R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Populatie' + ,'Geslacht' + ,'Connected' + ,'Seperate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Populatie','Geslacht','Connected','Seperate','Learning','Software','Happiness'),1:288)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '7' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '7' > #'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 Happiness Populatie Geslacht Connected Seperate Learning Software t 1 14 1 1 41 38 13 12 1 2 18 1 1 39 32 16 11 2 3 11 1 1 30 35 19 15 3 4 12 1 0 31 33 15 6 4 5 16 1 1 34 37 14 13 5 6 18 1 1 35 29 13 10 6 7 14 1 1 39 31 19 12 7 8 14 1 1 34 36 15 14 8 9 15 1 1 36 35 14 12 9 10 15 1 1 37 38 15 9 10 11 17 1 0 38 31 16 10 11 12 19 1 1 36 34 16 12 12 13 10 1 0 38 35 16 12 13 14 16 1 1 39 38 16 11 14 15 18 1 1 33 37 17 15 15 16 14 1 0 32 33 15 12 16 17 14 1 0 36 32 15 10 17 18 17 1 1 38 38 20 12 18 19 14 1 0 39 38 18 11 19 20 16 1 1 32 32 16 12 20 21 18 1 0 32 33 16 11 21 22 11 1 1 31 31 16 12 22 23 14 1 1 39 38 19 13 23 24 12 1 1 37 39 16 11 24 25 17 1 0 39 32 17 12 25 26 9 1 1 41 32 17 13 26 27 16 1 0 36 35 16 10 27 28 14 1 1 33 37 15 14 28 29 15 1 1 33 33 16 12 29 30 11 1 0 34 33 14 10 30 31 16 1 1 31 31 15 12 31 32 13 1 0 27 32 12 8 32 33 17 1 1 37 31 14 10 33 34 15 1 1 34 37 16 12 34 35 14 1 0 34 30 14 12 35 36 16 1 0 32 33 10 7 36 37 9 1 0 29 31 10 9 37 38 15 1 0 36 33 14 12 38 39 17 1 1 29 31 16 10 39 40 13 1 0 35 33 16 10 40 41 15 1 0 37 32 16 10 41 42 16 1 1 34 33 14 12 42 43 16 1 0 38 32 20 15 43 44 12 1 0 35 33 14 10 44 45 15 1 1 38 28 14 10 45 46 11 1 1 37 35 11 12 46 47 15 1 1 38 39 14 13 47 48 15 1 1 33 34 15 11 48 49 17 1 1 36 38 16 11 49 50 13 1 0 38 32 14 12 50 51 16 1 1 32 38 16 14 51 52 14 1 0 32 30 14 10 52 53 11 1 0 32 33 12 12 53 54 12 1 1 34 38 16 13 54 55 12 1 0 32 32 9 5 55 56 15 1 1 37 35 14 6 56 57 16 1 1 39 34 16 12 57 58 15 1 1 29 34 16 12 58 59 12 1 0 37 36 15 11 59 60 12 1 1 35 34 16 10 60 61 8 1 0 30 28 12 7 61 62 13 1 0 38 34 16 12 62 63 11 1 1 34 35 16 14 63 64 14 1 1 31 35 14 11 64 65 15 1 1 34 31 16 12 65 66 10 1 0 35 37 17 13 66 67 11 1 1 36 35 18 14 67 68 12 1 0 30 27 18 11 68 69 15 1 1 39 40 12 12 69 70 15 1 0 35 37 16 12 70 71 14 1 0 38 36 10 8 71 72 16 1 1 31 38 14 11 72 73 15 1 1 34 39 18 14 73 74 15 1 0 38 41 18 14 74 75 13 1 0 34 27 16 12 75 76 12 1 1 39 30 17 9 76 77 17 1 1 37 37 16 13 77 78 13 1 1 34 31 16 11 78 79 15 1 0 28 31 13 12 79 80 13 1 0 37 27 16 12 80 81 15 1 0 33 36 16 12 81 82 15 1 1 35 37 16 12 82 83 16 1 0 37 33 15 12 83 84 15 1 1 32 34 15 11 84 85 14 1 1 33 31 16 10 85 86 15 1 0 38 39 14 9 86 87 14 1 1 33 34 16 12 87 88 13 1 1 29 32 16 12 88 89 7 1 1 33 33 15 12 89 90 17 1 1 31 36 12 9 90 91 13 1 1 36 32 17 15 91 92 15 1 1 35 41 16 12 92 93 14 1 1 32 28 15 12 93 94 13 1 1 29 30 13 12 94 95 16 1 1 39 36 16 10 95 96 12 1 1 37 35 16 13 96 97 14 1 1 35 31 16 9 97 98 17 1 0 37 34 16 12 98 99 15 1 0 32 36 14 10 99 100 17 1 1 38 36 16 14 100 101 12 1 0 37 35 16 11 101 102 16 1 1 36 37 20 15 102 103 11 1 0 32 28 15 11 103 104 15 1 1 33 39 16 11 104 105 9 1 0 40 32 13 12 105 106 16 1 1 38 35 17 12 106 107 15 1 0 41 39 16 12 107 108 10 1 0 36 35 16 11 108 109 10 1 1 43 42 12 7 109 110 15 1 1 30 34 16 12 110 111 11 1 1 31 33 16 14 111 112 13 1 1 32 41 17 11 112 113 18 1 1 37 34 12 10 113 114 16 1 0 37 32 18 13 114 115 14 1 1 33 40 14 13 115 116 14 1 1 34 40 14 8 116 117 14 1 1 33 35 13 11 117 118 14 1 1 38 36 16 12 118 119 12 1 0 33 37 13 11 119 120 14 1 1 31 27 16 13 120 121 15 1 1 38 39 13 12 121 122 15 1 1 37 38 16 14 122 123 15 1 1 36 31 15 13 123 124 13 1 1 31 33 16 15 124 125 17 1 0 39 32 15 10 125 126 17 1 1 44 39 17 11 126 127 19 1 1 33 36 15 9 127 128 15 1 1 35 33 12 11 128 129 13 1 0 32 33 16 10 129 130 9 1 0 28 32 10 11 130 131 15 1 1 40 37 16 8 131 132 15 1 0 27 30 12 11 132 133 15 1 0 37 38 14 12 133 134 16 1 1 32 29 15 12 134 135 11 1 0 28 22 13 9 135 136 14 1 0 34 35 15 11 136 137 11 1 1 30 35 11 10 137 138 15 1 1 35 34 12 8 138 139 13 1 0 31 35 11 9 139 140 15 1 1 32 34 16 8 140 141 16 1 0 30 37 15 9 141 142 14 1 1 30 35 17 15 142 143 15 1 0 31 23 16 11 143 144 16 1 1 40 31 10 8 144 145 16 1 1 32 27 18 13 145 146 11 1 0 36 36 13 12 146 147 12 1 0 32 31 16 12 147 148 9 1 0 35 32 13 9 148 149 16 1 1 38 39 10 7 149 150 13 1 1 42 37 15 13 150 151 16 1 0 34 38 16 9 151 152 12 1 1 35 39 16 6 152 153 9 1 1 38 34 14 8 153 154 13 1 1 33 31 10 8 154 155 14 1 1 32 37 13 6 155 156 19 1 1 33 36 15 9 156 157 13 1 1 34 32 16 11 157 158 12 1 1 32 38 12 8 158 159 10 0 0 27 26 13 10 159 160 14 0 0 31 26 12 8 160 161 16 0 0 38 33 17 14 161 162 10 0 1 34 39 15 10 162 163 11 0 0 24 30 10 8 163 164 14 0 0 30 33 14 11 164 165 12 0 1 26 25 11 12 165 166 9 0 1 34 38 13 12 166 167 9 0 0 27 37 16 12 167 168 11 0 0 37 31 12 5 168 169 16 0 1 36 37 16 12 169 170 9 0 0 41 35 12 10 170 171 13 0 1 29 25 9 7 171 172 16 0 1 36 28 12 12 172 173 13 0 0 32 35 15 11 173 174 9 0 1 37 33 12 8 174 175 12 0 0 30 30 12 9 175 176 16 0 1 31 31 14 10 176 177 11 0 1 38 37 12 9 177 178 14 0 1 36 36 16 12 178 179 13 0 0 35 30 11 6 179 180 15 0 0 31 36 19 15 180 181 14 0 0 38 32 15 12 181 182 16 0 1 22 28 8 12 182 183 13 0 1 32 36 16 12 183 184 14 0 0 36 34 17 11 184 185 15 0 1 39 31 12 7 185 186 13 0 0 28 28 11 7 186 187 11 0 0 32 36 11 5 187 188 11 0 1 32 36 14 12 188 189 14 0 1 38 40 16 12 189 190 15 0 1 32 33 12 3 190 191 11 0 1 35 37 16 11 191 192 15 0 1 32 32 13 10 192 193 12 0 0 37 38 15 12 193 194 14 0 1 34 31 16 9 194 195 14 0 1 33 37 16 12 195 196 8 0 0 33 33 14 9 196 197 9 0 0 30 30 16 12 197 198 15 0 0 24 30 14 10 198 199 17 0 0 34 31 11 9 199 200 13 0 0 34 32 12 12 200 201 15 0 1 33 34 15 8 201 202 15 0 1 34 36 15 11 202 203 14 0 1 35 37 16 11 203 204 16 0 0 35 36 16 12 204 205 13 0 0 36 33 11 10 205 206 16 0 0 34 33 15 10 206 207 9 0 1 34 33 12 12 207 208 16 0 0 41 44 12 12 208 209 11 0 0 32 39 15 11 209 210 10 0 0 30 32 15 8 210 211 11 0 1 35 35 16 12 211 212 15 0 0 28 25 14 10 212 213 17 0 1 33 35 17 11 213 214 14 0 1 39 34 14 10 214 215 8 0 0 36 35 13 8 215 216 15 0 1 36 39 15 12 216 217 11 0 0 35 33 13 12 217 218 16 0 0 38 36 14 10 218 219 10 0 1 33 32 15 12 219 220 15 0 0 31 32 12 9 220 221 16 0 1 32 36 8 6 221 222 19 0 0 31 32 14 10 222 223 12 0 0 33 34 14 9 223 224 8 0 0 34 33 11 9 224 225 11 0 0 34 35 12 9 225 226 14 0 1 34 30 13 6 226 227 9 0 0 33 38 10 10 227 228 15 0 0 32 34 16 6 228 229 13 0 1 41 33 18 14 229 230 16 0 1 34 32 13 10 230 231 11 0 0 36 31 11 10 231 232 12 0 0 37 30 4 6 232 233 13 0 0 36 27 13 12 233 234 10 0 1 29 31 16 12 234 235 11 0 0 37 30 10 7 235 236 12 0 0 27 32 12 8 236 237 8 0 0 35 35 12 11 237 238 12 0 0 28 28 10 3 238 239 12 0 0 35 33 13 6 239 240 11 0 0 29 35 12 8 240 241 13 0 0 32 35 14 9 241 242 14 0 1 36 32 10 9 242 243 10 0 1 19 21 12 8 243 244 12 0 1 21 20 12 9 244 245 15 0 0 31 34 11 7 245 246 13 0 0 33 32 10 7 246 247 13 0 1 36 34 12 6 247 248 13 0 1 33 32 16 9 248 249 12 0 0 37 33 12 10 249 250 12 0 0 34 33 14 11 250 251 9 0 0 35 37 16 12 251 252 9 0 1 31 32 14 8 252 253 15 0 1 37 34 13 11 253 254 10 0 1 35 30 4 3 254 255 14 0 1 27 30 15 11 255 256 15 0 0 34 38 11 12 256 257 7 0 0 40 36 11 7 257 258 14 0 0 29 32 14 9 258 259 8 0 0 38 34 15 12 259 260 10 0 1 34 33 14 8 260 261 13 0 0 21 27 13 11 261 262 13 0 0 36 32 11 8 262 263 13 0 1 38 34 15 10 263 264 8 0 0 30 29 11 8 264 265 12 0 0 35 35 13 7 265 266 13 0 1 30 27 13 8 266 267 12 0 1 36 33 16 10 267 268 10 0 0 34 38 13 8 268 269 13 0 1 35 36 16 12 269 270 12 0 0 34 33 16 14 270 271 9 0 0 32 39 12 7 271 272 15 0 1 33 29 7 6 272 273 13 0 0 33 32 16 11 273 274 13 0 1 26 34 5 4 274 275 13 0 0 35 38 16 9 275 276 15 0 0 21 17 4 5 276 277 15 0 0 38 35 12 9 277 278 14 0 0 35 32 15 11 278 279 15 0 1 33 34 14 12 279 280 11 0 0 37 36 11 9 280 281 15 0 0 38 31 16 12 281 282 14 0 1 34 35 15 10 282 283 13 0 0 27 29 12 9 283 284 12 0 1 16 22 6 6 284 285 16 0 0 40 41 16 10 285 286 16 0 0 36 36 10 9 286 287 9 0 1 42 42 15 13 287 288 14 0 1 30 33 14 12 288 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Populatie Geslacht Connected Seperate Learning 11.37897 0.56595 0.79407 0.02548 -0.01293 0.11259 Software t -0.02314 -0.00315 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.2840 -1.3996 0.4132 1.6151 6.5993 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 11.37897 1.67468 6.795 6.52e-11 *** Populatie 0.56595 0.55792 1.014 0.31127 Geslacht 0.79407 0.28880 2.750 0.00636 ** Connected 0.02548 0.04136 0.616 0.53829 Seperate -0.01293 0.04309 -0.300 0.76430 Learning 0.11259 0.07439 1.513 0.13130 Software -0.02314 0.07977 -0.290 0.77195 t -0.00315 0.00339 -0.929 0.35362 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.369 on 280 degrees of freedom Multiple R-squared: 0.1195, Adjusted R-squared: 0.09745 F-statistic: 5.427 on 7 and 280 DF, p-value: 7.455e-06 > 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,] 0.55461768 0.89076464 0.44538232 [2,] 0.58964810 0.82070380 0.41035190 [3,] 0.71366240 0.57267520 0.28633760 [4,] 0.60303450 0.79393101 0.39696550 [5,] 0.65752349 0.68495302 0.34247651 [6,] 0.55973345 0.88053310 0.44026655 [7,] 0.49467611 0.98935222 0.50532389 [8,] 0.42324180 0.84648360 0.57675820 [9,] 0.35675434 0.71350868 0.64324566 [10,] 0.39670511 0.79341022 0.60329489 [11,] 0.46757818 0.93515636 0.53242182 [12,] 0.85813698 0.28372603 0.14186302 [13,] 0.84022141 0.31955718 0.15977859 [14,] 0.86779123 0.26441755 0.13220877 [15,] 0.84225658 0.31548684 0.15774342 [16,] 0.97507807 0.04984385 0.02492193 [17,] 0.96977469 0.06045061 0.03022531 [18,] 0.95751667 0.08496666 0.04248333 [19,] 0.94228790 0.11542420 0.05771210 [20,] 0.94894691 0.10210618 0.05105309 [21,] 0.93868025 0.12263950 0.06131975 [22,] 0.92092925 0.15814150 0.07907075 [23,] 0.91274958 0.17450083 0.08725042 [24,] 0.89023808 0.21952385 0.10976192 [25,] 0.86190583 0.27618835 0.13809417 [26,] 0.84540274 0.30919453 0.15459726 [27,] 0.91144943 0.17710114 0.08855057 [28,] 0.89638613 0.20722775 0.10361387 [29,] 0.89149046 0.21701909 0.10850954 [30,] 0.86912152 0.26175696 0.13087848 [31,] 0.84374852 0.31250295 0.15625148 [32,] 0.82066532 0.35866937 0.17933468 [33,] 0.80476101 0.39047798 0.19523899 [34,] 0.78977054 0.42045893 0.21022946 [35,] 0.75786557 0.48426886 0.24213443 [36,] 0.76959828 0.46080345 0.23040172 [37,] 0.74276593 0.51446813 0.25723407 [38,] 0.70429794 0.59140413 0.29570206 [39,] 0.70174133 0.59651733 0.29825867 [40,] 0.66160079 0.67679842 0.33839921 [41,] 0.63905738 0.72188524 0.36094262 [42,] 0.59549985 0.80900030 0.40450015 [43,] 0.57555274 0.84889451 0.42444726 [44,] 0.57528468 0.84943064 0.42471532 [45,] 0.53846750 0.92306500 0.46153250 [46,] 0.49519017 0.99038034 0.50480983 [47,] 0.46140297 0.92280594 0.53859703 [48,] 0.41919745 0.83839491 0.58080255 [49,] 0.38841201 0.77682401 0.61158799 [50,] 0.40703889 0.81407778 0.59296111 [51,] 0.55505252 0.88989496 0.44494748 [52,] 0.51276649 0.97446702 0.48723351 [53,] 0.53311629 0.93376742 0.46688371 [54,] 0.49294362 0.98588723 0.50705638 [55,] 0.45548834 0.91097668 0.54451166 [56,] 0.47157264 0.94314528 0.52842736 [57,] 0.49720196 0.99440392 0.50279804 [58,] 0.46645698 0.93291397 0.53354302 [59,] 0.45194837 0.90389674 0.54805163 [60,] 0.45799882 0.91599763 0.54200118 [61,] 0.43331579 0.86663159 0.56668421 [62,] 0.43818320 0.87636639 0.56181680 [63,] 0.40636621 0.81273241 0.59363379 [64,] 0.38977334 0.77954667 0.61022666 [65,] 0.35391449 0.70782899 0.64608551 [66,] 0.35647698 0.71295397 0.64352302 [67,] 0.37663038 0.75326075 0.62336962 [68,] 0.34552115 0.69104230 0.65447885 [69,] 0.35629834 0.71259668 0.64370166 [70,] 0.32229130 0.64458261 0.67770870 [71,] 0.30930266 0.61860532 0.69069734 [72,] 0.27982943 0.55965885 0.72017057 [73,] 0.29321931 0.58643862 0.70678069 [74,] 0.26680299 0.53360599 0.73319701 [75,] 0.23623575 0.47247149 0.76376425 [76,] 0.21873437 0.43746875 0.78126563 [77,] 0.19152463 0.38304926 0.80847537 [78,] 0.16982546 0.33965092 0.83017454 [79,] 0.39679577 0.79359155 0.60320423 [80,] 0.43347448 0.86694895 0.56652552 [81,] 0.40329201 0.80658402 0.59670799 [82,] 0.37012911 0.74025823 0.62987089 [83,] 0.33969624 0.67939248 0.66030376 [84,] 0.30835476 0.61670953 0.69164524 [85,] 0.28993018 0.57986036 0.71006982 [86,] 0.28364356 0.56728713 0.71635644 [87,] 0.25394114 0.50788228 0.74605886 [88,] 0.29582782 0.59165563 0.70417218 [89,] 0.28129709 0.56259418 0.71870291 [90,] 0.29222165 0.58444331 0.70777835 [91,] 0.27599577 0.55199155 0.72400423 [92,] 0.25619092 0.51238185 0.74380908 [93,] 0.24763793 0.49527585 0.75236207 [94,] 0.22186920 0.44373841 0.77813080 [95,] 0.27825210 0.55650419 0.72174790 [96,] 0.26190905 0.52381809 0.73809095 [97,] 0.24008904 0.48017808 0.75991096 [98,] 0.27479725 0.54959450 0.72520275 [99,] 0.35004300 0.70008600 0.64995700 [100,] 0.32434488 0.64868977 0.67565512 [101,] 0.33914585 0.67829170 0.66085415 [102,] 0.31797350 0.63594700 0.68202650 [103,] 0.39942500 0.79884999 0.60057500 [104,] 0.40400597 0.80801193 0.59599403 [105,] 0.37030482 0.74060965 0.62969518 [106,] 0.33758878 0.67517756 0.66241122 [107,] 0.30700637 0.61401274 0.69299363 [108,] 0.27736400 0.55472800 0.72263600 [109,] 0.25350707 0.50701413 0.74649293 [110,] 0.22949379 0.45898758 0.77050621 [111,] 0.20895394 0.41790788 0.79104606 [112,] 0.18733216 0.37466431 0.81266784 [113,] 0.16979784 0.33959568 0.83020216 [114,] 0.15225528 0.30451056 0.84774472 [115,] 0.18017006 0.36034012 0.81982994 [116,] 0.17673146 0.35346293 0.82326854 [117,] 0.25743751 0.51487502 0.74256249 [118,] 0.23793096 0.47586192 0.76206904 [119,] 0.21254338 0.42508676 0.78745662 [120,] 0.23795270 0.47590540 0.76204730 [121,] 0.21390603 0.42781206 0.78609397 [122,] 0.21817521 0.43635041 0.78182479 [123,] 0.20619607 0.41239213 0.79380393 [124,] 0.20068686 0.40137371 0.79931314 [125,] 0.19326417 0.38652834 0.80673583 [126,] 0.17220993 0.34441987 0.82779007 [127,] 0.17387173 0.34774346 0.82612827 [128,] 0.15727931 0.31455862 0.84272069 [129,] 0.13772569 0.27545138 0.86227431 [130,] 0.12140300 0.24280600 0.87859700 [131,] 0.12736546 0.25473093 0.87263454 [132,] 0.11027363 0.22054726 0.88972637 [133,] 0.10362506 0.20725012 0.89637494 [134,] 0.10269555 0.20539111 0.89730445 [135,] 0.09631925 0.19263849 0.90368075 [136,] 0.09153483 0.18306967 0.90846517 [137,] 0.08160326 0.16320652 0.91839674 [138,] 0.11223091 0.22446182 0.88776909 [139,] 0.11180942 0.22361884 0.88819058 [140,] 0.10081091 0.20162183 0.89918909 [141,] 0.10359417 0.20718833 0.89640583 [142,] 0.10588794 0.21177588 0.89411206 [143,] 0.17610757 0.35221514 0.82389243 [144,] 0.15802614 0.31605228 0.84197386 [145,] 0.13821467 0.27642934 0.86178533 [146,] 0.21424266 0.42848532 0.78575734 [147,] 0.19283793 0.38567586 0.80716207 [148,] 0.17685831 0.35371661 0.82314169 [149,] 0.17257198 0.34514396 0.82742802 [150,] 0.16642263 0.33284526 0.83357737 [151,] 0.17043430 0.34086861 0.82956570 [152,] 0.19915903 0.39831805 0.80084097 [153,] 0.17988588 0.35977177 0.82011412 [154,] 0.16690639 0.33381278 0.83309361 [155,] 0.15085762 0.30171525 0.84914238 [156,] 0.19243028 0.38486056 0.80756972 [157,] 0.22109969 0.44219939 0.77890031 [158,] 0.20235186 0.40470373 0.79764814 [159,] 0.20882959 0.41765918 0.79117041 [160,] 0.23393403 0.46786807 0.76606597 [161,] 0.21301352 0.42602704 0.78698648 [162,] 0.22574420 0.45148841 0.77425580 [163,] 0.20201425 0.40402849 0.79798575 [164,] 0.25296347 0.50592693 0.74703653 [165,] 0.22973986 0.45947971 0.77026014 [166,] 0.23882346 0.47764692 0.76117654 [167,] 0.23318869 0.46637739 0.76681131 [168,] 0.20837306 0.41674612 0.79162694 [169,] 0.18671599 0.37343198 0.81328401 [170,] 0.18013643 0.36027286 0.81986357 [171,] 0.16315007 0.32630014 0.83684993 [172,] 0.18943474 0.37886948 0.81056526 [173,] 0.16682263 0.33364527 0.83317737 [174,] 0.14951653 0.29903305 0.85048347 [175,] 0.13919479 0.27838958 0.86080521 [176,] 0.12224707 0.24449415 0.87775293 [177,] 0.11027432 0.22054863 0.88972568 [178,] 0.10747531 0.21495061 0.89252469 [179,] 0.09251915 0.18503831 0.90748085 [180,] 0.08637878 0.17275755 0.91362122 [181,] 0.08639396 0.17278791 0.91360604 [182,] 0.08016793 0.16033585 0.91983207 [183,] 0.06841036 0.13682072 0.93158964 [184,] 0.05784989 0.11569978 0.94215011 [185,] 0.04864940 0.09729880 0.95135060 [186,] 0.07696292 0.15392584 0.92303708 [187,] 0.09989446 0.19978893 0.90010554 [188,] 0.09921590 0.19843180 0.90078410 [189,] 0.14624022 0.29248043 0.85375978 [190,] 0.12722020 0.25444040 0.87277980 [191,] 0.11776632 0.23553265 0.88223368 [192,] 0.10964541 0.21929081 0.89035459 [193,] 0.09495249 0.18990498 0.90504751 [194,] 0.10970306 0.21940612 0.89029694 [195,] 0.09457285 0.18914570 0.90542715 [196,] 0.11319165 0.22638330 0.88680835 [197,] 0.14263125 0.28526249 0.85736875 [198,] 0.17995441 0.35990883 0.82004559 [199,] 0.16134730 0.32269461 0.83865270 [200,] 0.16075038 0.32150076 0.83924962 [201,] 0.15572534 0.31145069 0.84427466 [202,] 0.15656476 0.31312953 0.84343524 [203,] 0.19686632 0.39373264 0.80313368 [204,] 0.17819280 0.35638559 0.82180720 [205,] 0.23120976 0.46241953 0.76879024 [206,] 0.23233567 0.46467135 0.76766433 [207,] 0.20873604 0.41747208 0.79126396 [208,] 0.25721876 0.51443753 0.74278124 [209,] 0.26418505 0.52837009 0.73581495 [210,] 0.28341742 0.56683483 0.71658258 [211,] 0.36507695 0.73015391 0.63492305 [212,] 0.71060512 0.57878975 0.28939488 [213,] 0.67899588 0.64200824 0.32100412 [214,] 0.71769529 0.56460942 0.28230471 [215,] 0.68245743 0.63508514 0.31754257 [216,] 0.66692793 0.66614414 0.33307207 [217,] 0.65661792 0.68676416 0.34338208 [218,] 0.72242149 0.55515703 0.27757851 [219,] 0.69094914 0.61810173 0.30905086 [220,] 0.78406117 0.43187765 0.21593883 [221,] 0.75176434 0.49647133 0.24823566 [222,] 0.71580692 0.56838615 0.28419308 [223,] 0.68486914 0.63026171 0.31513086 [224,] 0.66856604 0.66286791 0.33143396 [225,] 0.62772219 0.74455562 0.37227781 [226,] 0.58872539 0.82254921 0.41127461 [227,] 0.65333146 0.69333707 0.34666854 [228,] 0.61703087 0.76593827 0.38296913 [229,] 0.58131342 0.83737316 0.41868658 [230,] 0.53464548 0.93070904 0.46535452 [231,] 0.51364163 0.97271675 0.48635837 [232,] 0.49753200 0.99506399 0.50246800 [233,] 0.48956163 0.97912325 0.51043837 [234,] 0.44500548 0.89001096 0.55499452 [235,] 0.54068486 0.91863028 0.45931514 [236,] 0.52875212 0.94249577 0.47124788 [237,] 0.53398274 0.93203451 0.46601726 [238,] 0.52491296 0.95017409 0.47508704 [239,] 0.47641592 0.95283185 0.52358408 [240,] 0.42796789 0.85593579 0.57203211 [241,] 0.42126414 0.84252829 0.57873586 [242,] 0.41690878 0.83381755 0.58309122 [243,] 0.46188595 0.92377190 0.53811405 [244,] 0.41035206 0.82070413 0.58964794 [245,] 0.40082572 0.80165143 0.59917428 [246,] 0.62646573 0.74706854 0.37353427 [247,] 0.70135078 0.59729843 0.29864922 [248,] 0.76562350 0.46875299 0.23437650 [249,] 0.81243190 0.37513619 0.18756810 [250,] 0.79509897 0.40980207 0.20490103 [251,] 0.85027376 0.29945249 0.14972624 [252,] 0.81691655 0.36616690 0.18308345 [253,] 0.76875733 0.46248533 0.23124267 [254,] 0.86434422 0.27131155 0.13565578 [255,] 0.81660326 0.36679349 0.18339674 [256,] 0.76345082 0.47309836 0.23654918 [257,] 0.74147280 0.51705441 0.25852720 [258,] 0.70117447 0.59765107 0.29882553 [259,] 0.63159965 0.73680070 0.36840035 [260,] 0.56540789 0.86918423 0.43459211 [261,] 0.58866036 0.82267927 0.41133964 [262,] 0.49541214 0.99082428 0.50458786 [263,] 0.39960876 0.79921752 0.60039124 [264,] 0.31580406 0.63160811 0.68419594 [265,] 0.24357059 0.48714117 0.75642941 [266,] 0.16651664 0.33303328 0.83348336 [267,] 0.10633604 0.21267208 0.89366396 > postscript(file="/var/wessaorg/rcomp/tmp/1kmte1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2nkgx1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3ayzm1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/40dis1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/517901351542721.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 = 288 Frequency = 1 1 2 3 4 5 6 -0.47520068 3.14041472 -3.83348615 -1.84552933 1.61340430 3.53077596 7 8 9 10 11 12 -1.17139345 -0.47952612 0.52603001 0.36048084 2.95224521 4.29736965 13 14 15 16 17 18 -3.94344302 1.25580657 3.37890241 0.30563214 0.14763230 1.86667603 19 20 21 22 23 24 -0.15954935 1.39863775 4.18565112 -3.58251143 -1.00732877 -2.64879586 25 26 27 28 29 30 2.91748485 -5.90126387 2.10533870 -0.37811438 0.41443455 -2.63493212 31 32 33 34 35 36 1.55842528 -0.28428460 2.47812897 0.45642944 0.38830215 2.81586467 37 38 39 40 41 42 -4.08411681 1.38558140 2.47571964 -0.85409526 1.08515480 1.65507539 43 44 45 46 47 48 1.73132251 -1.61631881 0.45164549 -3.04514567 0.66962540 0.57666022 49 50 51 52 53 54 2.44249999 -0.64052092 1.62015807 0.44653322 -2.24005988 -2.44450119 55 56 57 58 59 60 -1.07091576 0.50973848 1.36265894 0.62064533 -1.67068952 -2.57223964 61 62 63 64 65 66 -5.34426276 -0.80203601 -3.43180907 -0.19645485 0.47647830 -3.76363604 67 68 69 70 71 72 -3.69535457 -1.91811347 0.92840542 1.33841046 0.83514365 1.86754064 73 74 75 76 77 78 0.42624109 1.14739351 -0.74968103 -2.81123731 2.53856045 -1.50571580 79 80 81 82 83 84 1.80531567 -0.81038309 1.41109300 0.58213541 2.38924936 0.71553610 85 86 87 88 89 90 -0.48132494 1.49397429 -0.38994558 -1.31072596 -7.28398976 3.07726668 91 92 93 94 95 96 -1.52282703 0.66536287 -0.31056900 -0.97992610 1.46193297 -2.42745847 97 98 99 100 101 102 -0.51763614 3.33683987 1.67216751 2.59573070 -1.66391966 1.23871666 103 104 105 106 107 108 -2.50813998 0.70512150 -4.40566147 1.44282577 1.32791532 -3.61638751 109 110 111 112 113 114 -4.13737949 0.75895044 -3.23003327 -1.33092041 3.99408622 2.15933557 115 116 117 118 119 120 0.02416142 -0.11387889 0.03210503 -0.39385576 -1.14165812 -0.30242095 121 122 123 124 125 126 0.99215678 0.71637442 0.74392839 -1.16594479 3.41135798 2.38150782 127 128 129 130 131 132 4.80507539 1.10250918 -0.49731350 -3.70648826 0.51649137 2.07425284 133 134 135 136 137 138 1.72398925 1.83150455 -2.20411195 0.63536242 -2.62641228 1.07751556 139 140 141 142 143 144 0.12533481 0.70991149 2.73262822 -0.17048886 1.46608435 2.15537605 145 146 147 148 149 150 1.52566268 -2.12285605 -1.42019963 -4.21222632 2.30241027 -1.24633497 151 152 153 154 155 156 2.56253517 -2.31036263 -5.17686597 -0.63474048 0.08743886 4.89641913 157 158 159 160 161 162 -1.24395046 -1.73130815 -2.46221372 1.50530751 2.99650296 -3.48227848 163 164 165 166 167 168 -1.02995057 1.47816370 -0.95337634 -4.21115186 -3.58624236 -1.62715801 169 170 171 172 173 174 2.39663179 -3.55535702 0.09854226 2.74004411 0.36882059 -4.30704310 175 176 177 178 179 180 -0.34709116 2.64739888 -2.24820649 0.41204744 0.58125447 2.07149567 181 182 183 184 185 186 1.22546097 3.57866782 -0.47026900 1.06342385 1.62763110 0.77896506 187 188 189 190 191 192 -1.26264312 -2.22934276 0.44745737 1.75506531 -2.53173059 1.79783283 193 194 195 196 197 198 -0.63366352 0.37932541 0.55497716 -4.54377677 -3.65872668 2.67621981 199 200 201 202 203 204 4.75209008 0.72500747 1.55510215 1.62805695 0.50606682 3.31349795 205 206 207 208 209 210 0.76902756 3.37279004 -4.03408410 3.72700901 -1.46605756 -2.57189116 211 212 213 214 215 216 -2.47145843 2.55372011 3.45007851 0.60201861 -4.44506967 1.68312511 217 218 219 220 221 222 -1.34658611 3.46003868 -3.32150141 2.79503027 3.41128432 6.59929382 223 224 225 226 227 228 -0.44580020 -4.14330051 -1.22687457 0.73552820 -2.90797552 2.30083121 229 230 231 232 233 234 -0.77242384 2.86655730 -1.17494287 0.48534629 0.60073233 -3.29784107 235 236 237 238 239 240 -1.15759501 -0.07577983 -4.16827825 -0.03722267 -0.41613859 -1.07535079 241 242 243 244 245 246 0.64931198 1.16801206 -2.78619100 -0.82379966 2.96594577 1.00485207 247 248 249 250 251 252 -0.08497538 -0.41217019 -0.23045426 -0.35288952 -3.52552980 -4.14656771 253 254 255 256 257 258 1.91155745 -2.25788747 0.89578668 3.09157847 -5.19974496 1.74051207 259 260 261 262 263 264 -4.50299107 -3.18488801 1.04803990 0.88935026 -0.33074698 -3.99024542 265 266 267 268 269 270 -0.28523810 -0.02906045 -1.39270158 -2.18836658 -0.27583856 -0.44564730 271 272 273 274 275 276 -3.02557013 2.56850145 0.50692945 0.99674342 0.49357356 3.84041278 277 278 279 280 281 282 2.83497955 1.58429970 2.00593910 -1.00456637 2.41491830 0.84396597 283 284 285 286 287 288 1.05660408 0.06158290 3.45959161 4.15240465 -4.18420369 1.09780573 > postscript(file="/var/wessaorg/rcomp/tmp/6vey31351542722.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 = 288 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.47520068 NA 1 3.14041472 -0.47520068 2 -3.83348615 3.14041472 3 -1.84552933 -3.83348615 4 1.61340430 -1.84552933 5 3.53077596 1.61340430 6 -1.17139345 3.53077596 7 -0.47952612 -1.17139345 8 0.52603001 -0.47952612 9 0.36048084 0.52603001 10 2.95224521 0.36048084 11 4.29736965 2.95224521 12 -3.94344302 4.29736965 13 1.25580657 -3.94344302 14 3.37890241 1.25580657 15 0.30563214 3.37890241 16 0.14763230 0.30563214 17 1.86667603 0.14763230 18 -0.15954935 1.86667603 19 1.39863775 -0.15954935 20 4.18565112 1.39863775 21 -3.58251143 4.18565112 22 -1.00732877 -3.58251143 23 -2.64879586 -1.00732877 24 2.91748485 -2.64879586 25 -5.90126387 2.91748485 26 2.10533870 -5.90126387 27 -0.37811438 2.10533870 28 0.41443455 -0.37811438 29 -2.63493212 0.41443455 30 1.55842528 -2.63493212 31 -0.28428460 1.55842528 32 2.47812897 -0.28428460 33 0.45642944 2.47812897 34 0.38830215 0.45642944 35 2.81586467 0.38830215 36 -4.08411681 2.81586467 37 1.38558140 -4.08411681 38 2.47571964 1.38558140 39 -0.85409526 2.47571964 40 1.08515480 -0.85409526 41 1.65507539 1.08515480 42 1.73132251 1.65507539 43 -1.61631881 1.73132251 44 0.45164549 -1.61631881 45 -3.04514567 0.45164549 46 0.66962540 -3.04514567 47 0.57666022 0.66962540 48 2.44249999 0.57666022 49 -0.64052092 2.44249999 50 1.62015807 -0.64052092 51 0.44653322 1.62015807 52 -2.24005988 0.44653322 53 -2.44450119 -2.24005988 54 -1.07091576 -2.44450119 55 0.50973848 -1.07091576 56 1.36265894 0.50973848 57 0.62064533 1.36265894 58 -1.67068952 0.62064533 59 -2.57223964 -1.67068952 60 -5.34426276 -2.57223964 61 -0.80203601 -5.34426276 62 -3.43180907 -0.80203601 63 -0.19645485 -3.43180907 64 0.47647830 -0.19645485 65 -3.76363604 0.47647830 66 -3.69535457 -3.76363604 67 -1.91811347 -3.69535457 68 0.92840542 -1.91811347 69 1.33841046 0.92840542 70 0.83514365 1.33841046 71 1.86754064 0.83514365 72 0.42624109 1.86754064 73 1.14739351 0.42624109 74 -0.74968103 1.14739351 75 -2.81123731 -0.74968103 76 2.53856045 -2.81123731 77 -1.50571580 2.53856045 78 1.80531567 -1.50571580 79 -0.81038309 1.80531567 80 1.41109300 -0.81038309 81 0.58213541 1.41109300 82 2.38924936 0.58213541 83 0.71553610 2.38924936 84 -0.48132494 0.71553610 85 1.49397429 -0.48132494 86 -0.38994558 1.49397429 87 -1.31072596 -0.38994558 88 -7.28398976 -1.31072596 89 3.07726668 -7.28398976 90 -1.52282703 3.07726668 91 0.66536287 -1.52282703 92 -0.31056900 0.66536287 93 -0.97992610 -0.31056900 94 1.46193297 -0.97992610 95 -2.42745847 1.46193297 96 -0.51763614 -2.42745847 97 3.33683987 -0.51763614 98 1.67216751 3.33683987 99 2.59573070 1.67216751 100 -1.66391966 2.59573070 101 1.23871666 -1.66391966 102 -2.50813998 1.23871666 103 0.70512150 -2.50813998 104 -4.40566147 0.70512150 105 1.44282577 -4.40566147 106 1.32791532 1.44282577 107 -3.61638751 1.32791532 108 -4.13737949 -3.61638751 109 0.75895044 -4.13737949 110 -3.23003327 0.75895044 111 -1.33092041 -3.23003327 112 3.99408622 -1.33092041 113 2.15933557 3.99408622 114 0.02416142 2.15933557 115 -0.11387889 0.02416142 116 0.03210503 -0.11387889 117 -0.39385576 0.03210503 118 -1.14165812 -0.39385576 119 -0.30242095 -1.14165812 120 0.99215678 -0.30242095 121 0.71637442 0.99215678 122 0.74392839 0.71637442 123 -1.16594479 0.74392839 124 3.41135798 -1.16594479 125 2.38150782 3.41135798 126 4.80507539 2.38150782 127 1.10250918 4.80507539 128 -0.49731350 1.10250918 129 -3.70648826 -0.49731350 130 0.51649137 -3.70648826 131 2.07425284 0.51649137 132 1.72398925 2.07425284 133 1.83150455 1.72398925 134 -2.20411195 1.83150455 135 0.63536242 -2.20411195 136 -2.62641228 0.63536242 137 1.07751556 -2.62641228 138 0.12533481 1.07751556 139 0.70991149 0.12533481 140 2.73262822 0.70991149 141 -0.17048886 2.73262822 142 1.46608435 -0.17048886 143 2.15537605 1.46608435 144 1.52566268 2.15537605 145 -2.12285605 1.52566268 146 -1.42019963 -2.12285605 147 -4.21222632 -1.42019963 148 2.30241027 -4.21222632 149 -1.24633497 2.30241027 150 2.56253517 -1.24633497 151 -2.31036263 2.56253517 152 -5.17686597 -2.31036263 153 -0.63474048 -5.17686597 154 0.08743886 -0.63474048 155 4.89641913 0.08743886 156 -1.24395046 4.89641913 157 -1.73130815 -1.24395046 158 -2.46221372 -1.73130815 159 1.50530751 -2.46221372 160 2.99650296 1.50530751 161 -3.48227848 2.99650296 162 -1.02995057 -3.48227848 163 1.47816370 -1.02995057 164 -0.95337634 1.47816370 165 -4.21115186 -0.95337634 166 -3.58624236 -4.21115186 167 -1.62715801 -3.58624236 168 2.39663179 -1.62715801 169 -3.55535702 2.39663179 170 0.09854226 -3.55535702 171 2.74004411 0.09854226 172 0.36882059 2.74004411 173 -4.30704310 0.36882059 174 -0.34709116 -4.30704310 175 2.64739888 -0.34709116 176 -2.24820649 2.64739888 177 0.41204744 -2.24820649 178 0.58125447 0.41204744 179 2.07149567 0.58125447 180 1.22546097 2.07149567 181 3.57866782 1.22546097 182 -0.47026900 3.57866782 183 1.06342385 -0.47026900 184 1.62763110 1.06342385 185 0.77896506 1.62763110 186 -1.26264312 0.77896506 187 -2.22934276 -1.26264312 188 0.44745737 -2.22934276 189 1.75506531 0.44745737 190 -2.53173059 1.75506531 191 1.79783283 -2.53173059 192 -0.63366352 1.79783283 193 0.37932541 -0.63366352 194 0.55497716 0.37932541 195 -4.54377677 0.55497716 196 -3.65872668 -4.54377677 197 2.67621981 -3.65872668 198 4.75209008 2.67621981 199 0.72500747 4.75209008 200 1.55510215 0.72500747 201 1.62805695 1.55510215 202 0.50606682 1.62805695 203 3.31349795 0.50606682 204 0.76902756 3.31349795 205 3.37279004 0.76902756 206 -4.03408410 3.37279004 207 3.72700901 -4.03408410 208 -1.46605756 3.72700901 209 -2.57189116 -1.46605756 210 -2.47145843 -2.57189116 211 2.55372011 -2.47145843 212 3.45007851 2.55372011 213 0.60201861 3.45007851 214 -4.44506967 0.60201861 215 1.68312511 -4.44506967 216 -1.34658611 1.68312511 217 3.46003868 -1.34658611 218 -3.32150141 3.46003868 219 2.79503027 -3.32150141 220 3.41128432 2.79503027 221 6.59929382 3.41128432 222 -0.44580020 6.59929382 223 -4.14330051 -0.44580020 224 -1.22687457 -4.14330051 225 0.73552820 -1.22687457 226 -2.90797552 0.73552820 227 2.30083121 -2.90797552 228 -0.77242384 2.30083121 229 2.86655730 -0.77242384 230 -1.17494287 2.86655730 231 0.48534629 -1.17494287 232 0.60073233 0.48534629 233 -3.29784107 0.60073233 234 -1.15759501 -3.29784107 235 -0.07577983 -1.15759501 236 -4.16827825 -0.07577983 237 -0.03722267 -4.16827825 238 -0.41613859 -0.03722267 239 -1.07535079 -0.41613859 240 0.64931198 -1.07535079 241 1.16801206 0.64931198 242 -2.78619100 1.16801206 243 -0.82379966 -2.78619100 244 2.96594577 -0.82379966 245 1.00485207 2.96594577 246 -0.08497538 1.00485207 247 -0.41217019 -0.08497538 248 -0.23045426 -0.41217019 249 -0.35288952 -0.23045426 250 -3.52552980 -0.35288952 251 -4.14656771 -3.52552980 252 1.91155745 -4.14656771 253 -2.25788747 1.91155745 254 0.89578668 -2.25788747 255 3.09157847 0.89578668 256 -5.19974496 3.09157847 257 1.74051207 -5.19974496 258 -4.50299107 1.74051207 259 -3.18488801 -4.50299107 260 1.04803990 -3.18488801 261 0.88935026 1.04803990 262 -0.33074698 0.88935026 263 -3.99024542 -0.33074698 264 -0.28523810 -3.99024542 265 -0.02906045 -0.28523810 266 -1.39270158 -0.02906045 267 -2.18836658 -1.39270158 268 -0.27583856 -2.18836658 269 -0.44564730 -0.27583856 270 -3.02557013 -0.44564730 271 2.56850145 -3.02557013 272 0.50692945 2.56850145 273 0.99674342 0.50692945 274 0.49357356 0.99674342 275 3.84041278 0.49357356 276 2.83497955 3.84041278 277 1.58429970 2.83497955 278 2.00593910 1.58429970 279 -1.00456637 2.00593910 280 2.41491830 -1.00456637 281 0.84396597 2.41491830 282 1.05660408 0.84396597 283 0.06158290 1.05660408 284 3.45959161 0.06158290 285 4.15240465 3.45959161 286 -4.18420369 4.15240465 287 1.09780573 -4.18420369 288 NA 1.09780573 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 3.14041472 -0.47520068 [2,] -3.83348615 3.14041472 [3,] -1.84552933 -3.83348615 [4,] 1.61340430 -1.84552933 [5,] 3.53077596 1.61340430 [6,] -1.17139345 3.53077596 [7,] -0.47952612 -1.17139345 [8,] 0.52603001 -0.47952612 [9,] 0.36048084 0.52603001 [10,] 2.95224521 0.36048084 [11,] 4.29736965 2.95224521 [12,] -3.94344302 4.29736965 [13,] 1.25580657 -3.94344302 [14,] 3.37890241 1.25580657 [15,] 0.30563214 3.37890241 [16,] 0.14763230 0.30563214 [17,] 1.86667603 0.14763230 [18,] -0.15954935 1.86667603 [19,] 1.39863775 -0.15954935 [20,] 4.18565112 1.39863775 [21,] -3.58251143 4.18565112 [22,] -1.00732877 -3.58251143 [23,] -2.64879586 -1.00732877 [24,] 2.91748485 -2.64879586 [25,] -5.90126387 2.91748485 [26,] 2.10533870 -5.90126387 [27,] -0.37811438 2.10533870 [28,] 0.41443455 -0.37811438 [29,] -2.63493212 0.41443455 [30,] 1.55842528 -2.63493212 [31,] -0.28428460 1.55842528 [32,] 2.47812897 -0.28428460 [33,] 0.45642944 2.47812897 [34,] 0.38830215 0.45642944 [35,] 2.81586467 0.38830215 [36,] -4.08411681 2.81586467 [37,] 1.38558140 -4.08411681 [38,] 2.47571964 1.38558140 [39,] -0.85409526 2.47571964 [40,] 1.08515480 -0.85409526 [41,] 1.65507539 1.08515480 [42,] 1.73132251 1.65507539 [43,] -1.61631881 1.73132251 [44,] 0.45164549 -1.61631881 [45,] -3.04514567 0.45164549 [46,] 0.66962540 -3.04514567 [47,] 0.57666022 0.66962540 [48,] 2.44249999 0.57666022 [49,] -0.64052092 2.44249999 [50,] 1.62015807 -0.64052092 [51,] 0.44653322 1.62015807 [52,] -2.24005988 0.44653322 [53,] -2.44450119 -2.24005988 [54,] -1.07091576 -2.44450119 [55,] 0.50973848 -1.07091576 [56,] 1.36265894 0.50973848 [57,] 0.62064533 1.36265894 [58,] -1.67068952 0.62064533 [59,] -2.57223964 -1.67068952 [60,] -5.34426276 -2.57223964 [61,] -0.80203601 -5.34426276 [62,] -3.43180907 -0.80203601 [63,] -0.19645485 -3.43180907 [64,] 0.47647830 -0.19645485 [65,] -3.76363604 0.47647830 [66,] -3.69535457 -3.76363604 [67,] -1.91811347 -3.69535457 [68,] 0.92840542 -1.91811347 [69,] 1.33841046 0.92840542 [70,] 0.83514365 1.33841046 [71,] 1.86754064 0.83514365 [72,] 0.42624109 1.86754064 [73,] 1.14739351 0.42624109 [74,] -0.74968103 1.14739351 [75,] -2.81123731 -0.74968103 [76,] 2.53856045 -2.81123731 [77,] -1.50571580 2.53856045 [78,] 1.80531567 -1.50571580 [79,] -0.81038309 1.80531567 [80,] 1.41109300 -0.81038309 [81,] 0.58213541 1.41109300 [82,] 2.38924936 0.58213541 [83,] 0.71553610 2.38924936 [84,] -0.48132494 0.71553610 [85,] 1.49397429 -0.48132494 [86,] -0.38994558 1.49397429 [87,] -1.31072596 -0.38994558 [88,] -7.28398976 -1.31072596 [89,] 3.07726668 -7.28398976 [90,] -1.52282703 3.07726668 [91,] 0.66536287 -1.52282703 [92,] -0.31056900 0.66536287 [93,] -0.97992610 -0.31056900 [94,] 1.46193297 -0.97992610 [95,] -2.42745847 1.46193297 [96,] -0.51763614 -2.42745847 [97,] 3.33683987 -0.51763614 [98,] 1.67216751 3.33683987 [99,] 2.59573070 1.67216751 [100,] -1.66391966 2.59573070 [101,] 1.23871666 -1.66391966 [102,] -2.50813998 1.23871666 [103,] 0.70512150 -2.50813998 [104,] -4.40566147 0.70512150 [105,] 1.44282577 -4.40566147 [106,] 1.32791532 1.44282577 [107,] -3.61638751 1.32791532 [108,] -4.13737949 -3.61638751 [109,] 0.75895044 -4.13737949 [110,] -3.23003327 0.75895044 [111,] -1.33092041 -3.23003327 [112,] 3.99408622 -1.33092041 [113,] 2.15933557 3.99408622 [114,] 0.02416142 2.15933557 [115,] -0.11387889 0.02416142 [116,] 0.03210503 -0.11387889 [117,] -0.39385576 0.03210503 [118,] -1.14165812 -0.39385576 [119,] -0.30242095 -1.14165812 [120,] 0.99215678 -0.30242095 [121,] 0.71637442 0.99215678 [122,] 0.74392839 0.71637442 [123,] -1.16594479 0.74392839 [124,] 3.41135798 -1.16594479 [125,] 2.38150782 3.41135798 [126,] 4.80507539 2.38150782 [127,] 1.10250918 4.80507539 [128,] -0.49731350 1.10250918 [129,] -3.70648826 -0.49731350 [130,] 0.51649137 -3.70648826 [131,] 2.07425284 0.51649137 [132,] 1.72398925 2.07425284 [133,] 1.83150455 1.72398925 [134,] -2.20411195 1.83150455 [135,] 0.63536242 -2.20411195 [136,] -2.62641228 0.63536242 [137,] 1.07751556 -2.62641228 [138,] 0.12533481 1.07751556 [139,] 0.70991149 0.12533481 [140,] 2.73262822 0.70991149 [141,] -0.17048886 2.73262822 [142,] 1.46608435 -0.17048886 [143,] 2.15537605 1.46608435 [144,] 1.52566268 2.15537605 [145,] -2.12285605 1.52566268 [146,] -1.42019963 -2.12285605 [147,] -4.21222632 -1.42019963 [148,] 2.30241027 -4.21222632 [149,] -1.24633497 2.30241027 [150,] 2.56253517 -1.24633497 [151,] -2.31036263 2.56253517 [152,] -5.17686597 -2.31036263 [153,] -0.63474048 -5.17686597 [154,] 0.08743886 -0.63474048 [155,] 4.89641913 0.08743886 [156,] -1.24395046 4.89641913 [157,] -1.73130815 -1.24395046 [158,] -2.46221372 -1.73130815 [159,] 1.50530751 -2.46221372 [160,] 2.99650296 1.50530751 [161,] -3.48227848 2.99650296 [162,] -1.02995057 -3.48227848 [163,] 1.47816370 -1.02995057 [164,] -0.95337634 1.47816370 [165,] -4.21115186 -0.95337634 [166,] -3.58624236 -4.21115186 [167,] -1.62715801 -3.58624236 [168,] 2.39663179 -1.62715801 [169,] -3.55535702 2.39663179 [170,] 0.09854226 -3.55535702 [171,] 2.74004411 0.09854226 [172,] 0.36882059 2.74004411 [173,] -4.30704310 0.36882059 [174,] -0.34709116 -4.30704310 [175,] 2.64739888 -0.34709116 [176,] -2.24820649 2.64739888 [177,] 0.41204744 -2.24820649 [178,] 0.58125447 0.41204744 [179,] 2.07149567 0.58125447 [180,] 1.22546097 2.07149567 [181,] 3.57866782 1.22546097 [182,] -0.47026900 3.57866782 [183,] 1.06342385 -0.47026900 [184,] 1.62763110 1.06342385 [185,] 0.77896506 1.62763110 [186,] -1.26264312 0.77896506 [187,] -2.22934276 -1.26264312 [188,] 0.44745737 -2.22934276 [189,] 1.75506531 0.44745737 [190,] -2.53173059 1.75506531 [191,] 1.79783283 -2.53173059 [192,] -0.63366352 1.79783283 [193,] 0.37932541 -0.63366352 [194,] 0.55497716 0.37932541 [195,] -4.54377677 0.55497716 [196,] -3.65872668 -4.54377677 [197,] 2.67621981 -3.65872668 [198,] 4.75209008 2.67621981 [199,] 0.72500747 4.75209008 [200,] 1.55510215 0.72500747 [201,] 1.62805695 1.55510215 [202,] 0.50606682 1.62805695 [203,] 3.31349795 0.50606682 [204,] 0.76902756 3.31349795 [205,] 3.37279004 0.76902756 [206,] -4.03408410 3.37279004 [207,] 3.72700901 -4.03408410 [208,] -1.46605756 3.72700901 [209,] -2.57189116 -1.46605756 [210,] -2.47145843 -2.57189116 [211,] 2.55372011 -2.47145843 [212,] 3.45007851 2.55372011 [213,] 0.60201861 3.45007851 [214,] -4.44506967 0.60201861 [215,] 1.68312511 -4.44506967 [216,] -1.34658611 1.68312511 [217,] 3.46003868 -1.34658611 [218,] -3.32150141 3.46003868 [219,] 2.79503027 -3.32150141 [220,] 3.41128432 2.79503027 [221,] 6.59929382 3.41128432 [222,] -0.44580020 6.59929382 [223,] -4.14330051 -0.44580020 [224,] -1.22687457 -4.14330051 [225,] 0.73552820 -1.22687457 [226,] -2.90797552 0.73552820 [227,] 2.30083121 -2.90797552 [228,] -0.77242384 2.30083121 [229,] 2.86655730 -0.77242384 [230,] -1.17494287 2.86655730 [231,] 0.48534629 -1.17494287 [232,] 0.60073233 0.48534629 [233,] -3.29784107 0.60073233 [234,] -1.15759501 -3.29784107 [235,] -0.07577983 -1.15759501 [236,] -4.16827825 -0.07577983 [237,] -0.03722267 -4.16827825 [238,] -0.41613859 -0.03722267 [239,] -1.07535079 -0.41613859 [240,] 0.64931198 -1.07535079 [241,] 1.16801206 0.64931198 [242,] -2.78619100 1.16801206 [243,] -0.82379966 -2.78619100 [244,] 2.96594577 -0.82379966 [245,] 1.00485207 2.96594577 [246,] -0.08497538 1.00485207 [247,] -0.41217019 -0.08497538 [248,] -0.23045426 -0.41217019 [249,] -0.35288952 -0.23045426 [250,] -3.52552980 -0.35288952 [251,] -4.14656771 -3.52552980 [252,] 1.91155745 -4.14656771 [253,] -2.25788747 1.91155745 [254,] 0.89578668 -2.25788747 [255,] 3.09157847 0.89578668 [256,] -5.19974496 3.09157847 [257,] 1.74051207 -5.19974496 [258,] -4.50299107 1.74051207 [259,] -3.18488801 -4.50299107 [260,] 1.04803990 -3.18488801 [261,] 0.88935026 1.04803990 [262,] -0.33074698 0.88935026 [263,] -3.99024542 -0.33074698 [264,] -0.28523810 -3.99024542 [265,] -0.02906045 -0.28523810 [266,] -1.39270158 -0.02906045 [267,] -2.18836658 -1.39270158 [268,] -0.27583856 -2.18836658 [269,] -0.44564730 -0.27583856 [270,] -3.02557013 -0.44564730 [271,] 2.56850145 -3.02557013 [272,] 0.50692945 2.56850145 [273,] 0.99674342 0.50692945 [274,] 0.49357356 0.99674342 [275,] 3.84041278 0.49357356 [276,] 2.83497955 3.84041278 [277,] 1.58429970 2.83497955 [278,] 2.00593910 1.58429970 [279,] -1.00456637 2.00593910 [280,] 2.41491830 -1.00456637 [281,] 0.84396597 2.41491830 [282,] 1.05660408 0.84396597 [283,] 0.06158290 1.05660408 [284,] 3.45959161 0.06158290 [285,] 4.15240465 3.45959161 [286,] -4.18420369 4.15240465 [287,] 1.09780573 -4.18420369 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 3.14041472 -0.47520068 2 -3.83348615 3.14041472 3 -1.84552933 -3.83348615 4 1.61340430 -1.84552933 5 3.53077596 1.61340430 6 -1.17139345 3.53077596 7 -0.47952612 -1.17139345 8 0.52603001 -0.47952612 9 0.36048084 0.52603001 10 2.95224521 0.36048084 11 4.29736965 2.95224521 12 -3.94344302 4.29736965 13 1.25580657 -3.94344302 14 3.37890241 1.25580657 15 0.30563214 3.37890241 16 0.14763230 0.30563214 17 1.86667603 0.14763230 18 -0.15954935 1.86667603 19 1.39863775 -0.15954935 20 4.18565112 1.39863775 21 -3.58251143 4.18565112 22 -1.00732877 -3.58251143 23 -2.64879586 -1.00732877 24 2.91748485 -2.64879586 25 -5.90126387 2.91748485 26 2.10533870 -5.90126387 27 -0.37811438 2.10533870 28 0.41443455 -0.37811438 29 -2.63493212 0.41443455 30 1.55842528 -2.63493212 31 -0.28428460 1.55842528 32 2.47812897 -0.28428460 33 0.45642944 2.47812897 34 0.38830215 0.45642944 35 2.81586467 0.38830215 36 -4.08411681 2.81586467 37 1.38558140 -4.08411681 38 2.47571964 1.38558140 39 -0.85409526 2.47571964 40 1.08515480 -0.85409526 41 1.65507539 1.08515480 42 1.73132251 1.65507539 43 -1.61631881 1.73132251 44 0.45164549 -1.61631881 45 -3.04514567 0.45164549 46 0.66962540 -3.04514567 47 0.57666022 0.66962540 48 2.44249999 0.57666022 49 -0.64052092 2.44249999 50 1.62015807 -0.64052092 51 0.44653322 1.62015807 52 -2.24005988 0.44653322 53 -2.44450119 -2.24005988 54 -1.07091576 -2.44450119 55 0.50973848 -1.07091576 56 1.36265894 0.50973848 57 0.62064533 1.36265894 58 -1.67068952 0.62064533 59 -2.57223964 -1.67068952 60 -5.34426276 -2.57223964 61 -0.80203601 -5.34426276 62 -3.43180907 -0.80203601 63 -0.19645485 -3.43180907 64 0.47647830 -0.19645485 65 -3.76363604 0.47647830 66 -3.69535457 -3.76363604 67 -1.91811347 -3.69535457 68 0.92840542 -1.91811347 69 1.33841046 0.92840542 70 0.83514365 1.33841046 71 1.86754064 0.83514365 72 0.42624109 1.86754064 73 1.14739351 0.42624109 74 -0.74968103 1.14739351 75 -2.81123731 -0.74968103 76 2.53856045 -2.81123731 77 -1.50571580 2.53856045 78 1.80531567 -1.50571580 79 -0.81038309 1.80531567 80 1.41109300 -0.81038309 81 0.58213541 1.41109300 82 2.38924936 0.58213541 83 0.71553610 2.38924936 84 -0.48132494 0.71553610 85 1.49397429 -0.48132494 86 -0.38994558 1.49397429 87 -1.31072596 -0.38994558 88 -7.28398976 -1.31072596 89 3.07726668 -7.28398976 90 -1.52282703 3.07726668 91 0.66536287 -1.52282703 92 -0.31056900 0.66536287 93 -0.97992610 -0.31056900 94 1.46193297 -0.97992610 95 -2.42745847 1.46193297 96 -0.51763614 -2.42745847 97 3.33683987 -0.51763614 98 1.67216751 3.33683987 99 2.59573070 1.67216751 100 -1.66391966 2.59573070 101 1.23871666 -1.66391966 102 -2.50813998 1.23871666 103 0.70512150 -2.50813998 104 -4.40566147 0.70512150 105 1.44282577 -4.40566147 106 1.32791532 1.44282577 107 -3.61638751 1.32791532 108 -4.13737949 -3.61638751 109 0.75895044 -4.13737949 110 -3.23003327 0.75895044 111 -1.33092041 -3.23003327 112 3.99408622 -1.33092041 113 2.15933557 3.99408622 114 0.02416142 2.15933557 115 -0.11387889 0.02416142 116 0.03210503 -0.11387889 117 -0.39385576 0.03210503 118 -1.14165812 -0.39385576 119 -0.30242095 -1.14165812 120 0.99215678 -0.30242095 121 0.71637442 0.99215678 122 0.74392839 0.71637442 123 -1.16594479 0.74392839 124 3.41135798 -1.16594479 125 2.38150782 3.41135798 126 4.80507539 2.38150782 127 1.10250918 4.80507539 128 -0.49731350 1.10250918 129 -3.70648826 -0.49731350 130 0.51649137 -3.70648826 131 2.07425284 0.51649137 132 1.72398925 2.07425284 133 1.83150455 1.72398925 134 -2.20411195 1.83150455 135 0.63536242 -2.20411195 136 -2.62641228 0.63536242 137 1.07751556 -2.62641228 138 0.12533481 1.07751556 139 0.70991149 0.12533481 140 2.73262822 0.70991149 141 -0.17048886 2.73262822 142 1.46608435 -0.17048886 143 2.15537605 1.46608435 144 1.52566268 2.15537605 145 -2.12285605 1.52566268 146 -1.42019963 -2.12285605 147 -4.21222632 -1.42019963 148 2.30241027 -4.21222632 149 -1.24633497 2.30241027 150 2.56253517 -1.24633497 151 -2.31036263 2.56253517 152 -5.17686597 -2.31036263 153 -0.63474048 -5.17686597 154 0.08743886 -0.63474048 155 4.89641913 0.08743886 156 -1.24395046 4.89641913 157 -1.73130815 -1.24395046 158 -2.46221372 -1.73130815 159 1.50530751 -2.46221372 160 2.99650296 1.50530751 161 -3.48227848 2.99650296 162 -1.02995057 -3.48227848 163 1.47816370 -1.02995057 164 -0.95337634 1.47816370 165 -4.21115186 -0.95337634 166 -3.58624236 -4.21115186 167 -1.62715801 -3.58624236 168 2.39663179 -1.62715801 169 -3.55535702 2.39663179 170 0.09854226 -3.55535702 171 2.74004411 0.09854226 172 0.36882059 2.74004411 173 -4.30704310 0.36882059 174 -0.34709116 -4.30704310 175 2.64739888 -0.34709116 176 -2.24820649 2.64739888 177 0.41204744 -2.24820649 178 0.58125447 0.41204744 179 2.07149567 0.58125447 180 1.22546097 2.07149567 181 3.57866782 1.22546097 182 -0.47026900 3.57866782 183 1.06342385 -0.47026900 184 1.62763110 1.06342385 185 0.77896506 1.62763110 186 -1.26264312 0.77896506 187 -2.22934276 -1.26264312 188 0.44745737 -2.22934276 189 1.75506531 0.44745737 190 -2.53173059 1.75506531 191 1.79783283 -2.53173059 192 -0.63366352 1.79783283 193 0.37932541 -0.63366352 194 0.55497716 0.37932541 195 -4.54377677 0.55497716 196 -3.65872668 -4.54377677 197 2.67621981 -3.65872668 198 4.75209008 2.67621981 199 0.72500747 4.75209008 200 1.55510215 0.72500747 201 1.62805695 1.55510215 202 0.50606682 1.62805695 203 3.31349795 0.50606682 204 0.76902756 3.31349795 205 3.37279004 0.76902756 206 -4.03408410 3.37279004 207 3.72700901 -4.03408410 208 -1.46605756 3.72700901 209 -2.57189116 -1.46605756 210 -2.47145843 -2.57189116 211 2.55372011 -2.47145843 212 3.45007851 2.55372011 213 0.60201861 3.45007851 214 -4.44506967 0.60201861 215 1.68312511 -4.44506967 216 -1.34658611 1.68312511 217 3.46003868 -1.34658611 218 -3.32150141 3.46003868 219 2.79503027 -3.32150141 220 3.41128432 2.79503027 221 6.59929382 3.41128432 222 -0.44580020 6.59929382 223 -4.14330051 -0.44580020 224 -1.22687457 -4.14330051 225 0.73552820 -1.22687457 226 -2.90797552 0.73552820 227 2.30083121 -2.90797552 228 -0.77242384 2.30083121 229 2.86655730 -0.77242384 230 -1.17494287 2.86655730 231 0.48534629 -1.17494287 232 0.60073233 0.48534629 233 -3.29784107 0.60073233 234 -1.15759501 -3.29784107 235 -0.07577983 -1.15759501 236 -4.16827825 -0.07577983 237 -0.03722267 -4.16827825 238 -0.41613859 -0.03722267 239 -1.07535079 -0.41613859 240 0.64931198 -1.07535079 241 1.16801206 0.64931198 242 -2.78619100 1.16801206 243 -0.82379966 -2.78619100 244 2.96594577 -0.82379966 245 1.00485207 2.96594577 246 -0.08497538 1.00485207 247 -0.41217019 -0.08497538 248 -0.23045426 -0.41217019 249 -0.35288952 -0.23045426 250 -3.52552980 -0.35288952 251 -4.14656771 -3.52552980 252 1.91155745 -4.14656771 253 -2.25788747 1.91155745 254 0.89578668 -2.25788747 255 3.09157847 0.89578668 256 -5.19974496 3.09157847 257 1.74051207 -5.19974496 258 -4.50299107 1.74051207 259 -3.18488801 -4.50299107 260 1.04803990 -3.18488801 261 0.88935026 1.04803990 262 -0.33074698 0.88935026 263 -3.99024542 -0.33074698 264 -0.28523810 -3.99024542 265 -0.02906045 -0.28523810 266 -1.39270158 -0.02906045 267 -2.18836658 -1.39270158 268 -0.27583856 -2.18836658 269 -0.44564730 -0.27583856 270 -3.02557013 -0.44564730 271 2.56850145 -3.02557013 272 0.50692945 2.56850145 273 0.99674342 0.50692945 274 0.49357356 0.99674342 275 3.84041278 0.49357356 276 2.83497955 3.84041278 277 1.58429970 2.83497955 278 2.00593910 1.58429970 279 -1.00456637 2.00593910 280 2.41491830 -1.00456637 281 0.84396597 2.41491830 282 1.05660408 0.84396597 283 0.06158290 1.05660408 284 3.45959161 0.06158290 285 4.15240465 3.45959161 286 -4.18420369 4.15240465 287 1.09780573 -4.18420369 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7ob2l1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/89h4x1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9sk2v1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/107y021351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11c3oa1351542722.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12c7nw1351542722.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13lc9j1351542722.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14wvs51351542722.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15lsn11351542722.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/169zka1351542722.tab") + } > > try(system("convert tmp/1kmte1351542721.ps tmp/1kmte1351542721.png",intern=TRUE)) character(0) > try(system("convert tmp/2nkgx1351542721.ps tmp/2nkgx1351542721.png",intern=TRUE)) character(0) > try(system("convert tmp/3ayzm1351542721.ps tmp/3ayzm1351542721.png",intern=TRUE)) character(0) > try(system("convert tmp/40dis1351542721.ps tmp/40dis1351542721.png",intern=TRUE)) character(0) > try(system("convert tmp/517901351542721.ps tmp/517901351542721.png",intern=TRUE)) character(0) > try(system("convert tmp/6vey31351542722.ps tmp/6vey31351542722.png",intern=TRUE)) character(0) > try(system("convert tmp/7ob2l1351542722.ps tmp/7ob2l1351542722.png",intern=TRUE)) character(0) > try(system("convert tmp/89h4x1351542722.ps tmp/89h4x1351542722.png",intern=TRUE)) character(0) > try(system("convert tmp/9sk2v1351542722.ps tmp/9sk2v1351542722.png",intern=TRUE)) character(0) > try(system("convert tmp/107y021351542722.ps tmp/107y021351542722.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.011 1.430 16.517