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. 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+ ,7 + ,13 + ,17 + ,78 + ,47 + ,36 + ,34 + ,12 + ,6 + ,13 + ,11 + ,71 + ,44 + ,33 + ,32 + ,16 + ,9 + ,13 + ,13 + ,72 + ,45 + ,37 + ,33 + ,12 + ,10 + ,12 + ,17 + ,68 + ,44 + ,34 + ,33 + ,14 + ,11 + ,12 + ,15 + ,67 + ,43 + ,35 + ,37 + ,16 + ,12 + ,9 + ,21 + ,75 + ,43 + ,31 + ,32 + ,14 + ,8 + ,9 + ,18 + ,62 + ,40 + ,37 + ,34 + ,13 + ,11 + ,15 + ,15 + ,67 + ,41 + ,35 + ,30 + ,4 + ,3 + ,10 + ,8 + ,83 + ,52 + ,27 + ,30 + ,15 + ,11 + ,14 + ,12 + ,64 + ,38 + ,34 + ,38 + ,11 + ,12 + ,15 + ,12 + ,68 + ,41 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(8 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'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 Learning Connected Separate Software Happiness Depression Belonging 1 13 41 38 12 14 12.0 53 2 16 39 32 11 18 11.0 83 3 19 30 35 15 11 14.0 66 4 15 31 33 6 12 12.0 67 5 14 34 37 13 16 21.0 76 6 13 35 29 10 18 12.0 78 7 19 39 31 12 14 22.0 53 8 15 34 36 14 14 11.0 80 9 14 36 35 12 15 10.0 74 10 15 37 38 9 15 13.0 76 11 16 38 31 10 17 10.0 79 12 16 36 34 12 19 8.0 54 13 16 38 35 12 10 15.0 67 14 16 39 38 11 16 14.0 54 15 17 33 37 15 18 10.0 87 16 15 32 33 12 14 14.0 58 17 15 36 32 10 14 14.0 75 18 20 38 38 12 17 11.0 88 19 18 39 38 11 14 10.0 64 20 16 32 32 12 16 13.0 57 21 16 32 33 11 18 9.5 66 22 16 31 31 12 11 14.0 68 23 19 39 38 13 14 12.0 54 24 16 37 39 11 12 14.0 56 25 17 39 32 12 17 11.0 86 26 17 41 32 13 9 9.0 80 27 16 36 35 10 16 11.0 76 28 15 33 37 14 14 15.0 69 29 16 33 33 12 15 14.0 78 30 14 34 33 10 11 13.0 67 31 15 31 31 12 16 9.0 80 32 12 27 32 8 13 15.0 54 33 14 37 31 10 17 10.0 71 34 16 34 37 12 15 11.0 84 35 14 34 30 12 14 13.0 74 36 10 32 33 7 16 8.0 71 37 10 29 31 9 9 20.0 63 38 14 36 33 12 15 12.0 71 39 16 29 31 10 17 10.0 76 40 16 35 33 10 13 10.0 69 41 16 37 32 10 15 9.0 74 42 14 34 33 12 16 14.0 75 43 20 38 32 15 16 8.0 54 44 14 35 33 10 12 14.0 52 45 14 38 28 10 15 11.0 69 46 11 37 35 12 11 13.0 68 47 14 38 39 13 15 9.0 65 48 15 33 34 11 15 11.0 75 49 16 36 38 11 17 15.0 74 50 14 38 32 12 13 11.0 75 51 16 32 38 14 16 10.0 72 52 14 32 30 10 14 14.0 67 53 12 32 33 12 11 18.0 63 54 16 34 38 13 12 14.0 62 55 9 32 32 5 12 11.0 63 56 14 37 35 6 15 14.5 76 57 16 39 34 12 16 13.0 74 58 16 29 34 12 15 9.0 67 59 15 37 36 11 12 10.0 73 60 16 35 34 10 12 15.0 70 61 12 30 28 7 8 20.0 53 62 16 38 34 12 13 12.0 77 63 16 34 35 14 11 12.0 80 64 14 31 35 11 14 14.0 52 65 16 34 31 12 15 13.0 54 66 17 35 37 13 10 11.0 80 67 18 36 35 14 11 17.0 66 68 18 30 27 11 12 12.0 73 69 12 39 40 12 15 13.0 63 70 16 35 37 12 15 14.0 69 71 10 38 36 8 14 13.0 67 72 14 31 38 11 16 15.0 54 73 18 34 39 14 15 13.0 81 74 18 38 41 14 15 10.0 69 75 16 34 27 12 13 11.0 84 76 17 39 30 9 12 19.0 80 77 16 37 37 13 17 13.0 70 78 16 34 31 11 13 17.0 69 79 13 28 31 12 15 13.0 77 80 16 37 27 12 13 9.0 54 81 16 33 36 12 15 11.0 79 82 16 35 37 12 15 9.0 71 83 15 37 33 12 16 12.0 73 84 15 32 34 11 15 12.0 72 85 16 33 31 10 14 13.0 77 86 14 38 39 9 15 13.0 75 87 16 33 34 12 14 12.0 69 88 16 29 32 12 13 15.0 54 89 15 33 33 12 7 22.0 70 90 12 31 36 9 17 13.0 73 91 17 36 32 15 13 15.0 54 92 16 35 41 12 15 13.0 77 93 15 32 28 12 14 15.0 82 94 13 29 30 12 13 12.5 80 95 16 39 36 10 16 11.0 80 96 16 37 35 13 12 16.0 69 97 16 35 31 9 14 11.0 78 98 16 37 34 12 17 11.0 81 99 14 32 36 10 15 10.0 76 100 16 38 36 14 17 10.0 76 101 16 37 35 11 12 16.0 73 102 20 36 37 15 16 12.0 85 103 15 32 28 11 11 11.0 66 104 16 33 39 11 15 16.0 79 105 13 40 32 12 9 19.0 68 106 17 38 35 12 16 11.0 76 107 16 41 39 12 15 16.0 71 108 16 36 35 11 10 15.0 54 109 12 43 42 7 10 24.0 46 110 16 30 34 12 15 14.0 85 111 16 31 33 14 11 15.0 74 112 17 32 41 11 13 11.0 88 113 13 32 33 11 14 15.0 38 114 12 37 34 10 18 12.0 76 115 18 37 32 13 16 10.0 86 116 14 33 40 13 14 14.0 54 117 14 34 40 8 14 13.0 67 118 13 33 35 11 14 9.0 69 119 16 38 36 12 14 15.0 90 120 13 33 37 11 12 15.0 54 121 16 31 27 13 14 14.0 76 122 13 38 39 12 15 11.0 89 123 16 37 38 14 15 8.0 76 124 15 36 31 13 15 11.0 73 125 16 31 33 15 13 11.0 79 126 15 39 32 10 17 8.0 90 127 17 44 39 11 17 10.0 74 128 15 33 36 9 19 11.0 81 129 12 35 33 11 15 13.0 72 130 16 32 33 10 13 11.0 71 131 10 28 32 11 9 20.0 66 132 16 40 37 8 15 10.0 77 133 12 27 30 11 15 15.0 65 134 14 37 38 12 15 12.0 74 135 15 32 29 12 16 14.0 85 136 13 28 22 9 11 23.0 54 137 15 34 35 11 14 14.0 63 138 11 30 35 10 11 16.0 54 139 12 35 34 8 15 11.0 64 140 11 31 35 9 13 12.0 69 141 16 32 34 8 15 10.0 54 142 15 30 37 9 16 14.0 84 143 17 30 35 15 14 12.0 86 144 16 31 23 11 15 12.0 77 145 10 40 31 8 16 11.0 89 146 18 32 27 13 16 12.0 76 147 13 36 36 12 11 13.0 60 148 16 32 31 12 12 11.0 75 149 13 35 32 9 9 19.0 73 150 10 38 39 7 16 12.0 85 151 15 42 37 13 13 17.0 79 152 16 34 38 9 16 9.0 71 153 16 35 39 6 12 12.0 72 154 14 38 34 8 9 19.0 69 155 10 33 31 8 13 18.0 78 156 17 36 32 15 13 15.0 54 157 13 32 37 6 14 14.0 69 158 15 33 36 9 19 11.0 81 159 16 34 32 11 13 9.0 84 160 12 32 38 8 12 18.0 84 161 13 34 36 8 13 16.0 69 162 13 27 26 10 10 24.0 66 163 12 31 26 8 14 14.0 81 164 17 38 33 14 16 20.0 82 165 15 34 39 10 10 18.0 72 166 10 24 30 8 11 23.0 54 167 14 30 33 11 14 12.0 78 168 11 26 25 12 12 14.0 74 169 13 34 38 12 9 16.0 82 170 16 27 37 12 9 18.0 73 171 12 37 31 5 11 20.0 55 172 16 36 37 12 16 12.0 72 173 12 41 35 10 9 12.0 78 174 9 29 25 7 13 17.0 59 175 12 36 28 12 16 13.0 72 176 15 32 35 11 13 9.0 78 177 12 37 33 8 9 16.0 68 178 12 30 30 9 12 18.0 69 179 14 31 31 10 16 10.0 67 180 12 38 37 9 11 14.0 74 181 16 36 36 12 14 11.0 54 182 11 35 30 6 13 9.0 67 183 19 31 36 15 15 11.0 70 184 15 38 32 12 14 10.0 80 185 8 22 28 12 16 11.0 89 186 16 32 36 12 13 19.0 76 187 17 36 34 11 14 14.0 74 188 12 39 31 7 15 12.0 87 189 11 28 28 7 13 14.0 54 190 11 32 36 5 11 21.0 61 191 14 32 36 12 11 13.0 38 192 16 38 40 12 14 10.0 75 193 12 32 33 3 15 15.0 69 194 16 35 37 11 11 16.0 62 195 13 32 32 10 15 14.0 72 196 15 37 38 12 12 12.0 70 197 16 34 31 9 14 19.0 79 198 16 33 37 12 14 15.0 87 199 14 33 33 9 8 19.0 62 200 16 26 32 12 13 13.0 77 201 16 30 30 12 9 17.0 69 202 14 24 30 10 15 12.0 69 203 11 34 31 9 17 11.0 75 204 12 34 32 12 13 14.0 54 205 15 33 34 8 15 11.0 72 206 15 34 36 11 15 13.0 74 207 16 35 37 11 14 12.0 85 208 16 35 36 12 16 15.0 52 209 11 36 33 10 13 14.0 70 210 15 34 33 10 16 12.0 84 211 12 34 33 12 9 17.0 64 212 12 41 44 12 16 11.0 84 213 15 32 39 11 11 18.0 87 214 15 30 32 8 10 13.0 79 215 16 35 35 12 11 17.0 67 216 14 28 25 10 15 13.0 65 217 17 33 35 11 17 11.0 85 218 14 39 34 10 14 12.0 83 219 13 36 35 8 8 22.0 61 220 15 36 39 12 15 14.0 82 221 13 35 33 12 11 12.0 76 222 14 38 36 10 16 12.0 58 223 15 33 32 12 10 17.0 72 224 12 31 32 9 15 9.0 72 225 13 34 36 9 9 21.0 38 226 8 32 36 6 16 10.0 78 227 14 31 32 10 19 11.0 54 228 14 33 34 9 12 12.0 63 229 11 34 33 9 8 23.0 66 230 12 34 35 9 11 13.0 70 231 13 34 30 6 14 12.0 71 232 10 33 38 10 9 16.0 67 233 16 32 34 6 15 9.0 58 234 18 41 33 14 13 17.0 72 235 13 34 32 10 16 9.0 72 236 11 36 31 10 11 14.0 70 237 4 37 30 6 12 17.0 76 238 13 36 27 12 13 13.0 50 239 16 29 31 12 10 11.0 72 240 10 37 30 7 11 12.0 72 241 12 27 32 8 12 10.0 88 242 12 35 35 11 8 19.0 53 243 10 28 28 3 12 16.0 58 244 13 35 33 6 12 16.0 66 245 15 37 31 10 15 14.0 82 246 12 29 35 8 11 20.0 69 247 14 32 35 9 13 15.0 68 248 10 36 32 9 14 23.0 44 249 12 19 21 8 10 20.0 56 250 12 21 20 9 12 16.0 53 251 11 31 34 7 15 14.0 70 252 10 33 32 7 13 17.0 78 253 12 36 34 6 13 11.0 71 254 16 33 32 9 13 13.0 72 255 12 37 33 10 12 17.0 68 256 14 34 33 11 12 15.0 67 257 16 35 37 12 9 21.0 75 258 14 31 32 8 9 18.0 62 259 13 37 34 11 15 15.0 67 260 4 35 30 3 10 8.0 83 261 15 27 30 11 14 12.0 64 262 11 34 38 12 15 12.0 68 263 11 40 36 7 7 22.0 62 264 14 29 32 9 14 12.0 72 Belonging_Final 1 32 2 51 3 42 4 41 5 46 6 47 7 37 8 49 9 45 10 47 11 49 12 33 13 42 14 33 15 53 16 36 17 45 18 54 19 41 20 36 21 41 22 44 23 33 24 37 25 52 26 47 27 43 28 44 29 45 30 44 31 49 32 33 33 43 34 54 35 42 36 44 37 37 38 43 39 46 40 42 41 45 42 44 43 33 44 31 45 42 46 40 47 43 48 46 49 42 50 45 51 44 52 40 53 37 54 46 55 36 56 47 57 45 58 42 59 43 60 43 61 32 62 45 63 48 64 31 65 33 66 49 67 42 68 41 69 38 70 42 71 44 72 33 73 48 74 40 75 50 76 49 77 43 78 44 79 47 80 33 81 46 82 45 83 43 84 44 85 47 86 45 87 42 88 33 89 43 90 46 91 33 92 46 93 48 94 47 95 47 96 43 97 46 98 48 99 46 100 45 101 45 102 52 103 42 104 47 105 41 106 47 107 43 108 33 109 30 110 52 111 44 112 55 113 11 114 47 115 53 116 33 117 44 118 42 119 55 120 33 121 46 122 54 123 47 124 45 125 47 126 55 127 44 128 53 129 44 130 42 131 40 132 46 133 40 134 46 135 53 136 33 137 42 138 35 139 40 140 41 141 33 142 51 143 53 144 46 145 55 146 47 147 38 148 46 149 46 150 53 151 47 152 41 153 44 154 43 155 51 156 33 157 43 158 53 159 51 160 50 161 46 162 43 163 47 164 50 165 43 166 33 167 48 168 44 169 50 170 41 171 34 172 44 173 47 174 35 175 44 176 44 177 43 178 41 179 41 180 42 181 33 182 41 183 44 184 48 185 55 186 44 187 43 188 52 189 30 190 39 191 11 192 44 193 42 194 41 195 44 196 44 197 48 198 53 199 37 200 44 201 44 202 40 203 42 204 35 205 43 206 45 207 55 208 31 209 44 210 50 211 40 212 53 213 54 214 49 215 40 216 41 217 52 218 52 219 36 220 52 221 46 222 31 223 44 224 44 225 11 226 46 227 33 228 34 229 42 230 43 231 43 232 44 233 36 234 46 235 44 236 43 237 50 238 33 239 43 240 44 241 53 242 34 243 35 244 40 245 53 246 42 247 43 248 29 249 36 250 30 251 42 252 47 253 44 254 45 255 44 256 43 257 43 258 40 259 41 260 52 261 38 262 41 263 39 264 43 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software 3.77957 0.04694 0.04200 0.60741 Happiness Depression Belonging Belonging_Final 0.09863 -0.03938 0.01587 -0.01942 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.1700 -1.3147 0.2823 1.1739 4.2995 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.77957 1.91765 1.971 0.0498 * Connected 0.04694 0.03479 1.349 0.1784 Separate 0.04200 0.03565 1.178 0.2398 Software 0.60741 0.05185 11.716 <2e-16 *** Happiness 0.09863 0.05796 1.702 0.0900 . Depression -0.03938 0.04256 -0.925 0.3557 Belonging 0.01587 0.03784 0.419 0.6754 Belonging_Final -0.01942 0.05644 -0.344 0.7311 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.884 on 256 degrees of freedom Multiple R-squared: 0.4269, Adjusted R-squared: 0.4113 F-statistic: 27.24 on 7 and 256 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,] 0.234449524 0.468899048 0.7655505 [2,] 0.120293518 0.240587036 0.8797065 [3,] 0.081554157 0.163108315 0.9184458 [4,] 0.102874776 0.205749553 0.8971252 [5,] 0.058751976 0.117503951 0.9412480 [6,] 0.039168668 0.078337335 0.9608313 [7,] 0.063019118 0.126038237 0.9369809 [8,] 0.255864328 0.511728656 0.7441357 [9,] 0.194216633 0.388433266 0.8057834 [10,] 0.137764883 0.275529767 0.8622351 [11,] 0.098606262 0.197212523 0.9013937 [12,] 0.096853399 0.193706798 0.9031466 [13,] 0.257054378 0.514108756 0.7429456 [14,] 0.321495418 0.642990837 0.6785046 [15,] 0.294869539 0.589739077 0.7051305 [16,] 0.277447858 0.554895715 0.7225521 [17,] 0.352098889 0.704197778 0.6479011 [18,] 0.363849649 0.727699298 0.6361504 [19,] 0.346785592 0.693571184 0.6532144 [20,] 0.380898386 0.761796773 0.6191016 [21,] 0.328675803 0.657351606 0.6713242 [22,] 0.289492868 0.578985736 0.7105071 [23,] 0.262490777 0.524981555 0.7375092 [24,] 0.226304417 0.452608834 0.7736956 [25,] 0.187880542 0.375761084 0.8121195 [26,] 0.304929815 0.609859630 0.6950702 [27,] 0.334971074 0.669942148 0.6650289 [28,] 0.325963623 0.651927245 0.6740364 [29,] 0.356022740 0.712045480 0.6439773 [30,] 0.334209449 0.668418898 0.6657906 [31,] 0.298001365 0.596002731 0.7019986 [32,] 0.263835729 0.527671458 0.7361643 [33,] 0.278274265 0.556548530 0.7217257 [34,] 0.236536521 0.473073043 0.7634635 [35,] 0.214442280 0.428884560 0.7855577 [36,] 0.392689718 0.785379436 0.6073103 [37,] 0.545519033 0.908961933 0.4544810 [38,] 0.496548776 0.993097553 0.5034512 [39,] 0.483216161 0.966432321 0.5167838 [40,] 0.468390231 0.936780461 0.5316098 [41,] 0.423671720 0.847343441 0.5763283 [42,] 0.378169198 0.756338395 0.6218308 [43,] 0.386603610 0.773207219 0.6133964 [44,] 0.369709551 0.739419103 0.6302904 [45,] 0.360937557 0.721875114 0.6390624 [46,] 0.339385239 0.678770477 0.6606148 [47,] 0.299777754 0.599555509 0.7002222 [48,] 0.271157720 0.542315440 0.7288423 [49,] 0.236694270 0.473388540 0.7633057 [50,] 0.242458615 0.484917230 0.7575414 [51,] 0.218650159 0.437300318 0.7813498 [52,] 0.191735671 0.383471342 0.8082643 [53,] 0.163732058 0.327464116 0.8362679 [54,] 0.137978889 0.275957778 0.8620211 [55,] 0.119073866 0.238147733 0.8809261 [56,] 0.110228851 0.220457703 0.8897711 [57,] 0.105986150 0.211972300 0.8940138 [58,] 0.223058679 0.446117359 0.7769413 [59,] 0.313038853 0.626077705 0.6869611 [60,] 0.282142510 0.564285020 0.7178575 [61,] 0.397902377 0.795804755 0.6020976 [62,] 0.359897835 0.719795670 0.6401022 [63,] 0.351564646 0.703129293 0.6484354 [64,] 0.336197027 0.672394054 0.6638030 [65,] 0.304024155 0.608048310 0.6959758 [66,] 0.369544726 0.739089451 0.6304553 [67,] 0.333817781 0.667635562 0.6661822 [68,] 0.312977053 0.625954105 0.6870229 [69,] 0.326056569 0.652113138 0.6739434 [70,] 0.298478347 0.596956695 0.7015217 [71,] 0.270341907 0.540683814 0.7296581 [72,] 0.239446043 0.478892085 0.7605540 [73,] 0.215139663 0.430279326 0.7848603 [74,] 0.187913723 0.375827445 0.8120863 [75,] 0.184607192 0.369214383 0.8153928 [76,] 0.159572512 0.319145024 0.8404275 [77,] 0.140322537 0.280645075 0.8596775 [78,] 0.130738802 0.261477604 0.8692612 [79,] 0.112976438 0.225952876 0.8870236 [80,] 0.110203114 0.220406228 0.8897969 [81,] 0.094291937 0.188583874 0.9057081 [82,] 0.081424091 0.162848181 0.9185759 [83,] 0.069162205 0.138324409 0.9308378 [84,] 0.071050562 0.142101124 0.9289494 [85,] 0.064204775 0.128409550 0.9357952 [86,] 0.053797587 0.107595173 0.9462024 [87,] 0.059431515 0.118863029 0.9405685 [88,] 0.049227051 0.098454102 0.9507729 [89,] 0.040363797 0.080727594 0.9596362 [90,] 0.035314977 0.070629954 0.9646850 [91,] 0.031330014 0.062660028 0.9686700 [92,] 0.036882071 0.073764142 0.9631179 [93,] 0.031651390 0.063302781 0.9683486 [94,] 0.028585914 0.057171828 0.9714141 [95,] 0.033817136 0.067634272 0.9661829 [96,] 0.029717088 0.059434176 0.9702829 [97,] 0.024133079 0.048266157 0.9758669 [98,] 0.024007911 0.048015823 0.9759921 [99,] 0.019451505 0.038903010 0.9805485 [100,] 0.015913666 0.031827332 0.9840863 [101,] 0.012614652 0.025229305 0.9873853 [102,] 0.013038128 0.026076256 0.9869619 [103,] 0.011645215 0.023290429 0.9883548 [104,] 0.016720656 0.033441312 0.9832793 [105,] 0.015971023 0.031942047 0.9840290 [106,] 0.015281198 0.030562396 0.9847188 [107,] 0.012639030 0.025278060 0.9873610 [108,] 0.012621956 0.025243912 0.9873780 [109,] 0.010261202 0.020522403 0.9897388 [110,] 0.009038026 0.018076051 0.9909620 [111,] 0.007353568 0.014707136 0.9926464 [112,] 0.011319551 0.022639102 0.9886804 [113,] 0.009538496 0.019076992 0.9904615 [114,] 0.008473116 0.016946233 0.9915269 [115,] 0.006935560 0.013871119 0.9930644 [116,] 0.005650421 0.011300842 0.9943496 [117,] 0.005087164 0.010174328 0.9949128 [118,] 0.004098794 0.008197587 0.9959012 [119,] 0.006048870 0.012097740 0.9939511 [120,] 0.006553238 0.013106476 0.9934468 [121,] 0.013540294 0.027080589 0.9864597 [122,] 0.016298728 0.032597455 0.9837013 [123,] 0.017582365 0.035164730 0.9824176 [124,] 0.016983345 0.033966690 0.9830167 [125,] 0.013880374 0.027760749 0.9861196 [126,] 0.011712339 0.023424677 0.9882877 [127,] 0.009374643 0.018749287 0.9906254 [128,] 0.011416902 0.022833804 0.9885831 [129,] 0.009937140 0.019874280 0.9900629 [130,] 0.011292815 0.022585631 0.9887072 [131,] 0.017516565 0.035033130 0.9824834 [132,] 0.015928958 0.031857916 0.9840710 [133,] 0.012683164 0.025366327 0.9873168 [134,] 0.013445833 0.026891667 0.9865542 [135,] 0.025640313 0.051280627 0.9743597 [136,] 0.031418411 0.062836822 0.9685816 [137,] 0.032979578 0.065959156 0.9670204 [138,] 0.029773423 0.059546845 0.9702266 [139,] 0.024812729 0.049625457 0.9751873 [140,] 0.034436232 0.068872464 0.9655638 [141,] 0.029712419 0.059424838 0.9702876 [142,] 0.031280658 0.062561316 0.9687193 [143,] 0.063880433 0.127760866 0.9361196 [144,] 0.062761436 0.125522871 0.9372386 [145,] 0.072174777 0.144349554 0.9278252 [146,] 0.061747857 0.123495715 0.9382521 [147,] 0.055162879 0.110325759 0.9448371 [148,] 0.048270412 0.096540825 0.9517296 [149,] 0.047226109 0.094452217 0.9527739 [150,] 0.040493028 0.080986056 0.9595070 [151,] 0.033412431 0.066824861 0.9665876 [152,] 0.027466795 0.054933591 0.9725332 [153,] 0.022875653 0.045751307 0.9771243 [154,] 0.019296413 0.038592827 0.9807036 [155,] 0.016935356 0.033870713 0.9830646 [156,] 0.017184305 0.034368610 0.9828157 [157,] 0.013807624 0.027615249 0.9861924 [158,] 0.020373586 0.040747172 0.9796264 [159,] 0.020192886 0.040385772 0.9798071 [160,] 0.018858017 0.037716034 0.9811420 [161,] 0.018134965 0.036269931 0.9818650 [162,] 0.014694197 0.029388393 0.9853058 [163,] 0.014742291 0.029484581 0.9852577 [164,] 0.016422616 0.032845232 0.9835774 [165,] 0.021949460 0.043898919 0.9780505 [166,] 0.017653152 0.035306305 0.9823468 [167,] 0.014382692 0.028765385 0.9856173 [168,] 0.011742743 0.023485486 0.9882573 [169,] 0.009192160 0.018384320 0.9908078 [170,] 0.008005362 0.016010723 0.9919946 [171,] 0.006603604 0.013207208 0.9933964 [172,] 0.005345217 0.010690434 0.9946548 [173,] 0.005609366 0.011218732 0.9943906 [174,] 0.004436473 0.008872946 0.9955635 [175,] 0.094086540 0.188173080 0.9059135 [176,] 0.081684581 0.163369162 0.9183154 [177,] 0.092843980 0.185687961 0.9071560 [178,] 0.080212714 0.160425427 0.9197873 [179,] 0.067959634 0.135919268 0.9320404 [180,] 0.056356144 0.112712287 0.9436439 [181,] 0.049711162 0.099422324 0.9502888 [182,] 0.041961193 0.083922387 0.9580388 [183,] 0.048715528 0.097431056 0.9512845 [184,] 0.052289067 0.104578133 0.9477109 [185,] 0.044381174 0.088762348 0.9556188 [186,] 0.037026328 0.074052656 0.9629737 [187,] 0.048315706 0.096631412 0.9516843 [188,] 0.039586295 0.079172589 0.9604137 [189,] 0.036701156 0.073402313 0.9632988 [190,] 0.031039868 0.062079735 0.9689601 [191,] 0.029221972 0.058443945 0.9707780 [192,] 0.024380571 0.048761141 0.9756194 [193,] 0.031305369 0.062610738 0.9686946 [194,] 0.035680318 0.071360636 0.9643197 [195,] 0.038906336 0.077812673 0.9610937 [196,] 0.030879155 0.061758309 0.9691208 [197,] 0.030342223 0.060684446 0.9696578 [198,] 0.024949873 0.049899745 0.9750501 [199,] 0.027868141 0.055736282 0.9721319 [200,] 0.022541743 0.045083485 0.9774583 [201,] 0.024977533 0.049955067 0.9750225 [202,] 0.039973509 0.079947017 0.9600265 [203,] 0.031587106 0.063174212 0.9684129 [204,] 0.044158943 0.088317885 0.9558411 [205,] 0.038059309 0.076118619 0.9619407 [206,] 0.029750826 0.059501652 0.9702492 [207,] 0.032195531 0.064391063 0.9678045 [208,] 0.027529491 0.055058982 0.9724705 [209,] 0.024741807 0.049483614 0.9752582 [210,] 0.018963377 0.037926755 0.9810366 [211,] 0.016894734 0.033789468 0.9831053 [212,] 0.012420649 0.024841297 0.9875794 [213,] 0.009311742 0.018623484 0.9906883 [214,] 0.007502119 0.015004237 0.9924979 [215,] 0.006547056 0.013094112 0.9934529 [216,] 0.014995190 0.029990381 0.9850048 [217,] 0.011541618 0.023083237 0.9884584 [218,] 0.008350319 0.016700638 0.9916497 [219,] 0.006096834 0.012193668 0.9939032 [220,] 0.004410363 0.008820725 0.9955896 [221,] 0.004394615 0.008789229 0.9956054 [222,] 0.008144980 0.016289959 0.9918550 [223,] 0.037756544 0.075513088 0.9622435 [224,] 0.054294220 0.108588440 0.9457058 [225,] 0.040506586 0.081013172 0.9594934 [226,] 0.036110113 0.072220225 0.9638899 [227,] 0.246505701 0.493011401 0.7534943 [228,] 0.200782131 0.401564262 0.7992179 [229,] 0.180044405 0.360088811 0.8199556 [230,] 0.142128535 0.284257071 0.8578715 [231,] 0.113707343 0.227414686 0.8862927 [232,] 0.094530103 0.189060206 0.9054699 [233,] 0.087902319 0.175804637 0.9120977 [234,] 0.153550434 0.307100868 0.8464496 [235,] 0.137630340 0.275260679 0.8623697 [236,] 0.110109331 0.220218661 0.8898907 [237,] 0.077770601 0.155541202 0.9222294 [238,] 0.066694847 0.133389694 0.9333052 [239,] 0.062488827 0.124977654 0.9375112 [240,] 0.077380993 0.154761986 0.9226190 [241,] 0.046054976 0.092109953 0.9539450 [242,] 0.112285664 0.224571328 0.8877143 [243,] 0.234998511 0.469997022 0.7650015 > postscript(file="/var/fisher/rcomp/tmp/1zqgm1351977230.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2nvvd1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3a4mv1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4xxxa1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5ubh61351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 264 Frequency = 1 1 2 3 4 5 6 -2.716932667 0.695354701 2.465773394 3.756800929 -1.889687332 -1.342423946 7 8 9 10 11 12 4.161807991 -1.656841747 -1.614396605 1.160117737 1.475586780 0.038654809 13 14 15 16 17 18 1.034583103 0.869405423 -0.726605178 -0.007360227 0.966688731 3.960453337 19 20 21 22 23 24 2.905813838 0.813862378 0.998463167 1.416135174 2.773099012 1.361748946 25 26 27 28 29 30 1.158397864 1.165514867 1.470598801 -1.416929777 0.704482544 0.382605544 31 32 33 34 35 36 -0.367231470 -0.157794884 -0.467033523 0.450952519 -1.151997771 -2.454806771 37 38 39 40 41 42 -2.290802595 -1.442875350 1.887432350 1.949699309 1.640084339 -1.412909516 43 44 45 46 47 48 2.502440756 0.262000289 -0.139025784 -4.150559388 -2.419095997 0.218766088 49 50 51 52 53 54 0.808402277 -1.361515364 -0.853739839 0.268310726 -2.613865360 0.409310097 55 56 57 58 59 60 -1.713731807 2.167395395 0.306276310 0.769644429 0.176995441 2.206777551 61 62 63 64 65 66 1.163272051 0.562135982 -0.298986970 -0.438887734 0.849955730 1.256146159 67 68 69 70 71 72 1.909653498 3.923509508 -3.808460881 0.527152017 -3.012245827 -0.715665538 73 74 75 76 77 78 1.162010456 0.807171814 0.990531311 3.909747507 -0.407232544 1.787718277 79 80 81 82 83 84 -1.961485328 0.916861489 0.463901031 0.356770523 -0.620182287 0.313856292 85 86 87 88 89 90 2.117237372 0.048211388 0.766905814 1.318697005 0.896661826 -1.643303567 91 92 93 94 95 96 0.167875359 0.270514769 0.094207665 -1.836456819 1.301967194 0.303921953 97 98 99 100 101 102 2.516715321 0.169958299 -0.266125863 -1.194086507 1.494098871 2.420900716 103 104 105 106 107 108 0.977353986 1.161623748 -1.712444774 1.239562685 0.227935475 1.767395736 109 110 111 112 113 114 -0.002480266 0.828161450 0.061511967 2.137467172 -1.528640241 -2.614568461 115 116 117 118 119 120 1.723547556 -1.950475963 1.007537805 -1.785824239 0.485542044 -1.373030868 121 122 123 124 125 126 0.592730467 -2.900148395 -1.073803686 -0.998565253 -0.921755895 0.249852821 127 128 129 130 131 132 1.232789740 0.995768005 -2.745597967 2.098177534 -3.490018923 2.515270651 133 134 135 136 137 138 -2.131895571 -1.689160164 -0.134952532 1.120129701 0.459324011 -2.363957787 139 140 141 142 143 144 -0.994871780 -2.279773405 3.129332967 1.422202992 -0.012629669 1.782276715 145 146 147 148 149 150 -3.307648252 2.289181735 -2.057515845 1.080191400 0.362236196 -2.878325739 151 152 153 154 155 156 -1.155043271 2.007639148 4.295954949 1.750032454 -2.310629981 0.167875359 157 158 159 160 161 162 1.430460681 0.995768005 1.328604480 -0.573651755 0.399396308 0.533448646 163 164 165 166 167 168 -0.388160789 0.426210073 1.327399554 -1.420672833 -0.469161997 -3.290986580 169 170 171 172 173 174 -1.848292762 1.569125254 1.634688184 0.294051606 -1.988393653 -2.312033591 175 176 177 178 179 180 -3.288588788 0.255786884 -0.263297297 -0.687934672 -0.062106612 -1.476269705 181 182 183 184 185 186 0.565951923 -0.521748993 1.839536729 -0.520609608 -6.766288090 1.031907280 187 188 189 190 191 192 2.252323131 -0.541803289 -0.526967526 0.700677591 -1.044903908 0.145076363 193 194 195 196 197 198 2.342000107 1.699486741 -0.915984634 -0.368633528 2.901656852 0.687030247 199 200 201 202 203 204 1.512544799 1.229420219 1.804610223 0.434739187 -2.762297678 -2.916569977 205 206 207 208 209 210 2.030333482 0.163035580 1.152973504 0.566051290 -2.916763144 0.696840530 211 212 213 214 215 216 -2.507489607 -4.289684969 0.690829519 2.832504112 1.116710656 0.579212169 217 218 219 220 221 222 1.937338022 -0.327916940 1.009711098 -0.615891961 -2.022491333 -0.573303347 223 224 225 226 227 228 0.433554087 -1.458531370 0.195719260 -3.966868429 -0.309683138 0.726256447 229 230 231 232 233 234 -1.343269573 -1.160998557 1.520067648 -3.465040334 4.299546178 1.544133629 235 236 237 238 239 240 -1.305398824 -2.654920743 -7.170028137 -1.815214028 1.407636695 -1.928726129 241 242 243 244 245 246 -0.407197066 -1.795600382 1.113655299 1.722993120 0.907372684 -0.046770178 247 248 249 250 251 252 0.846117134 -2.890292844 1.199034180 0.116066371 -1.137919803 -1.862263458 253 254 255 256 257 258 0.336856218 2.821775236 -1.715206305 -0.264087860 1.318811443 2.176071319 259 260 261 262 263 264 -1.781640984 -5.483306743 0.825633349 -4.550209689 -0.471645252 0.832709147 > postscript(file="/var/fisher/rcomp/tmp/6oqio1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.716932667 NA 1 0.695354701 -2.716932667 2 2.465773394 0.695354701 3 3.756800929 2.465773394 4 -1.889687332 3.756800929 5 -1.342423946 -1.889687332 6 4.161807991 -1.342423946 7 -1.656841747 4.161807991 8 -1.614396605 -1.656841747 9 1.160117737 -1.614396605 10 1.475586780 1.160117737 11 0.038654809 1.475586780 12 1.034583103 0.038654809 13 0.869405423 1.034583103 14 -0.726605178 0.869405423 15 -0.007360227 -0.726605178 16 0.966688731 -0.007360227 17 3.960453337 0.966688731 18 2.905813838 3.960453337 19 0.813862378 2.905813838 20 0.998463167 0.813862378 21 1.416135174 0.998463167 22 2.773099012 1.416135174 23 1.361748946 2.773099012 24 1.158397864 1.361748946 25 1.165514867 1.158397864 26 1.470598801 1.165514867 27 -1.416929777 1.470598801 28 0.704482544 -1.416929777 29 0.382605544 0.704482544 30 -0.367231470 0.382605544 31 -0.157794884 -0.367231470 32 -0.467033523 -0.157794884 33 0.450952519 -0.467033523 34 -1.151997771 0.450952519 35 -2.454806771 -1.151997771 36 -2.290802595 -2.454806771 37 -1.442875350 -2.290802595 38 1.887432350 -1.442875350 39 1.949699309 1.887432350 40 1.640084339 1.949699309 41 -1.412909516 1.640084339 42 2.502440756 -1.412909516 43 0.262000289 2.502440756 44 -0.139025784 0.262000289 45 -4.150559388 -0.139025784 46 -2.419095997 -4.150559388 47 0.218766088 -2.419095997 48 0.808402277 0.218766088 49 -1.361515364 0.808402277 50 -0.853739839 -1.361515364 51 0.268310726 -0.853739839 52 -2.613865360 0.268310726 53 0.409310097 -2.613865360 54 -1.713731807 0.409310097 55 2.167395395 -1.713731807 56 0.306276310 2.167395395 57 0.769644429 0.306276310 58 0.176995441 0.769644429 59 2.206777551 0.176995441 60 1.163272051 2.206777551 61 0.562135982 1.163272051 62 -0.298986970 0.562135982 63 -0.438887734 -0.298986970 64 0.849955730 -0.438887734 65 1.256146159 0.849955730 66 1.909653498 1.256146159 67 3.923509508 1.909653498 68 -3.808460881 3.923509508 69 0.527152017 -3.808460881 70 -3.012245827 0.527152017 71 -0.715665538 -3.012245827 72 1.162010456 -0.715665538 73 0.807171814 1.162010456 74 0.990531311 0.807171814 75 3.909747507 0.990531311 76 -0.407232544 3.909747507 77 1.787718277 -0.407232544 78 -1.961485328 1.787718277 79 0.916861489 -1.961485328 80 0.463901031 0.916861489 81 0.356770523 0.463901031 82 -0.620182287 0.356770523 83 0.313856292 -0.620182287 84 2.117237372 0.313856292 85 0.048211388 2.117237372 86 0.766905814 0.048211388 87 1.318697005 0.766905814 88 0.896661826 1.318697005 89 -1.643303567 0.896661826 90 0.167875359 -1.643303567 91 0.270514769 0.167875359 92 0.094207665 0.270514769 93 -1.836456819 0.094207665 94 1.301967194 -1.836456819 95 0.303921953 1.301967194 96 2.516715321 0.303921953 97 0.169958299 2.516715321 98 -0.266125863 0.169958299 99 -1.194086507 -0.266125863 100 1.494098871 -1.194086507 101 2.420900716 1.494098871 102 0.977353986 2.420900716 103 1.161623748 0.977353986 104 -1.712444774 1.161623748 105 1.239562685 -1.712444774 106 0.227935475 1.239562685 107 1.767395736 0.227935475 108 -0.002480266 1.767395736 109 0.828161450 -0.002480266 110 0.061511967 0.828161450 111 2.137467172 0.061511967 112 -1.528640241 2.137467172 113 -2.614568461 -1.528640241 114 1.723547556 -2.614568461 115 -1.950475963 1.723547556 116 1.007537805 -1.950475963 117 -1.785824239 1.007537805 118 0.485542044 -1.785824239 119 -1.373030868 0.485542044 120 0.592730467 -1.373030868 121 -2.900148395 0.592730467 122 -1.073803686 -2.900148395 123 -0.998565253 -1.073803686 124 -0.921755895 -0.998565253 125 0.249852821 -0.921755895 126 1.232789740 0.249852821 127 0.995768005 1.232789740 128 -2.745597967 0.995768005 129 2.098177534 -2.745597967 130 -3.490018923 2.098177534 131 2.515270651 -3.490018923 132 -2.131895571 2.515270651 133 -1.689160164 -2.131895571 134 -0.134952532 -1.689160164 135 1.120129701 -0.134952532 136 0.459324011 1.120129701 137 -2.363957787 0.459324011 138 -0.994871780 -2.363957787 139 -2.279773405 -0.994871780 140 3.129332967 -2.279773405 141 1.422202992 3.129332967 142 -0.012629669 1.422202992 143 1.782276715 -0.012629669 144 -3.307648252 1.782276715 145 2.289181735 -3.307648252 146 -2.057515845 2.289181735 147 1.080191400 -2.057515845 148 0.362236196 1.080191400 149 -2.878325739 0.362236196 150 -1.155043271 -2.878325739 151 2.007639148 -1.155043271 152 4.295954949 2.007639148 153 1.750032454 4.295954949 154 -2.310629981 1.750032454 155 0.167875359 -2.310629981 156 1.430460681 0.167875359 157 0.995768005 1.430460681 158 1.328604480 0.995768005 159 -0.573651755 1.328604480 160 0.399396308 -0.573651755 161 0.533448646 0.399396308 162 -0.388160789 0.533448646 163 0.426210073 -0.388160789 164 1.327399554 0.426210073 165 -1.420672833 1.327399554 166 -0.469161997 -1.420672833 167 -3.290986580 -0.469161997 168 -1.848292762 -3.290986580 169 1.569125254 -1.848292762 170 1.634688184 1.569125254 171 0.294051606 1.634688184 172 -1.988393653 0.294051606 173 -2.312033591 -1.988393653 174 -3.288588788 -2.312033591 175 0.255786884 -3.288588788 176 -0.263297297 0.255786884 177 -0.687934672 -0.263297297 178 -0.062106612 -0.687934672 179 -1.476269705 -0.062106612 180 0.565951923 -1.476269705 181 -0.521748993 0.565951923 182 1.839536729 -0.521748993 183 -0.520609608 1.839536729 184 -6.766288090 -0.520609608 185 1.031907280 -6.766288090 186 2.252323131 1.031907280 187 -0.541803289 2.252323131 188 -0.526967526 -0.541803289 189 0.700677591 -0.526967526 190 -1.044903908 0.700677591 191 0.145076363 -1.044903908 192 2.342000107 0.145076363 193 1.699486741 2.342000107 194 -0.915984634 1.699486741 195 -0.368633528 -0.915984634 196 2.901656852 -0.368633528 197 0.687030247 2.901656852 198 1.512544799 0.687030247 199 1.229420219 1.512544799 200 1.804610223 1.229420219 201 0.434739187 1.804610223 202 -2.762297678 0.434739187 203 -2.916569977 -2.762297678 204 2.030333482 -2.916569977 205 0.163035580 2.030333482 206 1.152973504 0.163035580 207 0.566051290 1.152973504 208 -2.916763144 0.566051290 209 0.696840530 -2.916763144 210 -2.507489607 0.696840530 211 -4.289684969 -2.507489607 212 0.690829519 -4.289684969 213 2.832504112 0.690829519 214 1.116710656 2.832504112 215 0.579212169 1.116710656 216 1.937338022 0.579212169 217 -0.327916940 1.937338022 218 1.009711098 -0.327916940 219 -0.615891961 1.009711098 220 -2.022491333 -0.615891961 221 -0.573303347 -2.022491333 222 0.433554087 -0.573303347 223 -1.458531370 0.433554087 224 0.195719260 -1.458531370 225 -3.966868429 0.195719260 226 -0.309683138 -3.966868429 227 0.726256447 -0.309683138 228 -1.343269573 0.726256447 229 -1.160998557 -1.343269573 230 1.520067648 -1.160998557 231 -3.465040334 1.520067648 232 4.299546178 -3.465040334 233 1.544133629 4.299546178 234 -1.305398824 1.544133629 235 -2.654920743 -1.305398824 236 -7.170028137 -2.654920743 237 -1.815214028 -7.170028137 238 1.407636695 -1.815214028 239 -1.928726129 1.407636695 240 -0.407197066 -1.928726129 241 -1.795600382 -0.407197066 242 1.113655299 -1.795600382 243 1.722993120 1.113655299 244 0.907372684 1.722993120 245 -0.046770178 0.907372684 246 0.846117134 -0.046770178 247 -2.890292844 0.846117134 248 1.199034180 -2.890292844 249 0.116066371 1.199034180 250 -1.137919803 0.116066371 251 -1.862263458 -1.137919803 252 0.336856218 -1.862263458 253 2.821775236 0.336856218 254 -1.715206305 2.821775236 255 -0.264087860 -1.715206305 256 1.318811443 -0.264087860 257 2.176071319 1.318811443 258 -1.781640984 2.176071319 259 -5.483306743 -1.781640984 260 0.825633349 -5.483306743 261 -4.550209689 0.825633349 262 -0.471645252 -4.550209689 263 0.832709147 -0.471645252 264 NA 0.832709147 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.695354701 -2.716932667 [2,] 2.465773394 0.695354701 [3,] 3.756800929 2.465773394 [4,] -1.889687332 3.756800929 [5,] -1.342423946 -1.889687332 [6,] 4.161807991 -1.342423946 [7,] -1.656841747 4.161807991 [8,] -1.614396605 -1.656841747 [9,] 1.160117737 -1.614396605 [10,] 1.475586780 1.160117737 [11,] 0.038654809 1.475586780 [12,] 1.034583103 0.038654809 [13,] 0.869405423 1.034583103 [14,] -0.726605178 0.869405423 [15,] -0.007360227 -0.726605178 [16,] 0.966688731 -0.007360227 [17,] 3.960453337 0.966688731 [18,] 2.905813838 3.960453337 [19,] 0.813862378 2.905813838 [20,] 0.998463167 0.813862378 [21,] 1.416135174 0.998463167 [22,] 2.773099012 1.416135174 [23,] 1.361748946 2.773099012 [24,] 1.158397864 1.361748946 [25,] 1.165514867 1.158397864 [26,] 1.470598801 1.165514867 [27,] -1.416929777 1.470598801 [28,] 0.704482544 -1.416929777 [29,] 0.382605544 0.704482544 [30,] -0.367231470 0.382605544 [31,] -0.157794884 -0.367231470 [32,] -0.467033523 -0.157794884 [33,] 0.450952519 -0.467033523 [34,] -1.151997771 0.450952519 [35,] -2.454806771 -1.151997771 [36,] -2.290802595 -2.454806771 [37,] -1.442875350 -2.290802595 [38,] 1.887432350 -1.442875350 [39,] 1.949699309 1.887432350 [40,] 1.640084339 1.949699309 [41,] -1.412909516 1.640084339 [42,] 2.502440756 -1.412909516 [43,] 0.262000289 2.502440756 [44,] -0.139025784 0.262000289 [45,] -4.150559388 -0.139025784 [46,] -2.419095997 -4.150559388 [47,] 0.218766088 -2.419095997 [48,] 0.808402277 0.218766088 [49,] -1.361515364 0.808402277 [50,] -0.853739839 -1.361515364 [51,] 0.268310726 -0.853739839 [52,] -2.613865360 0.268310726 [53,] 0.409310097 -2.613865360 [54,] -1.713731807 0.409310097 [55,] 2.167395395 -1.713731807 [56,] 0.306276310 2.167395395 [57,] 0.769644429 0.306276310 [58,] 0.176995441 0.769644429 [59,] 2.206777551 0.176995441 [60,] 1.163272051 2.206777551 [61,] 0.562135982 1.163272051 [62,] -0.298986970 0.562135982 [63,] -0.438887734 -0.298986970 [64,] 0.849955730 -0.438887734 [65,] 1.256146159 0.849955730 [66,] 1.909653498 1.256146159 [67,] 3.923509508 1.909653498 [68,] -3.808460881 3.923509508 [69,] 0.527152017 -3.808460881 [70,] -3.012245827 0.527152017 [71,] -0.715665538 -3.012245827 [72,] 1.162010456 -0.715665538 [73,] 0.807171814 1.162010456 [74,] 0.990531311 0.807171814 [75,] 3.909747507 0.990531311 [76,] -0.407232544 3.909747507 [77,] 1.787718277 -0.407232544 [78,] -1.961485328 1.787718277 [79,] 0.916861489 -1.961485328 [80,] 0.463901031 0.916861489 [81,] 0.356770523 0.463901031 [82,] -0.620182287 0.356770523 [83,] 0.313856292 -0.620182287 [84,] 2.117237372 0.313856292 [85,] 0.048211388 2.117237372 [86,] 0.766905814 0.048211388 [87,] 1.318697005 0.766905814 [88,] 0.896661826 1.318697005 [89,] -1.643303567 0.896661826 [90,] 0.167875359 -1.643303567 [91,] 0.270514769 0.167875359 [92,] 0.094207665 0.270514769 [93,] -1.836456819 0.094207665 [94,] 1.301967194 -1.836456819 [95,] 0.303921953 1.301967194 [96,] 2.516715321 0.303921953 [97,] 0.169958299 2.516715321 [98,] -0.266125863 0.169958299 [99,] -1.194086507 -0.266125863 [100,] 1.494098871 -1.194086507 [101,] 2.420900716 1.494098871 [102,] 0.977353986 2.420900716 [103,] 1.161623748 0.977353986 [104,] -1.712444774 1.161623748 [105,] 1.239562685 -1.712444774 [106,] 0.227935475 1.239562685 [107,] 1.767395736 0.227935475 [108,] -0.002480266 1.767395736 [109,] 0.828161450 -0.002480266 [110,] 0.061511967 0.828161450 [111,] 2.137467172 0.061511967 [112,] -1.528640241 2.137467172 [113,] -2.614568461 -1.528640241 [114,] 1.723547556 -2.614568461 [115,] -1.950475963 1.723547556 [116,] 1.007537805 -1.950475963 [117,] -1.785824239 1.007537805 [118,] 0.485542044 -1.785824239 [119,] -1.373030868 0.485542044 [120,] 0.592730467 -1.373030868 [121,] -2.900148395 0.592730467 [122,] -1.073803686 -2.900148395 [123,] -0.998565253 -1.073803686 [124,] -0.921755895 -0.998565253 [125,] 0.249852821 -0.921755895 [126,] 1.232789740 0.249852821 [127,] 0.995768005 1.232789740 [128,] -2.745597967 0.995768005 [129,] 2.098177534 -2.745597967 [130,] -3.490018923 2.098177534 [131,] 2.515270651 -3.490018923 [132,] -2.131895571 2.515270651 [133,] -1.689160164 -2.131895571 [134,] -0.134952532 -1.689160164 [135,] 1.120129701 -0.134952532 [136,] 0.459324011 1.120129701 [137,] -2.363957787 0.459324011 [138,] -0.994871780 -2.363957787 [139,] -2.279773405 -0.994871780 [140,] 3.129332967 -2.279773405 [141,] 1.422202992 3.129332967 [142,] -0.012629669 1.422202992 [143,] 1.782276715 -0.012629669 [144,] -3.307648252 1.782276715 [145,] 2.289181735 -3.307648252 [146,] -2.057515845 2.289181735 [147,] 1.080191400 -2.057515845 [148,] 0.362236196 1.080191400 [149,] -2.878325739 0.362236196 [150,] -1.155043271 -2.878325739 [151,] 2.007639148 -1.155043271 [152,] 4.295954949 2.007639148 [153,] 1.750032454 4.295954949 [154,] -2.310629981 1.750032454 [155,] 0.167875359 -2.310629981 [156,] 1.430460681 0.167875359 [157,] 0.995768005 1.430460681 [158,] 1.328604480 0.995768005 [159,] -0.573651755 1.328604480 [160,] 0.399396308 -0.573651755 [161,] 0.533448646 0.399396308 [162,] -0.388160789 0.533448646 [163,] 0.426210073 -0.388160789 [164,] 1.327399554 0.426210073 [165,] -1.420672833 1.327399554 [166,] -0.469161997 -1.420672833 [167,] -3.290986580 -0.469161997 [168,] -1.848292762 -3.290986580 [169,] 1.569125254 -1.848292762 [170,] 1.634688184 1.569125254 [171,] 0.294051606 1.634688184 [172,] -1.988393653 0.294051606 [173,] -2.312033591 -1.988393653 [174,] -3.288588788 -2.312033591 [175,] 0.255786884 -3.288588788 [176,] -0.263297297 0.255786884 [177,] -0.687934672 -0.263297297 [178,] -0.062106612 -0.687934672 [179,] -1.476269705 -0.062106612 [180,] 0.565951923 -1.476269705 [181,] -0.521748993 0.565951923 [182,] 1.839536729 -0.521748993 [183,] -0.520609608 1.839536729 [184,] -6.766288090 -0.520609608 [185,] 1.031907280 -6.766288090 [186,] 2.252323131 1.031907280 [187,] -0.541803289 2.252323131 [188,] -0.526967526 -0.541803289 [189,] 0.700677591 -0.526967526 [190,] -1.044903908 0.700677591 [191,] 0.145076363 -1.044903908 [192,] 2.342000107 0.145076363 [193,] 1.699486741 2.342000107 [194,] -0.915984634 1.699486741 [195,] -0.368633528 -0.915984634 [196,] 2.901656852 -0.368633528 [197,] 0.687030247 2.901656852 [198,] 1.512544799 0.687030247 [199,] 1.229420219 1.512544799 [200,] 1.804610223 1.229420219 [201,] 0.434739187 1.804610223 [202,] -2.762297678 0.434739187 [203,] -2.916569977 -2.762297678 [204,] 2.030333482 -2.916569977 [205,] 0.163035580 2.030333482 [206,] 1.152973504 0.163035580 [207,] 0.566051290 1.152973504 [208,] -2.916763144 0.566051290 [209,] 0.696840530 -2.916763144 [210,] -2.507489607 0.696840530 [211,] -4.289684969 -2.507489607 [212,] 0.690829519 -4.289684969 [213,] 2.832504112 0.690829519 [214,] 1.116710656 2.832504112 [215,] 0.579212169 1.116710656 [216,] 1.937338022 0.579212169 [217,] -0.327916940 1.937338022 [218,] 1.009711098 -0.327916940 [219,] -0.615891961 1.009711098 [220,] -2.022491333 -0.615891961 [221,] -0.573303347 -2.022491333 [222,] 0.433554087 -0.573303347 [223,] -1.458531370 0.433554087 [224,] 0.195719260 -1.458531370 [225,] -3.966868429 0.195719260 [226,] -0.309683138 -3.966868429 [227,] 0.726256447 -0.309683138 [228,] -1.343269573 0.726256447 [229,] -1.160998557 -1.343269573 [230,] 1.520067648 -1.160998557 [231,] -3.465040334 1.520067648 [232,] 4.299546178 -3.465040334 [233,] 1.544133629 4.299546178 [234,] -1.305398824 1.544133629 [235,] -2.654920743 -1.305398824 [236,] -7.170028137 -2.654920743 [237,] -1.815214028 -7.170028137 [238,] 1.407636695 -1.815214028 [239,] -1.928726129 1.407636695 [240,] -0.407197066 -1.928726129 [241,] -1.795600382 -0.407197066 [242,] 1.113655299 -1.795600382 [243,] 1.722993120 1.113655299 [244,] 0.907372684 1.722993120 [245,] -0.046770178 0.907372684 [246,] 0.846117134 -0.046770178 [247,] -2.890292844 0.846117134 [248,] 1.199034180 -2.890292844 [249,] 0.116066371 1.199034180 [250,] -1.137919803 0.116066371 [251,] -1.862263458 -1.137919803 [252,] 0.336856218 -1.862263458 [253,] 2.821775236 0.336856218 [254,] -1.715206305 2.821775236 [255,] -0.264087860 -1.715206305 [256,] 1.318811443 -0.264087860 [257,] 2.176071319 1.318811443 [258,] -1.781640984 2.176071319 [259,] -5.483306743 -1.781640984 [260,] 0.825633349 -5.483306743 [261,] -4.550209689 0.825633349 [262,] -0.471645252 -4.550209689 [263,] 0.832709147 -0.471645252 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.695354701 -2.716932667 2 2.465773394 0.695354701 3 3.756800929 2.465773394 4 -1.889687332 3.756800929 5 -1.342423946 -1.889687332 6 4.161807991 -1.342423946 7 -1.656841747 4.161807991 8 -1.614396605 -1.656841747 9 1.160117737 -1.614396605 10 1.475586780 1.160117737 11 0.038654809 1.475586780 12 1.034583103 0.038654809 13 0.869405423 1.034583103 14 -0.726605178 0.869405423 15 -0.007360227 -0.726605178 16 0.966688731 -0.007360227 17 3.960453337 0.966688731 18 2.905813838 3.960453337 19 0.813862378 2.905813838 20 0.998463167 0.813862378 21 1.416135174 0.998463167 22 2.773099012 1.416135174 23 1.361748946 2.773099012 24 1.158397864 1.361748946 25 1.165514867 1.158397864 26 1.470598801 1.165514867 27 -1.416929777 1.470598801 28 0.704482544 -1.416929777 29 0.382605544 0.704482544 30 -0.367231470 0.382605544 31 -0.157794884 -0.367231470 32 -0.467033523 -0.157794884 33 0.450952519 -0.467033523 34 -1.151997771 0.450952519 35 -2.454806771 -1.151997771 36 -2.290802595 -2.454806771 37 -1.442875350 -2.290802595 38 1.887432350 -1.442875350 39 1.949699309 1.887432350 40 1.640084339 1.949699309 41 -1.412909516 1.640084339 42 2.502440756 -1.412909516 43 0.262000289 2.502440756 44 -0.139025784 0.262000289 45 -4.150559388 -0.139025784 46 -2.419095997 -4.150559388 47 0.218766088 -2.419095997 48 0.808402277 0.218766088 49 -1.361515364 0.808402277 50 -0.853739839 -1.361515364 51 0.268310726 -0.853739839 52 -2.613865360 0.268310726 53 0.409310097 -2.613865360 54 -1.713731807 0.409310097 55 2.167395395 -1.713731807 56 0.306276310 2.167395395 57 0.769644429 0.306276310 58 0.176995441 0.769644429 59 2.206777551 0.176995441 60 1.163272051 2.206777551 61 0.562135982 1.163272051 62 -0.298986970 0.562135982 63 -0.438887734 -0.298986970 64 0.849955730 -0.438887734 65 1.256146159 0.849955730 66 1.909653498 1.256146159 67 3.923509508 1.909653498 68 -3.808460881 3.923509508 69 0.527152017 -3.808460881 70 -3.012245827 0.527152017 71 -0.715665538 -3.012245827 72 1.162010456 -0.715665538 73 0.807171814 1.162010456 74 0.990531311 0.807171814 75 3.909747507 0.990531311 76 -0.407232544 3.909747507 77 1.787718277 -0.407232544 78 -1.961485328 1.787718277 79 0.916861489 -1.961485328 80 0.463901031 0.916861489 81 0.356770523 0.463901031 82 -0.620182287 0.356770523 83 0.313856292 -0.620182287 84 2.117237372 0.313856292 85 0.048211388 2.117237372 86 0.766905814 0.048211388 87 1.318697005 0.766905814 88 0.896661826 1.318697005 89 -1.643303567 0.896661826 90 0.167875359 -1.643303567 91 0.270514769 0.167875359 92 0.094207665 0.270514769 93 -1.836456819 0.094207665 94 1.301967194 -1.836456819 95 0.303921953 1.301967194 96 2.516715321 0.303921953 97 0.169958299 2.516715321 98 -0.266125863 0.169958299 99 -1.194086507 -0.266125863 100 1.494098871 -1.194086507 101 2.420900716 1.494098871 102 0.977353986 2.420900716 103 1.161623748 0.977353986 104 -1.712444774 1.161623748 105 1.239562685 -1.712444774 106 0.227935475 1.239562685 107 1.767395736 0.227935475 108 -0.002480266 1.767395736 109 0.828161450 -0.002480266 110 0.061511967 0.828161450 111 2.137467172 0.061511967 112 -1.528640241 2.137467172 113 -2.614568461 -1.528640241 114 1.723547556 -2.614568461 115 -1.950475963 1.723547556 116 1.007537805 -1.950475963 117 -1.785824239 1.007537805 118 0.485542044 -1.785824239 119 -1.373030868 0.485542044 120 0.592730467 -1.373030868 121 -2.900148395 0.592730467 122 -1.073803686 -2.900148395 123 -0.998565253 -1.073803686 124 -0.921755895 -0.998565253 125 0.249852821 -0.921755895 126 1.232789740 0.249852821 127 0.995768005 1.232789740 128 -2.745597967 0.995768005 129 2.098177534 -2.745597967 130 -3.490018923 2.098177534 131 2.515270651 -3.490018923 132 -2.131895571 2.515270651 133 -1.689160164 -2.131895571 134 -0.134952532 -1.689160164 135 1.120129701 -0.134952532 136 0.459324011 1.120129701 137 -2.363957787 0.459324011 138 -0.994871780 -2.363957787 139 -2.279773405 -0.994871780 140 3.129332967 -2.279773405 141 1.422202992 3.129332967 142 -0.012629669 1.422202992 143 1.782276715 -0.012629669 144 -3.307648252 1.782276715 145 2.289181735 -3.307648252 146 -2.057515845 2.289181735 147 1.080191400 -2.057515845 148 0.362236196 1.080191400 149 -2.878325739 0.362236196 150 -1.155043271 -2.878325739 151 2.007639148 -1.155043271 152 4.295954949 2.007639148 153 1.750032454 4.295954949 154 -2.310629981 1.750032454 155 0.167875359 -2.310629981 156 1.430460681 0.167875359 157 0.995768005 1.430460681 158 1.328604480 0.995768005 159 -0.573651755 1.328604480 160 0.399396308 -0.573651755 161 0.533448646 0.399396308 162 -0.388160789 0.533448646 163 0.426210073 -0.388160789 164 1.327399554 0.426210073 165 -1.420672833 1.327399554 166 -0.469161997 -1.420672833 167 -3.290986580 -0.469161997 168 -1.848292762 -3.290986580 169 1.569125254 -1.848292762 170 1.634688184 1.569125254 171 0.294051606 1.634688184 172 -1.988393653 0.294051606 173 -2.312033591 -1.988393653 174 -3.288588788 -2.312033591 175 0.255786884 -3.288588788 176 -0.263297297 0.255786884 177 -0.687934672 -0.263297297 178 -0.062106612 -0.687934672 179 -1.476269705 -0.062106612 180 0.565951923 -1.476269705 181 -0.521748993 0.565951923 182 1.839536729 -0.521748993 183 -0.520609608 1.839536729 184 -6.766288090 -0.520609608 185 1.031907280 -6.766288090 186 2.252323131 1.031907280 187 -0.541803289 2.252323131 188 -0.526967526 -0.541803289 189 0.700677591 -0.526967526 190 -1.044903908 0.700677591 191 0.145076363 -1.044903908 192 2.342000107 0.145076363 193 1.699486741 2.342000107 194 -0.915984634 1.699486741 195 -0.368633528 -0.915984634 196 2.901656852 -0.368633528 197 0.687030247 2.901656852 198 1.512544799 0.687030247 199 1.229420219 1.512544799 200 1.804610223 1.229420219 201 0.434739187 1.804610223 202 -2.762297678 0.434739187 203 -2.916569977 -2.762297678 204 2.030333482 -2.916569977 205 0.163035580 2.030333482 206 1.152973504 0.163035580 207 0.566051290 1.152973504 208 -2.916763144 0.566051290 209 0.696840530 -2.916763144 210 -2.507489607 0.696840530 211 -4.289684969 -2.507489607 212 0.690829519 -4.289684969 213 2.832504112 0.690829519 214 1.116710656 2.832504112 215 0.579212169 1.116710656 216 1.937338022 0.579212169 217 -0.327916940 1.937338022 218 1.009711098 -0.327916940 219 -0.615891961 1.009711098 220 -2.022491333 -0.615891961 221 -0.573303347 -2.022491333 222 0.433554087 -0.573303347 223 -1.458531370 0.433554087 224 0.195719260 -1.458531370 225 -3.966868429 0.195719260 226 -0.309683138 -3.966868429 227 0.726256447 -0.309683138 228 -1.343269573 0.726256447 229 -1.160998557 -1.343269573 230 1.520067648 -1.160998557 231 -3.465040334 1.520067648 232 4.299546178 -3.465040334 233 1.544133629 4.299546178 234 -1.305398824 1.544133629 235 -2.654920743 -1.305398824 236 -7.170028137 -2.654920743 237 -1.815214028 -7.170028137 238 1.407636695 -1.815214028 239 -1.928726129 1.407636695 240 -0.407197066 -1.928726129 241 -1.795600382 -0.407197066 242 1.113655299 -1.795600382 243 1.722993120 1.113655299 244 0.907372684 1.722993120 245 -0.046770178 0.907372684 246 0.846117134 -0.046770178 247 -2.890292844 0.846117134 248 1.199034180 -2.890292844 249 0.116066371 1.199034180 250 -1.137919803 0.116066371 251 -1.862263458 -1.137919803 252 0.336856218 -1.862263458 253 2.821775236 0.336856218 254 -1.715206305 2.821775236 255 -0.264087860 -1.715206305 256 1.318811443 -0.264087860 257 2.176071319 1.318811443 258 -1.781640984 2.176071319 259 -5.483306743 -1.781640984 260 0.825633349 -5.483306743 261 -4.550209689 0.825633349 262 -0.471645252 -4.550209689 263 0.832709147 -0.471645252 > 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/7vs061351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8i1xb1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9t3ya1351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10noc41351977230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11p9ul1351977230.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/12mg281351977230.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/13i7c31351977230.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/14v8wn1351977230.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/15k3sw1351977230.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/16qa2n1351977230.tab") + } > > try(system("convert tmp/1zqgm1351977230.ps tmp/1zqgm1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/2nvvd1351977230.ps tmp/2nvvd1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/3a4mv1351977230.ps tmp/3a4mv1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/4xxxa1351977230.ps tmp/4xxxa1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/5ubh61351977230.ps tmp/5ubh61351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/6oqio1351977230.ps tmp/6oqio1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/7vs061351977230.ps tmp/7vs061351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/8i1xb1351977230.ps tmp/8i1xb1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/9t3ya1351977230.ps tmp/9t3ya1351977230.png",intern=TRUE)) character(0) > try(system("convert tmp/10noc41351977230.ps tmp/10noc41351977230.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.014 1.078 12.387