R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43 + ,11) + ,dim=c(9 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Sport1' + ,'Sport2' + ,'Month') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Learning Connected Separate Software Happiness Depression Sport1 Sport2 1 13 41 38 12 14 12.0 53 32 2 16 39 32 11 18 11.0 83 51 3 19 30 35 15 11 14.0 66 42 4 15 31 33 6 12 12.0 67 41 5 14 34 37 13 16 21.0 76 46 6 13 35 29 10 18 12.0 78 47 7 19 39 31 12 14 22.0 53 37 8 15 34 36 14 14 11.0 80 49 9 14 36 35 12 15 10.0 74 45 10 15 37 38 9 15 13.0 76 47 11 16 38 31 10 17 10.0 79 49 12 16 36 34 12 19 8.0 54 33 13 16 38 35 12 10 15.0 67 42 14 16 39 38 11 16 14.0 54 33 15 17 33 37 15 18 10.0 87 53 16 15 32 33 12 14 14.0 58 36 17 15 36 32 10 14 14.0 75 45 18 20 38 38 12 17 11.0 88 54 19 18 39 38 11 14 10.0 64 41 20 16 32 32 12 16 13.0 57 36 21 16 32 33 11 18 9.5 66 41 22 16 31 31 12 11 14.0 68 44 23 19 39 38 13 14 12.0 54 33 24 16 37 39 11 12 14.0 56 37 25 17 39 32 12 17 11.0 86 52 26 17 41 32 13 9 9.0 80 47 27 16 36 35 10 16 11.0 76 43 28 15 33 37 14 14 15.0 69 44 29 16 33 33 12 15 14.0 78 45 30 14 34 33 10 11 13.0 67 44 31 15 31 31 12 16 9.0 80 49 32 12 27 32 8 13 15.0 54 33 33 14 37 31 10 17 10.0 71 43 34 16 34 37 12 15 11.0 84 54 35 14 34 30 12 14 13.0 74 42 36 10 32 33 7 16 8.0 71 44 37 10 29 31 9 9 20.0 63 37 38 14 36 33 12 15 12.0 71 43 39 16 29 31 10 17 10.0 76 46 40 16 35 33 10 13 10.0 69 42 41 16 37 32 10 15 9.0 74 45 42 14 34 33 12 16 14.0 75 44 43 20 38 32 15 16 8.0 54 33 44 14 35 33 10 12 14.0 52 31 45 14 38 28 10 15 11.0 69 42 46 11 37 35 12 11 13.0 68 40 47 14 38 39 13 15 9.0 65 43 48 15 33 34 11 15 11.0 75 46 49 16 36 38 11 17 15.0 74 42 50 14 38 32 12 13 11.0 75 45 51 16 32 38 14 16 10.0 72 44 52 14 32 30 10 14 14.0 67 40 53 12 32 33 12 11 18.0 63 37 54 16 34 38 13 12 14.0 62 46 55 9 32 32 5 12 11.0 63 36 56 14 37 35 6 15 14.5 76 47 57 16 39 34 12 16 13.0 74 45 58 16 29 34 12 15 9.0 67 42 59 15 37 36 11 12 10.0 73 43 60 16 35 34 10 12 15.0 70 43 61 12 30 28 7 8 20.0 53 32 62 16 38 34 12 13 12.0 77 45 63 16 34 35 14 11 12.0 80 48 64 14 31 35 11 14 14.0 52 31 65 16 34 31 12 15 13.0 54 33 66 17 35 37 13 10 11.0 80 49 67 18 36 35 14 11 17.0 66 42 68 18 30 27 11 12 12.0 73 41 69 12 39 40 12 15 13.0 63 38 70 16 35 37 12 15 14.0 69 42 71 10 38 36 8 14 13.0 67 44 72 14 31 38 11 16 15.0 54 33 73 18 34 39 14 15 13.0 81 48 74 18 38 41 14 15 10.0 69 40 75 16 34 27 12 13 11.0 84 50 76 17 39 30 9 12 19.0 80 49 77 16 37 37 13 17 13.0 70 43 78 16 34 31 11 13 17.0 69 44 79 13 28 31 12 15 13.0 77 47 80 16 37 27 12 13 9.0 54 33 81 16 33 36 12 15 11.0 79 46 82 16 35 37 12 15 9.0 71 45 83 15 37 33 12 16 12.0 73 43 84 15 32 34 11 15 12.0 72 44 85 16 33 31 10 14 13.0 77 47 86 14 38 39 9 15 13.0 75 45 87 16 33 34 12 14 12.0 69 42 88 16 29 32 12 13 15.0 54 33 89 15 33 33 12 7 22.0 70 43 90 12 31 36 9 17 13.0 73 46 91 17 36 32 15 13 15.0 54 33 92 16 35 41 12 15 13.0 77 46 93 15 32 28 12 14 15.0 82 48 94 13 29 30 12 13 12.5 80 47 95 16 39 36 10 16 11.0 80 47 96 16 37 35 13 12 16.0 69 43 97 16 35 31 9 14 11.0 78 46 98 16 37 34 12 17 11.0 81 48 99 14 32 36 10 15 10.0 76 46 100 16 38 36 14 17 10.0 76 45 101 16 37 35 11 12 16.0 73 45 102 20 36 37 15 16 12.0 85 52 103 15 32 28 11 11 11.0 66 42 104 16 33 39 11 15 16.0 79 47 105 13 40 32 12 9 19.0 68 41 106 17 38 35 12 16 11.0 76 47 107 16 41 39 12 15 16.0 71 43 108 16 36 35 11 10 15.0 54 33 109 12 43 42 7 10 24.0 46 30 110 16 30 34 12 15 14.0 85 52 111 16 31 33 14 11 15.0 74 44 112 17 32 41 11 13 11.0 88 55 113 13 32 33 11 14 15.0 38 11 114 12 37 34 10 18 12.0 76 47 115 18 37 32 13 16 10.0 86 53 116 14 33 40 13 14 14.0 54 33 117 14 34 40 8 14 13.0 67 44 118 13 33 35 11 14 9.0 69 42 119 16 38 36 12 14 15.0 90 55 120 13 33 37 11 12 15.0 54 33 121 16 31 27 13 14 14.0 76 46 122 13 38 39 12 15 11.0 89 54 123 16 37 38 14 15 8.0 76 47 124 15 36 31 13 15 11.0 73 45 125 16 31 33 15 13 11.0 79 47 126 15 39 32 10 17 8.0 90 55 127 17 44 39 11 17 10.0 74 44 128 15 33 36 9 19 11.0 81 53 129 12 35 33 11 15 13.0 72 44 130 16 32 33 10 13 11.0 71 42 131 10 28 32 11 9 20.0 66 40 132 16 40 37 8 15 10.0 77 46 133 12 27 30 11 15 15.0 65 40 134 14 37 38 12 15 12.0 74 46 135 15 32 29 12 16 14.0 85 53 136 13 28 22 9 11 23.0 54 33 137 15 34 35 11 14 14.0 63 42 138 11 30 35 10 11 16.0 54 35 139 12 35 34 8 15 11.0 64 40 140 11 31 35 9 13 12.0 69 41 141 16 32 34 8 15 10.0 54 33 142 15 30 37 9 16 14.0 84 51 143 17 30 35 15 14 12.0 86 53 144 16 31 23 11 15 12.0 77 46 145 10 40 31 8 16 11.0 89 55 146 18 32 27 13 16 12.0 76 47 147 13 36 36 12 11 13.0 60 38 148 16 32 31 12 12 11.0 75 46 149 13 35 32 9 9 19.0 73 46 150 10 38 39 7 16 12.0 85 53 151 15 42 37 13 13 17.0 79 47 152 16 34 38 9 16 9.0 71 41 153 16 35 39 6 12 12.0 72 44 154 14 38 34 8 9 19.0 69 43 155 10 33 31 8 13 18.0 78 51 156 17 36 32 15 13 15.0 54 33 157 13 32 37 6 14 14.0 69 43 158 15 33 36 9 19 11.0 81 53 159 16 34 32 11 13 9.0 84 51 160 12 32 38 8 12 18.0 84 50 161 13 34 36 8 13 16.0 69 46 162 13 27 26 10 10 24.0 66 43 163 12 31 26 8 14 14.0 81 47 164 17 38 33 14 16 20.0 82 50 165 15 34 39 10 10 18.0 72 43 166 10 24 30 8 11 23.0 54 33 167 14 30 33 11 14 12.0 78 48 168 11 26 25 12 12 14.0 74 44 169 13 34 38 12 9 16.0 82 50 170 16 27 37 12 9 18.0 73 41 171 12 37 31 5 11 20.0 55 34 172 16 36 37 12 16 12.0 72 44 173 12 41 35 10 9 12.0 78 47 174 9 29 25 7 13 17.0 59 35 175 12 36 28 12 16 13.0 72 44 176 15 32 35 11 13 9.0 78 44 177 12 37 33 8 9 16.0 68 43 178 12 30 30 9 12 18.0 69 41 179 14 31 31 10 16 10.0 67 41 180 12 38 37 9 11 14.0 74 42 181 16 36 36 12 14 11.0 54 33 182 11 35 30 6 13 9.0 67 41 183 19 31 36 15 15 11.0 70 44 184 15 38 32 12 14 10.0 80 48 185 8 22 28 12 16 11.0 89 55 186 16 32 36 12 13 19.0 76 44 187 17 36 34 11 14 14.0 74 43 188 12 39 31 7 15 12.0 87 52 189 11 28 28 7 13 14.0 54 30 190 11 32 36 5 11 21.0 61 39 191 14 32 36 12 11 13.0 38 11 192 16 38 40 12 14 10.0 75 44 193 12 32 33 3 15 15.0 69 42 194 16 35 37 11 11 16.0 62 41 195 13 32 32 10 15 14.0 72 44 196 15 37 38 12 12 12.0 70 44 197 16 34 31 9 14 19.0 79 48 198 16 33 37 12 14 15.0 87 53 199 14 33 33 9 8 19.0 62 37 200 16 26 32 12 13 13.0 77 44 201 16 30 30 12 9 17.0 69 44 202 14 24 30 10 15 12.0 69 40 203 11 34 31 9 17 11.0 75 42 204 12 34 32 12 13 14.0 54 35 205 15 33 34 8 15 11.0 72 43 206 15 34 36 11 15 13.0 74 45 207 16 35 37 11 14 12.0 85 55 208 16 35 36 12 16 15.0 52 31 209 11 36 33 10 13 14.0 70 44 210 15 34 33 10 16 12.0 84 50 211 12 34 33 12 9 17.0 64 40 212 12 41 44 12 16 11.0 84 53 213 15 32 39 11 11 18.0 87 54 214 15 30 32 8 10 13.0 79 49 215 16 35 35 12 11 17.0 67 40 216 14 28 25 10 15 13.0 65 41 217 17 33 35 11 17 11.0 85 52 218 14 39 34 10 14 12.0 83 52 219 13 36 35 8 8 22.0 61 36 220 15 36 39 12 15 14.0 82 52 221 13 35 33 12 11 12.0 76 46 222 14 38 36 10 16 12.0 58 31 223 15 33 32 12 10 17.0 72 44 224 12 31 32 9 15 9.0 72 44 225 13 34 36 9 9 21.0 38 11 226 8 32 36 6 16 10.0 78 46 227 14 31 32 10 19 11.0 54 33 228 14 33 34 9 12 12.0 63 34 229 11 34 33 9 8 23.0 66 42 230 12 34 35 9 11 13.0 70 43 231 13 34 30 6 14 12.0 71 43 232 10 33 38 10 9 16.0 67 44 233 16 32 34 6 15 9.0 58 36 234 18 41 33 14 13 17.0 72 46 235 13 34 32 10 16 9.0 72 44 236 11 36 31 10 11 14.0 70 43 237 4 37 30 6 12 17.0 76 50 238 13 36 27 12 13 13.0 50 33 239 16 29 31 12 10 11.0 72 43 240 10 37 30 7 11 12.0 72 44 241 12 27 32 8 12 10.0 88 53 242 12 35 35 11 8 19.0 53 34 243 10 28 28 3 12 16.0 58 35 244 13 35 33 6 12 16.0 66 40 245 15 37 31 10 15 14.0 82 53 246 12 29 35 8 11 20.0 69 42 247 14 32 35 9 13 15.0 68 43 248 10 36 32 9 14 23.0 44 29 249 12 19 21 8 10 20.0 56 36 250 12 21 20 9 12 16.0 53 30 251 11 31 34 7 15 14.0 70 42 252 10 33 32 7 13 17.0 78 47 253 12 36 34 6 13 11.0 71 44 254 16 33 32 9 13 13.0 72 45 255 12 37 33 10 12 17.0 68 44 256 14 34 33 11 12 15.0 67 43 257 16 35 37 12 9 21.0 75 43 258 14 31 32 8 9 18.0 62 40 259 13 37 34 11 15 15.0 67 41 260 4 35 30 3 10 8.0 83 52 261 15 27 30 11 14 12.0 64 38 262 11 34 38 12 15 12.0 68 41 263 11 40 36 7 7 22.0 62 39 264 14 29 32 9 14 12.0 72 43 Month 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 9 10 9 11 9 12 9 13 9 14 9 15 9 16 9 17 9 18 9 19 9 20 9 21 9 22 9 23 9 24 9 25 9 26 9 27 9 28 9 29 9 30 9 31 9 32 9 33 9 34 9 35 9 36 9 37 9 38 9 39 9 40 9 41 9 42 9 43 9 44 9 45 9 46 9 47 9 48 9 49 9 50 9 51 9 52 9 53 9 54 9 55 9 56 9 57 9 58 9 59 9 60 9 61 9 62 9 63 9 64 9 65 9 66 10 67 10 68 10 69 10 70 10 71 10 72 10 73 10 74 10 75 10 76 10 77 10 78 10 79 10 80 10 81 10 82 10 83 10 84 10 85 10 86 10 87 10 88 10 89 10 90 10 91 10 92 10 93 10 94 10 95 10 96 10 97 10 98 10 99 10 100 10 101 10 102 10 103 10 104 10 105 10 106 10 107 10 108 10 109 10 110 10 111 10 112 10 113 10 114 10 115 10 116 10 117 10 118 10 119 10 120 10 121 10 122 10 123 10 124 10 125 10 126 10 127 10 128 10 129 10 130 10 131 10 132 10 133 10 134 10 135 10 136 10 137 10 138 10 139 10 140 10 141 10 142 10 143 10 144 10 145 10 146 10 147 10 148 10 149 10 150 10 151 10 152 10 153 10 154 9 155 10 156 10 157 10 158 10 159 10 160 10 161 10 162 11 163 11 164 11 165 11 166 11 167 11 168 11 169 11 170 11 171 11 172 11 173 11 174 11 175 11 176 11 177 11 178 11 179 11 180 11 181 11 182 11 183 11 184 11 185 11 186 11 187 11 188 11 189 11 190 11 191 11 192 11 193 11 194 11 195 11 196 11 197 11 198 11 199 11 200 11 201 11 202 11 203 11 204 11 205 11 206 11 207 11 208 11 209 11 210 11 211 11 212 11 213 11 214 11 215 11 216 11 217 11 218 11 219 11 220 11 221 11 222 11 223 11 224 11 225 11 226 11 227 11 228 11 229 11 230 11 231 11 232 11 233 11 234 11 235 11 236 11 237 11 238 11 239 11 240 11 241 11 242 11 243 11 244 11 245 11 246 11 247 11 248 11 249 11 250 11 251 11 252 11 253 11 254 11 255 11 256 11 257 11 258 11 259 11 260 11 261 11 262 11 263 11 264 11 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software Happiness Depression 8.35047 0.03478 0.04334 0.57606 0.07962 -0.02610 Sport1 Sport2 Month 0.02829 -0.03265 -0.40460 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9727 -1.1695 0.2867 1.1592 4.6323 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.35047 2.61698 3.191 0.0016 ** Connected 0.03478 0.03476 1.001 0.3179 Separate 0.04334 0.03528 1.228 0.2204 Software 0.57606 0.05277 10.916 <2e-16 *** Happiness 0.07962 0.05784 1.376 0.1699 Depression -0.02610 0.04244 -0.615 0.5390 Sport1 0.02829 0.03777 0.749 0.4545 Sport2 -0.03265 0.05610 -0.582 0.5611 Month -0.40460 0.15952 -2.536 0.0118 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.865 on 255 degrees of freedom Multiple R-squared: 0.441, Adjusted R-squared: 0.4235 F-statistic: 25.15 on 8 and 255 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,] 0.249272273 0.498544547 0.7507277 [2,] 0.168954109 0.337908219 0.8310459 [3,] 0.188652474 0.377304949 0.8113475 [4,] 0.113822200 0.227644399 0.8861778 [5,] 0.077358179 0.154716357 0.9226418 [6,] 0.111089922 0.222179843 0.8889101 [7,] 0.350297244 0.700594488 0.6497028 [8,] 0.273076148 0.546152296 0.7269239 [9,] 0.200364780 0.400729560 0.7996352 [10,] 0.147329038 0.294658076 0.8526710 [11,] 0.142429822 0.284859644 0.8575702 [12,] 0.329613611 0.659227221 0.6703864 [13,] 0.396108111 0.792216222 0.6038919 [14,] 0.364643783 0.729287565 0.6353562 [15,] 0.343013785 0.686027571 0.6569862 [16,] 0.419522217 0.839044434 0.5804778 [17,] 0.431039270 0.862078539 0.5689607 [18,] 0.411178258 0.822356515 0.5888217 [19,] 0.444562864 0.889125729 0.5554371 [20,] 0.389182053 0.778364107 0.6108179 [21,] 0.346620753 0.693241507 0.6533792 [22,] 0.316677131 0.633354262 0.6833229 [23,] 0.275674898 0.551349797 0.7243251 [24,] 0.233139525 0.466279050 0.7668605 [25,] 0.363623900 0.727247799 0.6363761 [26,] 0.400480018 0.800960036 0.5995200 [27,] 0.392518457 0.785036914 0.6074815 [28,] 0.420646898 0.841293796 0.5793531 [29,] 0.394036669 0.788073338 0.6059633 [30,] 0.352608511 0.705217021 0.6473915 [31,] 0.317815561 0.635631122 0.6821844 [32,] 0.330602998 0.661205997 0.6693970 [33,] 0.284193040 0.568386080 0.7158070 [34,] 0.259307354 0.518614708 0.7406926 [35,] 0.457373137 0.914746273 0.5426269 [36,] 0.613015043 0.773969915 0.3869850 [37,] 0.564790965 0.870418070 0.4352090 [38,] 0.550115831 0.899768338 0.4498842 [39,] 0.538886872 0.922226256 0.4611131 [40,] 0.496029188 0.992058376 0.5039708 [41,] 0.448976552 0.897953103 0.5510234 [42,] 0.469409971 0.938819942 0.5305900 [43,] 0.449833836 0.899667672 0.5501662 [44,] 0.452066183 0.904132367 0.5479338 [45,] 0.420708590 0.841417180 0.5792914 [46,] 0.377122147 0.754244294 0.6228779 [47,] 0.343504288 0.687008576 0.6564957 [48,] 0.305381737 0.610763474 0.6946183 [49,] 0.304715665 0.609431330 0.6952843 [50,] 0.274885364 0.549770729 0.7251146 [51,] 0.243391513 0.486783027 0.7566085 [52,] 0.213205556 0.426411111 0.7867944 [53,] 0.184850255 0.369700511 0.8151497 [54,] 0.159612090 0.319224181 0.8403879 [55,] 0.135701683 0.271403365 0.8642983 [56,] 0.118078555 0.236157110 0.8819214 [57,] 0.147491712 0.294983424 0.8525083 [58,] 0.347255539 0.694511077 0.6527445 [59,] 0.309383521 0.618767042 0.6906165 [60,] 0.466018474 0.932036948 0.5339815 [61,] 0.427694883 0.855389767 0.5723051 [62,] 0.415756314 0.831512629 0.5842437 [63,] 0.394545849 0.789091698 0.6054542 [64,] 0.359444377 0.718888754 0.6405556 [65,] 0.407073803 0.814147605 0.5929262 [66,] 0.374300585 0.748601169 0.6256994 [67,] 0.346591345 0.693182689 0.6534087 [68,] 0.375124097 0.750248194 0.6248759 [69,] 0.344523837 0.689047674 0.6554762 [70,] 0.311892770 0.623785539 0.6881072 [71,] 0.277800972 0.555601945 0.7221990 [72,] 0.254136867 0.508273734 0.7458631 [73,] 0.223381796 0.446763591 0.7766182 [74,] 0.213624304 0.427248607 0.7863757 [75,] 0.186095184 0.372190367 0.8139048 [76,] 0.162784483 0.325568967 0.8372155 [77,] 0.147969314 0.295938628 0.8520307 [78,] 0.127536796 0.255073591 0.8724632 [79,] 0.129326333 0.258652666 0.8706737 [80,] 0.111535332 0.223070664 0.8884647 [81,] 0.096128398 0.192256796 0.9038716 [82,] 0.082550296 0.165100591 0.9174497 [83,] 0.087742539 0.175485078 0.9122575 [84,] 0.078087986 0.156175972 0.9219120 [85,] 0.065829406 0.131658811 0.9341706 [86,] 0.068906807 0.137813615 0.9310932 [87,] 0.057357952 0.114715903 0.9426420 [88,] 0.047590636 0.095181272 0.9524094 [89,] 0.042543858 0.085087717 0.9574561 [90,] 0.036985931 0.073971862 0.9630141 [91,] 0.042000243 0.084000487 0.9579998 [92,] 0.035665390 0.071330780 0.9643346 [93,] 0.031666520 0.063333040 0.9683335 [94,] 0.038809498 0.077618995 0.9611905 [95,] 0.033580103 0.067160206 0.9664199 [96,] 0.027290386 0.054580772 0.9727096 [97,] 0.026013415 0.052026830 0.9739866 [98,] 0.021222130 0.042444260 0.9787779 [99,] 0.017203818 0.034407637 0.9827962 [100,] 0.013632186 0.027264372 0.9863678 [101,] 0.013622757 0.027245513 0.9863772 [102,] 0.012487296 0.024974592 0.9875127 [103,] 0.019146176 0.038292352 0.9808538 [104,] 0.017761714 0.035523429 0.9822383 [105,] 0.017603646 0.035207292 0.9823964 [106,] 0.014365082 0.028730163 0.9856349 [107,] 0.014933316 0.029866632 0.9850667 [108,] 0.012069116 0.024138232 0.9879309 [109,] 0.011017215 0.022034430 0.9889828 [110,] 0.008849280 0.017698560 0.9911507 [111,] 0.014052017 0.028104034 0.9859480 [112,] 0.012043124 0.024086249 0.9879569 [113,] 0.010823044 0.021646088 0.9891770 [114,] 0.009037311 0.018074622 0.9909627 [115,] 0.007284510 0.014569021 0.9927155 [116,] 0.006399142 0.012798283 0.9936009 [117,] 0.005106591 0.010213182 0.9948934 [118,] 0.007833751 0.015667501 0.9921662 [119,] 0.008079630 0.016159261 0.9919204 [120,] 0.018094933 0.036189866 0.9819051 [121,] 0.020368964 0.040737929 0.9796310 [122,] 0.023261761 0.046523522 0.9767382 [123,] 0.023256224 0.046512448 0.9767438 [124,] 0.019036038 0.038072076 0.9809640 [125,] 0.015637249 0.031274498 0.9843628 [126,] 0.012443014 0.024886029 0.9875570 [127,] 0.016577269 0.033154538 0.9834227 [128,] 0.014924063 0.029848125 0.9850759 [129,] 0.019031155 0.038062311 0.9809688 [130,] 0.026112930 0.052225859 0.9738871 [131,] 0.023296615 0.046593229 0.9767034 [132,] 0.019013388 0.038026776 0.9809866 [133,] 0.018077456 0.036154912 0.9819225 [134,] 0.035502616 0.071005232 0.9644974 [135,] 0.038422197 0.076844393 0.9615778 [136,] 0.043198752 0.086397504 0.9568012 [137,] 0.036964585 0.073929170 0.9630354 [138,] 0.030326830 0.060653660 0.9696732 [139,] 0.045729337 0.091458674 0.9542707 [140,] 0.041381104 0.082762209 0.9586189 [141,] 0.041087092 0.082174183 0.9589129 [142,] 0.071708180 0.143416360 0.9282918 [143,] 0.063159712 0.126319425 0.9368403 [144,] 0.077112812 0.154225624 0.9228872 [145,] 0.064814840 0.129629680 0.9351852 [146,] 0.056421290 0.112842581 0.9435787 [147,] 0.047850295 0.095700591 0.9521497 [148,] 0.045457012 0.090914024 0.9545430 [149,] 0.039035534 0.078071068 0.9609645 [150,] 0.031868811 0.063737623 0.9681312 [151,] 0.026187933 0.052375865 0.9738121 [152,] 0.021581342 0.043162684 0.9784187 [153,] 0.018252422 0.036504845 0.9817476 [154,] 0.016138330 0.032276661 0.9838617 [155,] 0.016251666 0.032503332 0.9837483 [156,] 0.012956801 0.025913601 0.9870432 [157,] 0.018479417 0.036958835 0.9815206 [158,] 0.017894260 0.035788519 0.9821057 [159,] 0.017018918 0.034037836 0.9829811 [160,] 0.016561667 0.033123335 0.9834383 [161,] 0.013436048 0.026872095 0.9865640 [162,] 0.012958356 0.025916711 0.9870416 [163,] 0.013877447 0.027754894 0.9861226 [164,] 0.017240763 0.034481526 0.9827592 [165,] 0.013895174 0.027790348 0.9861048 [166,] 0.011051999 0.022103998 0.9889480 [167,] 0.008870743 0.017741487 0.9911293 [168,] 0.006883216 0.013766432 0.9931168 [169,] 0.005774436 0.011548871 0.9942256 [170,] 0.004853936 0.009707871 0.9951461 [171,] 0.003788799 0.007577598 0.9962112 [172,] 0.004241499 0.008482999 0.9957585 [173,] 0.003270547 0.006541094 0.9967295 [174,] 0.072840711 0.145681421 0.9271593 [175,] 0.063836939 0.127673878 0.9361631 [176,] 0.075551377 0.151102755 0.9244486 [177,] 0.064037139 0.128074277 0.9359629 [178,] 0.053654156 0.107308313 0.9463458 [179,] 0.044308331 0.088616662 0.9556917 [180,] 0.038733534 0.077467069 0.9612665 [181,] 0.032480446 0.064960891 0.9675196 [182,] 0.038777019 0.077554038 0.9612230 [183,] 0.042580372 0.085160744 0.9574196 [184,] 0.035541480 0.071082959 0.9644585 [185,] 0.029143015 0.058286030 0.9708570 [186,] 0.039343221 0.078686442 0.9606568 [187,] 0.032175175 0.064350349 0.9678248 [188,] 0.029969169 0.059938339 0.9700308 [189,] 0.025468536 0.050937072 0.9745315 [190,] 0.024209926 0.048419852 0.9757901 [191,] 0.020183007 0.040366014 0.9798170 [192,] 0.025527130 0.051054260 0.9744729 [193,] 0.028059869 0.056119738 0.9719401 [194,] 0.031222434 0.062444868 0.9687776 [195,] 0.024571621 0.049143242 0.9754284 [196,] 0.024269913 0.048539826 0.9757301 [197,] 0.019969288 0.039938576 0.9800307 [198,] 0.021672832 0.043345664 0.9783272 [199,] 0.017456347 0.034912694 0.9825437 [200,] 0.018929280 0.037858560 0.9810707 [201,] 0.029270319 0.058540638 0.9707297 [202,] 0.022869354 0.045738709 0.9771306 [203,] 0.033024021 0.066048042 0.9669760 [204,] 0.028456855 0.056913710 0.9715431 [205,] 0.022084984 0.044169968 0.9779150 [206,] 0.024359112 0.048718225 0.9756409 [207,] 0.020489589 0.040979178 0.9795104 [208,] 0.018320551 0.036641101 0.9816794 [209,] 0.013754798 0.027509597 0.9862452 [210,] 0.011927236 0.023854471 0.9880728 [211,] 0.008581221 0.017162442 0.9914188 [212,] 0.006346574 0.012693148 0.9936534 [213,] 0.004964717 0.009929434 0.9950353 [214,] 0.004294646 0.008589292 0.9957054 [215,] 0.009946292 0.019892583 0.9900537 [216,] 0.007534384 0.015068767 0.9924656 [217,] 0.005373386 0.010746773 0.9946266 [218,] 0.003807178 0.007614357 0.9961928 [219,] 0.002664712 0.005329424 0.9973353 [220,] 0.002671582 0.005343163 0.9973284 [221,] 0.004943364 0.009886728 0.9950566 [222,] 0.025946735 0.051893471 0.9740533 [223,] 0.038457941 0.076915881 0.9615421 [224,] 0.027718788 0.055437577 0.9722812 [225,] 0.023961814 0.047923628 0.9760382 [226,] 0.187771175 0.375542351 0.8122288 [227,] 0.147044861 0.294089722 0.8529551 [228,] 0.130216166 0.260432332 0.8697838 [229,] 0.098868434 0.197736868 0.9011316 [230,] 0.076600142 0.153200283 0.9233999 [231,] 0.061463418 0.122926836 0.9385366 [232,] 0.056209776 0.112419552 0.9437902 [233,] 0.103562676 0.207125352 0.8964373 [234,] 0.090783773 0.181567545 0.9092162 [235,] 0.069638293 0.139276586 0.9303617 [236,] 0.046529644 0.093059287 0.9534704 [237,] 0.037806000 0.075612000 0.9621940 [238,] 0.034074745 0.068149490 0.9659253 [239,] 0.041448869 0.082897738 0.9585511 [240,] 0.021822448 0.043644896 0.9781776 [241,] 0.054742676 0.109485353 0.9452573 > postscript(file="/var/fisher/rcomp/tmp/1thhz1383489303.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2gnid1383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/31flj1383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/451281383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5gxt41383489303.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 -2.950927427 0.381817133 2.083335628 3.127025193 -2.358051988 -1.736016272 7 8 9 10 11 12 3.846299991 -2.007756051 -1.948445497 0.701963529 1.137382762 -0.101807701 13 14 15 16 17 18 0.610689034 0.592021392 -1.004395063 -0.379815924 0.489441182 3.616619183 19 20 21 22 23 24 2.625165134 0.506467834 0.697230535 0.958823175 2.546938147 1.010762105 25 26 27 28 29 30 0.833157851 0.778791226 1.028275393 -1.763968014 0.233833016 -0.077895767 31 32 33 34 35 36 -0.746027284 -0.737377122 -0.797418619 0.071499880 -1.602200306 -2.921926401 37 38 39 40 41 42 -3.014672990 -1.789991690 1.437350999 1.527886673 1.272815400 -1.828353335 43 44 45 46 47 48 2.425987887 -0.166310878 -0.492903544 -4.579946776 -2.604228986 -0.194229434 49 50 51 52 53 54 0.370906087 -1.730933528 -1.147151290 -0.221677409 -3.145340373 0.130447599 55 56 57 58 59 60 -2.364552178 1.599325081 -0.010771712 0.412355641 -0.248651715 1.699039550 61 62 63 64 65 66 0.431959429 0.151906615 -0.732096822 -0.849165795 0.546781501 1.213271705 67 68 69 70 71 72 1.933602545 3.776399158 -3.703958865 0.552161863 -3.029195508 -0.699009795 73 74 75 76 77 78 1.178461283 0.952614163 0.938146133 3.731342200 -0.274453484 1.725904951 79 80 81 82 83 84 -2.033478554 1.075230233 0.434465248 0.463024793 -0.556378965 0.290823812 85 86 87 88 89 90 2.024351240 -0.008573324 0.779169251 1.293406531 0.645248973 -1.705080737 91 92 93 94 95 96 0.321735886 0.256979711 -0.019563985 -1.963598029 1.302627336 0.316937043 97 98 99 100 101 102 2.417701080 0.231489430 -0.319858082 -1.024703834 1.421202664 2.531232510 103 104 105 106 107 108 0.947695374 1.043666722 -1.801167422 1.341788163 0.285054577 1.734830472 109 110 111 112 113 114 -0.144502540 0.729968270 -0.019022897 2.027100876 -1.623526260 -2.561106515 115 116 117 118 119 120 1.817433595 -1.874239203 0.936575639 -1.766415230 0.427246196 -1.406745685 121 122 123 124 125 126 0.560835235 -2.891167271 -0.904258187 -0.892153489 -0.902236171 0.296367937 127 128 129 130 131 132 1.388689572 1.016139203 -2.744083838 2.006357737 -3.757672592 2.482367886 133 134 135 136 137 138 -2.216163118 -1.623796371 -0.169866756 0.857844295 0.499053080 -2.468624925 139 140 141 142 143 144 -1.015724374 -2.419446534 3.116868495 1.244149429 -0.009779507 1.726205342 145 146 147 148 149 150 -3.356731563 2.347253794 -2.022884772 1.037980818 0.122740838 -2.983866911 151 152 153 154 155 156 -1.149480124 1.972426862 4.088948985 1.118379703 -2.511077298 0.321735886 157 158 159 160 161 162 1.225159115 1.016139203 1.277952243 -0.902448709 0.276588644 0.640555249 163 164 165 166 167 168 -0.219714058 0.844074307 1.507288989 -1.369085649 -0.151183747 -3.047381403 169 170 171 172 173 174 -1.628420757 1.671359890 1.789630485 0.770582649 -1.678958916 -2.142243663 175 176 177 178 179 180 -2.813249493 0.563279913 -0.044313330 -0.527125898 0.347961979 -1.242363629 181 182 183 184 185 186 1.097128568 -0.230816591 2.369755033 -0.070957371 -6.500257228 1.261477725 187 188 189 190 191 192 2.598884232 -0.276935001 -0.337592691 0.766460632 -0.738347363 0.593164124 193 194 195 196 197 198 2.445135861 2.068898527 -0.589629361 0.067529462 3.102911981 0.981990529 199 200 201 202 203 204 1.650521446 1.458638605 2.055406796 0.677381539 -2.427517795 -2.436710987 205 206 207 208 209 210 2.330073087 0.541347575 1.532062754 1.068353080 -2.556278024 1.022060198 211 212 213 214 215 216 -2.202902206 -3.778443865 0.855979821 2.969296732 1.431517041 0.926865366 217 218 219 220 221 222 2.325387625 0.057640177 1.109497163 -0.205965436 -1.671017171 -0.131920566 223 224 225 226 227 228 0.699881753 -1.109293774 0.288333593 -3.747213134 0.198419826 0.979732572 229 230 231 232 233 234 -1.229759183 -0.896838278 1.754791518 -3.213065762 4.632282599 2.052583830 235 236 237 238 239 240 -0.869328864 -2.343002739 -6.972696730 -1.267817848 1.693092913 -1.682392341 241 242 243 244 245 246 -0.287911980 -1.501192112 1.148577467 1.897137210 1.290751900 0.031494268 247 248 249 250 251 252 1.122268014 -2.535800023 1.237586914 0.260609056 -0.922062430 -1.730463173 253 254 255 256 257 258 0.598033801 3.117443737 -1.376552283 0.095170915 1.480120220 2.331720115 259 260 261 262 263 264 -1.356690157 -5.383345806 1.152740672 -4.108360954 -0.347629194 1.085550529 > postscript(file="/var/fisher/rcomp/tmp/65rtk1383489303.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 -2.950927427 NA 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 264 NA 1.085550529 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.381817133 -2.950927427 [2,] 2.083335628 0.381817133 [3,] 3.127025193 2.083335628 [4,] -2.358051988 3.127025193 [5,] -1.736016272 -2.358051988 [6,] 3.846299991 -1.736016272 [7,] -2.007756051 3.846299991 [8,] -1.948445497 -2.007756051 [9,] 0.701963529 -1.948445497 [10,] 1.137382762 0.701963529 [11,] -0.101807701 1.137382762 [12,] 0.610689034 -0.101807701 [13,] 0.592021392 0.610689034 [14,] -1.004395063 0.592021392 [15,] -0.379815924 -1.004395063 [16,] 0.489441182 -0.379815924 [17,] 3.616619183 0.489441182 [18,] 2.625165134 3.616619183 [19,] 0.506467834 2.625165134 [20,] 0.697230535 0.506467834 [21,] 0.958823175 0.697230535 [22,] 2.546938147 0.958823175 [23,] 1.010762105 2.546938147 [24,] 0.833157851 1.010762105 [25,] 0.778791226 0.833157851 [26,] 1.028275393 0.778791226 [27,] -1.763968014 1.028275393 [28,] 0.233833016 -1.763968014 [29,] -0.077895767 0.233833016 [30,] -0.746027284 -0.077895767 [31,] -0.737377122 -0.746027284 [32,] -0.797418619 -0.737377122 [33,] 0.071499880 -0.797418619 [34,] -1.602200306 0.071499880 [35,] -2.921926401 -1.602200306 [36,] -3.014672990 -2.921926401 [37,] -1.789991690 -3.014672990 [38,] 1.437350999 -1.789991690 [39,] 1.527886673 1.437350999 [40,] 1.272815400 1.527886673 [41,] -1.828353335 1.272815400 [42,] 2.425987887 -1.828353335 [43,] -0.166310878 2.425987887 [44,] -0.492903544 -0.166310878 [45,] -4.579946776 -0.492903544 [46,] -2.604228986 -4.579946776 [47,] -0.194229434 -2.604228986 [48,] 0.370906087 -0.194229434 [49,] -1.730933528 0.370906087 [50,] -1.147151290 -1.730933528 [51,] -0.221677409 -1.147151290 [52,] -3.145340373 -0.221677409 [53,] 0.130447599 -3.145340373 [54,] -2.364552178 0.130447599 [55,] 1.599325081 -2.364552178 [56,] -0.010771712 1.599325081 [57,] 0.412355641 -0.010771712 [58,] -0.248651715 0.412355641 [59,] 1.699039550 -0.248651715 [60,] 0.431959429 1.699039550 [61,] 0.151906615 0.431959429 [62,] -0.732096822 0.151906615 [63,] -0.849165795 -0.732096822 [64,] 0.546781501 -0.849165795 [65,] 1.213271705 0.546781501 [66,] 1.933602545 1.213271705 [67,] 3.776399158 1.933602545 [68,] -3.703958865 3.776399158 [69,] 0.552161863 -3.703958865 [70,] -3.029195508 0.552161863 [71,] -0.699009795 -3.029195508 [72,] 1.178461283 -0.699009795 [73,] 0.952614163 1.178461283 [74,] 0.938146133 0.952614163 [75,] 3.731342200 0.938146133 [76,] -0.274453484 3.731342200 [77,] 1.725904951 -0.274453484 [78,] -2.033478554 1.725904951 [79,] 1.075230233 -2.033478554 [80,] 0.434465248 1.075230233 [81,] 0.463024793 0.434465248 [82,] -0.556378965 0.463024793 [83,] 0.290823812 -0.556378965 [84,] 2.024351240 0.290823812 [85,] -0.008573324 2.024351240 [86,] 0.779169251 -0.008573324 [87,] 1.293406531 0.779169251 [88,] 0.645248973 1.293406531 [89,] -1.705080737 0.645248973 [90,] 0.321735886 -1.705080737 [91,] 0.256979711 0.321735886 [92,] -0.019563985 0.256979711 [93,] -1.963598029 -0.019563985 [94,] 1.302627336 -1.963598029 [95,] 0.316937043 1.302627336 [96,] 2.417701080 0.316937043 [97,] 0.231489430 2.417701080 [98,] -0.319858082 0.231489430 [99,] -1.024703834 -0.319858082 [100,] 1.421202664 -1.024703834 [101,] 2.531232510 1.421202664 [102,] 0.947695374 2.531232510 [103,] 1.043666722 0.947695374 [104,] -1.801167422 1.043666722 [105,] 1.341788163 -1.801167422 [106,] 0.285054577 1.341788163 [107,] 1.734830472 0.285054577 [108,] -0.144502540 1.734830472 [109,] 0.729968270 -0.144502540 [110,] -0.019022897 0.729968270 [111,] 2.027100876 -0.019022897 [112,] -1.623526260 2.027100876 [113,] -2.561106515 -1.623526260 [114,] 1.817433595 -2.561106515 [115,] -1.874239203 1.817433595 [116,] 0.936575639 -1.874239203 [117,] -1.766415230 0.936575639 [118,] 0.427246196 -1.766415230 [119,] -1.406745685 0.427246196 [120,] 0.560835235 -1.406745685 [121,] -2.891167271 0.560835235 [122,] -0.904258187 -2.891167271 [123,] -0.892153489 -0.904258187 [124,] -0.902236171 -0.892153489 [125,] 0.296367937 -0.902236171 [126,] 1.388689572 0.296367937 [127,] 1.016139203 1.388689572 [128,] -2.744083838 1.016139203 [129,] 2.006357737 -2.744083838 [130,] -3.757672592 2.006357737 [131,] 2.482367886 -3.757672592 [132,] -2.216163118 2.482367886 [133,] -1.623796371 -2.216163118 [134,] -0.169866756 -1.623796371 [135,] 0.857844295 -0.169866756 [136,] 0.499053080 0.857844295 [137,] -2.468624925 0.499053080 [138,] -1.015724374 -2.468624925 [139,] -2.419446534 -1.015724374 [140,] 3.116868495 -2.419446534 [141,] 1.244149429 3.116868495 [142,] -0.009779507 1.244149429 [143,] 1.726205342 -0.009779507 [144,] -3.356731563 1.726205342 [145,] 2.347253794 -3.356731563 [146,] -2.022884772 2.347253794 [147,] 1.037980818 -2.022884772 [148,] 0.122740838 1.037980818 [149,] -2.983866911 0.122740838 [150,] -1.149480124 -2.983866911 [151,] 1.972426862 -1.149480124 [152,] 4.088948985 1.972426862 [153,] 1.118379703 4.088948985 [154,] -2.511077298 1.118379703 [155,] 0.321735886 -2.511077298 [156,] 1.225159115 0.321735886 [157,] 1.016139203 1.225159115 [158,] 1.277952243 1.016139203 [159,] -0.902448709 1.277952243 [160,] 0.276588644 -0.902448709 [161,] 0.640555249 0.276588644 [162,] -0.219714058 0.640555249 [163,] 0.844074307 -0.219714058 [164,] 1.507288989 0.844074307 [165,] -1.369085649 1.507288989 [166,] -0.151183747 -1.369085649 [167,] -3.047381403 -0.151183747 [168,] -1.628420757 -3.047381403 [169,] 1.671359890 -1.628420757 [170,] 1.789630485 1.671359890 [171,] 0.770582649 1.789630485 [172,] -1.678958916 0.770582649 [173,] -2.142243663 -1.678958916 [174,] -2.813249493 -2.142243663 [175,] 0.563279913 -2.813249493 [176,] -0.044313330 0.563279913 [177,] -0.527125898 -0.044313330 [178,] 0.347961979 -0.527125898 [179,] -1.242363629 0.347961979 [180,] 1.097128568 -1.242363629 [181,] -0.230816591 1.097128568 [182,] 2.369755033 -0.230816591 [183,] -0.070957371 2.369755033 [184,] -6.500257228 -0.070957371 [185,] 1.261477725 -6.500257228 [186,] 2.598884232 1.261477725 [187,] -0.276935001 2.598884232 [188,] -0.337592691 -0.276935001 [189,] 0.766460632 -0.337592691 [190,] -0.738347363 0.766460632 [191,] 0.593164124 -0.738347363 [192,] 2.445135861 0.593164124 [193,] 2.068898527 2.445135861 [194,] -0.589629361 2.068898527 [195,] 0.067529462 -0.589629361 [196,] 3.102911981 0.067529462 [197,] 0.981990529 3.102911981 [198,] 1.650521446 0.981990529 [199,] 1.458638605 1.650521446 [200,] 2.055406796 1.458638605 [201,] 0.677381539 2.055406796 [202,] -2.427517795 0.677381539 [203,] -2.436710987 -2.427517795 [204,] 2.330073087 -2.436710987 [205,] 0.541347575 2.330073087 [206,] 1.532062754 0.541347575 [207,] 1.068353080 1.532062754 [208,] -2.556278024 1.068353080 [209,] 1.022060198 -2.556278024 [210,] -2.202902206 1.022060198 [211,] -3.778443865 -2.202902206 [212,] 0.855979821 -3.778443865 [213,] 2.969296732 0.855979821 [214,] 1.431517041 2.969296732 [215,] 0.926865366 1.431517041 [216,] 2.325387625 0.926865366 [217,] 0.057640177 2.325387625 [218,] 1.109497163 0.057640177 [219,] -0.205965436 1.109497163 [220,] -1.671017171 -0.205965436 [221,] -0.131920566 -1.671017171 [222,] 0.699881753 -0.131920566 [223,] -1.109293774 0.699881753 [224,] 0.288333593 -1.109293774 [225,] -3.747213134 0.288333593 [226,] 0.198419826 -3.747213134 [227,] 0.979732572 0.198419826 [228,] -1.229759183 0.979732572 [229,] -0.896838278 -1.229759183 [230,] 1.754791518 -0.896838278 [231,] -3.213065762 1.754791518 [232,] 4.632282599 -3.213065762 [233,] 2.052583830 4.632282599 [234,] -0.869328864 2.052583830 [235,] -2.343002739 -0.869328864 [236,] -6.972696730 -2.343002739 [237,] -1.267817848 -6.972696730 [238,] 1.693092913 -1.267817848 [239,] -1.682392341 1.693092913 [240,] -0.287911980 -1.682392341 [241,] -1.501192112 -0.287911980 [242,] 1.148577467 -1.501192112 [243,] 1.897137210 1.148577467 [244,] 1.290751900 1.897137210 [245,] 0.031494268 1.290751900 [246,] 1.122268014 0.031494268 [247,] -2.535800023 1.122268014 [248,] 1.237586914 -2.535800023 [249,] 0.260609056 1.237586914 [250,] -0.922062430 0.260609056 [251,] -1.730463173 -0.922062430 [252,] 0.598033801 -1.730463173 [253,] 3.117443737 0.598033801 [254,] -1.376552283 3.117443737 [255,] 0.095170915 -1.376552283 [256,] 1.480120220 0.095170915 [257,] 2.331720115 1.480120220 [258,] -1.356690157 2.331720115 [259,] -5.383345806 -1.356690157 [260,] 1.152740672 -5.383345806 [261,] -4.108360954 1.152740672 [262,] -0.347629194 -4.108360954 [263,] 1.085550529 -0.347629194 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 > 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/74rk81383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/83h591383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9ctyu1383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10oife1383489303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11xxhl1383489303.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12x2c01383489304.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13i1vy1383489304.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14l5c21383489304.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/152fto1383489304.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/16slsb1383489304.tab") + } > > try(system("convert tmp/1thhz1383489303.ps tmp/1thhz1383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/2gnid1383489303.ps tmp/2gnid1383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/31flj1383489303.ps tmp/31flj1383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/451281383489303.ps tmp/451281383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/5gxt41383489303.ps tmp/5gxt41383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/65rtk1383489303.ps tmp/65rtk1383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/74rk81383489303.ps tmp/74rk81383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/83h591383489303.ps tmp/83h591383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/9ctyu1383489303.ps tmp/9ctyu1383489303.png",intern=TRUE)) character(0) > try(system("convert tmp/10oife1383489303.ps tmp/10oife1383489303.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.112 1.917 14.027