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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+ ,1 + ,11 + ,0 + ,1 + ,1 + ,4 + ,4 + ,3 + ,2 + ,0 + ,11 + ,0 + ,0 + ,1 + ,3 + ,3 + ,3 + ,3 + ,1 + ,10 + ,0 + ,1 + ,1 + ,3 + ,4 + ,2 + ,4 + ,0 + ,10 + ,0 + ,1 + ,1 + ,4 + ,3 + ,4 + ,2 + ,1 + ,10 + ,0 + ,0 + ,1 + ,5 + ,5 + ,5 + ,5 + ,1 + ,8 + ,0 + ,1 + ,1 + ,3 + ,3 + ,3 + ,2 + ,1 + ,11 + ,0 + ,1 + ,1 + ,4 + ,4 + ,4 + ,4 + ,1 + ,8 + ,0 + ,1 + ,1 + ,3 + ,1 + ,3 + ,2 + ,1 + ,4 + ,0 + ,1 + ,1 + ,3 + ,4 + ,4 + ,2 + ,1 + ,6 + ,0 + ,0 + ,3 + ,3 + ,3 + ,4 + ,2 + ,1 + ,11 + ,0 + ,0 + ,3 + ,3 + ,3 + ,2 + ,2 + ,0 + ,7 + ,0 + ,1 + ,1 + ,3 + ,3 + ,3 + ,3 + ,1 + ,6) + ,dim=c(9 + ,275) + ,dimnames=list(c('illness' + ,'sports.competition' + ,'addictive.drugs' + ,'fruits' + ,'fish' + ,'high.alcohol' + ,'milk' + ,'Gender' + ,'score') + ,1:275)) > y <- array(NA,dim=c(9,275),dimnames=list(c('illness','sports.competition','addictive.drugs','fruits','fish','high.alcohol','milk','Gender','score'),1:275)) > 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 = '9' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '9' > #'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 score illness sports.competition addictive.drugs fruits fish high.alcohol 1 11 0 1 1 4 4 2 2 13 0 1 1 3 2 3 3 7 0 0 1 4 4 3 4 10 0 0 1 3 3 2 5 10 0 0 1 5 4 1 6 5 0 0 1 3 3 3 7 7 1 0 1 5 3 2 8 9 0 1 1 3 3 2 9 14 0 0 2 5 4 3 10 8 1 0 1 3 3 4 11 14 0 1 1 4 4 1 12 11 0 1 1 4 3 2 13 9 0 1 2 4 4 4 14 12 0 0 1 3 2 2 15 16 0 1 1 5 4 3 16 10 0 0 1 4 3 3 17 8 0 0 1 3 3 1 18 9 0 1 1 3 4 3 19 7 0 0 2 3 3 3 20 13 0 0 1 4 3 2 21 10 0 1 2 5 5 2 22 12 1 1 1 4 4 2 23 10 1 1 4 5 4 2 24 3 0 1 1 3 3 4 25 8 1 1 1 4 5 3 26 15 1 0 1 2 2 1 27 13 1 1 1 5 4 2 28 9 0 0 1 2 3 1 29 12 0 1 1 3 2 3 30 12 0 1 1 3 3 2 31 9 0 1 1 3 4 1 32 9 0 1 1 3 4 3 33 10 0 0 1 4 1 2 34 8 0 0 1 3 2 3 35 8 0 1 1 5 5 1 36 8 0 0 1 2 2 3 37 12 0 1 1 3 4 3 38 9 1 1 1 3 3 2 39 8 1 1 1 4 1 3 40 15 0 1 1 5 3 3 41 9 0 1 1 3 4 3 42 6 0 0 3 3 3 3 43 13 0 0 2 3 4 3 44 9 0 1 1 3 1 3 45 12 1 1 1 3 4 2 46 17 0 0 1 4 3 1 47 13 0 0 1 3 4 3 48 11 0 0 1 2 3 3 49 10 0 1 1 3 3 1 50 7 1 0 1 3 1 1 51 14 0 1 1 3 2 1 52 11 0 1 1 4 5 4 53 9 0 1 1 2 4 3 54 8 0 0 1 5 3 1 55 12 0 1 1 4 3 2 56 13 0 0 2 2 3 4 57 2 0 1 1 3 4 3 58 18 0 0 1 4 3 2 59 11 0 1 1 3 4 3 60 10 0 0 1 3 3 3 61 13 0 1 1 4 3 2 62 6 0 0 1 3 4 3 63 8 0 0 1 3 4 1 64 12 0 1 1 4 3 3 65 12 0 0 1 2 3 2 66 14 0 0 1 2 4 2 67 8 0 0 1 4 3 4 68 7 0 0 1 2 4 2 69 10 0 1 1 4 3 3 70 10 1 1 1 3 4 2 71 14 0 1 1 5 5 4 72 16 0 1 1 3 2 2 73 14 0 1 1 3 2 3 74 6 0 1 2 4 4 3 75 10 0 0 1 3 2 1 76 8 0 0 1 3 4 2 77 9 0 0 1 4 2 2 78 7 0 1 1 3 4 3 79 11 0 0 1 3 3 1 80 13 0 1 3 3 3 2 81 12 0 0 1 3 4 3 82 12 0 1 2 5 3 2 83 11 0 1 1 3 3 3 84 6 0 1 1 5 4 4 85 10 0 0 1 4 4 3 86 5 0 1 1 3 4 3 87 8 1 0 1 3 4 3 88 13 0 1 1 5 4 2 89 18 0 0 1 4 4 3 90 15 0 1 2 5 4 3 91 13 0 1 3 4 4 3 92 7 0 0 1 2 4 3 93 9 0 1 1 5 4 2 94 9 0 1 1 4 3 2 95 7 0 0 1 2 4 4 96 13 0 1 1 2 2 2 97 6 1 0 1 3 4 1 98 7 0 0 1 4 4 3 99 15 0 0 1 2 5 2 100 11 0 0 1 4 1 1 101 11 0 1 1 3 2 3 102 7 0 1 1 3 3 2 103 8 0 1 3 3 3 2 104 7 0 1 1 4 4 3 105 13 0 1 1 3 3 2 106 9 0 0 3 3 4 3 107 15 1 1 2 4 4 1 108 10 0 1 1 3 3 3 109 11 0 0 1 4 4 2 110 15 0 1 1 3 4 3 111 8 0 1 2 4 3 2 112 9 0 1 3 4 4 2 113 8 1 1 1 3 3 3 114 19 0 1 1 5 4 2 115 8 0 0 1 3 3 2 116 10 0 1 1 4 2 2 117 15 0 1 1 4 4 3 118 8 0 1 1 2 4 2 119 7 0 1 1 4 3 4 120 15 0 1 1 4 4 1 121 9 0 1 1 4 4 3 122 8 0 0 1 3 4 1 123 11 0 1 1 5 3 2 124 9 0 1 1 4 3 4 125 11 1 0 3 3 4 3 126 10 1 0 2 3 4 2 127 8 0 0 1 4 4 1 128 7 0 1 1 5 4 2 129 8 0 0 1 3 4 3 130 9 0 1 1 3 4 2 131 11 0 1 1 2 3 3 132 12 1 1 1 5 4 3 133 14 0 0 1 3 4 3 134 10 0 1 1 3 3 3 135 10 0 1 1 4 3 3 136 9 0 1 1 2 3 1 137 12 0 0 2 4 4 3 138 5 1 1 1 3 3 4 139 14 0 0 1 3 1 3 140 14 1 0 1 5 1 2 141 13 0 1 1 5 3 5 142 17 0 1 1 3 3 2 143 11 0 1 1 4 4 1 144 8 0 1 1 5 3 2 145 10 0 1 1 2 4 2 146 5 0 0 1 4 3 1 147 6 1 0 2 2 3 1 148 5 0 0 1 4 4 2 149 10 0 0 1 3 2 2 150 9 0 0 1 4 4 4 151 10 0 0 1 4 4 1 152 9 0 1 1 4 4 1 153 8 0 1 1 5 5 3 154 7 0 0 1 5 5 3 155 13 0 1 1 4 3 4 156 11 0 0 1 3 4 2 157 11 0 0 1 4 4 3 158 11 0 0 2 5 1 1 159 12 0 1 1 4 3 4 160 11 0 1 1 5 5 5 161 10 0 1 1 4 4 2 162 9 0 0 1 2 2 3 163 13 0 1 1 2 1 2 164 10 1 1 1 5 4 2 165 7 0 1 1 3 4 4 166 10 0 1 1 3 3 3 167 11 1 1 1 3 3 2 168 14 0 0 1 3 4 2 169 10 0 1 1 4 4 1 170 12 0 0 1 4 2 3 171 13 0 0 1 3 4 2 172 8 0 0 1 5 2 2 173 8 0 1 1 3 4 3 174 8 0 0 1 4 4 2 175 11 0 0 1 3 4 2 176 11 0 0 1 4 4 2 177 9 0 1 1 5 4 1 178 12 0 0 1 3 4 1 179 7 0 1 1 5 4 1 180 7 1 1 1 3 3 3 181 9 0 0 1 4 2 3 182 11 1 0 1 5 3 2 183 8 0 0 1 4 3 2 184 10 0 1 1 4 2 5 185 7 1 0 1 3 4 3 186 6 0 1 1 5 1 2 187 8 0 1 1 4 4 1 188 11 0 1 1 4 4 3 189 11 0 1 1 3 3 2 190 13 0 0 1 5 4 2 191 13 0 1 1 3 3 2 192 11 1 1 1 4 4 3 193 7 0 1 3 4 4 3 194 7 0 1 1 3 4 2 195 10 0 1 1 3 4 4 196 8 0 0 1 5 1 1 197 8 0 0 1 4 4 1 198 9 0 0 1 4 3 1 199 9 0 0 1 3 1 1 200 17 0 0 1 5 3 1 201 11 0 1 1 2 3 3 202 11 0 0 1 3 4 1 203 11 0 0 1 3 4 1 204 12 1 0 1 4 4 3 205 10 0 0 2 2 2 2 206 8 0 1 1 4 4 4 207 6 0 0 1 2 4 2 208 10 0 0 1 4 4 3 209 10 0 1 1 4 4 3 210 4 0 0 1 5 4 2 211 10 0 1 1 5 4 2 212 7 1 0 1 2 2 1 213 12 0 1 1 3 4 3 214 7 0 0 1 4 3 2 215 12 1 1 1 4 2 3 216 9 0 1 1 4 4 3 217 10 0 0 1 3 4 2 218 11 0 1 1 3 2 3 219 12 0 1 1 3 4 2 220 12 0 1 1 3 2 1 221 2 0 1 2 2 2 3 222 9 0 1 3 3 3 3 223 9 0 0 1 4 3 3 224 4 0 1 1 3 3 2 225 9 0 1 1 5 5 4 226 8 0 0 1 2 3 2 227 7 1 1 1 2 4 4 228 10 0 0 1 4 3 3 229 14 0 0 1 4 3 2 230 11 0 1 1 4 3 4 231 8 0 0 1 4 2 1 232 8 0 1 1 5 4 2 233 13 0 1 4 5 4 3 234 6 0 0 1 5 4 3 235 10 0 0 1 3 3 1 236 9 0 1 1 3 3 1 237 11 0 0 1 4 4 2 238 8 0 0 1 4 4 4 239 6 0 0 1 4 4 2 240 9 0 0 1 4 3 2 241 8 0 0 1 3 2 1 242 11 1 0 1 5 2 3 243 10 0 0 1 2 4 2 244 7 0 1 1 4 4 3 245 7 0 0 1 5 2 2 246 8 0 1 1 2 4 3 247 7 0 0 1 2 4 4 248 10 0 0 3 3 1 3 249 9 0 1 1 5 4 2 250 13 1 1 1 4 1 3 251 11 0 0 1 4 5 3 252 6 0 0 1 5 5 1 253 8 0 1 1 2 3 3 254 8 0 1 1 3 4 2 255 7 0 0 1 5 3 1 256 11 0 1 1 3 4 4 257 10 0 0 1 4 3 2 258 8 0 1 1 3 3 3 259 7 0 0 1 4 4 3 260 7 0 0 1 5 5 1 261 9 0 1 1 4 3 3 262 11 0 1 1 4 4 4 263 11 0 0 2 4 4 3 264 11 0 1 1 4 4 3 265 10 0 0 1 3 3 3 266 10 0 1 1 3 4 2 267 10 0 1 1 4 3 4 268 8 0 0 1 5 5 5 269 11 0 1 1 3 3 3 270 8 0 1 1 4 4 4 271 4 0 1 1 3 1 3 272 6 0 1 1 3 4 4 273 11 0 0 3 3 3 4 274 7 0 0 3 3 3 2 275 6 0 1 1 3 3 3 milk Gender 1 5 1 2 4 1 3 5 0 4 2 0 5 3 0 6 2 0 7 2 1 8 4 1 9 3 0 10 3 1 11 5 1 12 5 1 13 2 0 14 2 0 15 5 1 16 4 0 17 2 0 18 2 1 19 4 0 20 5 0 21 5 1 22 1 0 23 4 1 24 5 1 25 4 1 26 1 0 27 5 0 28 2 0 29 4 1 30 1 0 31 4 1 32 4 1 33 3 0 34 4 1 35 5 0 36 2 1 37 4 0 38 5 1 39 4 0 40 3 1 41 4 1 42 3 0 43 4 1 44 4 1 45 4 1 46 3 0 47 3 0 48 2 1 49 4 1 50 2 1 51 4 0 52 4 1 53 2 0 54 5 0 55 5 1 56 5 1 57 5 1 58 4 0 59 2 1 60 5 1 61 4 1 62 4 1 63 3 0 64 3 1 65 2 1 66 2 1 67 1 0 68 2 0 69 2 1 70 2 1 71 4 1 72 4 1 73 2 1 74 4 1 75 5 0 76 5 1 77 5 1 78 3 1 79 2 0 80 3 1 81 4 0 82 2 0 83 5 1 84 5 1 85 4 0 86 3 1 87 4 1 88 3 1 89 3 0 90 4 1 91 1 1 92 4 0 93 1 0 94 4 1 95 3 1 96 1 1 97 1 0 98 3 0 99 3 1 100 1 0 101 1 1 102 1 1 103 3 1 104 4 1 105 2 1 106 3 1 107 1 1 108 3 1 109 3 0 110 5 0 111 4 0 112 4 0 113 1 0 114 3 1 115 2 1 116 2 0 117 4 1 118 4 1 119 5 0 120 4 1 121 1 1 122 5 0 123 2 0 124 2 1 125 4 1 126 1 1 127 2 0 128 5 0 129 2 0 130 3 1 131 3 1 132 5 1 133 4 1 134 2 0 135 2 0 136 2 1 137 1 1 138 3 1 139 1 0 140 4 0 141 5 0 142 3 1 143 2 0 144 3 0 145 5 1 146 1 0 147 3 0 148 1 0 149 2 1 150 1 0 151 5 0 152 5 1 153 3 0 154 4 1 155 5 1 156 3 1 157 3 0 158 2 0 159 5 1 160 2 0 161 2 1 162 5 0 163 3 1 164 3 1 165 3 1 166 2 1 167 3 1 168 1 0 169 2 1 170 2 0 171 3 0 172 4 0 173 4 0 174 4 0 175 1 1 176 3 1 177 4 0 178 5 0 179 5 1 180 3 1 181 3 0 182 2 0 183 4 0 184 5 1 185 1 0 186 4 1 187 2 0 188 4 1 189 2 1 190 3 0 191 4 1 192 2 0 193 2 1 194 5 0 195 5 1 196 3 0 197 3 0 198 5 0 199 3 0 200 4 0 201 2 1 202 5 0 203 3 0 204 4 1 205 2 0 206 3 1 207 4 0 208 5 0 209 4 0 210 5 0 211 2 0 212 4 0 213 5 1 214 4 0 215 3 0 216 5 1 217 3 0 218 5 1 219 3 0 220 3 1 221 4 1 222 3 1 223 2 0 224 2 0 225 5 1 226 1 1 227 1 0 228 4 0 229 3 1 230 2 1 231 2 0 232 5 0 233 4 0 234 4 0 235 2 0 236 4 0 237 2 0 238 5 1 239 2 0 240 2 1 241 4 0 242 4 1 243 3 1 244 5 0 245 3 0 246 4 1 247 1 1 248 3 0 249 1 1 250 1 1 251 3 0 252 5 0 253 2 0 254 1 1 255 1 0 256 1 1 257 4 0 258 3 1 259 4 1 260 4 1 261 2 1 262 2 1 263 5 1 264 2 0 265 3 1 266 4 0 267 2 1 268 5 1 269 2 1 270 4 1 271 2 1 272 2 1 273 2 1 274 2 0 275 3 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) illness sports.competition addictive.drugs 9.46356 -0.20271 0.25877 0.07930 fruits fish high.alcohol milk 0.37434 -0.18276 -0.27845 -0.06312 Gender 0.62518 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.8014 -1.8357 -0.1358 1.6226 8.7155 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.46356 1.03514 9.142 <2e-16 *** illness -0.20271 0.52264 -0.388 0.6984 sports.competition 0.25877 0.37503 0.690 0.4908 addictive.drugs 0.07930 0.32607 0.243 0.8080 fruits 0.37434 0.19855 1.885 0.0605 . fish -0.18276 0.18835 -0.970 0.3328 high.alcohol -0.27845 0.19007 -1.465 0.1441 milk -0.06312 0.13689 -0.461 0.6451 Gender 0.62518 0.38820 1.610 0.1085 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.822 on 266 degrees of freedom Multiple R-squared: 0.03146, Adjusted R-squared: 0.002336 F-statistic: 1.08 on 8 and 266 DF, p-value: 0.3772 > 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,] 0.5678189 0.864362189 0.432181094 [2,] 0.3981983 0.796396646 0.601801677 [3,] 0.2643209 0.528641880 0.735679060 [4,] 0.6490427 0.701914511 0.350957256 [5,] 0.5374024 0.925195179 0.462597590 [6,] 0.4286927 0.857385429 0.571307286 [7,] 0.3320214 0.664042824 0.667978588 [8,] 0.3725333 0.745066600 0.627466700 [9,] 0.3759384 0.751876897 0.624061552 [10,] 0.3397666 0.679533219 0.660233390 [11,] 0.3525096 0.705019161 0.647490420 [12,] 0.2785486 0.557097296 0.721451352 [13,] 0.6232250 0.753550026 0.376775013 [14,] 0.5529884 0.894023210 0.447011605 [15,] 0.7162840 0.567431958 0.283715979 [16,] 0.6771144 0.645771122 0.322885561 [17,] 0.6138883 0.772223339 0.386111670 [18,] 0.5561805 0.887638957 0.443819478 [19,] 0.4946054 0.989210862 0.505394569 [20,] 0.4393145 0.878628975 0.560685513 [21,] 0.3882845 0.776568950 0.611715525 [22,] 0.4149502 0.829900475 0.585049763 [23,] 0.3586283 0.717256686 0.641371657 [24,] 0.4424385 0.884876955 0.557561522 [25,] 0.3876096 0.775219194 0.612390403 [26,] 0.3769798 0.753959588 0.623020206 [27,] 0.3414893 0.682978533 0.658510733 [28,] 0.4218214 0.843642880 0.578178560 [29,] 0.4412781 0.882556134 0.558721933 [30,] 0.3883611 0.776722153 0.611638923 [31,] 0.3613564 0.722712768 0.638643616 [32,] 0.5028963 0.994207375 0.497103687 [33,] 0.4646589 0.929317838 0.535341081 [34,] 0.4486752 0.897350429 0.551324785 [35,] 0.6027416 0.794516769 0.397258385 [36,] 0.6552587 0.689482575 0.344741288 [37,] 0.6320706 0.735858733 0.367929366 [38,] 0.5941577 0.811684640 0.405842320 [39,] 0.6168081 0.766383768 0.383191884 [40,] 0.6204587 0.759082542 0.379541271 [41,] 0.5824949 0.835010176 0.417505088 [42,] 0.5393485 0.921302918 0.460651459 [43,] 0.5603553 0.879289369 0.439644685 [44,] 0.5223706 0.955258837 0.477629418 [45,] 0.6231389 0.753722291 0.376861146 [46,] 0.8404432 0.319113646 0.159556823 [47,] 0.9462385 0.107522979 0.053761489 [48,] 0.9348800 0.130240016 0.065120008 [49,] 0.9206657 0.158668671 0.079334336 [50,] 0.9124414 0.175117200 0.087558600 [51,] 0.9172538 0.165492429 0.082746215 [52,] 0.9111694 0.177661188 0.088830594 [53,] 0.8964393 0.207121407 0.103560703 [54,] 0.8900265 0.219947094 0.109973547 [55,] 0.9112479 0.177504228 0.088752114 [56,] 0.9023704 0.195259247 0.097629624 [57,] 0.8971399 0.205720185 0.102860092 [58,] 0.8797969 0.240406117 0.120203059 [59,] 0.8580332 0.283933527 0.141966764 [60,] 0.8707202 0.258559562 0.129279781 [61,] 0.9136028 0.172794352 0.086397176 [62,] 0.9174471 0.165105861 0.082552931 [63,] 0.9350774 0.129845285 0.064922642 [64,] 0.9221778 0.155644407 0.077822203 [65,] 0.9114500 0.177100000 0.088550000 [66,] 0.9001489 0.199702293 0.099851146 [67,] 0.9019373 0.196125392 0.098062696 [68,] 0.8860307 0.227938576 0.113969288 [69,] 0.8797520 0.240496040 0.120248020 [70,] 0.8836913 0.232617353 0.116308676 [71,] 0.8670174 0.265965207 0.132982603 [72,] 0.8487766 0.302446752 0.151223376 [73,] 0.8718954 0.256209160 0.128104580 [74,] 0.8518458 0.296308376 0.148154188 [75,] 0.8901796 0.219640746 0.109820373 [76,] 0.8735975 0.252804983 0.126402492 [77,] 0.8602869 0.279426204 0.139713102 [78,] 0.9623353 0.075329497 0.037664748 [79,] 0.9699890 0.060022042 0.030011021 [80,] 0.9670521 0.065895779 0.032947889 [81,] 0.9613498 0.077300368 0.038650184 [82,] 0.9616387 0.076722513 0.038361256 [83,] 0.9573310 0.085338084 0.042669042 [84,] 0.9514511 0.097097800 0.048548900 [85,] 0.9492428 0.101514327 0.050757164 [86,] 0.9549782 0.090043618 0.045021809 [87,] 0.9534985 0.093003020 0.046501510 [88,] 0.9749435 0.050113081 0.025056540 [89,] 0.9705747 0.058850645 0.029425323 [90,] 0.9644245 0.071151023 0.035575511 [91,] 0.9695139 0.060972244 0.030486122 [92,] 0.9685602 0.062879614 0.031439807 [93,] 0.9701696 0.059660829 0.029830415 [94,] 0.9689147 0.062170547 0.031085273 [95,] 0.9624251 0.075149705 0.037574852 [96,] 0.9700043 0.059991358 0.029995679 [97,] 0.9633707 0.073258643 0.036629321 [98,] 0.9572274 0.085545205 0.042772603 [99,] 0.9778019 0.044396271 0.022198135 [100,] 0.9766194 0.046761169 0.023380584 [101,] 0.9724191 0.055161765 0.027580882 [102,] 0.9674903 0.065019491 0.032509746 [103,] 0.9930334 0.013933214 0.006966607 [104,] 0.9924475 0.015105048 0.007552524 [105,] 0.9907504 0.018499105 0.009249552 [106,] 0.9943656 0.011268747 0.005634374 [107,] 0.9933340 0.013332074 0.006666037 [108,] 0.9927577 0.014484576 0.007242288 [109,] 0.9951021 0.009795840 0.004897920 [110,] 0.9942309 0.011538234 0.005769117 [111,] 0.9932396 0.013520816 0.006760408 [112,] 0.9918930 0.016213984 0.008106992 [113,] 0.9900901 0.019819861 0.009909930 [114,] 0.9888801 0.022239789 0.011119894 [115,] 0.9860509 0.027898239 0.013949120 [116,] 0.9853444 0.029311277 0.014655638 [117,] 0.9862806 0.027438890 0.013719445 [118,] 0.9834661 0.033067834 0.016533917 [119,] 0.9802550 0.039489924 0.019744962 [120,] 0.9771124 0.045775106 0.022887553 [121,] 0.9746622 0.050675567 0.025337783 [122,] 0.9819044 0.036191173 0.018095586 [123,] 0.9778347 0.044330547 0.022165273 [124,] 0.9728657 0.054268622 0.027134311 [125,] 0.9682451 0.063509795 0.031754897 [126,] 0.9652350 0.069530050 0.034765025 [127,] 0.9743001 0.051399850 0.025699925 [128,] 0.9809575 0.038085010 0.019042505 [129,] 0.9837413 0.032517390 0.016258695 [130,] 0.9860443 0.027911314 0.013955657 [131,] 0.9965508 0.006898311 0.003449155 [132,] 0.9959276 0.008144845 0.004072423 [133,] 0.9956725 0.008655037 0.004327519 [134,] 0.9945172 0.010965533 0.005482767 [135,] 0.9971912 0.005617682 0.002808841 [136,] 0.9974797 0.005040640 0.002520320 [137,] 0.9985737 0.002852675 0.001426338 [138,] 0.9981239 0.003752109 0.001876054 [139,] 0.9975189 0.004962225 0.002481113 [140,] 0.9968021 0.006395766 0.003197883 [141,] 0.9961236 0.007752805 0.003876402 [142,] 0.9953751 0.009249731 0.004624866 [143,] 0.9956209 0.008758135 0.004379068 [144,] 0.9963066 0.007386854 0.003693427 [145,] 0.9954818 0.009036360 0.004518180 [146,] 0.9946846 0.010630765 0.005315382 [147,] 0.9934784 0.013043172 0.006521586 [148,] 0.9933933 0.013213461 0.006606731 [149,] 0.9924774 0.015045285 0.007522643 [150,] 0.9905274 0.018945199 0.009472599 [151,] 0.9879965 0.024006962 0.012003481 [152,] 0.9901239 0.019752216 0.009876108 [153,] 0.9875457 0.024908651 0.012454325 [154,] 0.9865446 0.026910822 0.013455411 [155,] 0.9833577 0.033284641 0.016642320 [156,] 0.9799262 0.040147502 0.020073751 [157,] 0.9873221 0.025355722 0.012677861 [158,] 0.9843465 0.031307023 0.015653511 [159,] 0.9845451 0.030909733 0.015454867 [160,] 0.9884525 0.023095049 0.011547525 [161,] 0.9869986 0.026002831 0.013001416 [162,] 0.9838636 0.032272893 0.016136446 [163,] 0.9807604 0.038479300 0.019239650 [164,] 0.9775217 0.044956531 0.022478265 [165,] 0.9735309 0.052938242 0.026469121 [166,] 0.9683035 0.063392937 0.031696468 [167,] 0.9702698 0.059460460 0.029730230 [168,] 0.9736199 0.052760137 0.026380068 [169,] 0.9749720 0.050055916 0.025027958 [170,] 0.9694066 0.061186852 0.030593426 [171,] 0.9626761 0.074647801 0.037323900 [172,] 0.9562260 0.087547960 0.043773980 [173,] 0.9480941 0.103811712 0.051905856 [174,] 0.9475613 0.104877416 0.052438708 [175,] 0.9624314 0.075137118 0.037568559 [176,] 0.9575307 0.084938619 0.042469310 [177,] 0.9505746 0.098850824 0.049425412 [178,] 0.9432180 0.113563957 0.056781979 [179,] 0.9512035 0.097592925 0.048796462 [180,] 0.9587773 0.082445409 0.041222704 [181,] 0.9506064 0.098787247 0.049393623 [182,] 0.9556865 0.088627097 0.044313548 [183,] 0.9502543 0.099491483 0.049745742 [184,] 0.9415857 0.116828529 0.058414264 [185,] 0.9355804 0.128839277 0.064419639 [186,] 0.9258224 0.148355160 0.074177580 [187,] 0.9110259 0.177948161 0.088974081 [188,] 0.8947635 0.210472982 0.105236491 [189,] 0.9767221 0.046555736 0.023277868 [190,] 0.9748075 0.050384996 0.025192498 [191,] 0.9750565 0.049887036 0.024943518 [192,] 0.9751696 0.049660823 0.024830412 [193,] 0.9715129 0.056974211 0.028487106 [194,] 0.9670084 0.065983193 0.032991596 [195,] 0.9612840 0.077432035 0.038716017 [196,] 0.9560989 0.087802142 0.043901071 [197,] 0.9503921 0.099215720 0.049607860 [198,] 0.9415770 0.116845974 0.058422987 [199,] 0.9664983 0.067003388 0.033501694 [200,] 0.9578302 0.084339545 0.042169772 [201,] 0.9552698 0.089460412 0.044730206 [202,] 0.9609110 0.078178081 0.039089040 [203,] 0.9550554 0.089889265 0.044944633 [204,] 0.9490326 0.101934816 0.050967408 [205,] 0.9362914 0.127417101 0.063708550 [206,] 0.9278528 0.144294381 0.072147191 [207,] 0.9299383 0.140123406 0.070061703 [208,] 0.9478537 0.104292542 0.052146271 [209,] 0.9615648 0.076870363 0.038435181 [210,] 0.9927234 0.014553260 0.007276630 [211,] 0.9907850 0.018429947 0.009214973 [212,] 0.9874755 0.025048970 0.012524485 [213,] 0.9935549 0.012890281 0.006445141 [214,] 0.9908687 0.018262598 0.009131299 [215,] 0.9876120 0.024775963 0.012387981 [216,] 0.9921597 0.015680661 0.007840331 [217,] 0.9908496 0.018300719 0.009150360 [218,] 0.9988127 0.002374511 0.001187255 [219,] 0.9988149 0.002370294 0.001185147 [220,] 0.9982151 0.003569815 0.001784907 [221,] 0.9972723 0.005455497 0.002727748 [222,] 0.9966667 0.006666628 0.003333314 [223,] 0.9968200 0.006360097 0.003180048 [224,] 0.9960520 0.007896083 0.003948041 [225,] 0.9946240 0.010752067 0.005376034 [226,] 0.9946534 0.010693191 0.005346596 [227,] 0.9919012 0.016197502 0.008098751 [228,] 0.9929932 0.014013525 0.007006763 [229,] 0.9903200 0.019359917 0.009679959 [230,] 0.9868459 0.026308218 0.013154109 [231,] 0.9817839 0.036432171 0.018216085 [232,] 0.9819181 0.036163805 0.018081903 [233,] 0.9804476 0.039104703 0.019552352 [234,] 0.9721413 0.055717446 0.027858723 [235,] 0.9586388 0.082722363 0.041361182 [236,] 0.9534900 0.093020005 0.046510003 [237,] 0.9384503 0.123099357 0.061549678 [238,] 0.9150849 0.169830236 0.084915118 [239,] 0.8812380 0.237523926 0.118761963 [240,] 0.8427101 0.314579802 0.157289901 [241,] 0.8314635 0.337073094 0.168536547 [242,] 0.7904788 0.419042313 0.209521156 [243,] 0.7279004 0.544199180 0.272099590 [244,] 0.6750015 0.649996943 0.324998472 [245,] 0.6023038 0.795392366 0.397696183 [246,] 0.5468802 0.906239522 0.453119761 [247,] 0.4461950 0.892389964 0.553805018 [248,] 0.3636772 0.727354404 0.636322798 [249,] 0.4637189 0.927437751 0.536281125 [250,] 0.3859351 0.771870226 0.614064887 [251,] 0.2616548 0.523309503 0.738345249 [252,] 0.1543774 0.308754728 0.845622636 > postscript(file="/var/fisher/rcomp/tmp/1xrvl1387448176.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2x9891387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3yxkm1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/43wrp1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5o3es1387448176.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 = 275 Frequency = 1 1 2 3 4 5 6 0.67938170 2.90352210 -2.15821729 0.56554669 -0.21569237 -4.15600593 7 8 9 10 11 12 -3.60559630 -1.19216391 4.26189945 -1.23690546 3.40093432 0.49662032 13 14 15 16 17 18 -0.40721035 2.38278531 5.58349180 0.59589982 -1.71290069 -0.85719817 19 20 21 22 23 24 -2.10906585 3.38057395 -0.59149715 2.25478856 -0.79327370 -6.57214763 25 26 27 28 29 30 -1.71981881 5.61826594 3.13293732 -0.33856341 1.90352210 2.24365222 31 32 33 34 35 36 -1.28784991 -0.73095515 -0.11119184 -1.83770494 -2.16546092 -1.58961069 37 38 39 40 41 42 2.89422552 -0.92633015 -1.82568366 4.27448740 -0.73095515 -3.25149031 43 44 45 46 47 48 3.44851486 -1.27923928 2.19330971 6.97588354 4.08987697 1.59315069 49 50 51 52 53 54 -0.47061129 -3.50089187 3.97180800 1.35591633 0.14231978 -2.27221072 55 56 57 58 59 60 1.49662032 3.98165966 -7.66783363 8.31745243 1.14280183 0.40817795 61 62 63 64 65 66 2.43349881 -3.47218219 -1.46701780 1.64882468 2.31470331 4.49746469 67 68 69 70 71 72 -1.31501734 -1.87735465 -0.41429683 0.06706669 3.98157905 5.62507472 73 74 75 76 77 78 3.77727907 -4.18459538 0.29370247 -1.68750806 -1.42736810 -2.79407666 79 80 81 82 83 84 1.28709931 2.58610868 3.15299848 1.47879622 1.14940499 -4.13806082 85 86 87 88 89 90 0.77866119 -4.79407666 -1.26946995 2.17880139 8.71553968 4.44106734 91 92 93 94 95 96 2.54673714 -1.47266424 -1.32226097 -1.56650119 -1.88251903 2.81004746 97 98 99 100 101 102 -3.39054858 -2.28446032 5.74334758 0.48411776 0.71415756 -3.38152844 103 104 105 106 107 108 -2.41389132 -3.10529243 2.68159307 -0.69390960 4.27185756 0.02316196 109 110 111 112 113 114 1.43709230 5.95734703 -2.02062347 -0.91716504 -1.27518816 8.17880139 115 116 117 118 119 120 -2.05963397 -0.25032493 4.89470757 -1.63506524 -2.32130425 4.33781281 121 122 123 124 125 126 -1.29465697 -1.34077477 0.55809917 -1.13584945 1.57192415 0.18341518 127 128 129 130 131 132 -1.90447659 -3.06977492 -0.97324455 -1.07252404 1.39749925 1.78620404 133 134 135 136 137 138 4.52781781 0.58522112 0.21088383 -1.22251703 1.88481304 -4.49567841 139 140 141 142 143 144 4.41534980 3.78030463 3.58280585 6.74471458 0.83675045 -2.37877932 145 146 147 148 149 150 0.42805627 -5.15035949 -3.15203260 -4.68915073 -0.24239535 -0.13225596 151 152 153 154 155 156 0.28488794 -1.59906568 -1.73480918 -3.03809538 3.05351509 1.18624892 157 158 159 160 161 162 1.71553968 0.09359904 2.05351509 1.75896407 -0.50998284 0.22493452 163 164 165 166 167 168 2.75352911 -0.61848637 -2.51562928 -0.03995955 0.94742682 4.68518656 169 170 171 172 173 174 -0.78843022 2.28689541 3.81142958 -2.23964623 -1.10577448 -1.49978619 175 176 177 178 179 180 1.06000589 0.81191163 -1.41134381 2.65922523 -3.97340296 -2.77412580 181 182 183 184 185 186 -0.64998308 1.01958437 -1.68254757 0.14920109 -1.83365382 -5.30636123 187 188 189 190 191 192 -2.16324955 0.89470757 0.68159307 3.06275502 2.80783609 1.59635745 193 194 195 196 197 198 -3.39014135 -2.32110035 0.61061375 -2.76397650 -1.84135508 -0.89787344 199 200 201 202 203 204 -1.01530194 6.66466777 1.33437773 1.65922523 1.53298220 2.35619277 205 206 207 208 209 210 0.67781965 -1.88996656 -2.75111162 0.84178271 0.51988824 -5.81100196 211 212 213 214 215 216 -0.25913945 -2.19236952 2.33216637 -2.68254757 2.29395621 -1.04217092 217 218 219 220 221 222 0.81142958 0.96664361 2.55265663 1.28350582 -7.80144357 -1.13544393 223 224 225 226 227 228 -0.53034321 -5.69322627 -0.95529944 -1.74841820 -1.43964211 0.59589982 229 230 231 232 233 234 3.62915026 0.86415055 -2.26999935 -2.06977492 2.90764211 -3.59567609 235 236 237 238 239 240 0.28709931 -0.84543062 1.37397079 -1.50495058 -3.62602921 -1.43397126 241 242 243 244 245 246 -1.76941904 0.61633273 0.56058620 -2.41699025 -3.30276774 -1.35661786 247 248 249 250 251 252 -2.00876206 0.38298693 -1.94744163 2.35977114 1.89830106 -3.90668796 253 254 255 256 257 258 -1.04044160 -2.19876707 -3.52469677 1.35812770 0.31745243 -1.97683804 259 260 261 262 263 264 -2.84651947 -3.59499014 -1.41429683 1.04691193 1.13729909 1.39364521 265 266 267 268 269 270 0.28193492 0.61577814 -0.13584945 -1.41807910 0.96004045 -1.82684505 271 272 273 274 275 -6.40548231 -3.57875079 1.33865489 -2.59305921 -3.97683804 > postscript(file="/var/fisher/rcomp/tmp/632lk1387448176.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 = 275 Frequency = 1 lag(myerror, k = 1) myerror 0 0.67938170 NA 1 2.90352210 0.67938170 2 -2.15821729 2.90352210 3 0.56554669 -2.15821729 4 -0.21569237 0.56554669 5 -4.15600593 -0.21569237 6 -3.60559630 -4.15600593 7 -1.19216391 -3.60559630 8 4.26189945 -1.19216391 9 -1.23690546 4.26189945 10 3.40093432 -1.23690546 11 0.49662032 3.40093432 12 -0.40721035 0.49662032 13 2.38278531 -0.40721035 14 5.58349180 2.38278531 15 0.59589982 5.58349180 16 -1.71290069 0.59589982 17 -0.85719817 -1.71290069 18 -2.10906585 -0.85719817 19 3.38057395 -2.10906585 20 -0.59149715 3.38057395 21 2.25478856 -0.59149715 22 -0.79327370 2.25478856 23 -6.57214763 -0.79327370 24 -1.71981881 -6.57214763 25 5.61826594 -1.71981881 26 3.13293732 5.61826594 27 -0.33856341 3.13293732 28 1.90352210 -0.33856341 29 2.24365222 1.90352210 30 -1.28784991 2.24365222 31 -0.73095515 -1.28784991 32 -0.11119184 -0.73095515 33 -1.83770494 -0.11119184 34 -2.16546092 -1.83770494 35 -1.58961069 -2.16546092 36 2.89422552 -1.58961069 37 -0.92633015 2.89422552 38 -1.82568366 -0.92633015 39 4.27448740 -1.82568366 40 -0.73095515 4.27448740 41 -3.25149031 -0.73095515 42 3.44851486 -3.25149031 43 -1.27923928 3.44851486 44 2.19330971 -1.27923928 45 6.97588354 2.19330971 46 4.08987697 6.97588354 47 1.59315069 4.08987697 48 -0.47061129 1.59315069 49 -3.50089187 -0.47061129 50 3.97180800 -3.50089187 51 1.35591633 3.97180800 52 0.14231978 1.35591633 53 -2.27221072 0.14231978 54 1.49662032 -2.27221072 55 3.98165966 1.49662032 56 -7.66783363 3.98165966 57 8.31745243 -7.66783363 58 1.14280183 8.31745243 59 0.40817795 1.14280183 60 2.43349881 0.40817795 61 -3.47218219 2.43349881 62 -1.46701780 -3.47218219 63 1.64882468 -1.46701780 64 2.31470331 1.64882468 65 4.49746469 2.31470331 66 -1.31501734 4.49746469 67 -1.87735465 -1.31501734 68 -0.41429683 -1.87735465 69 0.06706669 -0.41429683 70 3.98157905 0.06706669 71 5.62507472 3.98157905 72 3.77727907 5.62507472 73 -4.18459538 3.77727907 74 0.29370247 -4.18459538 75 -1.68750806 0.29370247 76 -1.42736810 -1.68750806 77 -2.79407666 -1.42736810 78 1.28709931 -2.79407666 79 2.58610868 1.28709931 80 3.15299848 2.58610868 81 1.47879622 3.15299848 82 1.14940499 1.47879622 83 -4.13806082 1.14940499 84 0.77866119 -4.13806082 85 -4.79407666 0.77866119 86 -1.26946995 -4.79407666 87 2.17880139 -1.26946995 88 8.71553968 2.17880139 89 4.44106734 8.71553968 90 2.54673714 4.44106734 91 -1.47266424 2.54673714 92 -1.32226097 -1.47266424 93 -1.56650119 -1.32226097 94 -1.88251903 -1.56650119 95 2.81004746 -1.88251903 96 -3.39054858 2.81004746 97 -2.28446032 -3.39054858 98 5.74334758 -2.28446032 99 0.48411776 5.74334758 100 0.71415756 0.48411776 101 -3.38152844 0.71415756 102 -2.41389132 -3.38152844 103 -3.10529243 -2.41389132 104 2.68159307 -3.10529243 105 -0.69390960 2.68159307 106 4.27185756 -0.69390960 107 0.02316196 4.27185756 108 1.43709230 0.02316196 109 5.95734703 1.43709230 110 -2.02062347 5.95734703 111 -0.91716504 -2.02062347 112 -1.27518816 -0.91716504 113 8.17880139 -1.27518816 114 -2.05963397 8.17880139 115 -0.25032493 -2.05963397 116 4.89470757 -0.25032493 117 -1.63506524 4.89470757 118 -2.32130425 -1.63506524 119 4.33781281 -2.32130425 120 -1.29465697 4.33781281 121 -1.34077477 -1.29465697 122 0.55809917 -1.34077477 123 -1.13584945 0.55809917 124 1.57192415 -1.13584945 125 0.18341518 1.57192415 126 -1.90447659 0.18341518 127 -3.06977492 -1.90447659 128 -0.97324455 -3.06977492 129 -1.07252404 -0.97324455 130 1.39749925 -1.07252404 131 1.78620404 1.39749925 132 4.52781781 1.78620404 133 0.58522112 4.52781781 134 0.21088383 0.58522112 135 -1.22251703 0.21088383 136 1.88481304 -1.22251703 137 -4.49567841 1.88481304 138 4.41534980 -4.49567841 139 3.78030463 4.41534980 140 3.58280585 3.78030463 141 6.74471458 3.58280585 142 0.83675045 6.74471458 143 -2.37877932 0.83675045 144 0.42805627 -2.37877932 145 -5.15035949 0.42805627 146 -3.15203260 -5.15035949 147 -4.68915073 -3.15203260 148 -0.24239535 -4.68915073 149 -0.13225596 -0.24239535 150 0.28488794 -0.13225596 151 -1.59906568 0.28488794 152 -1.73480918 -1.59906568 153 -3.03809538 -1.73480918 154 3.05351509 -3.03809538 155 1.18624892 3.05351509 156 1.71553968 1.18624892 157 0.09359904 1.71553968 158 2.05351509 0.09359904 159 1.75896407 2.05351509 160 -0.50998284 1.75896407 161 0.22493452 -0.50998284 162 2.75352911 0.22493452 163 -0.61848637 2.75352911 164 -2.51562928 -0.61848637 165 -0.03995955 -2.51562928 166 0.94742682 -0.03995955 167 4.68518656 0.94742682 168 -0.78843022 4.68518656 169 2.28689541 -0.78843022 170 3.81142958 2.28689541 171 -2.23964623 3.81142958 172 -1.10577448 -2.23964623 173 -1.49978619 -1.10577448 174 1.06000589 -1.49978619 175 0.81191163 1.06000589 176 -1.41134381 0.81191163 177 2.65922523 -1.41134381 178 -3.97340296 2.65922523 179 -2.77412580 -3.97340296 180 -0.64998308 -2.77412580 181 1.01958437 -0.64998308 182 -1.68254757 1.01958437 183 0.14920109 -1.68254757 184 -1.83365382 0.14920109 185 -5.30636123 -1.83365382 186 -2.16324955 -5.30636123 187 0.89470757 -2.16324955 188 0.68159307 0.89470757 189 3.06275502 0.68159307 190 2.80783609 3.06275502 191 1.59635745 2.80783609 192 -3.39014135 1.59635745 193 -2.32110035 -3.39014135 194 0.61061375 -2.32110035 195 -2.76397650 0.61061375 196 -1.84135508 -2.76397650 197 -0.89787344 -1.84135508 198 -1.01530194 -0.89787344 199 6.66466777 -1.01530194 200 1.33437773 6.66466777 201 1.65922523 1.33437773 202 1.53298220 1.65922523 203 2.35619277 1.53298220 204 0.67781965 2.35619277 205 -1.88996656 0.67781965 206 -2.75111162 -1.88996656 207 0.84178271 -2.75111162 208 0.51988824 0.84178271 209 -5.81100196 0.51988824 210 -0.25913945 -5.81100196 211 -2.19236952 -0.25913945 212 2.33216637 -2.19236952 213 -2.68254757 2.33216637 214 2.29395621 -2.68254757 215 -1.04217092 2.29395621 216 0.81142958 -1.04217092 217 0.96664361 0.81142958 218 2.55265663 0.96664361 219 1.28350582 2.55265663 220 -7.80144357 1.28350582 221 -1.13544393 -7.80144357 222 -0.53034321 -1.13544393 223 -5.69322627 -0.53034321 224 -0.95529944 -5.69322627 225 -1.74841820 -0.95529944 226 -1.43964211 -1.74841820 227 0.59589982 -1.43964211 228 3.62915026 0.59589982 229 0.86415055 3.62915026 230 -2.26999935 0.86415055 231 -2.06977492 -2.26999935 232 2.90764211 -2.06977492 233 -3.59567609 2.90764211 234 0.28709931 -3.59567609 235 -0.84543062 0.28709931 236 1.37397079 -0.84543062 237 -1.50495058 1.37397079 238 -3.62602921 -1.50495058 239 -1.43397126 -3.62602921 240 -1.76941904 -1.43397126 241 0.61633273 -1.76941904 242 0.56058620 0.61633273 243 -2.41699025 0.56058620 244 -3.30276774 -2.41699025 245 -1.35661786 -3.30276774 246 -2.00876206 -1.35661786 247 0.38298693 -2.00876206 248 -1.94744163 0.38298693 249 2.35977114 -1.94744163 250 1.89830106 2.35977114 251 -3.90668796 1.89830106 252 -1.04044160 -3.90668796 253 -2.19876707 -1.04044160 254 -3.52469677 -2.19876707 255 1.35812770 -3.52469677 256 0.31745243 1.35812770 257 -1.97683804 0.31745243 258 -2.84651947 -1.97683804 259 -3.59499014 -2.84651947 260 -1.41429683 -3.59499014 261 1.04691193 -1.41429683 262 1.13729909 1.04691193 263 1.39364521 1.13729909 264 0.28193492 1.39364521 265 0.61577814 0.28193492 266 -0.13584945 0.61577814 267 -1.41807910 -0.13584945 268 0.96004045 -1.41807910 269 -1.82684505 0.96004045 270 -6.40548231 -1.82684505 271 -3.57875079 -6.40548231 272 1.33865489 -3.57875079 273 -2.59305921 1.33865489 274 -3.97683804 -2.59305921 275 NA -3.97683804 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.90352210 0.67938170 [2,] -2.15821729 2.90352210 [3,] 0.56554669 -2.15821729 [4,] -0.21569237 0.56554669 [5,] -4.15600593 -0.21569237 [6,] -3.60559630 -4.15600593 [7,] -1.19216391 -3.60559630 [8,] 4.26189945 -1.19216391 [9,] -1.23690546 4.26189945 [10,] 3.40093432 -1.23690546 [11,] 0.49662032 3.40093432 [12,] -0.40721035 0.49662032 [13,] 2.38278531 -0.40721035 [14,] 5.58349180 2.38278531 [15,] 0.59589982 5.58349180 [16,] -1.71290069 0.59589982 [17,] -0.85719817 -1.71290069 [18,] -2.10906585 -0.85719817 [19,] 3.38057395 -2.10906585 [20,] -0.59149715 3.38057395 [21,] 2.25478856 -0.59149715 [22,] -0.79327370 2.25478856 [23,] -6.57214763 -0.79327370 [24,] -1.71981881 -6.57214763 [25,] 5.61826594 -1.71981881 [26,] 3.13293732 5.61826594 [27,] -0.33856341 3.13293732 [28,] 1.90352210 -0.33856341 [29,] 2.24365222 1.90352210 [30,] -1.28784991 2.24365222 [31,] -0.73095515 -1.28784991 [32,] -0.11119184 -0.73095515 [33,] -1.83770494 -0.11119184 [34,] -2.16546092 -1.83770494 [35,] -1.58961069 -2.16546092 [36,] 2.89422552 -1.58961069 [37,] -0.92633015 2.89422552 [38,] -1.82568366 -0.92633015 [39,] 4.27448740 -1.82568366 [40,] -0.73095515 4.27448740 [41,] -3.25149031 -0.73095515 [42,] 3.44851486 -3.25149031 [43,] -1.27923928 3.44851486 [44,] 2.19330971 -1.27923928 [45,] 6.97588354 2.19330971 [46,] 4.08987697 6.97588354 [47,] 1.59315069 4.08987697 [48,] -0.47061129 1.59315069 [49,] -3.50089187 -0.47061129 [50,] 3.97180800 -3.50089187 [51,] 1.35591633 3.97180800 [52,] 0.14231978 1.35591633 [53,] -2.27221072 0.14231978 [54,] 1.49662032 -2.27221072 [55,] 3.98165966 1.49662032 [56,] -7.66783363 3.98165966 [57,] 8.31745243 -7.66783363 [58,] 1.14280183 8.31745243 [59,] 0.40817795 1.14280183 [60,] 2.43349881 0.40817795 [61,] -3.47218219 2.43349881 [62,] -1.46701780 -3.47218219 [63,] 1.64882468 -1.46701780 [64,] 2.31470331 1.64882468 [65,] 4.49746469 2.31470331 [66,] -1.31501734 4.49746469 [67,] -1.87735465 -1.31501734 [68,] -0.41429683 -1.87735465 [69,] 0.06706669 -0.41429683 [70,] 3.98157905 0.06706669 [71,] 5.62507472 3.98157905 [72,] 3.77727907 5.62507472 [73,] -4.18459538 3.77727907 [74,] 0.29370247 -4.18459538 [75,] -1.68750806 0.29370247 [76,] -1.42736810 -1.68750806 [77,] -2.79407666 -1.42736810 [78,] 1.28709931 -2.79407666 [79,] 2.58610868 1.28709931 [80,] 3.15299848 2.58610868 [81,] 1.47879622 3.15299848 [82,] 1.14940499 1.47879622 [83,] -4.13806082 1.14940499 [84,] 0.77866119 -4.13806082 [85,] -4.79407666 0.77866119 [86,] -1.26946995 -4.79407666 [87,] 2.17880139 -1.26946995 [88,] 8.71553968 2.17880139 [89,] 4.44106734 8.71553968 [90,] 2.54673714 4.44106734 [91,] -1.47266424 2.54673714 [92,] -1.32226097 -1.47266424 [93,] -1.56650119 -1.32226097 [94,] -1.88251903 -1.56650119 [95,] 2.81004746 -1.88251903 [96,] -3.39054858 2.81004746 [97,] -2.28446032 -3.39054858 [98,] 5.74334758 -2.28446032 [99,] 0.48411776 5.74334758 [100,] 0.71415756 0.48411776 [101,] -3.38152844 0.71415756 [102,] -2.41389132 -3.38152844 [103,] -3.10529243 -2.41389132 [104,] 2.68159307 -3.10529243 [105,] -0.69390960 2.68159307 [106,] 4.27185756 -0.69390960 [107,] 0.02316196 4.27185756 [108,] 1.43709230 0.02316196 [109,] 5.95734703 1.43709230 [110,] -2.02062347 5.95734703 [111,] -0.91716504 -2.02062347 [112,] -1.27518816 -0.91716504 [113,] 8.17880139 -1.27518816 [114,] -2.05963397 8.17880139 [115,] -0.25032493 -2.05963397 [116,] 4.89470757 -0.25032493 [117,] -1.63506524 4.89470757 [118,] -2.32130425 -1.63506524 [119,] 4.33781281 -2.32130425 [120,] -1.29465697 4.33781281 [121,] -1.34077477 -1.29465697 [122,] 0.55809917 -1.34077477 [123,] -1.13584945 0.55809917 [124,] 1.57192415 -1.13584945 [125,] 0.18341518 1.57192415 [126,] -1.90447659 0.18341518 [127,] -3.06977492 -1.90447659 [128,] -0.97324455 -3.06977492 [129,] -1.07252404 -0.97324455 [130,] 1.39749925 -1.07252404 [131,] 1.78620404 1.39749925 [132,] 4.52781781 1.78620404 [133,] 0.58522112 4.52781781 [134,] 0.21088383 0.58522112 [135,] -1.22251703 0.21088383 [136,] 1.88481304 -1.22251703 [137,] -4.49567841 1.88481304 [138,] 4.41534980 -4.49567841 [139,] 3.78030463 4.41534980 [140,] 3.58280585 3.78030463 [141,] 6.74471458 3.58280585 [142,] 0.83675045 6.74471458 [143,] -2.37877932 0.83675045 [144,] 0.42805627 -2.37877932 [145,] -5.15035949 0.42805627 [146,] -3.15203260 -5.15035949 [147,] -4.68915073 -3.15203260 [148,] -0.24239535 -4.68915073 [149,] -0.13225596 -0.24239535 [150,] 0.28488794 -0.13225596 [151,] -1.59906568 0.28488794 [152,] -1.73480918 -1.59906568 [153,] -3.03809538 -1.73480918 [154,] 3.05351509 -3.03809538 [155,] 1.18624892 3.05351509 [156,] 1.71553968 1.18624892 [157,] 0.09359904 1.71553968 [158,] 2.05351509 0.09359904 [159,] 1.75896407 2.05351509 [160,] -0.50998284 1.75896407 [161,] 0.22493452 -0.50998284 [162,] 2.75352911 0.22493452 [163,] -0.61848637 2.75352911 [164,] -2.51562928 -0.61848637 [165,] -0.03995955 -2.51562928 [166,] 0.94742682 -0.03995955 [167,] 4.68518656 0.94742682 [168,] -0.78843022 4.68518656 [169,] 2.28689541 -0.78843022 [170,] 3.81142958 2.28689541 [171,] -2.23964623 3.81142958 [172,] -1.10577448 -2.23964623 [173,] -1.49978619 -1.10577448 [174,] 1.06000589 -1.49978619 [175,] 0.81191163 1.06000589 [176,] -1.41134381 0.81191163 [177,] 2.65922523 -1.41134381 [178,] -3.97340296 2.65922523 [179,] -2.77412580 -3.97340296 [180,] -0.64998308 -2.77412580 [181,] 1.01958437 -0.64998308 [182,] -1.68254757 1.01958437 [183,] 0.14920109 -1.68254757 [184,] -1.83365382 0.14920109 [185,] -5.30636123 -1.83365382 [186,] -2.16324955 -5.30636123 [187,] 0.89470757 -2.16324955 [188,] 0.68159307 0.89470757 [189,] 3.06275502 0.68159307 [190,] 2.80783609 3.06275502 [191,] 1.59635745 2.80783609 [192,] -3.39014135 1.59635745 [193,] -2.32110035 -3.39014135 [194,] 0.61061375 -2.32110035 [195,] -2.76397650 0.61061375 [196,] -1.84135508 -2.76397650 [197,] -0.89787344 -1.84135508 [198,] -1.01530194 -0.89787344 [199,] 6.66466777 -1.01530194 [200,] 1.33437773 6.66466777 [201,] 1.65922523 1.33437773 [202,] 1.53298220 1.65922523 [203,] 2.35619277 1.53298220 [204,] 0.67781965 2.35619277 [205,] -1.88996656 0.67781965 [206,] -2.75111162 -1.88996656 [207,] 0.84178271 -2.75111162 [208,] 0.51988824 0.84178271 [209,] -5.81100196 0.51988824 [210,] -0.25913945 -5.81100196 [211,] -2.19236952 -0.25913945 [212,] 2.33216637 -2.19236952 [213,] -2.68254757 2.33216637 [214,] 2.29395621 -2.68254757 [215,] -1.04217092 2.29395621 [216,] 0.81142958 -1.04217092 [217,] 0.96664361 0.81142958 [218,] 2.55265663 0.96664361 [219,] 1.28350582 2.55265663 [220,] -7.80144357 1.28350582 [221,] -1.13544393 -7.80144357 [222,] -0.53034321 -1.13544393 [223,] -5.69322627 -0.53034321 [224,] -0.95529944 -5.69322627 [225,] -1.74841820 -0.95529944 [226,] -1.43964211 -1.74841820 [227,] 0.59589982 -1.43964211 [228,] 3.62915026 0.59589982 [229,] 0.86415055 3.62915026 [230,] -2.26999935 0.86415055 [231,] -2.06977492 -2.26999935 [232,] 2.90764211 -2.06977492 [233,] -3.59567609 2.90764211 [234,] 0.28709931 -3.59567609 [235,] -0.84543062 0.28709931 [236,] 1.37397079 -0.84543062 [237,] -1.50495058 1.37397079 [238,] -3.62602921 -1.50495058 [239,] -1.43397126 -3.62602921 [240,] -1.76941904 -1.43397126 [241,] 0.61633273 -1.76941904 [242,] 0.56058620 0.61633273 [243,] -2.41699025 0.56058620 [244,] -3.30276774 -2.41699025 [245,] -1.35661786 -3.30276774 [246,] -2.00876206 -1.35661786 [247,] 0.38298693 -2.00876206 [248,] -1.94744163 0.38298693 [249,] 2.35977114 -1.94744163 [250,] 1.89830106 2.35977114 [251,] -3.90668796 1.89830106 [252,] -1.04044160 -3.90668796 [253,] -2.19876707 -1.04044160 [254,] -3.52469677 -2.19876707 [255,] 1.35812770 -3.52469677 [256,] 0.31745243 1.35812770 [257,] -1.97683804 0.31745243 [258,] -2.84651947 -1.97683804 [259,] -3.59499014 -2.84651947 [260,] -1.41429683 -3.59499014 [261,] 1.04691193 -1.41429683 [262,] 1.13729909 1.04691193 [263,] 1.39364521 1.13729909 [264,] 0.28193492 1.39364521 [265,] 0.61577814 0.28193492 [266,] -0.13584945 0.61577814 [267,] -1.41807910 -0.13584945 [268,] 0.96004045 -1.41807910 [269,] -1.82684505 0.96004045 [270,] -6.40548231 -1.82684505 [271,] -3.57875079 -6.40548231 [272,] 1.33865489 -3.57875079 [273,] -2.59305921 1.33865489 [274,] -3.97683804 -2.59305921 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.90352210 0.67938170 2 -2.15821729 2.90352210 3 0.56554669 -2.15821729 4 -0.21569237 0.56554669 5 -4.15600593 -0.21569237 6 -3.60559630 -4.15600593 7 -1.19216391 -3.60559630 8 4.26189945 -1.19216391 9 -1.23690546 4.26189945 10 3.40093432 -1.23690546 11 0.49662032 3.40093432 12 -0.40721035 0.49662032 13 2.38278531 -0.40721035 14 5.58349180 2.38278531 15 0.59589982 5.58349180 16 -1.71290069 0.59589982 17 -0.85719817 -1.71290069 18 -2.10906585 -0.85719817 19 3.38057395 -2.10906585 20 -0.59149715 3.38057395 21 2.25478856 -0.59149715 22 -0.79327370 2.25478856 23 -6.57214763 -0.79327370 24 -1.71981881 -6.57214763 25 5.61826594 -1.71981881 26 3.13293732 5.61826594 27 -0.33856341 3.13293732 28 1.90352210 -0.33856341 29 2.24365222 1.90352210 30 -1.28784991 2.24365222 31 -0.73095515 -1.28784991 32 -0.11119184 -0.73095515 33 -1.83770494 -0.11119184 34 -2.16546092 -1.83770494 35 -1.58961069 -2.16546092 36 2.89422552 -1.58961069 37 -0.92633015 2.89422552 38 -1.82568366 -0.92633015 39 4.27448740 -1.82568366 40 -0.73095515 4.27448740 41 -3.25149031 -0.73095515 42 3.44851486 -3.25149031 43 -1.27923928 3.44851486 44 2.19330971 -1.27923928 45 6.97588354 2.19330971 46 4.08987697 6.97588354 47 1.59315069 4.08987697 48 -0.47061129 1.59315069 49 -3.50089187 -0.47061129 50 3.97180800 -3.50089187 51 1.35591633 3.97180800 52 0.14231978 1.35591633 53 -2.27221072 0.14231978 54 1.49662032 -2.27221072 55 3.98165966 1.49662032 56 -7.66783363 3.98165966 57 8.31745243 -7.66783363 58 1.14280183 8.31745243 59 0.40817795 1.14280183 60 2.43349881 0.40817795 61 -3.47218219 2.43349881 62 -1.46701780 -3.47218219 63 1.64882468 -1.46701780 64 2.31470331 1.64882468 65 4.49746469 2.31470331 66 -1.31501734 4.49746469 67 -1.87735465 -1.31501734 68 -0.41429683 -1.87735465 69 0.06706669 -0.41429683 70 3.98157905 0.06706669 71 5.62507472 3.98157905 72 3.77727907 5.62507472 73 -4.18459538 3.77727907 74 0.29370247 -4.18459538 75 -1.68750806 0.29370247 76 -1.42736810 -1.68750806 77 -2.79407666 -1.42736810 78 1.28709931 -2.79407666 79 2.58610868 1.28709931 80 3.15299848 2.58610868 81 1.47879622 3.15299848 82 1.14940499 1.47879622 83 -4.13806082 1.14940499 84 0.77866119 -4.13806082 85 -4.79407666 0.77866119 86 -1.26946995 -4.79407666 87 2.17880139 -1.26946995 88 8.71553968 2.17880139 89 4.44106734 8.71553968 90 2.54673714 4.44106734 91 -1.47266424 2.54673714 92 -1.32226097 -1.47266424 93 -1.56650119 -1.32226097 94 -1.88251903 -1.56650119 95 2.81004746 -1.88251903 96 -3.39054858 2.81004746 97 -2.28446032 -3.39054858 98 5.74334758 -2.28446032 99 0.48411776 5.74334758 100 0.71415756 0.48411776 101 -3.38152844 0.71415756 102 -2.41389132 -3.38152844 103 -3.10529243 -2.41389132 104 2.68159307 -3.10529243 105 -0.69390960 2.68159307 106 4.27185756 -0.69390960 107 0.02316196 4.27185756 108 1.43709230 0.02316196 109 5.95734703 1.43709230 110 -2.02062347 5.95734703 111 -0.91716504 -2.02062347 112 -1.27518816 -0.91716504 113 8.17880139 -1.27518816 114 -2.05963397 8.17880139 115 -0.25032493 -2.05963397 116 4.89470757 -0.25032493 117 -1.63506524 4.89470757 118 -2.32130425 -1.63506524 119 4.33781281 -2.32130425 120 -1.29465697 4.33781281 121 -1.34077477 -1.29465697 122 0.55809917 -1.34077477 123 -1.13584945 0.55809917 124 1.57192415 -1.13584945 125 0.18341518 1.57192415 126 -1.90447659 0.18341518 127 -3.06977492 -1.90447659 128 -0.97324455 -3.06977492 129 -1.07252404 -0.97324455 130 1.39749925 -1.07252404 131 1.78620404 1.39749925 132 4.52781781 1.78620404 133 0.58522112 4.52781781 134 0.21088383 0.58522112 135 -1.22251703 0.21088383 136 1.88481304 -1.22251703 137 -4.49567841 1.88481304 138 4.41534980 -4.49567841 139 3.78030463 4.41534980 140 3.58280585 3.78030463 141 6.74471458 3.58280585 142 0.83675045 6.74471458 143 -2.37877932 0.83675045 144 0.42805627 -2.37877932 145 -5.15035949 0.42805627 146 -3.15203260 -5.15035949 147 -4.68915073 -3.15203260 148 -0.24239535 -4.68915073 149 -0.13225596 -0.24239535 150 0.28488794 -0.13225596 151 -1.59906568 0.28488794 152 -1.73480918 -1.59906568 153 -3.03809538 -1.73480918 154 3.05351509 -3.03809538 155 1.18624892 3.05351509 156 1.71553968 1.18624892 157 0.09359904 1.71553968 158 2.05351509 0.09359904 159 1.75896407 2.05351509 160 -0.50998284 1.75896407 161 0.22493452 -0.50998284 162 2.75352911 0.22493452 163 -0.61848637 2.75352911 164 -2.51562928 -0.61848637 165 -0.03995955 -2.51562928 166 0.94742682 -0.03995955 167 4.68518656 0.94742682 168 -0.78843022 4.68518656 169 2.28689541 -0.78843022 170 3.81142958 2.28689541 171 -2.23964623 3.81142958 172 -1.10577448 -2.23964623 173 -1.49978619 -1.10577448 174 1.06000589 -1.49978619 175 0.81191163 1.06000589 176 -1.41134381 0.81191163 177 2.65922523 -1.41134381 178 -3.97340296 2.65922523 179 -2.77412580 -3.97340296 180 -0.64998308 -2.77412580 181 1.01958437 -0.64998308 182 -1.68254757 1.01958437 183 0.14920109 -1.68254757 184 -1.83365382 0.14920109 185 -5.30636123 -1.83365382 186 -2.16324955 -5.30636123 187 0.89470757 -2.16324955 188 0.68159307 0.89470757 189 3.06275502 0.68159307 190 2.80783609 3.06275502 191 1.59635745 2.80783609 192 -3.39014135 1.59635745 193 -2.32110035 -3.39014135 194 0.61061375 -2.32110035 195 -2.76397650 0.61061375 196 -1.84135508 -2.76397650 197 -0.89787344 -1.84135508 198 -1.01530194 -0.89787344 199 6.66466777 -1.01530194 200 1.33437773 6.66466777 201 1.65922523 1.33437773 202 1.53298220 1.65922523 203 2.35619277 1.53298220 204 0.67781965 2.35619277 205 -1.88996656 0.67781965 206 -2.75111162 -1.88996656 207 0.84178271 -2.75111162 208 0.51988824 0.84178271 209 -5.81100196 0.51988824 210 -0.25913945 -5.81100196 211 -2.19236952 -0.25913945 212 2.33216637 -2.19236952 213 -2.68254757 2.33216637 214 2.29395621 -2.68254757 215 -1.04217092 2.29395621 216 0.81142958 -1.04217092 217 0.96664361 0.81142958 218 2.55265663 0.96664361 219 1.28350582 2.55265663 220 -7.80144357 1.28350582 221 -1.13544393 -7.80144357 222 -0.53034321 -1.13544393 223 -5.69322627 -0.53034321 224 -0.95529944 -5.69322627 225 -1.74841820 -0.95529944 226 -1.43964211 -1.74841820 227 0.59589982 -1.43964211 228 3.62915026 0.59589982 229 0.86415055 3.62915026 230 -2.26999935 0.86415055 231 -2.06977492 -2.26999935 232 2.90764211 -2.06977492 233 -3.59567609 2.90764211 234 0.28709931 -3.59567609 235 -0.84543062 0.28709931 236 1.37397079 -0.84543062 237 -1.50495058 1.37397079 238 -3.62602921 -1.50495058 239 -1.43397126 -3.62602921 240 -1.76941904 -1.43397126 241 0.61633273 -1.76941904 242 0.56058620 0.61633273 243 -2.41699025 0.56058620 244 -3.30276774 -2.41699025 245 -1.35661786 -3.30276774 246 -2.00876206 -1.35661786 247 0.38298693 -2.00876206 248 -1.94744163 0.38298693 249 2.35977114 -1.94744163 250 1.89830106 2.35977114 251 -3.90668796 1.89830106 252 -1.04044160 -3.90668796 253 -2.19876707 -1.04044160 254 -3.52469677 -2.19876707 255 1.35812770 -3.52469677 256 0.31745243 1.35812770 257 -1.97683804 0.31745243 258 -2.84651947 -1.97683804 259 -3.59499014 -2.84651947 260 -1.41429683 -3.59499014 261 1.04691193 -1.41429683 262 1.13729909 1.04691193 263 1.39364521 1.13729909 264 0.28193492 1.39364521 265 0.61577814 0.28193492 266 -0.13584945 0.61577814 267 -1.41807910 -0.13584945 268 0.96004045 -1.41807910 269 -1.82684505 0.96004045 270 -6.40548231 -1.82684505 271 -3.57875079 -6.40548231 272 1.33865489 -3.57875079 273 -2.59305921 1.33865489 274 -3.97683804 -2.59305921 > 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/fisher/rcomp/tmp/7zipb1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8v5921387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9vkwv1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10l9dk1387448176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/119mby1387448176.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/fisher/rcomp/tmp/127bb71387448176.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/fisher/rcomp/tmp/13h4901387448177.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/fisher/rcomp/tmp/14luay1387448177.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/fisher/rcomp/tmp/153clw1387448177.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/fisher/rcomp/tmp/16s9ls1387448177.tab") + } > > try(system("convert tmp/1xrvl1387448176.ps tmp/1xrvl1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/2x9891387448176.ps tmp/2x9891387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/3yxkm1387448176.ps tmp/3yxkm1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/43wrp1387448176.ps tmp/43wrp1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/5o3es1387448176.ps tmp/5o3es1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/632lk1387448176.ps tmp/632lk1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/7zipb1387448176.ps tmp/7zipb1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/8v5921387448176.ps tmp/8v5921387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/9vkwv1387448176.ps tmp/9vkwv1387448176.png",intern=TRUE)) character(0) > try(system("convert tmp/10l9dk1387448176.ps tmp/10l9dk1387448176.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 19.852 3.391 23.256