.9365527 [113,] 0.06055195 0.12110391 0.9394480 [114,] 0.05722199 0.11444398 0.9427780 [115,] 0.07054883 0.14109766 0.9294512 [116,] 0.12680549 0.25361099 0.8731945 [117,] 0.12407023 0.24814045 0.8759298 [118,] 0.11043435 0.22086871 0.8895656 [119,] 0.10172611 0.20345222 0.8982739 [120,] 0.11045106 0.22090211 0.8895489 [121,] 0.11806407 0.23612815 0.8819359 [122,] 0.14211655 0.28423310 0.8578835 [123,] 0.12550691 0.25101381 0.8744931 [124,] 0.10844070 0.21688140 0.8915593 [125,] 0.09430599 0.18861198 0.9056940 [126,] 0.08099901 0.16199802 0.9190010 [127,] 0.08946784 0.17893567 0.9105322 [128,] 0.07648437 0.15296875 0.9235156 [129,] 0.07638977 0.15277954 0.9236102 [130,] 0.07402650 0.14805299 0.9259735 [131,] 0.10583260 0.21166519 0.8941674 [132,] 0.13164455 0.26328910 0.8683554 [133,] 0.11945759 0.23891518 0.8805424 [134,] 0.19533571 0.39067142 0.8046643 [135,] 0.17634231 0.35268462 0.8236577 [136,] 0.15917596 0.31835191 0.8408240 [137,] 0.14074683 0.28149367 0.8592532 [138,] 0.12730867 0.25461734 0.8726913 [139,] 0.11309372 0.22618744 0.8869063 [140,] 0.17837228 0.35674457 0.8216277 [141,] 0.16545820 0.33091640 0.8345418 [142,] 0.15093502 0.30187004 0.8490650 [143,] 0.16515929 0.33031858 0.8348407 [144,] 0.14517068 0.29034136 0.8548293 [145,] 0.15936405 0.31872811 0.8406359 [146,] 0.15310809 0.30621619 0.8468919 [147,] 0.14089453 0.28178905 0.8591055 [148,] 0.13583307 0.27166614 0.8641669 [149,] 0.12615807 0.25231614 0.8738419 [150,] 0.10822457 0.21644915 0.8917754 [151,] 0.10041901 0.20083802 0.8995810 [152,] 0.09209178 0.18418356 0.9079082 [153,] 0.12138356 0.24276712 0.8786164 [154,] 0.10885759 0.21771518 0.8911424 [155,] 0.18069083 0.36138165 0.8193092 [156,] 0.17600294 0.35200587 0.8239971 [157,] 0.16207883 0.32415766 0.8379212 [158,] 0.14533395 0.29066790 0.8546661 [159,] 0.23538626 0.47077251 0.7646137 [160,] 0.27809199 0.55618398 0.7219080 [161,] 0.24927240 0.49854480 0.7507276 [162,] 0.36227978 0.72455955 0.6377202 [163,] 0.32767970 0.65535940 0.6723203 [164,] 0.39980872 0.79961745 0.6001913 [165,] 0.37212659 0.74425318 0.6278734 [166,] 0.37835233 0.75670466 0.6216477 [167,] 0.34561620 0.69123240 0.6543838 [168,] 0.31521039 0.63042077 0.6847896 [169,] 0.31403471 0.62806943 0.6859653 [170,] 0.28361733 0.56723466 0.7163827 [171,] 0.27977959 0.55955918 0.7202204 [172,] 0.29495071 0.58990142 0.7050493 [173,] 0.36486413 0.72972827 0.6351359 [174,] 0.60221188 0.79557625 0.3977881 [175,] 0.58225519 0.83548963 0.4177448 [176,] 0.57877007 0.84245986 0.4212299 [177,] 0.72888179 0.54223641 0.2711182 [178,] 0.70545939 0.58908122 0.2945406 [179,] 0.70989602 0.58020795 0.2901040 [180,] 0.67440645 0.65118711 0.3255936 [181,] 0.64516516 0.70966967 0.3548348 [182,] 0.61059616 0.77880769 0.3894038 [183,] 0.57655026 0.84689949 0.4234497 [184,] 0.53674782 0.92650436 0.4632522 [185,] 0.49857614 0.99715229 0.5014239 [186,] 0.49131299 0.98262597 0.5086870 [187,] 0.46249016 0.92498031 0.5375098 [188,] 0.42099083 0.84198166 0.5790092 [189,] 0.49260456 0.98520912 0.5073954 [190,] 0.45808006 0.91616013 0.5419199 [191,] 0.60747474 0.78505051 0.3925253 [192,] 0.58770578 0.82458844 0.4122942 [193,] 0.55167120 0.89665760 0.4483288 [194,] 0.50862092 0.98275816 0.4913791 [195,] 0.46705870 0.93411739 0.5329413 [196,] 0.42665944 0.85331888 0.5733406 [197,] 0.38460989 0.76921979 0.6153901 [198,] 0.35939550 0.71879101 0.6406045 [199,] 0.32714161 0.65428321 0.6728584 [200,] 0.29081309 0.58162618 0.7091869 [201,] 0.26537754 0.53075509 0.7346225 [202,] 0.31330631 0.62661262 0.6866937 [203,] 0.29108745 0.58217491 0.7089125 [204,] 0.25279605 0.50559211 0.7472039 [205,] 0.21829979 0.43659959 0.7817002 [206,] 0.19000312 0.38000624 0.8099969 [207,] 0.22031317 0.44062633 0.7796868 [208,] 0.19656197 0.39312394 0.8034380 [209,] 0.17696660 0.35393320 0.8230334 [210,] 0.15140397 0.30280795 0.8485960 [211,] 0.16443476 0.32886953 0.8355652 [212,] 0.13438717 0.26877435 0.8656128 [213,] 0.11331942 0.22663883 0.8866806 [214,] 0.16998610 0.33997220 0.8300139 [215,] 0.15442270 0.30884541 0.8455773 [216,] 0.13385577 0.26771155 0.8661442 [217,] 0.14137912 0.28275824 0.8586209 [218,] 0.11374603 0.22749205 0.8862540 [219,] 0.08879973 0.17759947 0.9112003 [220,] 0.08843341 0.17686683 0.9115666 [221,] 0.20946572 0.41893144 0.7905343 [222,] 0.17152488 0.34304976 0.8284751 [223,] 0.28142757 0.56285514 0.7185724 [224,] 0.23617491 0.47234983 0.7638251 [225,] 0.24061804 0.48123608 0.7593820 [226,] 0.20650733 0.41301467 0.7934927 [227,] 0.28846774 0.57693548 0.7115323 [228,] 0.23707101 0.47414201 0.7629290 [229,] 0.39622008 0.79244016 0.6037799 [230,] 0.46480580 0.92961159 0.5351942 [231,] 0.38720388 0.77440777 0.6127961 [232,] 0.31428516 0.62857031 0.6857148 [233,] 0.30119019 0.60238039 0.6988098 [234,] 0.32308984 0.64617967 0.6769102 [235,] 0.53420784 0.93158432 0.4657922 [236,] 0.52663240 0.94673520 0.4733676 [237,] 0.45765106 0.91530213 0.5423489 [238,] 0.67207880 0.65584240 0.3279212 [239,] 0.60077133 0.79845734 0.3992287 [240,] 0.56117976 0.87764047 0.4388202 [241,] 0.66347881 0.67304238 0.3365212 > postscript(file="/var/wessaorg/rcomp/tmp/1gcro1384526060.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2d6ue1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3m1sg1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ji421384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5s7ke1384526060.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 4.981001822 4.390800158 -5.620051568 -3.766063581 -1.748357336 2.210717473 7 8 9 10 11 12 5.124191770 -1.910496452 0.620402430 0.034099795 3.757458676 1.072043230 13 14 15 16 17 18 2.645275789 2.578195760 -3.698278618 -2.229618323 1.906040423 0.343515537 19 20 21 22 23 24 1.783435204 -2.029726495 -2.805612927 -2.877434817 2.298739476 -0.178868696 25 26 27 28 29 30 4.388167558 6.733593421 0.652701691 -3.159511768 -1.368958673 -0.723295374 31 32 33 34 35 36 -2.988806295 -6.500811632 3.275905566 -2.967581598 1.259531044 -2.501027198 37 38 39 40 41 42 -3.537598272 1.641222292 -5.042734241 0.250956415 2.522703450 -0.194641151 43 44 45 46 47 48 3.691110364 1.066630811 5.640024468 2.406264582 0.684038660 -2.147546543 49 50 51 52 53 54 -0.407330730 4.024041935 -4.770722667 -0.949649786 -1.547844470 -3.390057941 55 56 57 58 59 60 -1.376664031 1.317967311 3.937339525 -6.229325827 1.239334103 -0.026850594 61 62 63 64 65 66 -1.515139279 3.088540640 -1.399619456 -3.754401823 0.610200444 -0.994082819 67 68 69 70 71 72 1.142335695 -1.329970612 2.663433742 -0.618421214 2.872950723 -4.570404088 73 74 75 76 77 78 -2.705548623 0.633011698 2.408440290 6.080557938 1.310612363 0.803300961 79 80 81 82 83 84 -4.922137291 5.768209125 -2.261467521 -1.077969864 3.208858298 -2.440958839 85 86 87 88 89 90 -0.366867419 1.488949996 -1.402968803 -4.128786246 -0.436907924 -4.247109365 91 92 93 94 95 96 2.936357147 -2.429929923 0.379347067 -3.332383435 3.542398399 2.295151329 97 98 99 100 101 102 1.663978795 2.441097222 -3.355446454 2.753854893 2.149655414 -0.332155666 103 104 105 106 107 108 0.057147951 -3.520665242 7.204011606 2.765091078 4.592705352 1.558352478 109 110 111 112 113 114 6.074943302 -4.679069048 -2.672211890 -6.155350041 0.492941189 2.657169327 115 116 117 118 119 120 2.770122941 -3.329095412 -3.292969830 -1.677752694 2.421695823 -1.984453702 121 122 123 124 125 126 -0.365298627 1.362263204 0.596085707 2.856523823 -2.894268121 4.809063884 127 128 129 130 131 132 7.190289808 -3.102605099 1.366813004 -2.016476156 -4.462885045 3.925242889 133 134 135 136 137 138 -5.156283082 0.881512184 -0.552361138 -0.326027748 -1.014397192 -4.147574613 139 140 141 142 143 144 0.731680955 -3.316421708 -2.452534357 -5.975734524 -5.181084783 1.191568280 145 146 147 148 149 150 6.764105371 0.185174684 1.152250876 -1.295349292 1.693378564 1.341297711 151 152 153 154 155 156 6.631802489 -2.217055582 -1.961130863 3.225326207 -0.018210758 2.936357147 157 158 159 160 161 162 -3.744776436 -3.102605099 0.001885863 -3.797032207 -1.462561919 -2.905168467 163 164 165 166 167 168 0.948331665 4.658019786 -1.636801189 -6.894711203 -3.463539171 -3.263563604 169 170 171 172 173 174 -1.243015602 -7.469353556 5.083099168 0.792754398 7.019456502 0.059299535 175 176 177 178 179 180 5.145022440 -2.068256719 3.872431551 -1.439645970 -1.515678220 3.548755032 181 182 183 184 185 186 1.563851395 3.005945960 -4.074397023 4.935643289 -8.901995296 -2.243292575 187 188 189 190 191 192 2.238770324 6.370373196 -2.243659281 -2.188048767 -0.311547360 1.604495052 193 194 195 196 197 198 -1.358919905 -0.217144097 -0.749425160 1.414577230 1.393509172 -2.342054784 199 200 201 202 203 204 0.241422720 -6.722238016 -2.078910971 -7.767099523 2.097594703 1.625569874 205 206 207 208 209 210 -0.955143801 -0.686059843 -0.870720564 0.840165061 2.962640392 0.421581989 211 212 213 214 215 216 1.297341483 2.758076893 -4.109091715 -3.305432054 1.126374842 -1.912661806 217 218 219 220 221 222 -1.840890992 4.834903493 2.627299308 -0.285678676 1.874703159 4.141067435 223 224 225 226 227 228 0.323629169 -1.902834479 1.976232506 -2.237293855 -1.669185706 -0.109348927 229 230 231 232 233 234 1.358127032 0.033513831 1.866799003 -2.101935401 -2.173921685 7.402927128 235 236 237 238 239 240 1.032777194 3.962543948 5.535675314 5.643849243 -3.488198638 5.230189938 241 242 243 244 245 246 -6.115343729 1.608226167 -2.660293445 1.861055429 3.908261679 -4.669738769 247 248 249 250 251 252 -2.237127911 4.221179152 -8.624405295 -5.764976500 -2.426890575 0.516007673 253 254 255 256 257 258 2.113385371 -0.284830507 3.879165196 0.697346914 0.521449773 -1.800227173 259 260 261 262 263 264 3.586797465 3.140367740 -4.835232451 -0.915995514 6.069233137 -3.873000711 > postscript(file="/var/wessaorg/rcomp/tmp/6c5b41384526060.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 4.981001822 NA 1 4.390800158 4.981001822 2 -5.620051568 4.390800158 3 -3.766063581 -5.620051568 4 -1.748357336 -3.766063581 5 2.210717473 -1.748357336 6 5.124191770 2.210717473 7 -1.910496452 5.124191770 8 0.620402430 -1.910496452 9 0.034099795 0.620402430 10 3.757458676 0.034099795 11 1.072043230 3.757458676 12 2.645275789 1.072043230 13 2.578195760 2.645275789 14 -3.698278618 2.578195760 15 -2.229618323 -3.698278618 16 1.906040423 -2.229618323 17 0.343515537 1.906040423 18 1.783435204 0.343515537 19 -2.029726495 1.783435204 20 -2.805612927 -2.029726495 21 -2.877434817 -2.805612927 22 2.298739476 -2.877434817 23 -0.178868696 2.298739476 24 4.388167558 -0.178868696 25 6.733593421 4.388167558 26 0.652701691 6.733593421 27 -3.159511768 0.652701691 28 -1.368958673 -3.159511768 29 -0.723295374 -1.368958673 30 -2.988806295 -0.723295374 31 -6.500811632 -2.988806295 32 3.275905566 -6.500811632 33 -2.967581598 3.275905566 34 1.259531044 -2.967581598 35 -2.501027198 1.259531044 36 -3.537598272 -2.501027198 37 1.641222292 -3.537598272 38 -5.042734241 1.641222292 39 0.250956415 -5.042734241 40 2.522703450 0.250956415 41 -0.194641151 2.522703450 42 3.691110364 -0.194641151 43 1.066630811 3.691110364 44 5.640024468 1.066630811 45 2.406264582 5.640024468 46 0.684038660 2.406264582 47 -2.147546543 0.684038660 48 -0.407330730 -2.147546543 49 4.024041935 -0.407330730 50 -4.770722667 4.024041935 51 -0.949649786 -4.770722667 52 -1.547844470 -0.949649786 53 -3.390057941 -1.547844470 54 -1.376664031 -3.390057941 55 1.317967311 -1.376664031 56 3.937339525 1.317967311 57 -6.229325827 3.937339525 58 1.239334103 -6.229325827 59 -0.026850594 1.239334103 60 -1.515139279 -0.026850594 61 3.088540640 -1.515139279 62 -1.399619456 3.088540640 63 -3.754401823 -1.399619456 64 0.610200444 -3.754401823 65 -0.994082819 0.610200444 66 1.142335695 -0.994082819 67 -1.329970612 1.142335695 68 2.663433742 -1.329970612 69 -0.618421214 2.663433742 70 2.872950723 -0.618421214 71 -4.570404088 2.872950723 72 -2.705548623 -4.570404088 73 0.633011698 -2.705548623 74 2.408440290 0.633011698 75 6.080557938 2.408440290 76 1.310612363 6.080557938 77 0.803300961 1.310612363 78 -4.922137291 0.803300961 79 5.768209125 -4.922137291 80 -2.261467521 5.768209125 81 -1.077969864 -2.261467521 82 3.208858298 -1.077969864 83 -2.440958839 3.208858298 84 -0.366867419 -2.440958839 85 1.488949996 -0.366867419 86 -1.402968803 1.488949996 87 -4.128786246 -1.402968803 88 -0.436907924 -4.128786246 89 -4.247109365 -0.436907924 90 2.936357147 -4.247109365 91 -2.429929923 2.936357147 92 0.379347067 -2.429929923 93 -3.332383435 0.379347067 94 3.542398399 -3.332383435 95 2.295151329 3.542398399 96 1.663978795 2.295151329 97 2.441097222 1.663978795 98 -3.355446454 2.441097222 99 2.753854893 -3.355446454 100 2.149655414 2.753854893 101 -0.332155666 2.149655414 102 0.057147951 -0.332155666 103 -3.520665242 0.057147951 104 7.204011606 -3.520665242 105 2.765091078 7.204011606 106 4.592705352 2.765091078 107 1.558352478 4.592705352 108 6.074943302 1.558352478 109 -4.679069048 6.074943302 110 -2.672211890 -4.679069048 111 -6.155350041 -2.672211890 112 0.492941189 -6.155350041 113 2.657169327 0.492941189 114 2.770122941 2.657169327 115 -3.329095412 2.770122941 116 -3.292969830 -3.329095412 117 -1.677752694 -3.292969830 118 2.421695823 -1.677752694 119 -1.984453702 2.421695823 120 -0.365298627 -1.984453702 121 1.362263204 -0.365298627 122 0.596085707 1.362263204 123 2.856523823 0.596085707 124 -2.894268121 2.856523823 125 4.809063884 -2.894268121 126 7.190289808 4.809063884 127 -3.102605099 7.190289808 128 1.366813004 -3.102605099 129 -2.016476156 1.366813004 130 -4.462885045 -2.016476156 131 3.925242889 -4.462885045 132 -5.156283082 3.925242889 133 0.881512184 -5.156283082 134 -0.552361138 0.881512184 135 -0.326027748 -0.552361138 136 -1.014397192 -0.326027748 137 -4.147574613 -1.014397192 138 0.731680955 -4.147574613 139 -3.316421708 0.731680955 140 -2.452534357 -3.316421708 141 -5.975734524 -2.452534357 142 -5.181084783 -5.975734524 143 1.191568280 -5.181084783 144 6.764105371 1.191568280 145 0.185174684 6.764105371 146 1.152250876 0.185174684 147 -1.295349292 1.152250876 148 1.693378564 -1.295349292 149 1.341297711 1.693378564 150 6.631802489 1.341297711 151 -2.217055582 6.631802489 152 -1.961130863 -2.217055582 153 3.225326207 -1.961130863 154 -0.018210758 3.225326207 155 2.936357147 -0.018210758 156 -3.744776436 2.936357147 157 -3.102605099 -3.744776436 158 0.001885863 -3.102605099 159 -3.797032207 0.001885863 160 -1.462561919 -3.797032207 161 -2.905168467 -1.462561919 162 0.948331665 -2.905168467 163 4.658019786 0.948331665 164 -1.636801189 4.658019786 165 -6.894711203 -1.636801189 166 -3.463539171 -6.894711203 167 -3.263563604 -3.463539171 168 -1.243015602 -3.263563604 169 -7.469353556 -1.243015602 170 5.083099168 -7.469353556 171 0.792754398 5.083099168 172 7.019456502 0.792754398 173 0.059299535 7.019456502 174 5.145022440 0.059299535 175 -2.068256719 5.145022440 176 3.872431551 -2.068256719 177 -1.439645970 3.872431551 178 -1.515678220 -1.439645970 179 3.548755032 -1.515678220 180 1.563851395 3.548755032 181 3.005945960 1.563851395 182 -4.074397023 3.005945960 183 4.935643289 -4.074397023 184 -8.901995296 4.935643289 185 -2.243292575 -8.901995296 186 2.238770324 -2.243292575 187 6.370373196 2.238770324 188 -2.243659281 6.370373196 189 -2.188048767 -2.243659281 190 -0.311547360 -2.188048767 191 1.604495052 -0.311547360 192 -1.358919905 1.604495052 193 -0.217144097 -1.358919905 194 -0.749425160 -0.217144097 195 1.414577230 -0.749425160 196 1.393509172 1.414577230 197 -2.342054784 1.393509172 198 0.241422720 -2.342054784 199 -6.722238016 0.241422720 200 -2.078910971 -6.722238016 201 -7.767099523 -2.078910971 202 2.097594703 -7.767099523 203 1.625569874 2.097594703 204 -0.955143801 1.625569874 205 -0.686059843 -0.955143801 206 -0.870720564 -0.686059843 207 0.840165061 -0.870720564 208 2.962640392 0.840165061 209 0.421581989 2.962640392 210 1.297341483 0.421581989 211 2.758076893 1.297341483 212 -4.109091715 2.758076893 213 -3.305432054 -4.109091715 214 1.126374842 -3.305432054 215 -1.912661806 1.126374842 216 -1.840890992 -1.912661806 217 4.834903493 -1.840890992 218 2.627299308 4.834903493 219 -0.285678676 2.627299308 220 1.874703159 -0.285678676 221 4.141067435 1.874703159 222 0.323629169 4.141067435 223 -1.902834479 0.323629169 224 1.976232506 -1.902834479 225 -2.237293855 1.976232506 226 -1.669185706 -2.237293855 227 -0.109348927 -1.669185706 228 1.358127032 -0.109348927 229 0.033513831 1.358127032 230 1.866799003 0.033513831 231 -2.101935401 1.866799003 232 -2.173921685 -2.101935401 233 7.402927128 -2.173921685 234 1.032777194 7.402927128 235 3.962543948 1.032777194 236 5.535675314 3.962543948 237 5.643849243 5.535675314 238 -3.488198638 5.643849243 239 5.230189938 -3.488198638 240 -6.115343729 5.230189938 241 1.608226167 -6.115343729 242 -2.660293445 1.608226167 243 1.861055429 -2.660293445 244 3.908261679 1.861055429 245 -4.669738769 3.908261679 246 -2.237127911 -4.669738769 247 4.221179152 -2.237127911 248 -8.624405295 4.221179152 249 -5.764976500 -8.624405295 250 -2.426890575 -5.764976500 251 0.516007673 -2.426890575 252 2.113385371 0.516007673 253 -0.284830507 2.113385371 254 3.879165196 -0.284830507 255 0.697346914 3.879165196 256 0.521449773 0.697346914 257 -1.800227173 0.521449773 258 3.586797465 -1.800227173 259 3.140367740 3.586797465 260 -4.835232451 3.140367740 261 -0.915995514 -4.835232451 262 6.069233137 -0.915995514 263 -3.873000711 6.069233137 264 NA -3.873000711 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 4.390800158 4.981001822 [2,] -5.620051568 4.390800158 [3,] -3.766063581 -5.620051568 [4,] -1.748357336 -3.766063581 [5,] 2.210717473 -1.748357336 [6,] 5.124191770 2.210717473 [7,] -1.910496452 5.124191770 [8,] 0.620402430 -1.910496452 [9,] 0.034099795 0.620402430 [10,] 3.757458676 0.034099795 [11,] 1.072043230 3.757458676 [12,] 2.645275789 1.072043230 [13,] 2.578195760 2.645275789 [14,] -3.698278618 2.578195760 [15,] -2.229618323 -3.698278618 [16,] 1.906040423 -2.229618323 [17,] 0.343515537 1.906040423 [18,] 1.783435204 0.343515537 [19,] -2.029726495 1.783435204 [20,] -2.805612927 -2.029726495 [21,] -2.877434817 -2.805612927 [22,] 2.298739476 -2.877434817 [23,] -0.178868696 2.298739476 [24,] 4.388167558 -0.178868696 [25,] 6.733593421 4.388167558 [26,] 0.652701691 6.733593421 [27,] -3.159511768 0.652701691 [28,] -1.368958673 -3.159511768 [29,] -0.723295374 -1.368958673 [30,] -2.988806295 -0.723295374 [31,] -6.500811632 -2.988806295 [32,] 3.275905566 -6.500811632 [33,] -2.967581598 3.275905566 [34,] 1.259531044 -2.967581598 [35,] -2.501027198 1.259531044 [36,] -3.537598272 -2.501027198 [37,] 1.641222292 -3.537598272 [38,] -5.042734241 1.641222292 [39,] 0.250956415 -5.042734241 [40,] 2.522703450 0.250956415 [41,] -0.194641151 2.522703450 [42,] 3.691110364 -0.194641151 [43,] 1.066630811 3.691110364 [44,] 5.640024468 1.066630811 [45,] 2.406264582 5.640024468 [46,] 0.684038660 2.406264582 [47,] -2.147546543 0.684038660 [48,] -0.407330730 -2.147546543 [49,] 4.024041935 -0.407330730 [50,] -4.770722667 4.024041935 [51,] -0.949649786 -4.770722667 [52,] -1.547844470 -0.949649786 [53,] -3.390057941 -1.547844470 [54,] -1.376664031 -3.390057941 [55,] 1.317967311 -1.376664031 [56,] 3.937339525 1.317967311 [57,] -6.229325827 3.937339525 [58,] 1.239334103 -6.229325827 [59,] -0.026850594 1.239334103 [60,] -1.515139279 -0.026850594 [61,] 3.088540640 -1.515139279 [62,] -1.399619456 3.088540640 [63,] -3.754401823 -1.399619456 [64,] 0.610200444 -3.754401823 [65,] -0.994082819 0.610200444 [66,] 1.142335695 -0.994082819 [67,] -1.329970612 1.142335695 [68,] 2.663433742 -1.329970612 [69,] -0.618421214 2.663433742 [70,] 2.872950723 -0.618421214 [71,] -4.570404088 2.872950723 [72,] -2.705548623 -4.570404088 [73,] 0.633011698 -2.705548623 [74,] 2.408440290 0.633011698 [75,] 6.080557938 2.408440290 [76,] 1.310612363 6.080557938 [77,] 0.803300961 1.310612363 [78,] -4.922137291 0.803300961 [79,] 5.768209125 -4.922137291 [80,] -2.261467521 5.768209125 [81,] -1.077969864 -2.261467521 [82,] 3.208858298 -1.077969864 [83,] -2.440958839 3.208858298 [84,] -0.366867419 -2.440958839 [85,] 1.488949996 -0.366867419 [86,] -1.402968803 1.488949996 [87,] -4.128786246 -1.402968803 [88,] -0.436907924 -4.128786246 [89,] -4.247109365 -0.436907924 [90,] 2.936357147 -4.247109365 [91,] -2.429929923 2.936357147 [92,] 0.379347067 -2.429929923 [93,] -3.332383435 0.379347067 [94,] 3.542398399 -3.332383435 [95,] 2.295151329 3.542398399 [96,] 1.663978795 2.295151329 [97,] 2.441097222 1.663978795 [98,] -3.355446454 2.441097222 [99,] 2.753854893 -3.355446454 [100,] 2.149655414 2.753854893 [101,] -0.332155666 2.149655414 [102,] 0.057147951 -0.332155666 [103,] -3.520665242 0.057147951 [104,] 7.204011606 -3.520665242 [105,] 2.765091078 7.204011606 [106,] 4.592705352 2.765091078 [107,] 1.558352478 4.592705352 [108,] 6.074943302 1.558352478 [109,] -4.679069048 6.074943302 [110,] -2.672211890 -4.679069048 [111,] -6.155350041 -2.672211890 [112,] 0.492941189 -6.155350041 [113,] 2.657169327 0.492941189 [114,] 2.770122941 2.657169327 [115,] -3.329095412 2.770122941 [116,] -3.292969830 -3.329095412 [117,] -1.677752694 -3.292969830 [118,] 2.421695823 -1.677752694 [119,] -1.984453702 2.421695823 [120,] -0.365298627 -1.984453702 [121,] 1.362263204 -0.365298627 [122,] 0.596085707 1.362263204 [123,] 2.856523823 0.596085707 [124,] -2.894268121 2.856523823 [125,] 4.809063884 -2.894268121 [126,] 7.190289808 4.809063884 [127,] -3.102605099 7.190289808 [128,] 1.366813004 -3.102605099 [129,] -2.016476156 1.366813004 [130,] -4.462885045 -2.016476156 [131,] 3.925242889 -4.462885045 [132,] -5.156283082 3.925242889 [133,] 0.881512184 -5.156283082 [134,] -0.552361138 0.881512184 [135,] -0.326027748 -0.552361138 [136,] -1.014397192 -0.326027748 [137,] -4.147574613 -1.014397192 [138,] 0.731680955 -4.147574613 [139,] -3.316421708 0.731680955 [140,] -2.452534357 -3.316421708 [141,] -5.975734524 -2.452534357 [142,] -5.181084783 -5.975734524 [143,] 1.191568280 -5.181084783 [144,] 6.764105371 1.191568280 [145,] 0.185174684 6.764105371 [146,] 1.152250876 0.185174684 [147,] -1.295349292 1.152250876 [148,] 1.693378564 -1.295349292 [149,] 1.341297711 1.693378564 [150,] 6.631802489 1.341297711 [151,] -2.217055582 6.631802489 [152,] -1.961130863 -2.217055582 [153,] 3.225326207 -1.961130863 [154,] -0.018210758 3.225326207 [155,] 2.936357147 -0.018210758 [156,] -3.744776436 2.936357147 [157,] -3.102605099 -3.744776436 [158,] 0.001885863 -3.102605099 [159,] -3.797032207 0.001885863 [160,] -1.462561919 -3.797032207 [161,] -2.905168467 -1.462561919 [162,] 0.948331665 -2.905168467 [163,] 4.658019786 0.948331665 [164,] -1.636801189 4.658019786 [165,] -6.894711203 -1.636801189 [166,] -3.463539171 -6.894711203 [167,] -3.263563604 -3.463539171 [168,] -1.243015602 -3.263563604 [169,] -7.469353556 -1.243015602 [170,] 5.083099168 -7.469353556 [171,] 0.792754398 5.083099168 [172,] 7.019456502 0.792754398 [173,] 0.059299535 7.019456502 [174,] 5.145022440 0.059299535 [175,] -2.068256719 5.145022440 [176,] 3.872431551 -2.068256719 [177,] -1.439645970 3.872431551 [178,] -1.515678220 -1.439645970 [179,] 3.548755032 -1.515678220 [180,] 1.563851395 3.548755032 [181,] 3.005945960 1.563851395 [182,] -4.074397023 3.005945960 [183,] 4.935643289 -4.074397023 [184,] -8.901995296 4.935643289 [185,] -2.243292575 -8.901995296 [186,] 2.238770324 -2.243292575 [187,] 6.370373196 2.238770324 [188,] -2.243659281 6.370373196 [189,] -2.188048767 -2.243659281 [190,] -0.311547360 -2.188048767 [191,] 1.604495052 -0.311547360 [192,] -1.358919905 1.604495052 [193,] -0.217144097 -1.358919905 [194,] -0.749425160 -0.217144097 [195,] 1.414577230 -0.749425160 [196,] 1.393509172 1.414577230 [197,] -2.342054784 1.393509172 [198,] 0.241422720 -2.342054784 [199,] -6.722238016 0.241422720 [200,] -2.078910971 -6.722238016 [201,] -7.767099523 -2.078910971 [202,] 2.097594703 -7.767099523 [203,] 1.625569874 2.097594703 [204,] -0.955143801 1.625569874 [205,] -0.686059843 -0.955143801 [206,] -0.870720564 -0.686059843 [207,] 0.840165061 -0.870720564 [208,] 2.962640392 0.840165061 [209,] 0.421581989 2.962640392 [210,] 1.297341483 0.421581989 [211,] 2.758076893 1.297341483 [212,] -4.109091715 2.758076893 [213,] -3.305432054 -4.109091715 [214,] 1.126374842 -3.305432054 [215,] -1.912661806 1.126374842 [216,] -1.840890992 -1.912661806 [217,] 4.834903493 -1.840890992 [218,] 2.627299308 4.834903493 [219,] -0.285678676 2.627299308 [220,] 1.874703159 -0.285678676 [221,] 4.141067435 1.874703159 [222,] 0.323629169 4.141067435 [223,] -1.902834479 0.323629169 [224,] 1.976232506 -1.902834479 [225,] -2.237293855 1.976232506 [226,] -1.669185706 -2.237293855 [227,] -0.109348927 -1.669185706 [228,] 1.358127032 -0.109348927 [229,] 0.033513831 1.358127032 [230,] 1.866799003 0.033513831 [231,] -2.101935401 1.866799003 [232,] -2.173921685 -2.101935401 [233,] 7.402927128 -2.173921685 [234,] 1.032777194 7.402927128 [235,] 3.962543948 1.032777194 [236,] 5.535675314 3.962543948 [237,] 5.643849243 5.535675314 [238,] -3.488198638 5.643849243 [239,] 5.230189938 -3.488198638 [240,] -6.115343729 5.230189938 [241,] 1.608226167 -6.115343729 [242,] -2.660293445 1.608226167 [243,] 1.861055429 -2.660293445 [244,] 3.908261679 1.861055429 [245,] -4.669738769 3.908261679 [246,] -2.237127911 -4.669738769 [247,] 4.221179152 -2.237127911 [248,] -8.624405295 4.221179152 [249,] -5.764976500 -8.624405295 [250,] -2.426890575 -5.764976500 [251,] 0.516007673 -2.426890575 [252,] 2.113385371 0.516007673 [253,] -0.284830507 2.113385371 [254,] 3.879165196 -0.284830507 [255,] 0.697346914 3.879165196 [256,] 0.521449773 0.697346914 [257,] -1.800227173 0.521449773 [258,] 3.586797465 -1.800227173 [259,] 3.140367740 3.586797465 [260,] -4.835232451 3.140367740 [261,] -0.915995514 -4.835232451 [262,] 6.069233137 -0.915995514 [263,] -3.873000711 6.069233137 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 4.390800158 4.981001822 2 -5.620051568 4.390800158 3 -3.766063581 -5.620051568 4 -1.748357336 -3.766063581 5 2.210717473 -1.748357336 6 5.124191770 2.210717473 7 -1.910496452 5.124191770 8 0.620402430 -1.910496452 9 0.034099795 0.620402430 10 3.757458676 0.034099795 11 1.072043230 3.757458676 12 2.645275789 1.072043230 13 2.578195760 2.645275789 14 -3.698278618 2.578195760 15 -2.229618323 -3.698278618 16 1.906040423 -2.229618323 17 0.343515537 1.906040423 18 1.783435204 0.343515537 19 -2.029726495 1.783435204 20 -2.805612927 -2.029726495 21 -2.877434817 -2.805612927 22 2.298739476 -2.877434817 23 -0.178868696 2.298739476 24 4.388167558 -0.178868696 25 6.733593421 4.388167558 26 0.652701691 6.733593421 27 -3.159511768 0.652701691 28 -1.368958673 -3.159511768 29 -0.723295374 -1.368958673 30 -2.988806295 -0.723295374 31 -6.500811632 -2.988806295 32 3.275905566 -6.500811632 33 -2.967581598 3.275905566 34 1.259531044 -2.967581598 35 -2.501027198 1.259531044 36 -3.537598272 -2.501027198 37 1.641222292 -3.537598272 38 -5.042734241 1.641222292 39 0.250956415 -5.042734241 40 2.522703450 0.250956415 41 -0.194641151 2.522703450 42 3.691110364 -0.194641151 43 1.066630811 3.691110364 44 5.640024468 1.066630811 45 2.406264582 5.640024468 46 0.684038660 2.406264582 47 -2.147546543 0.684038660 48 -0.407330730 -2.147546543 49 4.024041935 -0.407330730 50 -4.770722667 4.024041935 51 -0.949649786 -4.770722667 52 -1.547844470 -0.949649786 53 -3.390057941 -1.547844470 54 -1.376664031 -3.390057941 55 1.317967311 -1.376664031 56 3.937339525 1.317967311 57 -6.229325827 3.937339525 58 1.239334103 -6.229325827 59 -0.026850594 1.239334103 60 -1.515139279 -0.026850594 61 3.088540640 -1.515139279 62 -1.399619456 3.088540640 63 -3.754401823 -1.399619456 64 0.610200444 -3.754401823 65 -0.994082819 0.610200444 66 1.142335695 -0.994082819 67 -1.329970612 1.142335695 68 2.663433742 -1.329970612 69 -0.618421214 2.663433742 70 2.872950723 -0.618421214 71 -4.570404088 2.872950723 72 -2.705548623 -4.570404088 73 0.633011698 -2.705548623 74 2.408440290 0.633011698 75 6.080557938 2.408440290 76 1.310612363 6.080557938 77 0.803300961 1.310612363 78 -4.922137291 0.803300961 79 5.768209125 -4.922137291 80 -2.261467521 5.768209125 81 -1.077969864 -2.261467521 82 3.208858298 -1.077969864 83 -2.440958839 3.208858298 84 -0.366867419 -2.440958839 85 1.488949996 -0.366867419 86 -1.402968803 1.488949996 87 -4.128786246 -1.402968803 88 -0.436907924 -4.128786246 89 -4.247109365 -0.436907924 90 2.936357147 -4.247109365 91 -2.429929923 2.936357147 92 0.379347067 -2.429929923 93 -3.332383435 0.379347067 94 3.542398399 -3.332383435 95 2.295151329 3.542398399 96 1.663978795 2.295151329 97 2.441097222 1.663978795 98 -3.355446454 2.441097222 99 2.753854893 -3.355446454 100 2.149655414 2.753854893 101 -0.332155666 2.149655414 102 0.057147951 -0.332155666 103 -3.520665242 0.057147951 104 7.204011606 -3.520665242 105 2.765091078 7.204011606 106 4.592705352 2.765091078 107 1.558352478 4.592705352 108 6.074943302 1.558352478 109 -4.679069048 6.074943302 110 -2.672211890 -4.679069048 111 -6.155350041 -2.672211890 112 0.492941189 -6.155350041 113 2.657169327 0.492941189 114 2.770122941 2.657169327 115 -3.329095412 2.770122941 116 -3.292969830 -3.329095412 117 -1.677752694 -3.292969830 118 2.421695823 -1.677752694 119 -1.984453702 2.421695823 120 -0.365298627 -1.984453702 121 1.362263204 -0.365298627 122 0.596085707 1.362263204 123 2.856523823 0.596085707 124 -2.894268121 2.856523823 125 4.809063884 -2.894268121 126 7.190289808 4.809063884 127 -3.102605099 7.190289808 128 1.366813004 -3.102605099 129 -2.016476156 1.366813004 130 -4.462885045 -2.016476156 131 3.925242889 -4.462885045 132 -5.156283082 3.925242889 133 0.881512184 -5.156283082 134 -0.552361138 0.881512184 135 -0.326027748 -0.552361138 136 -1.014397192 -0.326027748 137 -4.147574613 -1.014397192 138 0.731680955 -4.147574613 139 -3.316421708 0.731680955 140 -2.452534357 -3.316421708 141 -5.975734524 -2.452534357 142 -5.181084783 -5.975734524 143 1.191568280 -5.181084783 144 6.764105371 1.191568280 145 0.185174684 6.764105371 146 1.152250876 0.185174684 147 -1.295349292 1.152250876 148 1.693378564 -1.295349292 149 1.341297711 1.693378564 150 6.631802489 1.341297711 151 -2.217055582 6.631802489 152 -1.961130863 -2.217055582 153 3.225326207 -1.961130863 154 -0.018210758 3.225326207 155 2.936357147 -0.018210758 156 -3.744776436 2.936357147 157 -3.102605099 -3.744776436 158 0.001885863 -3.102605099 159 -3.797032207 0.001885863 160 -1.462561919 -3.797032207 161 -2.905168467 -1.462561919 162 0.948331665 -2.905168467 163 4.658019786 0.948331665 164 -1.636801189 4.658019786 165 -6.894711203 -1.636801189 166 -3.463539171 -6.894711203 167 -3.263563604 -3.463539171 168 -1.243015602 -3.263563604 169 -7.469353556 -1.243015602 170 5.083099168 -7.469353556 171 0.792754398 5.083099168 172 7.019456502 0.792754398 173 0.059299535 7.019456502 174 5.145022440 0.059299535 175 -2.068256719 5.145022440 176 3.872431551 -2.068256719 177 -1.439645970 3.872431551 178 -1.515678220 -1.439645970 179 3.548755032 -1.515678220 180 1.563851395 3.548755032 181 3.005945960 1.563851395 182 -4.074397023 3.005945960 183 4.935643289 -4.074397023 184 -8.901995296 4.935643289 185 -2.243292575 -8.901995296 186 2.238770324 -2.243292575 187 6.370373196 2.238770324 188 -2.243659281 6.370373196 189 -2.188048767 -2.243659281 190 -0.311547360 -2.188048767 191 1.604495052 -0.311547360 192 -1.358919905 1.604495052 193 -0.217144097 -1.358919905 194 -0.749425160 -0.217144097 195 1.414577230 -0.749425160 196 1.393509172 1.414577230 197 -2.342054784 1.393509172 198 0.241422720 -2.342054784 199 -6.722238016 0.241422720 200 -2.078910971 -6.722238016 201 -7.767099523 -2.078910971 202 2.097594703 -7.767099523 203 1.625569874 2.097594703 204 -0.955143801 1.625569874 205 -0.686059843 -0.955143801 206 -0.870720564 -0.686059843 207 0.840165061 -0.870720564 208 2.962640392 0.840165061 209 0.421581989 2.962640392 210 1.297341483 0.421581989 211 2.758076893 1.297341483 212 -4.109091715 2.758076893 213 -3.305432054 -4.109091715 214 1.126374842 -3.305432054 215 -1.912661806 1.126374842 216 -1.840890992 -1.912661806 217 4.834903493 -1.840890992 218 2.627299308 4.834903493 219 -0.285678676 2.627299308 220 1.874703159 -0.285678676 221 4.141067435 1.874703159 222 0.323629169 4.141067435 223 -1.902834479 0.323629169 224 1.976232506 -1.902834479 225 -2.237293855 1.976232506 226 -1.669185706 -2.237293855 227 -0.109348927 -1.669185706 228 1.358127032 -0.109348927 229 0.033513831 1.358127032 230 1.866799003 0.033513831 231 -2.101935401 1.866799003 232 -2.173921685 -2.101935401 233 7.402927128 -2.173921685 234 1.032777194 7.402927128 235 3.962543948 1.032777194 236 5.535675314 3.962543948 237 5.643849243 5.535675314 238 -3.488198638 5.643849243 239 5.230189938 -3.488198638 240 -6.115343729 5.230189938 241 1.608226167 -6.115343729 242 -2.660293445 1.608226167 243 1.861055429 -2.660293445 244 3.908261679 1.861055429 245 -4.669738769 3.908261679 246 -2.237127911 -4.669738769 247 4.221179152 -2.237127911 248 -8.624405295 4.221179152 249 -5.764976500 -8.624405295 250 -2.426890575 -5.764976500 251 0.516007673 -2.426890575 252 2.113385371 0.516007673 253 -0.284830507 2.113385371 254 3.879165196 -0.284830507 255 0.697346914 3.879165196 256 0.521449773 0.697346914 257 -1.800227173 0.521449773 258 3.586797465 -1.800227173 259 3.140367740 3.586797465 260 -4.835232451 3.140367740 261 -0.915995514 -4.835232451 262 6.069233137 -0.915995514 263 -3.873000711 6.069233137 > 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/7uod41384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8nypa1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/93zu91384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10scur1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/111xpi1384526060.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12vsxz1384526060.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13gps11384526061.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14u7sp1384526061.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15923o1384526061.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16p5r51384526061.tab") + } > > try(system("convert tmp/1gcro1384526060.ps tmp/1gcro1384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/2d6ue1384526060.ps tmp/2d6ue1384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/3m1sg1384526060.ps tmp/3m1sg1384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/4ji421384526060.ps tmp/4ji421384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/5s7ke1384526060.ps tmp/5s7ke1384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/6c5b41384526060.ps tmp/6c5b41384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/7uod41384526060.ps tmp/7uod41384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/8nypa1384526060.ps tmp/8nypa1384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/93zu91384526060.ps tmp/93zu91384526060.png",intern=TRUE)) character(0) > try(system("convert tmp/10scur1384526060.ps tmp/10scur1384526060.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.898 3.125 20.108