R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,4 + ,3 + ,3 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,4 + ,5 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,4 + ,3 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,2 + ,1 + ,2 + ,4 + ,0 + ,0 + ,0 + ,2 + ,4 + ,2 + ,1 + ,2 + ,3 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,2 + ,2 + ,4 + ,1 + ,2 + ,2 + ,0 + ,0 + ,0 + ,4 + ,4 + ,2 + ,4 + ,3 + ,4 + ,0 + ,0 + ,0 + ,2 + ,3 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,3 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,2 + ,3 + ,4 + ,1 + ,4 + ,1 + ,1 + ,3 + ,2 + ,3 + ,3 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,3 + ,5 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,4 + ,2 + ,0 + ,0 + ,0 + ,2 + ,3 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,4 + ,2 + ,1 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,5 + ,5 + ,1 + ,4 + ,2 + ,5 + ,1 + ,4 + ,2 + ,2 + ,4 + ,4 + ,3 + ,4 + ,2 + ,1 + ,3 + ,5 + ,0 + ,0 + ,0 + ,4 + ,4 + ,4 + ,0 + ,0 + ,0 + ,2 + ,4 + ,2 + ,1 + ,4 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,3 + ,4 + ,2 + ,1 + ,2 + ,3 + ,2 + ,2 + ,4 + ,3 + ,2 + ,5 + ,1 + ,2 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,3 + ,2 + ,2 + ,3 + ,2 + ,3 + ,4 + ,3 + ,1 + ,2 + ,4 + ,5 + ,5 + ,4 + ,2 + ,4 + ,4 + ,1 + ,3 + ,2 + ,2 + ,3 + ,2 + ,1 + ,2 + ,2 + ,1 + ,3 + ,3 + ,0 + ,0 + ,0 + ,2 + ,4 + ,2 + ,2 + ,2 + ,3 + ,3 + ,3 + ,4 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,3 + ,2 + ,0 + ,0 + ,0 + ,1 + ,4 + ,4 + ,1 + ,3 + ,2 + ,2 + ,3 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,3 + ,2 + ,0 + ,0 + ,0 + ,2 + ,4 + ,3 + ,2 + ,4 + ,4 + ,1 + ,2 + ,3 + ,2 + ,4 + ,3 + ,0 + ,0 + ,0 + ,3 + ,4 + ,4 + ,3 + ,4 + ,5 + ,3 + ,4 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,3 + ,2 + ,3 + ,4 + ,1 + ,4 + ,3 + ,2 + ,2 + ,2 + ,2 + ,4 + ,5 + ,4 + ,4 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,5 + ,0 + ,0 + ,0 + ,2 + ,4 + ,5 + ,1 + ,1 + ,1 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,4 + ,3 + ,2 + ,0 + ,0 + ,0 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,2 + ,3 + ,4 + ,3 + ,4 + ,3 + ,1 + ,4 + ,5 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,3 + ,3 + ,4 + ,2 + ,1 + ,4 + ,1 + ,3 + ,2 + ,3 + ,4 + ,5 + ,1 + ,3 + ,5 + ,0 + ,0 + ,0 + ,1 + ,1 + ,2 + ,3 + ,4 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,4 + ,2 + ,2 + ,4 + ,4 + ,2 + ,3 + ,4 + ,3 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,2 + ,3 + ,2 + ,2 + ,2 + ,3 + ,2 + ,5 + ,2 + ,4 + ,2 + ,1 + ,1 + ,4 + ,1 + ,2 + ,2 + ,2 + ,2 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,2 + ,2 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,3 + ,4 + ,1 + ,3 + ,2 + ,4 + ,0 + ,0 + ,0 + ,1 + ,4 + ,2 + ,2 + ,2 + ,4 + ,3 + ,3 + ,5 + ,1 + ,4 + ,2 + ,2 + ,3 + ,4 + ,4 + ,4 + ,5 + ,0 + ,0 + ,0 + ,2 + ,4 + ,4 + ,2 + ,3 + ,4 + ,2 + ,2 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,3 + ,2 + ,2 + ,0 + ,0 + ,0 + ,2 + ,3 + ,2 + ,2 + ,3 + ,4 + ,0 + ,0 + ,0 + ,2 + ,2 + ,4 + ,1 + ,4 + ,2 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,2 + ,2 + ,2 + ,1 + ,4 + ,1 + ,3 + ,4 + ,4 + ,0 + ,0 + ,0 + ,3 + ,3 + ,5 + ,0 + ,0 + ,0 + ,3 + ,3 + ,3 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,3 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,4 + ,2 + ,2 + ,5 + ,3 + ,2 + ,2 + ,2 + ,0 + ,0 + ,0 + ,3 + ,2 + ,2 + ,3 + ,3 + ,2 + ,2 + ,4 + ,4 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,2 + ,4 + ,5 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,3 + ,2 + ,3 + ,2 + ,2 + ,2 + ,2 + ,4 + ,5 + ,0 + ,0 + ,0 + ,3 + ,4 + ,4 + ,1 + ,2 + ,1 + ,2 + ,4 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,3 + ,2 + ,0 + ,0 + ,0 + ,2 + ,3 + ,4 + ,3 + ,2 + ,2 + ,0 + ,0 + ,0 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,2 + ,3 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,3 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0) + ,dim=c(3 + ,289) + ,dimnames=list(c('Beter' + ,'Fout' + ,'Ouders') + ,1:289)) > y <- array(NA,dim=c(3,289),dimnames=list(c('Beter','Fout','Ouders'),1:289)) > 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. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Beter Fout Ouders 1 1 4 4 2 4 3 3 3 4 4 3 4 0 0 0 5 0 0 0 6 1 4 4 7 3 3 3 8 0 0 0 9 0 0 0 10 4 4 5 11 0 0 0 12 2 1 1 13 0 0 0 14 2 2 2 15 0 0 0 16 0 0 0 17 4 4 4 18 4 4 4 19 2 2 2 20 0 0 0 21 4 3 4 22 2 2 2 23 2 3 2 24 0 0 0 25 0 0 0 26 1 4 2 27 1 2 4 28 0 0 0 29 2 4 2 30 1 2 3 31 2 4 4 32 0 0 0 33 2 2 4 34 1 2 2 35 0 0 0 36 4 4 2 37 4 3 4 38 0 0 0 39 2 3 3 40 0 0 0 41 0 0 0 42 1 2 4 43 4 4 4 44 2 4 4 45 0 0 0 46 3 2 4 47 3 4 3 48 3 4 2 49 0 0 0 50 0 0 0 51 2 4 4 52 0 0 0 53 2 3 4 54 1 4 1 55 1 3 2 56 3 3 4 57 0 0 0 58 0 0 0 59 0 0 0 60 0 0 0 61 3 5 3 62 4 4 4 63 4 4 4 64 2 4 3 65 0 0 0 66 0 0 0 67 0 0 0 68 2 4 4 69 0 0 0 70 0 0 0 71 0 0 0 72 2 4 2 73 0 0 0 74 2 3 2 75 2 4 4 76 2 4 4 77 2 1 3 78 0 0 0 79 0 0 0 80 0 0 0 81 1 1 4 82 2 4 4 83 0 0 0 84 5 5 1 85 4 2 5 86 1 4 2 87 2 4 4 88 3 4 2 89 1 3 5 90 0 0 0 91 4 4 4 92 0 0 0 93 2 4 2 94 1 4 4 95 2 2 4 96 2 4 4 97 3 3 2 98 4 4 4 99 2 3 4 100 3 4 2 101 1 2 3 102 2 2 4 103 3 2 5 104 1 2 4 105 0 0 0 106 0 0 0 107 1 4 4 108 2 4 3 109 3 4 4 110 2 4 4 111 0 0 0 112 0 0 0 113 0 0 0 114 0 0 0 115 2 3 2 116 2 3 2 117 3 4 3 118 1 2 4 119 5 5 4 120 2 4 4 121 1 3 2 122 2 3 2 123 1 2 2 124 1 3 3 125 0 0 0 126 2 4 2 127 2 2 3 128 3 3 4 129 2 2 2 130 0 0 0 131 0 0 0 132 4 3 2 133 0 0 0 134 1 4 4 135 1 3 2 136 2 3 3 137 0 0 0 138 0 0 0 139 2 3 2 140 0 0 0 141 2 4 3 142 2 4 4 143 1 2 3 144 2 4 3 145 0 0 0 146 3 4 4 147 3 4 5 148 3 4 4 149 0 0 0 150 0 0 0 151 1 2 3 152 2 3 4 153 1 4 3 154 2 2 2 155 2 4 5 156 4 4 2 157 0 0 0 158 0 0 0 159 0 0 0 160 2 1 5 161 0 0 0 162 2 4 5 163 1 1 1 164 4 4 4 165 2 3 4 166 4 3 2 167 0 0 0 168 2 2 2 169 0 0 0 170 2 3 4 171 3 4 3 172 1 4 5 173 2 2 4 174 4 4 2 175 0 0 0 176 0 0 0 177 0 0 0 178 3 3 4 179 2 1 4 180 1 3 2 181 3 4 5 182 1 3 5 183 0 0 0 184 1 1 2 185 3 4 4 186 0 0 0 187 0 0 0 188 0 0 0 189 2 4 2 190 2 4 4 191 2 3 4 192 3 3 1 193 0 0 0 194 2 2 3 195 2 2 2 196 3 2 5 197 2 4 2 198 1 1 4 199 1 2 2 200 2 2 4 201 0 0 0 202 0 0 0 203 4 2 2 204 4 4 3 205 4 4 3 206 0 0 0 207 0 0 0 208 2 2 4 209 2 2 2 210 0 0 0 211 3 4 1 212 3 2 4 213 0 0 0 214 1 4 2 215 2 2 4 216 3 3 5 217 1 4 2 218 2 3 4 219 4 4 5 220 0 0 0 221 2 4 4 222 2 3 4 223 2 2 4 224 0 0 0 225 0 0 0 226 3 2 2 227 0 0 0 228 2 3 2 229 2 3 4 230 0 0 0 231 2 2 4 232 1 4 2 233 2 4 4 234 0 0 0 235 2 2 2 236 1 4 1 237 3 4 4 238 0 0 0 239 3 3 5 240 0 0 0 241 3 3 3 242 0 0 0 243 0 0 0 244 1 2 2 245 0 0 0 246 0 0 0 247 2 3 4 248 0 0 0 249 0 0 0 250 4 4 2 251 2 5 3 252 2 2 2 253 0 0 0 254 3 2 2 255 3 3 2 256 2 4 4 257 1 1 1 258 0 0 0 259 2 4 5 260 0 0 0 261 0 0 0 262 0 0 0 263 0 0 0 264 3 2 3 265 2 2 2 266 2 4 5 267 0 0 0 268 3 4 4 269 1 2 1 270 2 4 4 271 0 0 0 272 0 0 0 273 0 0 0 274 1 3 2 275 0 0 0 276 2 3 4 277 3 2 2 278 0 0 0 279 3 3 4 280 3 4 4 281 2 3 4 282 0 0 0 283 0 0 0 284 0 0 0 285 4 3 2 286 0 0 0 287 0 0 0 288 0 0 0 289 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Fout Ouders 0.1004 0.4787 0.1972 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.0014 -0.3255 -0.1004 0.1958 2.5477 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.10042 0.07171 1.400 0.162 Fout 0.47873 0.04548 10.527 < 2e-16 *** Ouders 0.19721 0.04338 4.546 8.08e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7776 on 286 degrees of freedom Multiple R-squared: 0.6745, Adjusted R-squared: 0.6722 F-statistic: 296.3 on 2 and 286 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.9966377 6.724556e-03 3.362278e-03 [2,] 0.9977302 4.539641e-03 2.269821e-03 [3,] 0.9951257 9.748608e-03 4.874304e-03 [4,] 0.9902724 1.945512e-02 9.727559e-03 [5,] 0.9980862 3.827572e-03 1.913786e-03 [6,] 0.9962314 7.537244e-03 3.768622e-03 [7,] 0.9968364 6.327236e-03 3.163618e-03 [8,] 0.9944414 1.111723e-02 5.558613e-03 [9,] 0.9911970 1.760593e-02 8.802965e-03 [10,] 0.9858756 2.824881e-02 1.412441e-02 [11,] 0.9781194 4.376126e-02 2.188063e-02 [12,] 0.9781476 4.370474e-02 2.185237e-02 [13,] 0.9762115 4.757709e-02 2.378855e-02 [14,] 0.9662870 6.742592e-02 3.371296e-02 [15,] 0.9522604 9.547912e-02 4.773956e-02 [16,] 0.9665202 6.695956e-02 3.347978e-02 [17,] 0.9542007 9.159851e-02 4.579926e-02 [18,] 0.9373989 1.252022e-01 6.260110e-02 [19,] 0.9169754 1.660491e-01 8.302457e-02 [20,] 0.8921434 2.157133e-01 1.078566e-01 [21,] 0.9060530 1.878940e-01 9.394699e-02 [22,] 0.9669841 6.603171e-02 3.301585e-02 [23,] 0.9553821 8.923574e-02 4.461787e-02 [24,] 0.9427493 1.145014e-01 5.725068e-02 [25,] 0.9489455 1.021090e-01 5.105450e-02 [26,] 0.9553929 8.921411e-02 4.460706e-02 [27,] 0.9415846 1.168308e-01 5.841540e-02 [28,] 0.9263913 1.472174e-01 7.360871e-02 [29,] 0.9149268 1.701464e-01 8.507320e-02 [30,] 0.8932055 2.135890e-01 1.067945e-01 [31,] 0.9396701 1.206598e-01 6.032989e-02 [32,] 0.9637841 7.243182e-02 3.621591e-02 [33,] 0.9529901 9.401982e-02 4.700991e-02 [34,] 0.9423256 1.153488e-01 5.767441e-02 [35,] 0.9271141 1.457718e-01 7.288590e-02 [36,] 0.9091425 1.817150e-01 9.085752e-02 [37,] 0.9203757 1.592486e-01 7.962429e-02 [38,] 0.9259002 1.481996e-01 7.409979e-02 [39,] 0.9362652 1.274696e-01 6.373481e-02 [40,] 0.9207044 1.585912e-01 7.929562e-02 [41,] 0.9283273 1.433453e-01 7.167266e-02 [42,] 0.9129635 1.740730e-01 8.703650e-02 [43,] 0.8994764 2.010471e-01 1.005236e-01 [44,] 0.8785307 2.429385e-01 1.214693e-01 [45,] 0.8548294 2.903412e-01 1.451706e-01 [46,] 0.8716593 2.566815e-01 1.283407e-01 [47,] 0.8475039 3.049922e-01 1.524961e-01 [48,] 0.8316126 3.367748e-01 1.683874e-01 [49,] 0.8660121 2.679758e-01 1.339879e-01 [50,] 0.8748117 2.503767e-01 1.251883e-01 [51,] 0.8606256 2.787488e-01 1.393744e-01 [52,] 0.8360131 3.279738e-01 1.639869e-01 [53,] 0.8088735 3.822529e-01 1.911265e-01 [54,] 0.7792826 4.414347e-01 2.207174e-01 [55,] 0.7473754 5.052492e-01 2.526246e-01 [56,] 0.7135544 5.728911e-01 2.864456e-01 [57,] 0.7379365 5.241271e-01 2.620635e-01 [58,] 0.7587870 4.824260e-01 2.412130e-01 [59,] 0.7513788 4.972425e-01 2.486212e-01 [60,] 0.7184514 5.630972e-01 2.815486e-01 [61,] 0.6836954 6.326093e-01 3.163046e-01 [62,] 0.6473803 7.052393e-01 3.526197e-01 [63,] 0.6683511 6.632977e-01 3.316489e-01 [64,] 0.6317962 7.364076e-01 3.682038e-01 [65,] 0.5941462 8.117076e-01 4.058538e-01 [66,] 0.5557434 8.885131e-01 4.442566e-01 [67,] 0.5241601 9.516798e-01 4.758399e-01 [68,] 0.4853474 9.706949e-01 5.146526e-01 [69,] 0.4469226 8.938453e-01 5.530774e-01 [70,] 0.4662090 9.324180e-01 5.337910e-01 [71,] 0.4819625 9.639250e-01 5.180375e-01 [72,] 0.4691913 9.383825e-01 5.308087e-01 [73,] 0.4315231 8.630462e-01 5.684769e-01 [74,] 0.3945774 7.891549e-01 6.054226e-01 [75,] 0.3586615 7.173229e-01 6.413385e-01 [76,] 0.3524607 7.049214e-01 6.475393e-01 [77,] 0.3640869 7.281739e-01 6.359131e-01 [78,] 0.3295590 6.591180e-01 6.704410e-01 [79,] 0.6285837 7.428327e-01 3.714163e-01 [80,] 0.7700516 4.598968e-01 2.299484e-01 [81,] 0.8270039 3.459923e-01 1.729961e-01 [82,] 0.8338778 3.322444e-01 1.661222e-01 [83,] 0.8242741 3.514519e-01 1.757259e-01 [84,] 0.8908751 2.182498e-01 1.091249e-01 [85,] 0.8732403 2.535193e-01 1.267597e-01 [86,] 0.8918660 2.162680e-01 1.081340e-01 [87,] 0.8744558 2.510885e-01 1.255442e-01 [88,] 0.8606436 2.787128e-01 1.393564e-01 [89,] 0.9306252 1.387496e-01 6.937482e-02 [90,] 0.9183046 1.633907e-01 8.169535e-02 [91,] 0.9197207 1.605585e-01 8.027927e-02 [92,] 0.9298420 1.403160e-01 7.015798e-02 [93,] 0.9423374 1.153252e-01 5.766258e-02 [94,] 0.9339958 1.320083e-01 6.600417e-02 [95,] 0.9286634 1.426732e-01 7.133658e-02 [96,] 0.9252361 1.495278e-01 7.476392e-02 [97,] 0.9124668 1.750664e-01 8.753319e-02 [98,] 0.9167985 1.664029e-01 8.320146e-02 [99,] 0.9201523 1.596954e-01 7.984768e-02 [100,] 0.9066167 1.867666e-01 9.338329e-02 [101,] 0.8914809 2.170383e-01 1.085191e-01 [102,] 0.9463341 1.073318e-01 5.366588e-02 [103,] 0.9424421 1.151157e-01 5.755786e-02 [104,] 0.9324182 1.351636e-01 6.758179e-02 [105,] 0.9330900 1.338200e-01 6.690998e-02 [106,] 0.9213624 1.572752e-01 7.863760e-02 [107,] 0.9081564 1.836872e-01 9.184359e-02 [108,] 0.8933942 2.132116e-01 1.066058e-01 [109,] 0.8770123 2.459754e-01 1.229877e-01 [110,] 0.8588509 2.822983e-01 1.411491e-01 [111,] 0.8389689 3.220622e-01 1.610311e-01 [112,] 0.8227103 3.545794e-01 1.772897e-01 [113,] 0.8266269 3.467463e-01 1.733731e-01 [114,] 0.8957852 2.084295e-01 1.042148e-01 [115,] 0.8968009 2.063981e-01 1.031991e-01 [116,] 0.9026908 1.946185e-01 9.730924e-02 [117,] 0.8873791 2.252418e-01 1.126209e-01 [118,] 0.8760228 2.479545e-01 1.239772e-01 [119,] 0.8934293 2.131415e-01 1.065707e-01 [120,] 0.8773077 2.453847e-01 1.226923e-01 [121,] 0.8642501 2.714997e-01 1.357499e-01 [122,] 0.8489691 3.020618e-01 1.510309e-01 [123,] 0.8436228 3.127545e-01 1.563772e-01 [124,] 0.8331372 3.337256e-01 1.668628e-01 [125,] 0.8115273 3.769453e-01 1.884727e-01 [126,] 0.7882932 4.234136e-01 2.117068e-01 [127,] 0.9043916 1.912168e-01 9.560842e-02 [128,] 0.8895467 2.209066e-01 1.104533e-01 [129,] 0.9438369 1.123262e-01 5.616312e-02 [130,] 0.9476594 1.046812e-01 5.234058e-02 [131,] 0.9382760 1.234481e-01 6.172404e-02 [132,] 0.9275444 1.449113e-01 7.245564e-02 [133,] 0.9154462 1.691077e-01 8.455383e-02 [134,] 0.9017212 1.965575e-01 9.827875e-02 [135,] 0.8866319 2.267363e-01 1.133681e-01 [136,] 0.8801022 2.397957e-01 1.198978e-01 [137,] 0.8812184 2.375632e-01 1.187816e-01 [138,] 0.8760243 2.479514e-01 1.239757e-01 [139,] 0.8693535 2.612930e-01 1.306465e-01 [140,] 0.8510040 2.979921e-01 1.489960e-01 [141,] 0.8318391 3.363217e-01 1.681609e-01 [142,] 0.8097886 3.804229e-01 1.902114e-01 [143,] 0.7874952 4.250096e-01 2.125048e-01 [144,] 0.7627508 4.744984e-01 2.372492e-01 [145,] 0.7365133 5.269734e-01 2.634867e-01 [146,] 0.7281931 5.436138e-01 2.718069e-01 [147,] 0.7047570 5.904860e-01 2.952430e-01 [148,] 0.7940912 4.118177e-01 2.059088e-01 [149,] 0.7812892 4.374217e-01 2.187108e-01 [150,] 0.7988867 4.022266e-01 2.011133e-01 [151,] 0.8636627 2.726746e-01 1.363373e-01 [152,] 0.8446981 3.106038e-01 1.553019e-01 [153,] 0.8240729 3.518541e-01 1.759271e-01 [154,] 0.8017961 3.964077e-01 1.982039e-01 [155,] 0.7850949 4.298101e-01 2.149051e-01 [156,] 0.7600614 4.798773e-01 2.399386e-01 [157,] 0.7790884 4.418231e-01 2.209116e-01 [158,] 0.7550308 4.899383e-01 2.449692e-01 [159,] 0.7881783 4.236435e-01 2.118217e-01 [160,] 0.7679125 4.641750e-01 2.320875e-01 [161,] 0.8945079 2.109842e-01 1.054921e-01 [162,] 0.8784026 2.431947e-01 1.215974e-01 [163,] 0.8691829 2.616341e-01 1.308171e-01 [164,] 0.8504879 2.990243e-01 1.495121e-01 [165,] 0.8339317 3.321366e-01 1.660683e-01 [166,] 0.8175038 3.649924e-01 1.824962e-01 [167,] 0.9224882 1.550235e-01 7.751176e-02 [168,] 0.9096790 1.806421e-01 9.032104e-02 [169,] 0.9494226 1.011547e-01 5.057735e-02 [170,] 0.9399100 1.201800e-01 6.009002e-02 [171,] 0.9290436 1.419127e-01 7.095636e-02 [172,] 0.9167203 1.665595e-01 8.327973e-02 [173,] 0.9125049 1.749902e-01 8.749509e-02 [174,] 0.9057658 1.884684e-01 9.423421e-02 [175,] 0.9120771 1.758458e-01 8.792292e-02 [176,] 0.8973229 2.053542e-01 1.026771e-01 [177,] 0.9425077 1.149845e-01 5.749227e-02 [178,] 0.9318357 1.363285e-01 6.816427e-02 [179,] 0.9195763 1.608473e-01 8.042366e-02 [180,] 0.9061241 1.877518e-01 9.387592e-02 [181,] 0.8907217 2.185567e-01 1.092783e-01 [182,] 0.8735683 2.528633e-01 1.264317e-01 [183,] 0.8546080 2.907841e-01 1.453920e-01 [184,] 0.8384423 3.231154e-01 1.615577e-01 [185,] 0.8439915 3.120170e-01 1.560085e-01 [186,] 0.8290158 3.419683e-01 1.709842e-01 [187,] 0.8707024 2.585952e-01 1.292976e-01 [188,] 0.8511113 2.977774e-01 1.488887e-01 [189,] 0.8320965 3.358069e-01 1.679035e-01 [190,] 0.8193007 3.613987e-01 1.806993e-01 [191,] 0.8209023 3.581953e-01 1.790977e-01 [192,] 0.8016595 3.966810e-01 1.983405e-01 [193,] 0.7910815 4.178370e-01 2.089185e-01 [194,] 0.7751652 4.496697e-01 2.248348e-01 [195,] 0.7478827 5.042346e-01 2.521173e-01 [196,] 0.7182824 5.634352e-01 2.817176e-01 [197,] 0.6870597 6.258806e-01 3.129403e-01 [198,] 0.9202323 1.595355e-01 7.976775e-02 [199,] 0.9516371 9.672576e-02 4.836288e-02 [200,] 0.9743150 5.136997e-02 2.568499e-02 [201,] 0.9681938 6.361243e-02 3.180622e-02 [202,] 0.9609010 7.819800e-02 3.909900e-02 [203,] 0.9523790 9.524206e-02 4.762103e-02 [204,] 0.9477996 1.044008e-01 5.220038e-02 [205,] 0.9368994 1.262013e-01 6.310065e-02 [206,] 0.9549509 9.009826e-02 4.504913e-02 [207,] 0.9600232 7.995354e-02 3.997677e-02 [208,] 0.9510065 9.798707e-02 4.899354e-02 [209,] 0.9632266 7.354689e-02 3.677344e-02 [210,] 0.9546822 9.063556e-02 4.531778e-02 [211,] 0.9463927 1.072145e-01 5.360726e-02 [212,] 0.9625503 7.489931e-02 3.744966e-02 [213,] 0.9560877 8.782456e-02 4.391228e-02 [214,] 0.9623605 7.527903e-02 3.763952e-02 [215,] 0.9534397 9.312056e-02 4.656028e-02 [216,] 0.9542572 9.148550e-02 4.574275e-02 [217,] 0.9464283 1.071434e-01 5.357172e-02 [218,] 0.9341928 1.316143e-01 6.580715e-02 [219,] 0.9201693 1.596615e-01 7.983074e-02 [220,] 0.9039495 1.921010e-01 9.605048e-02 [221,] 0.9512081 9.758386e-02 4.879193e-02 [222,] 0.9397202 1.205595e-01 6.027976e-02 [223,] 0.9264234 1.471533e-01 7.357664e-02 [224,] 0.9144891 1.710218e-01 8.551092e-02 [225,] 0.8967477 2.065046e-01 1.032523e-01 [226,] 0.8759172 2.481657e-01 1.240828e-01 [227,] 0.9167013 1.665974e-01 8.329872e-02 [228,] 0.9213815 1.572371e-01 7.861853e-02 [229,] 0.9040835 1.918330e-01 9.591652e-02 [230,] 0.8935278 2.129444e-01 1.064722e-01 [231,] 0.9384163 1.231674e-01 6.158370e-02 [232,] 0.9233839 1.532321e-01 7.661605e-02 [233,] 0.9057507 1.884985e-01 9.424927e-02 [234,] 0.9034413 1.931174e-01 9.655868e-02 [235,] 0.8823412 2.353176e-01 1.176588e-01 [236,] 0.8882576 2.234848e-01 1.117424e-01 [237,] 0.8645116 2.709767e-01 1.354884e-01 [238,] 0.8373863 3.252275e-01 1.626137e-01 [239,] 0.8195959 3.608082e-01 1.804041e-01 [240,] 0.7868896 4.262208e-01 2.131104e-01 [241,] 0.7507980 4.984040e-01 2.492020e-01 [242,] 0.7126933 5.746134e-01 2.873067e-01 [243,] 0.6704400 6.591200e-01 3.295600e-01 [244,] 0.6256964 7.486073e-01 3.743036e-01 [245,] 0.7276808 5.446383e-01 2.723192e-01 [246,] 0.8204712 3.590577e-01 1.795288e-01 [247,] 0.7984851 4.030298e-01 2.015149e-01 [248,] 0.7598808 4.802384e-01 2.401192e-01 [249,] 0.8695533 2.608933e-01 1.304467e-01 [250,] 0.8804259 2.391483e-01 1.195741e-01 [251,] 0.8880428 2.239143e-01 1.119572e-01 [252,] 0.8604428 2.791145e-01 1.395572e-01 [253,] 0.8258956 3.482087e-01 1.741044e-01 [254,] 0.8526779 2.946443e-01 1.473221e-01 [255,] 0.8155345 3.689309e-01 1.844655e-01 [256,] 0.7725752 4.548495e-01 2.274248e-01 [257,] 0.7239353 5.521294e-01 2.760647e-01 [258,] 0.6700643 6.598714e-01 3.299357e-01 [259,] 0.8178251 3.643498e-01 1.821749e-01 [260,] 0.7951129 4.097742e-01 2.048871e-01 [261,] 0.8213198 3.573603e-01 1.786802e-01 [262,] 0.7719170 4.561661e-01 2.280830e-01 [263,] 0.7144574 5.710852e-01 2.855426e-01 [264,] 0.6834274 6.331453e-01 3.165726e-01 [265,] 0.7765907 4.468187e-01 2.234093e-01 [266,] 0.7140029 5.719942e-01 2.859971e-01 [267,] 0.6424815 7.150370e-01 3.575185e-01 [268,] 0.5636678 8.726645e-01 4.363322e-01 [269,] 0.9735128 5.297435e-02 2.648717e-02 [270,] 0.9543540 9.129195e-02 4.564598e-02 [271,] 0.9307880 1.384240e-01 6.921199e-02 [272,] 0.9791534 4.169318e-02 2.084659e-02 [273,] 0.9586311 8.273782e-02 4.136891e-02 [274,] 0.9966993 6.601461e-03 3.300730e-03 [275,] 1.0000000 4.392488e-95 2.196244e-95 [276,] 1.0000000 8.047249e-80 4.023624e-80 [277,] 1.0000000 1.004010e-60 5.020049e-61 [278,] 1.0000000 2.355905e-49 1.177953e-49 > postscript(file="/var/fisher/rcomp/tmp/1u5d61353330689.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/28qvr1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3nuaj1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4j4d21353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5qy3i1353330689.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 = 289 Frequency = 1 1 2 3 4 5 6 -1.804191602 1.871750230 1.393015249 -0.100424276 -0.100424276 -1.804191602 7 8 9 10 11 12 0.871750230 -0.100424276 -0.100424276 0.998601548 -0.100424276 1.223633893 13 14 15 16 17 18 -0.100424276 0.547692061 -0.100424276 -0.100424276 1.195808398 1.195808398 19 20 21 22 23 24 0.547692061 -0.100424276 1.674543379 0.547692061 0.068957080 -0.100424276 25 26 27 28 29 30 -0.100424276 -1.409777901 -0.846721639 -0.100424276 -0.409777901 -0.649514789 31 32 33 34 35 36 -0.804191602 -0.100424276 0.153278361 -0.452307939 -0.100424276 1.590222099 37 38 39 40 41 42 1.674543379 -0.100424276 -0.128249770 -0.100424276 -0.100424276 -0.846721639 43 44 45 46 47 48 1.195808398 -0.804191602 -0.100424276 1.153278361 0.393015249 0.590222099 49 50 51 52 53 54 -0.100424276 -0.100424276 -0.804191602 -0.100424276 -0.325456621 -1.212571051 55 56 57 58 59 60 -0.931042920 0.674543379 -0.100424276 -0.100424276 -0.100424276 -0.100424276 61 62 63 64 65 66 -0.085719732 1.195808398 1.195808398 -0.606984751 -0.100424276 -0.100424276 67 68 69 70 71 72 -0.100424276 -0.804191602 -0.100424276 -0.100424276 -0.100424276 -0.409777901 73 74 75 76 77 78 -0.100424276 0.068957080 -0.804191602 -0.804191602 0.829220192 -0.100424276 79 80 81 82 83 84 -0.100424276 -0.100424276 -0.367986658 -0.804191602 -0.100424276 2.308693968 85 86 87 88 89 90 1.956071510 -1.409777901 -0.804191602 0.590222099 -1.522663471 -0.100424276 91 92 93 94 95 96 1.195808398 -0.100424276 -0.409777901 -1.804191602 0.153278361 -0.804191602 97 98 99 100 101 102 1.068957080 1.195808398 -0.325456621 0.590222099 -0.649514789 0.153278361 103 104 105 106 107 108 0.956071510 -0.846721639 -0.100424276 -0.100424276 -1.804191602 -0.606984751 109 110 111 112 113 114 0.195808398 -0.804191602 -0.100424276 -0.100424276 -0.100424276 -0.100424276 115 116 117 118 119 120 0.068957080 0.068957080 0.393015249 -0.846721639 1.717073417 -0.804191602 121 122 123 124 125 126 -0.931042920 0.068957080 -0.452307939 -1.128249770 -0.100424276 -0.409777901 127 128 129 130 131 132 0.350485211 0.674543379 0.547692061 -0.100424276 -0.100424276 2.068957080 133 134 135 136 137 138 -0.100424276 -1.804191602 -0.931042920 -0.128249770 -0.100424276 -0.100424276 139 140 141 142 143 144 0.068957080 -0.100424276 -0.606984751 -0.804191602 -0.649514789 -0.606984751 145 146 147 148 149 150 -0.100424276 0.195808398 -0.001398452 0.195808398 -0.100424276 -0.100424276 151 152 153 154 155 156 -0.649514789 -0.325456621 -1.606984751 0.547692061 -1.001398452 1.590222099 157 158 159 160 161 162 -0.100424276 -0.100424276 -0.100424276 0.434806491 -0.100424276 -1.001398452 163 164 165 166 167 168 0.223633893 1.195808398 -0.325456621 2.068957080 -0.100424276 0.547692061 169 170 171 172 173 174 -0.100424276 -0.325456621 0.393015249 -2.001398452 0.153278361 1.590222099 175 176 177 178 179 180 -0.100424276 -0.100424276 -0.100424276 0.674543379 0.632013342 -0.931042920 181 182 183 184 185 186 -0.001398452 -1.522663471 -0.100424276 0.026427042 0.195808398 -0.100424276 187 188 189 190 191 192 -0.100424276 -0.100424276 -0.409777901 -0.804191602 -0.325456621 1.266163931 193 194 195 196 197 198 -0.100424276 0.350485211 0.547692061 0.956071510 -0.409777901 -0.367986658 199 200 201 202 203 204 -0.452307939 0.153278361 -0.100424276 -0.100424276 2.547692061 1.393015249 205 206 207 208 209 210 1.393015249 -0.100424276 -0.100424276 0.153278361 0.547692061 -0.100424276 211 212 213 214 215 216 0.787428949 1.153278361 -0.100424276 -1.409777901 0.153278361 0.477336529 217 218 219 220 221 222 -1.409777901 -0.325456621 0.998601548 -0.100424276 -0.804191602 -0.325456621 223 224 225 226 227 228 0.153278361 -0.100424276 -0.100424276 1.547692061 -0.100424276 0.068957080 229 230 231 232 233 234 -0.325456621 -0.100424276 0.153278361 -1.409777901 -0.804191602 -0.100424276 235 236 237 238 239 240 0.547692061 -1.212571051 0.195808398 -0.100424276 0.477336529 -0.100424276 241 242 243 244 245 246 0.871750230 -0.100424276 -0.100424276 -0.452307939 -0.100424276 -0.100424276 247 248 249 250 251 252 -0.325456621 -0.100424276 -0.100424276 1.590222099 -1.085719732 0.547692061 253 254 255 256 257 258 -0.100424276 1.547692061 1.068957080 -0.804191602 0.223633893 -0.100424276 259 260 261 262 263 264 -1.001398452 -0.100424276 -0.100424276 -0.100424276 -0.100424276 1.350485211 265 266 267 268 269 270 0.547692061 -1.001398452 -0.100424276 0.195808398 -0.255101088 -0.804191602 271 272 273 274 275 276 -0.100424276 -0.100424276 -0.100424276 -0.931042920 -0.100424276 -0.325456621 277 278 279 280 281 282 1.547692061 -0.100424276 0.674543379 0.195808398 -0.325456621 -0.100424276 283 284 285 286 287 288 -0.100424276 -0.100424276 2.068957080 -0.100424276 -0.100424276 -0.100424276 289 -0.100424276 > postscript(file="/var/fisher/rcomp/tmp/6v4wg1353330689.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 = 289 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.804191602 NA 1 1.871750230 -1.804191602 2 1.393015249 1.871750230 3 -0.100424276 1.393015249 4 -0.100424276 -0.100424276 5 -1.804191602 -0.100424276 6 0.871750230 -1.804191602 7 -0.100424276 0.871750230 8 -0.100424276 -0.100424276 9 0.998601548 -0.100424276 10 -0.100424276 0.998601548 11 1.223633893 -0.100424276 12 -0.100424276 1.223633893 13 0.547692061 -0.100424276 14 -0.100424276 0.547692061 15 -0.100424276 -0.100424276 16 1.195808398 -0.100424276 17 1.195808398 1.195808398 18 0.547692061 1.195808398 19 -0.100424276 0.547692061 20 1.674543379 -0.100424276 21 0.547692061 1.674543379 22 0.068957080 0.547692061 23 -0.100424276 0.068957080 24 -0.100424276 -0.100424276 25 -1.409777901 -0.100424276 26 -0.846721639 -1.409777901 27 -0.100424276 -0.846721639 28 -0.409777901 -0.100424276 29 -0.649514789 -0.409777901 30 -0.804191602 -0.649514789 31 -0.100424276 -0.804191602 32 0.153278361 -0.100424276 33 -0.452307939 0.153278361 34 -0.100424276 -0.452307939 35 1.590222099 -0.100424276 36 1.674543379 1.590222099 37 -0.100424276 1.674543379 38 -0.128249770 -0.100424276 39 -0.100424276 -0.128249770 40 -0.100424276 -0.100424276 41 -0.846721639 -0.100424276 42 1.195808398 -0.846721639 43 -0.804191602 1.195808398 44 -0.100424276 -0.804191602 45 1.153278361 -0.100424276 46 0.393015249 1.153278361 47 0.590222099 0.393015249 48 -0.100424276 0.590222099 49 -0.100424276 -0.100424276 50 -0.804191602 -0.100424276 51 -0.100424276 -0.804191602 52 -0.325456621 -0.100424276 53 -1.212571051 -0.325456621 54 -0.931042920 -1.212571051 55 0.674543379 -0.931042920 56 -0.100424276 0.674543379 57 -0.100424276 -0.100424276 58 -0.100424276 -0.100424276 59 -0.100424276 -0.100424276 60 -0.085719732 -0.100424276 61 1.195808398 -0.085719732 62 1.195808398 1.195808398 63 -0.606984751 1.195808398 64 -0.100424276 -0.606984751 65 -0.100424276 -0.100424276 66 -0.100424276 -0.100424276 67 -0.804191602 -0.100424276 68 -0.100424276 -0.804191602 69 -0.100424276 -0.100424276 70 -0.100424276 -0.100424276 71 -0.409777901 -0.100424276 72 -0.100424276 -0.409777901 73 0.068957080 -0.100424276 74 -0.804191602 0.068957080 75 -0.804191602 -0.804191602 76 0.829220192 -0.804191602 77 -0.100424276 0.829220192 78 -0.100424276 -0.100424276 79 -0.100424276 -0.100424276 80 -0.367986658 -0.100424276 81 -0.804191602 -0.367986658 82 -0.100424276 -0.804191602 83 2.308693968 -0.100424276 84 1.956071510 2.308693968 85 -1.409777901 1.956071510 86 -0.804191602 -1.409777901 87 0.590222099 -0.804191602 88 -1.522663471 0.590222099 89 -0.100424276 -1.522663471 90 1.195808398 -0.100424276 91 -0.100424276 1.195808398 92 -0.409777901 -0.100424276 93 -1.804191602 -0.409777901 94 0.153278361 -1.804191602 95 -0.804191602 0.153278361 96 1.068957080 -0.804191602 97 1.195808398 1.068957080 98 -0.325456621 1.195808398 99 0.590222099 -0.325456621 100 -0.649514789 0.590222099 101 0.153278361 -0.649514789 102 0.956071510 0.153278361 103 -0.846721639 0.956071510 104 -0.100424276 -0.846721639 105 -0.100424276 -0.100424276 106 -1.804191602 -0.100424276 107 -0.606984751 -1.804191602 108 0.195808398 -0.606984751 109 -0.804191602 0.195808398 110 -0.100424276 -0.804191602 111 -0.100424276 -0.100424276 112 -0.100424276 -0.100424276 113 -0.100424276 -0.100424276 114 0.068957080 -0.100424276 115 0.068957080 0.068957080 116 0.393015249 0.068957080 117 -0.846721639 0.393015249 118 1.717073417 -0.846721639 119 -0.804191602 1.717073417 120 -0.931042920 -0.804191602 121 0.068957080 -0.931042920 122 -0.452307939 0.068957080 123 -1.128249770 -0.452307939 124 -0.100424276 -1.128249770 125 -0.409777901 -0.100424276 126 0.350485211 -0.409777901 127 0.674543379 0.350485211 128 0.547692061 0.674543379 129 -0.100424276 0.547692061 130 -0.100424276 -0.100424276 131 2.068957080 -0.100424276 132 -0.100424276 2.068957080 133 -1.804191602 -0.100424276 134 -0.931042920 -1.804191602 135 -0.128249770 -0.931042920 136 -0.100424276 -0.128249770 137 -0.100424276 -0.100424276 138 0.068957080 -0.100424276 139 -0.100424276 0.068957080 140 -0.606984751 -0.100424276 141 -0.804191602 -0.606984751 142 -0.649514789 -0.804191602 143 -0.606984751 -0.649514789 144 -0.100424276 -0.606984751 145 0.195808398 -0.100424276 146 -0.001398452 0.195808398 147 0.195808398 -0.001398452 148 -0.100424276 0.195808398 149 -0.100424276 -0.100424276 150 -0.649514789 -0.100424276 151 -0.325456621 -0.649514789 152 -1.606984751 -0.325456621 153 0.547692061 -1.606984751 154 -1.001398452 0.547692061 155 1.590222099 -1.001398452 156 -0.100424276 1.590222099 157 -0.100424276 -0.100424276 158 -0.100424276 -0.100424276 159 0.434806491 -0.100424276 160 -0.100424276 0.434806491 161 -1.001398452 -0.100424276 162 0.223633893 -1.001398452 163 1.195808398 0.223633893 164 -0.325456621 1.195808398 165 2.068957080 -0.325456621 166 -0.100424276 2.068957080 167 0.547692061 -0.100424276 168 -0.100424276 0.547692061 169 -0.325456621 -0.100424276 170 0.393015249 -0.325456621 171 -2.001398452 0.393015249 172 0.153278361 -2.001398452 173 1.590222099 0.153278361 174 -0.100424276 1.590222099 175 -0.100424276 -0.100424276 176 -0.100424276 -0.100424276 177 0.674543379 -0.100424276 178 0.632013342 0.674543379 179 -0.931042920 0.632013342 180 -0.001398452 -0.931042920 181 -1.522663471 -0.001398452 182 -0.100424276 -1.522663471 183 0.026427042 -0.100424276 184 0.195808398 0.026427042 185 -0.100424276 0.195808398 186 -0.100424276 -0.100424276 187 -0.100424276 -0.100424276 188 -0.409777901 -0.100424276 189 -0.804191602 -0.409777901 190 -0.325456621 -0.804191602 191 1.266163931 -0.325456621 192 -0.100424276 1.266163931 193 0.350485211 -0.100424276 194 0.547692061 0.350485211 195 0.956071510 0.547692061 196 -0.409777901 0.956071510 197 -0.367986658 -0.409777901 198 -0.452307939 -0.367986658 199 0.153278361 -0.452307939 200 -0.100424276 0.153278361 201 -0.100424276 -0.100424276 202 2.547692061 -0.100424276 203 1.393015249 2.547692061 204 1.393015249 1.393015249 205 -0.100424276 1.393015249 206 -0.100424276 -0.100424276 207 0.153278361 -0.100424276 208 0.547692061 0.153278361 209 -0.100424276 0.547692061 210 0.787428949 -0.100424276 211 1.153278361 0.787428949 212 -0.100424276 1.153278361 213 -1.409777901 -0.100424276 214 0.153278361 -1.409777901 215 0.477336529 0.153278361 216 -1.409777901 0.477336529 217 -0.325456621 -1.409777901 218 0.998601548 -0.325456621 219 -0.100424276 0.998601548 220 -0.804191602 -0.100424276 221 -0.325456621 -0.804191602 222 0.153278361 -0.325456621 223 -0.100424276 0.153278361 224 -0.100424276 -0.100424276 225 1.547692061 -0.100424276 226 -0.100424276 1.547692061 227 0.068957080 -0.100424276 228 -0.325456621 0.068957080 229 -0.100424276 -0.325456621 230 0.153278361 -0.100424276 231 -1.409777901 0.153278361 232 -0.804191602 -1.409777901 233 -0.100424276 -0.804191602 234 0.547692061 -0.100424276 235 -1.212571051 0.547692061 236 0.195808398 -1.212571051 237 -0.100424276 0.195808398 238 0.477336529 -0.100424276 239 -0.100424276 0.477336529 240 0.871750230 -0.100424276 241 -0.100424276 0.871750230 242 -0.100424276 -0.100424276 243 -0.452307939 -0.100424276 244 -0.100424276 -0.452307939 245 -0.100424276 -0.100424276 246 -0.325456621 -0.100424276 247 -0.100424276 -0.325456621 248 -0.100424276 -0.100424276 249 1.590222099 -0.100424276 250 -1.085719732 1.590222099 251 0.547692061 -1.085719732 252 -0.100424276 0.547692061 253 1.547692061 -0.100424276 254 1.068957080 1.547692061 255 -0.804191602 1.068957080 256 0.223633893 -0.804191602 257 -0.100424276 0.223633893 258 -1.001398452 -0.100424276 259 -0.100424276 -1.001398452 260 -0.100424276 -0.100424276 261 -0.100424276 -0.100424276 262 -0.100424276 -0.100424276 263 1.350485211 -0.100424276 264 0.547692061 1.350485211 265 -1.001398452 0.547692061 266 -0.100424276 -1.001398452 267 0.195808398 -0.100424276 268 -0.255101088 0.195808398 269 -0.804191602 -0.255101088 270 -0.100424276 -0.804191602 271 -0.100424276 -0.100424276 272 -0.100424276 -0.100424276 273 -0.931042920 -0.100424276 274 -0.100424276 -0.931042920 275 -0.325456621 -0.100424276 276 1.547692061 -0.325456621 277 -0.100424276 1.547692061 278 0.674543379 -0.100424276 279 0.195808398 0.674543379 280 -0.325456621 0.195808398 281 -0.100424276 -0.325456621 282 -0.100424276 -0.100424276 283 -0.100424276 -0.100424276 284 2.068957080 -0.100424276 285 -0.100424276 2.068957080 286 -0.100424276 -0.100424276 287 -0.100424276 -0.100424276 288 -0.100424276 -0.100424276 289 NA -0.100424276 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.871750230 -1.804191602 [2,] 1.393015249 1.871750230 [3,] -0.100424276 1.393015249 [4,] -0.100424276 -0.100424276 [5,] -1.804191602 -0.100424276 [6,] 0.871750230 -1.804191602 [7,] -0.100424276 0.871750230 [8,] -0.100424276 -0.100424276 [9,] 0.998601548 -0.100424276 [10,] -0.100424276 0.998601548 [11,] 1.223633893 -0.100424276 [12,] -0.100424276 1.223633893 [13,] 0.547692061 -0.100424276 [14,] -0.100424276 0.547692061 [15,] -0.100424276 -0.100424276 [16,] 1.195808398 -0.100424276 [17,] 1.195808398 1.195808398 [18,] 0.547692061 1.195808398 [19,] -0.100424276 0.547692061 [20,] 1.674543379 -0.100424276 [21,] 0.547692061 1.674543379 [22,] 0.068957080 0.547692061 [23,] -0.100424276 0.068957080 [24,] -0.100424276 -0.100424276 [25,] -1.409777901 -0.100424276 [26,] -0.846721639 -1.409777901 [27,] -0.100424276 -0.846721639 [28,] -0.409777901 -0.100424276 [29,] -0.649514789 -0.409777901 [30,] -0.804191602 -0.649514789 [31,] -0.100424276 -0.804191602 [32,] 0.153278361 -0.100424276 [33,] -0.452307939 0.153278361 [34,] -0.100424276 -0.452307939 [35,] 1.590222099 -0.100424276 [36,] 1.674543379 1.590222099 [37,] -0.100424276 1.674543379 [38,] -0.128249770 -0.100424276 [39,] -0.100424276 -0.128249770 [40,] -0.100424276 -0.100424276 [41,] -0.846721639 -0.100424276 [42,] 1.195808398 -0.846721639 [43,] -0.804191602 1.195808398 [44,] -0.100424276 -0.804191602 [45,] 1.153278361 -0.100424276 [46,] 0.393015249 1.153278361 [47,] 0.590222099 0.393015249 [48,] -0.100424276 0.590222099 [49,] -0.100424276 -0.100424276 [50,] -0.804191602 -0.100424276 [51,] -0.100424276 -0.804191602 [52,] -0.325456621 -0.100424276 [53,] -1.212571051 -0.325456621 [54,] -0.931042920 -1.212571051 [55,] 0.674543379 -0.931042920 [56,] -0.100424276 0.674543379 [57,] -0.100424276 -0.100424276 [58,] -0.100424276 -0.100424276 [59,] -0.100424276 -0.100424276 [60,] -0.085719732 -0.100424276 [61,] 1.195808398 -0.085719732 [62,] 1.195808398 1.195808398 [63,] -0.606984751 1.195808398 [64,] -0.100424276 -0.606984751 [65,] -0.100424276 -0.100424276 [66,] -0.100424276 -0.100424276 [67,] -0.804191602 -0.100424276 [68,] -0.100424276 -0.804191602 [69,] -0.100424276 -0.100424276 [70,] -0.100424276 -0.100424276 [71,] -0.409777901 -0.100424276 [72,] -0.100424276 -0.409777901 [73,] 0.068957080 -0.100424276 [74,] -0.804191602 0.068957080 [75,] -0.804191602 -0.804191602 [76,] 0.829220192 -0.804191602 [77,] -0.100424276 0.829220192 [78,] -0.100424276 -0.100424276 [79,] -0.100424276 -0.100424276 [80,] -0.367986658 -0.100424276 [81,] -0.804191602 -0.367986658 [82,] -0.100424276 -0.804191602 [83,] 2.308693968 -0.100424276 [84,] 1.956071510 2.308693968 [85,] -1.409777901 1.956071510 [86,] -0.804191602 -1.409777901 [87,] 0.590222099 -0.804191602 [88,] -1.522663471 0.590222099 [89,] -0.100424276 -1.522663471 [90,] 1.195808398 -0.100424276 [91,] -0.100424276 1.195808398 [92,] -0.409777901 -0.100424276 [93,] -1.804191602 -0.409777901 [94,] 0.153278361 -1.804191602 [95,] -0.804191602 0.153278361 [96,] 1.068957080 -0.804191602 [97,] 1.195808398 1.068957080 [98,] -0.325456621 1.195808398 [99,] 0.590222099 -0.325456621 [100,] -0.649514789 0.590222099 [101,] 0.153278361 -0.649514789 [102,] 0.956071510 0.153278361 [103,] -0.846721639 0.956071510 [104,] -0.100424276 -0.846721639 [105,] -0.100424276 -0.100424276 [106,] -1.804191602 -0.100424276 [107,] -0.606984751 -1.804191602 [108,] 0.195808398 -0.606984751 [109,] -0.804191602 0.195808398 [110,] -0.100424276 -0.804191602 [111,] -0.100424276 -0.100424276 [112,] -0.100424276 -0.100424276 [113,] -0.100424276 -0.100424276 [114,] 0.068957080 -0.100424276 [115,] 0.068957080 0.068957080 [116,] 0.393015249 0.068957080 [117,] -0.846721639 0.393015249 [118,] 1.717073417 -0.846721639 [119,] -0.804191602 1.717073417 [120,] -0.931042920 -0.804191602 [121,] 0.068957080 -0.931042920 [122,] -0.452307939 0.068957080 [123,] -1.128249770 -0.452307939 [124,] -0.100424276 -1.128249770 [125,] -0.409777901 -0.100424276 [126,] 0.350485211 -0.409777901 [127,] 0.674543379 0.350485211 [128,] 0.547692061 0.674543379 [129,] -0.100424276 0.547692061 [130,] -0.100424276 -0.100424276 [131,] 2.068957080 -0.100424276 [132,] -0.100424276 2.068957080 [133,] -1.804191602 -0.100424276 [134,] -0.931042920 -1.804191602 [135,] -0.128249770 -0.931042920 [136,] -0.100424276 -0.128249770 [137,] -0.100424276 -0.100424276 [138,] 0.068957080 -0.100424276 [139,] -0.100424276 0.068957080 [140,] -0.606984751 -0.100424276 [141,] -0.804191602 -0.606984751 [142,] -0.649514789 -0.804191602 [143,] -0.606984751 -0.649514789 [144,] -0.100424276 -0.606984751 [145,] 0.195808398 -0.100424276 [146,] -0.001398452 0.195808398 [147,] 0.195808398 -0.001398452 [148,] -0.100424276 0.195808398 [149,] -0.100424276 -0.100424276 [150,] -0.649514789 -0.100424276 [151,] -0.325456621 -0.649514789 [152,] -1.606984751 -0.325456621 [153,] 0.547692061 -1.606984751 [154,] -1.001398452 0.547692061 [155,] 1.590222099 -1.001398452 [156,] -0.100424276 1.590222099 [157,] -0.100424276 -0.100424276 [158,] -0.100424276 -0.100424276 [159,] 0.434806491 -0.100424276 [160,] -0.100424276 0.434806491 [161,] -1.001398452 -0.100424276 [162,] 0.223633893 -1.001398452 [163,] 1.195808398 0.223633893 [164,] -0.325456621 1.195808398 [165,] 2.068957080 -0.325456621 [166,] -0.100424276 2.068957080 [167,] 0.547692061 -0.100424276 [168,] -0.100424276 0.547692061 [169,] -0.325456621 -0.100424276 [170,] 0.393015249 -0.325456621 [171,] -2.001398452 0.393015249 [172,] 0.153278361 -2.001398452 [173,] 1.590222099 0.153278361 [174,] -0.100424276 1.590222099 [175,] -0.100424276 -0.100424276 [176,] -0.100424276 -0.100424276 [177,] 0.674543379 -0.100424276 [178,] 0.632013342 0.674543379 [179,] -0.931042920 0.632013342 [180,] -0.001398452 -0.931042920 [181,] -1.522663471 -0.001398452 [182,] -0.100424276 -1.522663471 [183,] 0.026427042 -0.100424276 [184,] 0.195808398 0.026427042 [185,] -0.100424276 0.195808398 [186,] -0.100424276 -0.100424276 [187,] -0.100424276 -0.100424276 [188,] -0.409777901 -0.100424276 [189,] -0.804191602 -0.409777901 [190,] -0.325456621 -0.804191602 [191,] 1.266163931 -0.325456621 [192,] -0.100424276 1.266163931 [193,] 0.350485211 -0.100424276 [194,] 0.547692061 0.350485211 [195,] 0.956071510 0.547692061 [196,] -0.409777901 0.956071510 [197,] -0.367986658 -0.409777901 [198,] -0.452307939 -0.367986658 [199,] 0.153278361 -0.452307939 [200,] -0.100424276 0.153278361 [201,] -0.100424276 -0.100424276 [202,] 2.547692061 -0.100424276 [203,] 1.393015249 2.547692061 [204,] 1.393015249 1.393015249 [205,] -0.100424276 1.393015249 [206,] -0.100424276 -0.100424276 [207,] 0.153278361 -0.100424276 [208,] 0.547692061 0.153278361 [209,] -0.100424276 0.547692061 [210,] 0.787428949 -0.100424276 [211,] 1.153278361 0.787428949 [212,] -0.100424276 1.153278361 [213,] -1.409777901 -0.100424276 [214,] 0.153278361 -1.409777901 [215,] 0.477336529 0.153278361 [216,] -1.409777901 0.477336529 [217,] -0.325456621 -1.409777901 [218,] 0.998601548 -0.325456621 [219,] -0.100424276 0.998601548 [220,] -0.804191602 -0.100424276 [221,] -0.325456621 -0.804191602 [222,] 0.153278361 -0.325456621 [223,] -0.100424276 0.153278361 [224,] -0.100424276 -0.100424276 [225,] 1.547692061 -0.100424276 [226,] -0.100424276 1.547692061 [227,] 0.068957080 -0.100424276 [228,] -0.325456621 0.068957080 [229,] -0.100424276 -0.325456621 [230,] 0.153278361 -0.100424276 [231,] -1.409777901 0.153278361 [232,] -0.804191602 -1.409777901 [233,] -0.100424276 -0.804191602 [234,] 0.547692061 -0.100424276 [235,] -1.212571051 0.547692061 [236,] 0.195808398 -1.212571051 [237,] -0.100424276 0.195808398 [238,] 0.477336529 -0.100424276 [239,] -0.100424276 0.477336529 [240,] 0.871750230 -0.100424276 [241,] -0.100424276 0.871750230 [242,] -0.100424276 -0.100424276 [243,] -0.452307939 -0.100424276 [244,] -0.100424276 -0.452307939 [245,] -0.100424276 -0.100424276 [246,] -0.325456621 -0.100424276 [247,] -0.100424276 -0.325456621 [248,] -0.100424276 -0.100424276 [249,] 1.590222099 -0.100424276 [250,] -1.085719732 1.590222099 [251,] 0.547692061 -1.085719732 [252,] -0.100424276 0.547692061 [253,] 1.547692061 -0.100424276 [254,] 1.068957080 1.547692061 [255,] -0.804191602 1.068957080 [256,] 0.223633893 -0.804191602 [257,] -0.100424276 0.223633893 [258,] -1.001398452 -0.100424276 [259,] -0.100424276 -1.001398452 [260,] -0.100424276 -0.100424276 [261,] -0.100424276 -0.100424276 [262,] -0.100424276 -0.100424276 [263,] 1.350485211 -0.100424276 [264,] 0.547692061 1.350485211 [265,] -1.001398452 0.547692061 [266,] -0.100424276 -1.001398452 [267,] 0.195808398 -0.100424276 [268,] -0.255101088 0.195808398 [269,] -0.804191602 -0.255101088 [270,] -0.100424276 -0.804191602 [271,] -0.100424276 -0.100424276 [272,] -0.100424276 -0.100424276 [273,] -0.931042920 -0.100424276 [274,] -0.100424276 -0.931042920 [275,] -0.325456621 -0.100424276 [276,] 1.547692061 -0.325456621 [277,] -0.100424276 1.547692061 [278,] 0.674543379 -0.100424276 [279,] 0.195808398 0.674543379 [280,] -0.325456621 0.195808398 [281,] -0.100424276 -0.325456621 [282,] -0.100424276 -0.100424276 [283,] -0.100424276 -0.100424276 [284,] 2.068957080 -0.100424276 [285,] -0.100424276 2.068957080 [286,] -0.100424276 -0.100424276 [287,] -0.100424276 -0.100424276 [288,] -0.100424276 -0.100424276 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.871750230 -1.804191602 2 1.393015249 1.871750230 3 -0.100424276 1.393015249 4 -0.100424276 -0.100424276 5 -1.804191602 -0.100424276 6 0.871750230 -1.804191602 7 -0.100424276 0.871750230 8 -0.100424276 -0.100424276 9 0.998601548 -0.100424276 10 -0.100424276 0.998601548 11 1.223633893 -0.100424276 12 -0.100424276 1.223633893 13 0.547692061 -0.100424276 14 -0.100424276 0.547692061 15 -0.100424276 -0.100424276 16 1.195808398 -0.100424276 17 1.195808398 1.195808398 18 0.547692061 1.195808398 19 -0.100424276 0.547692061 20 1.674543379 -0.100424276 21 0.547692061 1.674543379 22 0.068957080 0.547692061 23 -0.100424276 0.068957080 24 -0.100424276 -0.100424276 25 -1.409777901 -0.100424276 26 -0.846721639 -1.409777901 27 -0.100424276 -0.846721639 28 -0.409777901 -0.100424276 29 -0.649514789 -0.409777901 30 -0.804191602 -0.649514789 31 -0.100424276 -0.804191602 32 0.153278361 -0.100424276 33 -0.452307939 0.153278361 34 -0.100424276 -0.452307939 35 1.590222099 -0.100424276 36 1.674543379 1.590222099 37 -0.100424276 1.674543379 38 -0.128249770 -0.100424276 39 -0.100424276 -0.128249770 40 -0.100424276 -0.100424276 41 -0.846721639 -0.100424276 42 1.195808398 -0.846721639 43 -0.804191602 1.195808398 44 -0.100424276 -0.804191602 45 1.153278361 -0.100424276 46 0.393015249 1.153278361 47 0.590222099 0.393015249 48 -0.100424276 0.590222099 49 -0.100424276 -0.100424276 50 -0.804191602 -0.100424276 51 -0.100424276 -0.804191602 52 -0.325456621 -0.100424276 53 -1.212571051 -0.325456621 54 -0.931042920 -1.212571051 55 0.674543379 -0.931042920 56 -0.100424276 0.674543379 57 -0.100424276 -0.100424276 58 -0.100424276 -0.100424276 59 -0.100424276 -0.100424276 60 -0.085719732 -0.100424276 61 1.195808398 -0.085719732 62 1.195808398 1.195808398 63 -0.606984751 1.195808398 64 -0.100424276 -0.606984751 65 -0.100424276 -0.100424276 66 -0.100424276 -0.100424276 67 -0.804191602 -0.100424276 68 -0.100424276 -0.804191602 69 -0.100424276 -0.100424276 70 -0.100424276 -0.100424276 71 -0.409777901 -0.100424276 72 -0.100424276 -0.409777901 73 0.068957080 -0.100424276 74 -0.804191602 0.068957080 75 -0.804191602 -0.804191602 76 0.829220192 -0.804191602 77 -0.100424276 0.829220192 78 -0.100424276 -0.100424276 79 -0.100424276 -0.100424276 80 -0.367986658 -0.100424276 81 -0.804191602 -0.367986658 82 -0.100424276 -0.804191602 83 2.308693968 -0.100424276 84 1.956071510 2.308693968 85 -1.409777901 1.956071510 86 -0.804191602 -1.409777901 87 0.590222099 -0.804191602 88 -1.522663471 0.590222099 89 -0.100424276 -1.522663471 90 1.195808398 -0.100424276 91 -0.100424276 1.195808398 92 -0.409777901 -0.100424276 93 -1.804191602 -0.409777901 94 0.153278361 -1.804191602 95 -0.804191602 0.153278361 96 1.068957080 -0.804191602 97 1.195808398 1.068957080 98 -0.325456621 1.195808398 99 0.590222099 -0.325456621 100 -0.649514789 0.590222099 101 0.153278361 -0.649514789 102 0.956071510 0.153278361 103 -0.846721639 0.956071510 104 -0.100424276 -0.846721639 105 -0.100424276 -0.100424276 106 -1.804191602 -0.100424276 107 -0.606984751 -1.804191602 108 0.195808398 -0.606984751 109 -0.804191602 0.195808398 110 -0.100424276 -0.804191602 111 -0.100424276 -0.100424276 112 -0.100424276 -0.100424276 113 -0.100424276 -0.100424276 114 0.068957080 -0.100424276 115 0.068957080 0.068957080 116 0.393015249 0.068957080 117 -0.846721639 0.393015249 118 1.717073417 -0.846721639 119 -0.804191602 1.717073417 120 -0.931042920 -0.804191602 121 0.068957080 -0.931042920 122 -0.452307939 0.068957080 123 -1.128249770 -0.452307939 124 -0.100424276 -1.128249770 125 -0.409777901 -0.100424276 126 0.350485211 -0.409777901 127 0.674543379 0.350485211 128 0.547692061 0.674543379 129 -0.100424276 0.547692061 130 -0.100424276 -0.100424276 131 2.068957080 -0.100424276 132 -0.100424276 2.068957080 133 -1.804191602 -0.100424276 134 -0.931042920 -1.804191602 135 -0.128249770 -0.931042920 136 -0.100424276 -0.128249770 137 -0.100424276 -0.100424276 138 0.068957080 -0.100424276 139 -0.100424276 0.068957080 140 -0.606984751 -0.100424276 141 -0.804191602 -0.606984751 142 -0.649514789 -0.804191602 143 -0.606984751 -0.649514789 144 -0.100424276 -0.606984751 145 0.195808398 -0.100424276 146 -0.001398452 0.195808398 147 0.195808398 -0.001398452 148 -0.100424276 0.195808398 149 -0.100424276 -0.100424276 150 -0.649514789 -0.100424276 151 -0.325456621 -0.649514789 152 -1.606984751 -0.325456621 153 0.547692061 -1.606984751 154 -1.001398452 0.547692061 155 1.590222099 -1.001398452 156 -0.100424276 1.590222099 157 -0.100424276 -0.100424276 158 -0.100424276 -0.100424276 159 0.434806491 -0.100424276 160 -0.100424276 0.434806491 161 -1.001398452 -0.100424276 162 0.223633893 -1.001398452 163 1.195808398 0.223633893 164 -0.325456621 1.195808398 165 2.068957080 -0.325456621 166 -0.100424276 2.068957080 167 0.547692061 -0.100424276 168 -0.100424276 0.547692061 169 -0.325456621 -0.100424276 170 0.393015249 -0.325456621 171 -2.001398452 0.393015249 172 0.153278361 -2.001398452 173 1.590222099 0.153278361 174 -0.100424276 1.590222099 175 -0.100424276 -0.100424276 176 -0.100424276 -0.100424276 177 0.674543379 -0.100424276 178 0.632013342 0.674543379 179 -0.931042920 0.632013342 180 -0.001398452 -0.931042920 181 -1.522663471 -0.001398452 182 -0.100424276 -1.522663471 183 0.026427042 -0.100424276 184 0.195808398 0.026427042 185 -0.100424276 0.195808398 186 -0.100424276 -0.100424276 187 -0.100424276 -0.100424276 188 -0.409777901 -0.100424276 189 -0.804191602 -0.409777901 190 -0.325456621 -0.804191602 191 1.266163931 -0.325456621 192 -0.100424276 1.266163931 193 0.350485211 -0.100424276 194 0.547692061 0.350485211 195 0.956071510 0.547692061 196 -0.409777901 0.956071510 197 -0.367986658 -0.409777901 198 -0.452307939 -0.367986658 199 0.153278361 -0.452307939 200 -0.100424276 0.153278361 201 -0.100424276 -0.100424276 202 2.547692061 -0.100424276 203 1.393015249 2.547692061 204 1.393015249 1.393015249 205 -0.100424276 1.393015249 206 -0.100424276 -0.100424276 207 0.153278361 -0.100424276 208 0.547692061 0.153278361 209 -0.100424276 0.547692061 210 0.787428949 -0.100424276 211 1.153278361 0.787428949 212 -0.100424276 1.153278361 213 -1.409777901 -0.100424276 214 0.153278361 -1.409777901 215 0.477336529 0.153278361 216 -1.409777901 0.477336529 217 -0.325456621 -1.409777901 218 0.998601548 -0.325456621 219 -0.100424276 0.998601548 220 -0.804191602 -0.100424276 221 -0.325456621 -0.804191602 222 0.153278361 -0.325456621 223 -0.100424276 0.153278361 224 -0.100424276 -0.100424276 225 1.547692061 -0.100424276 226 -0.100424276 1.547692061 227 0.068957080 -0.100424276 228 -0.325456621 0.068957080 229 -0.100424276 -0.325456621 230 0.153278361 -0.100424276 231 -1.409777901 0.153278361 232 -0.804191602 -1.409777901 233 -0.100424276 -0.804191602 234 0.547692061 -0.100424276 235 -1.212571051 0.547692061 236 0.195808398 -1.212571051 237 -0.100424276 0.195808398 238 0.477336529 -0.100424276 239 -0.100424276 0.477336529 240 0.871750230 -0.100424276 241 -0.100424276 0.871750230 242 -0.100424276 -0.100424276 243 -0.452307939 -0.100424276 244 -0.100424276 -0.452307939 245 -0.100424276 -0.100424276 246 -0.325456621 -0.100424276 247 -0.100424276 -0.325456621 248 -0.100424276 -0.100424276 249 1.590222099 -0.100424276 250 -1.085719732 1.590222099 251 0.547692061 -1.085719732 252 -0.100424276 0.547692061 253 1.547692061 -0.100424276 254 1.068957080 1.547692061 255 -0.804191602 1.068957080 256 0.223633893 -0.804191602 257 -0.100424276 0.223633893 258 -1.001398452 -0.100424276 259 -0.100424276 -1.001398452 260 -0.100424276 -0.100424276 261 -0.100424276 -0.100424276 262 -0.100424276 -0.100424276 263 1.350485211 -0.100424276 264 0.547692061 1.350485211 265 -1.001398452 0.547692061 266 -0.100424276 -1.001398452 267 0.195808398 -0.100424276 268 -0.255101088 0.195808398 269 -0.804191602 -0.255101088 270 -0.100424276 -0.804191602 271 -0.100424276 -0.100424276 272 -0.100424276 -0.100424276 273 -0.931042920 -0.100424276 274 -0.100424276 -0.931042920 275 -0.325456621 -0.100424276 276 1.547692061 -0.325456621 277 -0.100424276 1.547692061 278 0.674543379 -0.100424276 279 0.195808398 0.674543379 280 -0.325456621 0.195808398 281 -0.100424276 -0.325456621 282 -0.100424276 -0.100424276 283 -0.100424276 -0.100424276 284 2.068957080 -0.100424276 285 -0.100424276 2.068957080 286 -0.100424276 -0.100424276 287 -0.100424276 -0.100424276 288 -0.100424276 -0.100424276 > 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/7ojs91353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8zn5g1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9i8p01353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10rvz21353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11hit71353330689.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12jip71353330689.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/1350vh1353330689.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14afu31353330689.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/15duvb1353330689.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/160m4m1353330689.tab") + } > > try(system("convert tmp/1u5d61353330689.ps tmp/1u5d61353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/28qvr1353330689.ps tmp/28qvr1353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/3nuaj1353330689.ps tmp/3nuaj1353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/4j4d21353330689.ps tmp/4j4d21353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/5qy3i1353330689.ps tmp/5qy3i1353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/6v4wg1353330689.ps tmp/6v4wg1353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/7ojs91353330689.ps tmp/7ojs91353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/8zn5g1353330689.ps tmp/8zn5g1353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/9i8p01353330689.ps tmp/9i8p01353330689.png",intern=TRUE)) character(0) > try(system("convert tmp/10rvz21353330689.ps tmp/10rvz21353330689.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.945 1.370 12.310