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 = '6' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '6' > #'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 Depression Connected Separate Learning Software Happiness Sport1 Sport2 1 12.0 41 38 13 12 14 53 32 2 11.0 39 32 16 11 18 83 51 3 14.0 30 35 19 15 11 66 42 4 12.0 31 33 15 6 12 67 41 5 21.0 34 37 14 13 16 76 46 6 12.0 35 29 13 10 18 78 47 7 22.0 39 31 19 12 14 53 37 8 11.0 34 36 15 14 14 80 49 9 10.0 36 35 14 12 15 74 45 10 13.0 37 38 15 9 15 76 47 11 10.0 38 31 16 10 17 79 49 12 8.0 36 34 16 12 19 54 33 13 15.0 38 35 16 12 10 67 42 14 14.0 39 38 16 11 16 54 33 15 10.0 33 37 17 15 18 87 53 16 14.0 32 33 15 12 14 58 36 17 14.0 36 32 15 10 14 75 45 18 11.0 38 38 20 12 17 88 54 19 10.0 39 38 18 11 14 64 41 20 13.0 32 32 16 12 16 57 36 21 9.5 32 33 16 11 18 66 41 22 14.0 31 31 16 12 11 68 44 23 12.0 39 38 19 13 14 54 33 24 14.0 37 39 16 11 12 56 37 25 11.0 39 32 17 12 17 86 52 26 9.0 41 32 17 13 9 80 47 27 11.0 36 35 16 10 16 76 43 28 15.0 33 37 15 14 14 69 44 29 14.0 33 33 16 12 15 78 45 30 13.0 34 33 14 10 11 67 44 31 9.0 31 31 15 12 16 80 49 32 15.0 27 32 12 8 13 54 33 33 10.0 37 31 14 10 17 71 43 34 11.0 34 37 16 12 15 84 54 35 13.0 34 30 14 12 14 74 42 36 8.0 32 33 10 7 16 71 44 37 20.0 29 31 10 9 9 63 37 38 12.0 36 33 14 12 15 71 43 39 10.0 29 31 16 10 17 76 46 40 10.0 35 33 16 10 13 69 42 41 9.0 37 32 16 10 15 74 45 42 14.0 34 33 14 12 16 75 44 43 8.0 38 32 20 15 16 54 33 44 14.0 35 33 14 10 12 52 31 45 11.0 38 28 14 10 15 69 42 46 13.0 37 35 11 12 11 68 40 47 9.0 38 39 14 13 15 65 43 48 11.0 33 34 15 11 15 75 46 49 15.0 36 38 16 11 17 74 42 50 11.0 38 32 14 12 13 75 45 51 10.0 32 38 16 14 16 72 44 52 14.0 32 30 14 10 14 67 40 53 18.0 32 33 12 12 11 63 37 54 14.0 34 38 16 13 12 62 46 55 11.0 32 32 9 5 12 63 36 56 14.5 37 35 14 6 15 76 47 57 13.0 39 34 16 12 16 74 45 58 9.0 29 34 16 12 15 67 42 59 10.0 37 36 15 11 12 73 43 60 15.0 35 34 16 10 12 70 43 61 20.0 30 28 12 7 8 53 32 62 12.0 38 34 16 12 13 77 45 63 12.0 34 35 16 14 11 80 48 64 14.0 31 35 14 11 14 52 31 65 13.0 34 31 16 12 15 54 33 66 11.0 35 37 17 13 10 80 49 67 17.0 36 35 18 14 11 66 42 68 12.0 30 27 18 11 12 73 41 69 13.0 39 40 12 12 15 63 38 70 14.0 35 37 16 12 15 69 42 71 13.0 38 36 10 8 14 67 44 72 15.0 31 38 14 11 16 54 33 73 13.0 34 39 18 14 15 81 48 74 10.0 38 41 18 14 15 69 40 75 11.0 34 27 16 12 13 84 50 76 19.0 39 30 17 9 12 80 49 77 13.0 37 37 16 13 17 70 43 78 17.0 34 31 16 11 13 69 44 79 13.0 28 31 13 12 15 77 47 80 9.0 37 27 16 12 13 54 33 81 11.0 33 36 16 12 15 79 46 82 9.0 35 37 16 12 15 71 45 83 12.0 37 33 15 12 16 73 43 84 12.0 32 34 15 11 15 72 44 85 13.0 33 31 16 10 14 77 47 86 13.0 38 39 14 9 15 75 45 87 12.0 33 34 16 12 14 69 42 88 15.0 29 32 16 12 13 54 33 89 22.0 33 33 15 12 7 70 43 90 13.0 31 36 12 9 17 73 46 91 15.0 36 32 17 15 13 54 33 92 13.0 35 41 16 12 15 77 46 93 15.0 32 28 15 12 14 82 48 94 12.5 29 30 13 12 13 80 47 95 11.0 39 36 16 10 16 80 47 96 16.0 37 35 16 13 12 69 43 97 11.0 35 31 16 9 14 78 46 98 11.0 37 34 16 12 17 81 48 99 10.0 32 36 14 10 15 76 46 100 10.0 38 36 16 14 17 76 45 101 16.0 37 35 16 11 12 73 45 102 12.0 36 37 20 15 16 85 52 103 11.0 32 28 15 11 11 66 42 104 16.0 33 39 16 11 15 79 47 105 19.0 40 32 13 12 9 68 41 106 11.0 38 35 17 12 16 76 47 107 16.0 41 39 16 12 15 71 43 108 15.0 36 35 16 11 10 54 33 109 24.0 43 42 12 7 10 46 30 110 14.0 30 34 16 12 15 85 52 111 15.0 31 33 16 14 11 74 44 112 11.0 32 41 17 11 13 88 55 113 15.0 32 33 13 11 14 38 11 114 12.0 37 34 12 10 18 76 47 115 10.0 37 32 18 13 16 86 53 116 14.0 33 40 14 13 14 54 33 117 13.0 34 40 14 8 14 67 44 118 9.0 33 35 13 11 14 69 42 119 15.0 38 36 16 12 14 90 55 120 15.0 33 37 13 11 12 54 33 121 14.0 31 27 16 13 14 76 46 122 11.0 38 39 13 12 15 89 54 123 8.0 37 38 16 14 15 76 47 124 11.0 36 31 15 13 15 73 45 125 11.0 31 33 16 15 13 79 47 126 8.0 39 32 15 10 17 90 55 127 10.0 44 39 17 11 17 74 44 128 11.0 33 36 15 9 19 81 53 129 13.0 35 33 12 11 15 72 44 130 11.0 32 33 16 10 13 71 42 131 20.0 28 32 10 11 9 66 40 132 10.0 40 37 16 8 15 77 46 133 15.0 27 30 12 11 15 65 40 134 12.0 37 38 14 12 15 74 46 135 14.0 32 29 15 12 16 85 53 136 23.0 28 22 13 9 11 54 33 137 14.0 34 35 15 11 14 63 42 138 16.0 30 35 11 10 11 54 35 139 11.0 35 34 12 8 15 64 40 140 12.0 31 35 11 9 13 69 41 141 10.0 32 34 16 8 15 54 33 142 14.0 30 37 15 9 16 84 51 143 12.0 30 35 17 15 14 86 53 144 12.0 31 23 16 11 15 77 46 145 11.0 40 31 10 8 16 89 55 146 12.0 32 27 18 13 16 76 47 147 13.0 36 36 13 12 11 60 38 148 11.0 32 31 16 12 12 75 46 149 19.0 35 32 13 9 9 73 46 150 12.0 38 39 10 7 16 85 53 151 17.0 42 37 15 13 13 79 47 152 9.0 34 38 16 9 16 71 41 153 12.0 35 39 16 6 12 72 44 154 19.0 38 34 14 8 9 69 43 155 18.0 33 31 10 8 13 78 51 156 15.0 36 32 17 15 13 54 33 157 14.0 32 37 13 6 14 69 43 158 11.0 33 36 15 9 19 81 53 159 9.0 34 32 16 11 13 84 51 160 18.0 32 38 12 8 12 84 50 161 16.0 34 36 13 8 13 69 46 162 24.0 27 26 13 10 10 66 43 163 14.0 31 26 12 8 14 81 47 164 20.0 38 33 17 14 16 82 50 165 18.0 34 39 15 10 10 72 43 166 23.0 24 30 10 8 11 54 33 167 12.0 30 33 14 11 14 78 48 168 14.0 26 25 11 12 12 74 44 169 16.0 34 38 13 12 9 82 50 170 18.0 27 37 16 12 9 73 41 171 20.0 37 31 12 5 11 55 34 172 12.0 36 37 16 12 16 72 44 173 12.0 41 35 12 10 9 78 47 174 17.0 29 25 9 7 13 59 35 175 13.0 36 28 12 12 16 72 44 176 9.0 32 35 15 11 13 78 44 177 16.0 37 33 12 8 9 68 43 178 18.0 30 30 12 9 12 69 41 179 10.0 31 31 14 10 16 67 41 180 14.0 38 37 12 9 11 74 42 181 11.0 36 36 16 12 14 54 33 182 9.0 35 30 11 6 13 67 41 183 11.0 31 36 19 15 15 70 44 184 10.0 38 32 15 12 14 80 48 185 11.0 22 28 8 12 16 89 55 186 19.0 32 36 16 12 13 76 44 187 14.0 36 34 17 11 14 74 43 188 12.0 39 31 12 7 15 87 52 189 14.0 28 28 11 7 13 54 30 190 21.0 32 36 11 5 11 61 39 191 13.0 32 36 14 12 11 38 11 192 10.0 38 40 16 12 14 75 44 193 15.0 32 33 12 3 15 69 42 194 16.0 35 37 16 11 11 62 41 195 14.0 32 32 13 10 15 72 44 196 12.0 37 38 15 12 12 70 44 197 19.0 34 31 16 9 14 79 48 198 15.0 33 37 16 12 14 87 53 199 19.0 33 33 14 9 8 62 37 200 13.0 26 32 16 12 13 77 44 201 17.0 30 30 16 12 9 69 44 202 12.0 24 30 14 10 15 69 40 203 11.0 34 31 11 9 17 75 42 204 14.0 34 32 12 12 13 54 35 205 11.0 33 34 15 8 15 72 43 206 13.0 34 36 15 11 15 74 45 207 12.0 35 37 16 11 14 85 55 208 15.0 35 36 16 12 16 52 31 209 14.0 36 33 11 10 13 70 44 210 12.0 34 33 15 10 16 84 50 211 17.0 34 33 12 12 9 64 40 212 11.0 41 44 12 12 16 84 53 213 18.0 32 39 15 11 11 87 54 214 13.0 30 32 15 8 10 79 49 215 17.0 35 35 16 12 11 67 40 216 13.0 28 25 14 10 15 65 41 217 11.0 33 35 17 11 17 85 52 218 12.0 39 34 14 10 14 83 52 219 22.0 36 35 13 8 8 61 36 220 14.0 36 39 15 12 15 82 52 221 12.0 35 33 13 12 11 76 46 222 12.0 38 36 14 10 16 58 31 223 17.0 33 32 15 12 10 72 44 224 9.0 31 32 12 9 15 72 44 225 21.0 34 36 13 9 9 38 11 226 10.0 32 36 8 6 16 78 46 227 11.0 31 32 14 10 19 54 33 228 12.0 33 34 14 9 12 63 34 229 23.0 34 33 11 9 8 66 42 230 13.0 34 35 12 9 11 70 43 231 12.0 34 30 13 6 14 71 43 232 16.0 33 38 10 10 9 67 44 233 9.0 32 34 16 6 15 58 36 234 17.0 41 33 18 14 13 72 46 235 9.0 34 32 13 10 16 72 44 236 14.0 36 31 11 10 11 70 43 237 17.0 37 30 4 6 12 76 50 238 13.0 36 27 13 12 13 50 33 239 11.0 29 31 16 12 10 72 43 240 12.0 37 30 10 7 11 72 44 241 10.0 27 32 12 8 12 88 53 242 19.0 35 35 12 11 8 53 34 243 16.0 28 28 10 3 12 58 35 244 16.0 35 33 13 6 12 66 40 245 14.0 37 31 15 10 15 82 53 246 20.0 29 35 12 8 11 69 42 247 15.0 32 35 14 9 13 68 43 248 23.0 36 32 10 9 14 44 29 249 20.0 19 21 12 8 10 56 36 250 16.0 21 20 12 9 12 53 30 251 14.0 31 34 11 7 15 70 42 252 17.0 33 32 10 7 13 78 47 253 11.0 36 34 12 6 13 71 44 254 13.0 33 32 16 9 13 72 45 255 17.0 37 33 12 10 12 68 44 256 15.0 34 33 14 11 12 67 43 257 21.0 35 37 16 12 9 75 43 258 18.0 31 32 14 8 9 62 40 259 15.0 37 34 13 11 15 67 41 260 8.0 35 30 4 3 10 83 52 261 12.0 27 30 15 11 14 64 38 262 12.0 34 38 11 12 15 68 41 263 22.0 40 36 11 7 7 62 39 264 12.0 29 32 14 9 14 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 Learning Software Happiness 24.475536 -0.027795 0.003352 -0.056755 -0.005343 -0.679487 Sport1 Sport2 Month -0.158205 0.155997 0.440018 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.3864 -1.8147 -0.2068 1.5693 9.9688 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 24.475536 3.624457 6.753 9.74e-11 *** Connected -0.027795 0.051321 -0.542 0.58858 Separate 0.003352 0.052176 0.064 0.94883 Learning -0.056755 0.092273 -0.615 0.53905 Software -0.005343 0.094258 -0.057 0.95484 Happiness -0.679487 0.074290 -9.146 < 2e-16 *** Sport1 -0.158205 0.054863 -2.884 0.00427 ** Sport2 0.155997 0.082197 1.898 0.05885 . Month 0.440018 0.236568 1.860 0.06404 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.75 on 255 degrees of freedom Multiple R-squared: 0.391, Adjusted R-squared: 0.3719 F-statistic: 20.46 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,] 0.97883524 0.04232951 0.02116476 [2,] 0.97328791 0.05342417 0.02671209 [3,] 0.97962883 0.04074234 0.02037117 [4,] 0.96454920 0.07090161 0.03545080 [5,] 0.94071213 0.11857575 0.05928787 [6,] 0.97156728 0.05686544 0.02843272 [7,] 0.95942653 0.08114694 0.04057347 [8,] 0.97472657 0.05054686 0.02527343 [9,] 0.96143024 0.07713951 0.03856976 [10,] 0.95313576 0.09372848 0.04686424 [11,] 0.94890480 0.10219041 0.05109520 [12,] 0.92809092 0.14381817 0.07190908 [13,] 0.91229705 0.17540590 0.08770295 [14,] 0.88194296 0.23611408 0.11805704 [15,] 0.87956090 0.24087819 0.12043910 [16,] 0.88794366 0.22411268 0.11205634 [17,] 0.85528880 0.28942239 0.14471120 [18,] 0.88172538 0.23654925 0.11827462 [19,] 0.87181825 0.25636351 0.12818175 [20,] 0.87516487 0.24967027 0.12483513 [21,] 0.84825204 0.30349593 0.15174796 [22,] 0.82073449 0.35853102 0.17926551 [23,] 0.79825923 0.40348154 0.20174077 [24,] 0.76844836 0.46310329 0.23155164 [25,] 0.79503140 0.40993721 0.20496860 [26,] 0.86792297 0.26415406 0.13207703 [27,] 0.83733128 0.32533744 0.16266872 [28,] 0.80692402 0.38615195 0.19307598 [29,] 0.80336490 0.39327020 0.19663510 [30,] 0.79037679 0.41924643 0.20962321 [31,] 0.78005594 0.43988811 0.21994406 [32,] 0.82538615 0.34922771 0.17461385 [33,] 0.79201074 0.41597853 0.20798926 [34,] 0.75714464 0.48571073 0.24285536 [35,] 0.73063609 0.53872782 0.26936391 [36,] 0.77972619 0.44054761 0.22027381 [37,] 0.74698989 0.50602022 0.25301011 [38,] 0.79929906 0.40140188 0.20070094 [39,] 0.77343525 0.45312951 0.22656475 [40,] 0.76093815 0.47812371 0.23906185 [41,] 0.72879137 0.54241727 0.27120863 [42,] 0.73288517 0.53422967 0.26711483 [43,] 0.69965026 0.60069947 0.30034974 [44,] 0.70619166 0.58761667 0.29380834 [45,] 0.72245711 0.55508578 0.27754289 [46,] 0.70201837 0.59596326 0.29798163 [47,] 0.73363061 0.53273879 0.26636939 [48,] 0.74524244 0.50951513 0.25475756 [49,] 0.72269432 0.55461137 0.27730568 [50,] 0.73433327 0.53133345 0.26566673 [51,] 0.69836249 0.60327501 0.30163751 [52,] 0.67401167 0.65197665 0.32598833 [53,] 0.63446063 0.73107873 0.36553937 [54,] 0.59378341 0.81243319 0.40621659 [55,] 0.58124984 0.83750032 0.41875016 [56,] 0.60421916 0.79156168 0.39578084 [57,] 0.57560751 0.84878497 0.42439249 [58,] 0.53482817 0.93034367 0.46517183 [59,] 0.50640996 0.98718009 0.49359004 [60,] 0.46859885 0.93719771 0.53140115 [61,] 0.43924014 0.87848028 0.56075986 [62,] 0.40681536 0.81363073 0.59318464 [63,] 0.39130528 0.78261057 0.60869472 [64,] 0.36445566 0.72891133 0.63554434 [65,] 0.49768366 0.99536733 0.50231634 [66,] 0.46666067 0.93332134 0.53333933 [67,] 0.45859272 0.91718544 0.54140728 [68,] 0.42054932 0.84109865 0.57945068 [69,] 0.55421433 0.89157135 0.44578567 [70,] 0.52111379 0.95777242 0.47888621 [71,] 0.55777970 0.88444059 0.44222030 [72,] 0.51952630 0.96094740 0.48047370 [73,] 0.48297254 0.96594508 0.51702746 [74,] 0.44400450 0.88800899 0.55599550 [75,] 0.40802914 0.81605828 0.59197086 [76,] 0.37815834 0.75631669 0.62184166 [77,] 0.34320172 0.68640345 0.65679828 [78,] 0.42014395 0.84028791 0.57985605 [79,] 0.38763391 0.77526783 0.61236609 [80,] 0.35488643 0.70977286 0.64511357 [81,] 0.32279191 0.64558382 0.67720809 [82,] 0.31462932 0.62925863 0.68537068 [83,] 0.28724435 0.57448869 0.71275565 [84,] 0.25619026 0.51238052 0.74380974 [85,] 0.23535909 0.47071818 0.76464091 [86,] 0.21812543 0.43625086 0.78187457 [87,] 0.19232397 0.38464793 0.80767603 [88,] 0.19014103 0.38028205 0.80985897 [89,] 0.16747992 0.33495983 0.83252008 [90,] 0.15372891 0.30745782 0.84627109 [91,] 0.13569223 0.27138446 0.86430777 [92,] 0.18190909 0.36381818 0.81809091 [93,] 0.20648277 0.41296553 0.79351723 [94,] 0.20765275 0.41530551 0.79234725 [95,] 0.18349471 0.36698941 0.81650529 [96,] 0.19987035 0.39974070 0.80012965 [97,] 0.18648962 0.37297924 0.81351038 [98,] 0.28849226 0.57698452 0.71150774 [99,] 0.27565367 0.55130733 0.72434633 [100,] 0.24630434 0.49260868 0.75369566 [101,] 0.23922145 0.47844291 0.76077855 [102,] 0.21730995 0.43461991 0.78269005 [103,] 0.19829708 0.39659417 0.80170292 [104,] 0.17608904 0.35217808 0.82391096 [105,] 0.15474714 0.30949429 0.84525286 [106,] 0.13944281 0.27888561 0.86055719 [107,] 0.18094912 0.36189824 0.81905088 [108,] 0.18362201 0.36724403 0.81637799 [109,] 0.16281364 0.32562727 0.83718636 [110,] 0.14625176 0.29250352 0.85374824 [111,] 0.12932417 0.25864834 0.87067583 [112,] 0.15955306 0.31910612 0.84044694 [113,] 0.14422110 0.28844220 0.85577890 [114,] 0.13669031 0.27338063 0.86330969 [115,] 0.12983554 0.25967109 0.87016446 [116,] 0.11458864 0.22917728 0.88541136 [117,] 0.10041994 0.20083988 0.89958006 [118,] 0.08548338 0.17096677 0.91451662 [119,] 0.08678502 0.17357005 0.91321498 [120,] 0.08610564 0.17221128 0.91389436 [121,] 0.08065253 0.16130506 0.91934747 [122,] 0.07172351 0.14344702 0.92827649 [123,] 0.06078615 0.12157231 0.93921385 [124,] 0.06023618 0.12047236 0.93976382 [125,] 0.11580236 0.23160471 0.88419764 [126,] 0.09955183 0.19910365 0.90044817 [127,] 0.08761903 0.17523806 0.91238097 [128,] 0.08590665 0.17181330 0.91409335 [129,] 0.08275186 0.16550371 0.91724814 [130,] 0.09776266 0.19552531 0.90223734 [131,] 0.09667314 0.19334628 0.90332686 [132,] 0.08252299 0.16504598 0.91747701 [133,] 0.06970751 0.13941502 0.93029249 [134,] 0.05903937 0.11807875 0.94096063 [135,] 0.04923968 0.09847936 0.95076032 [136,] 0.05646010 0.11292020 0.94353990 [137,] 0.06558482 0.13116964 0.93441518 [138,] 0.05972924 0.11945849 0.94027076 [139,] 0.04996089 0.09992179 0.95003911 [140,] 0.05830857 0.11661713 0.94169143 [141,] 0.05783011 0.11566021 0.94216989 [142,] 0.05915678 0.11831357 0.94084322 [143,] 0.05492974 0.10985948 0.94507026 [144,] 0.05845969 0.11691937 0.94154031 [145,] 0.04935380 0.09870760 0.95064620 [146,] 0.04092533 0.08185067 0.95907467 [147,] 0.03410417 0.06820834 0.96589583 [148,] 0.05008547 0.10017094 0.94991453 [149,] 0.05358259 0.10716519 0.94641741 [150,] 0.04490823 0.08981647 0.95509177 [151,] 0.09088860 0.18177720 0.90911140 [152,] 0.08214389 0.16428777 0.91785611 [153,] 0.29185982 0.58371963 0.70814018 [154,] 0.26834090 0.53668180 0.73165910 [155,] 0.34295208 0.68590417 0.65704792 [156,] 0.32457237 0.64914473 0.67542763 [157,] 0.30525869 0.61051737 0.69474131 [158,] 0.27611106 0.55222212 0.72388894 [159,] 0.25277546 0.50555092 0.74722454 [160,] 0.24910918 0.49821837 0.75089082 [161,] 0.22175091 0.44350182 0.77824909 [162,] 0.27682080 0.55364160 0.72317920 [163,] 0.26448913 0.52897827 0.73551087 [164,] 0.24601632 0.49203264 0.75398368 [165,] 0.29246608 0.58493216 0.70753392 [166,] 0.27444770 0.54889539 0.72555230 [167,] 0.28034917 0.56069833 0.71965083 [168,] 0.27803493 0.55606987 0.72196507 [169,] 0.25161288 0.50322576 0.74838712 [170,] 0.28696148 0.57392295 0.71303852 [171,] 0.40103335 0.80206671 0.59896665 [172,] 0.39042132 0.78084265 0.60957868 [173,] 0.38468707 0.76937414 0.61531293 [174,] 0.37042225 0.74084451 0.62957775 [175,] 0.48635136 0.97270272 0.51364864 [176,] 0.45126494 0.90252988 0.54873506 [177,] 0.41919997 0.83839995 0.58080003 [178,] 0.38630766 0.77261532 0.61369234 [179,] 0.41799309 0.83598617 0.58200691 [180,] 0.44971682 0.89943364 0.55028318 [181,] 0.47116725 0.94233451 0.52883275 [182,] 0.46016295 0.92032590 0.53983705 [183,] 0.42989058 0.85978116 0.57010942 [184,] 0.40128447 0.80256894 0.59871553 [185,] 0.44052069 0.88104139 0.55947931 [186,] 0.64143833 0.71712334 0.35856167 [187,] 0.64674659 0.70650681 0.35325341 [188,] 0.60629774 0.78740452 0.39370226 [189,] 0.56545686 0.86908627 0.43454314 [190,] 0.52421656 0.95156689 0.47578344 [191,] 0.48440879 0.96881757 0.51559121 [192,] 0.45992110 0.91984219 0.54007890 [193,] 0.45830152 0.91660303 0.54169848 [194,] 0.42396394 0.84792787 0.57603606 [195,] 0.38163620 0.76327240 0.61836380 [196,] 0.34362716 0.68725432 0.65637284 [197,] 0.30757243 0.61514487 0.69242757 [198,] 0.27019445 0.54038889 0.72980555 [199,] 0.26267857 0.52535715 0.73732143 [200,] 0.23957804 0.47915609 0.76042196 [201,] 0.20898643 0.41797286 0.79101357 [202,] 0.23136302 0.46272604 0.76863698 [203,] 0.21739042 0.43478085 0.78260958 [204,] 0.18639102 0.37278203 0.81360898 [205,] 0.15861011 0.31722022 0.84138989 [206,] 0.14949531 0.29899063 0.85050469 [207,] 0.12483581 0.24967162 0.87516419 [208,] 0.12564647 0.25129293 0.87435353 [209,] 0.11752290 0.23504580 0.88247710 [210,] 0.11797938 0.23595877 0.88202062 [211,] 0.09582724 0.19165448 0.90417276 [212,] 0.07711800 0.15423599 0.92288200 [213,] 0.07920926 0.15841852 0.92079074 [214,] 0.07043453 0.14086907 0.92956547 [215,] 0.05664095 0.11328190 0.94335905 [216,] 0.04323475 0.08646950 0.95676525 [217,] 0.04068835 0.08137670 0.95931165 [218,] 0.05539356 0.11078712 0.94460644 [219,] 0.05284088 0.10568176 0.94715912 [220,] 0.04006529 0.08013058 0.95993471 [221,] 0.04120584 0.08241167 0.95879416 [222,] 0.09159083 0.18318166 0.90840917 [223,] 0.09583202 0.19166404 0.90416798 [224,] 0.09120932 0.18241864 0.90879068 [225,] 0.06959991 0.13919981 0.93040009 [226,] 0.09167430 0.18334861 0.90832570 [227,] 0.12808777 0.25617555 0.87191223 [228,] 0.21529170 0.43058340 0.78470830 [229,] 0.21302002 0.42604004 0.78697998 [230,] 0.17227384 0.34454768 0.82772616 [231,] 0.21140140 0.42280280 0.78859860 [232,] 0.15897735 0.31795469 0.84102265 [233,] 0.11432055 0.22864110 0.88567945 [234,] 0.17345194 0.34690387 0.82654806 [235,] 0.18795056 0.37590113 0.81204944 [236,] 0.13137148 0.26274297 0.86862852 [237,] 0.16130773 0.32261546 0.83869227 [238,] 0.24423794 0.48847587 0.75576206 [239,] 0.16075173 0.32150347 0.83924827 [240,] 0.17568193 0.35136385 0.82431807 [241,] 0.79882032 0.40235935 0.20117968 > postscript(file="/var/wessaorg/rcomp/tmp/1bxqu1384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/23cq51384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3vofs1384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/497jn1384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5z6sm1384952393.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 -1.71577203 1.91381741 -1.19667177 -2.44359290 9.96883665 2.47004903 7 8 9 10 11 12 7.81264756 -1.15986004 -1.81411223 1.24876835 -0.11628064 -2.27142971 13 14 15 16 17 18 -0.68188953 1.75474287 1.12924026 0.33137825 1.72073001 1.74182029 19 20 21 22 23 24 -3.15665074 0.59225522 -0.91360841 -1.33734910 -1.42327959 -1.32972691 25 26 27 28 29 30 1.61504554 -5.92916213 0.59660296 1.84872684 2.85553593 -2.54306936 31 32 33 34 35 36 -1.87819723 0.15980220 -0.58724128 -0.58482219 0.93556124 -3.81145473 37 38 39 40 41 42 3.19248153 0.02997163 -0.37305839 -3.41438576 -2.67343716 3.13069059 43 44 45 46 47 48 -4.00454728 -1.18089550 -1.06877741 -1.84377485 -3.87843482 -0.84052549 49 50 51 52 53 54 5.11096531 -1.94923638 -1.29207618 1.07384639 2.75767704 -1.85381936 55 56 57 58 59 60 -3.61115421 2.68603948 2.06562245 -3.53125631 -3.62292921 0.90498272 61 62 63 64 65 66 1.85160781 -0.52602020 -1.98221752 0.06553833 -0.03491300 -4.18521602 67 68 69 70 71 72 1.46798221 -1.74508468 0.05071304 1.50184975 -1.08120824 1.97885479 73 74 75 76 77 78 1.55402158 -1.99198194 -1.72630704 5.28702932 1.92396492 2.81785438 79 80 81 82 83 84 0.64278247 -5.73711254 -0.59232971 -3.64973209 0.67040095 -0.47095799 85 86 87 88 89 90 0.26184986 0.93022803 -1.22317180 0.02376809 3.96922170 1.51877624 91 92 93 94 95 96 0.29111756 1.13009142 2.83306782 -0.71043106 0.24544891 1.37502778 97 98 99 100 101 102 -1.37370147 0.88894375 -2.21893553 -0.40231111 1.68516623 1.42348670 103 104 105 106 107 108 -4.80602963 4.23627445 2.40818721 -0.34437150 3.82232822 -1.83552749 109 110 111 112 113 114 6.28953440 2.34423479 0.17584386 -1.92457791 1.50834009 1.69569729 115 116 117 118 119 120 -0.65394798 -0.32054733 -0.97877461 -4.40213263 3.20343591 -0.73690761 121 122 123 124 125 126 1.23348909 -0.29960599 -4.10777853 -1.33682857 -2.13680633 -1.78434125 127 128 129 130 131 132 -0.36528312 1.27726457 0.44551301 -2.62137892 2.73862608 -1.73889841 133 134 135 136 137 138 1.74976423 -0.39238758 2.88331911 6.48422240 -0.21005523 -0.92392385 139 140 141 142 143 144 -2.21551737 -2.10540952 -3.56194858 2.93867421 -0.26361219 0.07390243 145 146 147 148 149 150 0.11465108 0.57777207 -3.15507214 -3.27464576 2.26422108 0.70607827 151 152 153 154 155 156 4.08808953 -2.39343267 -2.41275436 2.66750451 3.76536111 0.29111756 157 158 159 160 161 162 0.38065609 1.27726457 -3.90440666 4.25335144 1.30280565 6.66734364 163 164 165 166 167 168 1.17811321 8.71423779 1.88107271 5.73060078 -1.37421635 -0.99130890 169 170 171 172 173 174 -0.40781966 1.55136463 3.18827269 -0.06826567 -4.43546806 1.66224062 175 176 177 178 179 180 0.73488020 -4.32407380 -1.50868902 2.82080540 -2.63435825 -1.02475878 181 182 183 184 185 186 -3.55560489 -5.74992634 -2.01349024 -2.76999678 -0.90775887 5.41826330 187 188 189 190 191 192 1.10663418 0.22710060 -1.27313755 4.14502576 -2.91809654 -2.90709178 193 194 195 196 197 198 1.71674585 -0.61289568 0.97687455 -3.13493623 6.00469649 2.45847156 199 200 201 202 203 204 0.80625174 -0.57689438 -0.44259717 -1.03265206 0.06254549 -1.81628986 205 206 207 208 209 210 -1.74321276 0.29832293 -1.11968509 1.77115938 -0.70419184 0.78458431 211 212 213 214 215 216 -0.73552919 -0.68529250 3.16741560 -3.04587979 1.34617174 -0.69352850 217 218 219 220 221 222 0.39463698 -0.96572126 3.81862694 1.52285832 -3.32953057 -0.32042424 223 224 225 226 227 228 0.73143005 -4.11301881 3.70573441 -2.02496054 -0.40793279 -2.85295524 229 230 231 232 233 234 4.51661425 -2.91805081 -1.66389889 -2.05365461 -4.84782480 2.85785769 235 236 237 238 239 240 -3.28804823 -1.90046547 1.24875793 -3.00800975 -5.16364565 -3.78169082 241 242 243 244 245 246 -4.14070220 -0.20353747 -0.17791904 0.67183370 1.41078519 3.93542354 247 248 249 250 251 252 0.18243489 7.14318308 1.90423434 -0.21113782 0.80842072 2.94063686 253 254 255 256 257 258 -3.51395609 -1.34537926 1.38446181 -0.58227712 4.77813977 -0.03983342 259 260 261 262 263 264 1.79145657 -8.38639710 -2.04568528 -1.25529876 2.81832756 -1.57858838 > postscript(file="/var/wessaorg/rcomp/tmp/6buq71384952393.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 -1.71577203 NA 1 1.91381741 -1.71577203 2 -1.19667177 1.91381741 3 -2.44359290 -1.19667177 4 9.96883665 -2.44359290 5 2.47004903 9.96883665 6 7.81264756 2.47004903 7 -1.15986004 7.81264756 8 -1.81411223 -1.15986004 9 1.24876835 -1.81411223 10 -0.11628064 1.24876835 11 -2.27142971 -0.11628064 12 -0.68188953 -2.27142971 13 1.75474287 -0.68188953 14 1.12924026 1.75474287 15 0.33137825 1.12924026 16 1.72073001 0.33137825 17 1.74182029 1.72073001 18 -3.15665074 1.74182029 19 0.59225522 -3.15665074 20 -0.91360841 0.59225522 21 -1.33734910 -0.91360841 22 -1.42327959 -1.33734910 23 -1.32972691 -1.42327959 24 1.61504554 -1.32972691 25 -5.92916213 1.61504554 26 0.59660296 -5.92916213 27 1.84872684 0.59660296 28 2.85553593 1.84872684 29 -2.54306936 2.85553593 30 -1.87819723 -2.54306936 31 0.15980220 -1.87819723 32 -0.58724128 0.15980220 33 -0.58482219 -0.58724128 34 0.93556124 -0.58482219 35 -3.81145473 0.93556124 36 3.19248153 -3.81145473 37 0.02997163 3.19248153 38 -0.37305839 0.02997163 39 -3.41438576 -0.37305839 40 -2.67343716 -3.41438576 41 3.13069059 -2.67343716 42 -4.00454728 3.13069059 43 -1.18089550 -4.00454728 44 -1.06877741 -1.18089550 45 -1.84377485 -1.06877741 46 -3.87843482 -1.84377485 47 -0.84052549 -3.87843482 48 5.11096531 -0.84052549 49 -1.94923638 5.11096531 50 -1.29207618 -1.94923638 51 1.07384639 -1.29207618 52 2.75767704 1.07384639 53 -1.85381936 2.75767704 54 -3.61115421 -1.85381936 55 2.68603948 -3.61115421 56 2.06562245 2.68603948 57 -3.53125631 2.06562245 58 -3.62292921 -3.53125631 59 0.90498272 -3.62292921 60 1.85160781 0.90498272 61 -0.52602020 1.85160781 62 -1.98221752 -0.52602020 63 0.06553833 -1.98221752 64 -0.03491300 0.06553833 65 -4.18521602 -0.03491300 66 1.46798221 -4.18521602 67 -1.74508468 1.46798221 68 0.05071304 -1.74508468 69 1.50184975 0.05071304 70 -1.08120824 1.50184975 71 1.97885479 -1.08120824 72 1.55402158 1.97885479 73 -1.99198194 1.55402158 74 -1.72630704 -1.99198194 75 5.28702932 -1.72630704 76 1.92396492 5.28702932 77 2.81785438 1.92396492 78 0.64278247 2.81785438 79 -5.73711254 0.64278247 80 -0.59232971 -5.73711254 81 -3.64973209 -0.59232971 82 0.67040095 -3.64973209 83 -0.47095799 0.67040095 84 0.26184986 -0.47095799 85 0.93022803 0.26184986 86 -1.22317180 0.93022803 87 0.02376809 -1.22317180 88 3.96922170 0.02376809 89 1.51877624 3.96922170 90 0.29111756 1.51877624 91 1.13009142 0.29111756 92 2.83306782 1.13009142 93 -0.71043106 2.83306782 94 0.24544891 -0.71043106 95 1.37502778 0.24544891 96 -1.37370147 1.37502778 97 0.88894375 -1.37370147 98 -2.21893553 0.88894375 99 -0.40231111 -2.21893553 100 1.68516623 -0.40231111 101 1.42348670 1.68516623 102 -4.80602963 1.42348670 103 4.23627445 -4.80602963 104 2.40818721 4.23627445 105 -0.34437150 2.40818721 106 3.82232822 -0.34437150 107 -1.83552749 3.82232822 108 6.28953440 -1.83552749 109 2.34423479 6.28953440 110 0.17584386 2.34423479 111 -1.92457791 0.17584386 112 1.50834009 -1.92457791 113 1.69569729 1.50834009 114 -0.65394798 1.69569729 115 -0.32054733 -0.65394798 116 -0.97877461 -0.32054733 117 -4.40213263 -0.97877461 118 3.20343591 -4.40213263 119 -0.73690761 3.20343591 120 1.23348909 -0.73690761 121 -0.29960599 1.23348909 122 -4.10777853 -0.29960599 123 -1.33682857 -4.10777853 124 -2.13680633 -1.33682857 125 -1.78434125 -2.13680633 126 -0.36528312 -1.78434125 127 1.27726457 -0.36528312 128 0.44551301 1.27726457 129 -2.62137892 0.44551301 130 2.73862608 -2.62137892 131 -1.73889841 2.73862608 132 1.74976423 -1.73889841 133 -0.39238758 1.74976423 134 2.88331911 -0.39238758 135 6.48422240 2.88331911 136 -0.21005523 6.48422240 137 -0.92392385 -0.21005523 138 -2.21551737 -0.92392385 139 -2.10540952 -2.21551737 140 -3.56194858 -2.10540952 141 2.93867421 -3.56194858 142 -0.26361219 2.93867421 143 0.07390243 -0.26361219 144 0.11465108 0.07390243 145 0.57777207 0.11465108 146 -3.15507214 0.57777207 147 -3.27464576 -3.15507214 148 2.26422108 -3.27464576 149 0.70607827 2.26422108 150 4.08808953 0.70607827 151 -2.39343267 4.08808953 152 -2.41275436 -2.39343267 153 2.66750451 -2.41275436 154 3.76536111 2.66750451 155 0.29111756 3.76536111 156 0.38065609 0.29111756 157 1.27726457 0.38065609 158 -3.90440666 1.27726457 159 4.25335144 -3.90440666 160 1.30280565 4.25335144 161 6.66734364 1.30280565 162 1.17811321 6.66734364 163 8.71423779 1.17811321 164 1.88107271 8.71423779 165 5.73060078 1.88107271 166 -1.37421635 5.73060078 167 -0.99130890 -1.37421635 168 -0.40781966 -0.99130890 169 1.55136463 -0.40781966 170 3.18827269 1.55136463 171 -0.06826567 3.18827269 172 -4.43546806 -0.06826567 173 1.66224062 -4.43546806 174 0.73488020 1.66224062 175 -4.32407380 0.73488020 176 -1.50868902 -4.32407380 177 2.82080540 -1.50868902 178 -2.63435825 2.82080540 179 -1.02475878 -2.63435825 180 -3.55560489 -1.02475878 181 -5.74992634 -3.55560489 182 -2.01349024 -5.74992634 183 -2.76999678 -2.01349024 184 -0.90775887 -2.76999678 185 5.41826330 -0.90775887 186 1.10663418 5.41826330 187 0.22710060 1.10663418 188 -1.27313755 0.22710060 189 4.14502576 -1.27313755 190 -2.91809654 4.14502576 191 -2.90709178 -2.91809654 192 1.71674585 -2.90709178 193 -0.61289568 1.71674585 194 0.97687455 -0.61289568 195 -3.13493623 0.97687455 196 6.00469649 -3.13493623 197 2.45847156 6.00469649 198 0.80625174 2.45847156 199 -0.57689438 0.80625174 200 -0.44259717 -0.57689438 201 -1.03265206 -0.44259717 202 0.06254549 -1.03265206 203 -1.81628986 0.06254549 204 -1.74321276 -1.81628986 205 0.29832293 -1.74321276 206 -1.11968509 0.29832293 207 1.77115938 -1.11968509 208 -0.70419184 1.77115938 209 0.78458431 -0.70419184 210 -0.73552919 0.78458431 211 -0.68529250 -0.73552919 212 3.16741560 -0.68529250 213 -3.04587979 3.16741560 214 1.34617174 -3.04587979 215 -0.69352850 1.34617174 216 0.39463698 -0.69352850 217 -0.96572126 0.39463698 218 3.81862694 -0.96572126 219 1.52285832 3.81862694 220 -3.32953057 1.52285832 221 -0.32042424 -3.32953057 222 0.73143005 -0.32042424 223 -4.11301881 0.73143005 224 3.70573441 -4.11301881 225 -2.02496054 3.70573441 226 -0.40793279 -2.02496054 227 -2.85295524 -0.40793279 228 4.51661425 -2.85295524 229 -2.91805081 4.51661425 230 -1.66389889 -2.91805081 231 -2.05365461 -1.66389889 232 -4.84782480 -2.05365461 233 2.85785769 -4.84782480 234 -3.28804823 2.85785769 235 -1.90046547 -3.28804823 236 1.24875793 -1.90046547 237 -3.00800975 1.24875793 238 -5.16364565 -3.00800975 239 -3.78169082 -5.16364565 240 -4.14070220 -3.78169082 241 -0.20353747 -4.14070220 242 -0.17791904 -0.20353747 243 0.67183370 -0.17791904 244 1.41078519 0.67183370 245 3.93542354 1.41078519 246 0.18243489 3.93542354 247 7.14318308 0.18243489 248 1.90423434 7.14318308 249 -0.21113782 1.90423434 250 0.80842072 -0.21113782 251 2.94063686 0.80842072 252 -3.51395609 2.94063686 253 -1.34537926 -3.51395609 254 1.38446181 -1.34537926 255 -0.58227712 1.38446181 256 4.77813977 -0.58227712 257 -0.03983342 4.77813977 258 1.79145657 -0.03983342 259 -8.38639710 1.79145657 260 -2.04568528 -8.38639710 261 -1.25529876 -2.04568528 262 2.81832756 -1.25529876 263 -1.57858838 2.81832756 264 NA -1.57858838 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.91381741 -1.71577203 [2,] -1.19667177 1.91381741 [3,] -2.44359290 -1.19667177 [4,] 9.96883665 -2.44359290 [5,] 2.47004903 9.96883665 [6,] 7.81264756 2.47004903 [7,] -1.15986004 7.81264756 [8,] -1.81411223 -1.15986004 [9,] 1.24876835 -1.81411223 [10,] -0.11628064 1.24876835 [11,] -2.27142971 -0.11628064 [12,] -0.68188953 -2.27142971 [13,] 1.75474287 -0.68188953 [14,] 1.12924026 1.75474287 [15,] 0.33137825 1.12924026 [16,] 1.72073001 0.33137825 [17,] 1.74182029 1.72073001 [18,] -3.15665074 1.74182029 [19,] 0.59225522 -3.15665074 [20,] -0.91360841 0.59225522 [21,] -1.33734910 -0.91360841 [22,] -1.42327959 -1.33734910 [23,] -1.32972691 -1.42327959 [24,] 1.61504554 -1.32972691 [25,] -5.92916213 1.61504554 [26,] 0.59660296 -5.92916213 [27,] 1.84872684 0.59660296 [28,] 2.85553593 1.84872684 [29,] -2.54306936 2.85553593 [30,] -1.87819723 -2.54306936 [31,] 0.15980220 -1.87819723 [32,] -0.58724128 0.15980220 [33,] -0.58482219 -0.58724128 [34,] 0.93556124 -0.58482219 [35,] -3.81145473 0.93556124 [36,] 3.19248153 -3.81145473 [37,] 0.02997163 3.19248153 [38,] -0.37305839 0.02997163 [39,] -3.41438576 -0.37305839 [40,] -2.67343716 -3.41438576 [41,] 3.13069059 -2.67343716 [42,] -4.00454728 3.13069059 [43,] -1.18089550 -4.00454728 [44,] -1.06877741 -1.18089550 [45,] -1.84377485 -1.06877741 [46,] -3.87843482 -1.84377485 [47,] -0.84052549 -3.87843482 [48,] 5.11096531 -0.84052549 [49,] -1.94923638 5.11096531 [50,] -1.29207618 -1.94923638 [51,] 1.07384639 -1.29207618 [52,] 2.75767704 1.07384639 [53,] -1.85381936 2.75767704 [54,] -3.61115421 -1.85381936 [55,] 2.68603948 -3.61115421 [56,] 2.06562245 2.68603948 [57,] -3.53125631 2.06562245 [58,] -3.62292921 -3.53125631 [59,] 0.90498272 -3.62292921 [60,] 1.85160781 0.90498272 [61,] -0.52602020 1.85160781 [62,] -1.98221752 -0.52602020 [63,] 0.06553833 -1.98221752 [64,] -0.03491300 0.06553833 [65,] -4.18521602 -0.03491300 [66,] 1.46798221 -4.18521602 [67,] -1.74508468 1.46798221 [68,] 0.05071304 -1.74508468 [69,] 1.50184975 0.05071304 [70,] -1.08120824 1.50184975 [71,] 1.97885479 -1.08120824 [72,] 1.55402158 1.97885479 [73,] -1.99198194 1.55402158 [74,] -1.72630704 -1.99198194 [75,] 5.28702932 -1.72630704 [76,] 1.92396492 5.28702932 [77,] 2.81785438 1.92396492 [78,] 0.64278247 2.81785438 [79,] -5.73711254 0.64278247 [80,] -0.59232971 -5.73711254 [81,] -3.64973209 -0.59232971 [82,] 0.67040095 -3.64973209 [83,] -0.47095799 0.67040095 [84,] 0.26184986 -0.47095799 [85,] 0.93022803 0.26184986 [86,] -1.22317180 0.93022803 [87,] 0.02376809 -1.22317180 [88,] 3.96922170 0.02376809 [89,] 1.51877624 3.96922170 [90,] 0.29111756 1.51877624 [91,] 1.13009142 0.29111756 [92,] 2.83306782 1.13009142 [93,] -0.71043106 2.83306782 [94,] 0.24544891 -0.71043106 [95,] 1.37502778 0.24544891 [96,] -1.37370147 1.37502778 [97,] 0.88894375 -1.37370147 [98,] -2.21893553 0.88894375 [99,] -0.40231111 -2.21893553 [100,] 1.68516623 -0.40231111 [101,] 1.42348670 1.68516623 [102,] -4.80602963 1.42348670 [103,] 4.23627445 -4.80602963 [104,] 2.40818721 4.23627445 [105,] -0.34437150 2.40818721 [106,] 3.82232822 -0.34437150 [107,] -1.83552749 3.82232822 [108,] 6.28953440 -1.83552749 [109,] 2.34423479 6.28953440 [110,] 0.17584386 2.34423479 [111,] -1.92457791 0.17584386 [112,] 1.50834009 -1.92457791 [113,] 1.69569729 1.50834009 [114,] -0.65394798 1.69569729 [115,] -0.32054733 -0.65394798 [116,] -0.97877461 -0.32054733 [117,] -4.40213263 -0.97877461 [118,] 3.20343591 -4.40213263 [119,] -0.73690761 3.20343591 [120,] 1.23348909 -0.73690761 [121,] -0.29960599 1.23348909 [122,] -4.10777853 -0.29960599 [123,] -1.33682857 -4.10777853 [124,] -2.13680633 -1.33682857 [125,] -1.78434125 -2.13680633 [126,] -0.36528312 -1.78434125 [127,] 1.27726457 -0.36528312 [128,] 0.44551301 1.27726457 [129,] -2.62137892 0.44551301 [130,] 2.73862608 -2.62137892 [131,] -1.73889841 2.73862608 [132,] 1.74976423 -1.73889841 [133,] -0.39238758 1.74976423 [134,] 2.88331911 -0.39238758 [135,] 6.48422240 2.88331911 [136,] -0.21005523 6.48422240 [137,] -0.92392385 -0.21005523 [138,] -2.21551737 -0.92392385 [139,] -2.10540952 -2.21551737 [140,] -3.56194858 -2.10540952 [141,] 2.93867421 -3.56194858 [142,] -0.26361219 2.93867421 [143,] 0.07390243 -0.26361219 [144,] 0.11465108 0.07390243 [145,] 0.57777207 0.11465108 [146,] -3.15507214 0.57777207 [147,] -3.27464576 -3.15507214 [148,] 2.26422108 -3.27464576 [149,] 0.70607827 2.26422108 [150,] 4.08808953 0.70607827 [151,] -2.39343267 4.08808953 [152,] -2.41275436 -2.39343267 [153,] 2.66750451 -2.41275436 [154,] 3.76536111 2.66750451 [155,] 0.29111756 3.76536111 [156,] 0.38065609 0.29111756 [157,] 1.27726457 0.38065609 [158,] -3.90440666 1.27726457 [159,] 4.25335144 -3.90440666 [160,] 1.30280565 4.25335144 [161,] 6.66734364 1.30280565 [162,] 1.17811321 6.66734364 [163,] 8.71423779 1.17811321 [164,] 1.88107271 8.71423779 [165,] 5.73060078 1.88107271 [166,] -1.37421635 5.73060078 [167,] -0.99130890 -1.37421635 [168,] -0.40781966 -0.99130890 [169,] 1.55136463 -0.40781966 [170,] 3.18827269 1.55136463 [171,] -0.06826567 3.18827269 [172,] -4.43546806 -0.06826567 [173,] 1.66224062 -4.43546806 [174,] 0.73488020 1.66224062 [175,] -4.32407380 0.73488020 [176,] -1.50868902 -4.32407380 [177,] 2.82080540 -1.50868902 [178,] -2.63435825 2.82080540 [179,] -1.02475878 -2.63435825 [180,] -3.55560489 -1.02475878 [181,] -5.74992634 -3.55560489 [182,] -2.01349024 -5.74992634 [183,] -2.76999678 -2.01349024 [184,] -0.90775887 -2.76999678 [185,] 5.41826330 -0.90775887 [186,] 1.10663418 5.41826330 [187,] 0.22710060 1.10663418 [188,] -1.27313755 0.22710060 [189,] 4.14502576 -1.27313755 [190,] -2.91809654 4.14502576 [191,] -2.90709178 -2.91809654 [192,] 1.71674585 -2.90709178 [193,] -0.61289568 1.71674585 [194,] 0.97687455 -0.61289568 [195,] -3.13493623 0.97687455 [196,] 6.00469649 -3.13493623 [197,] 2.45847156 6.00469649 [198,] 0.80625174 2.45847156 [199,] -0.57689438 0.80625174 [200,] -0.44259717 -0.57689438 [201,] -1.03265206 -0.44259717 [202,] 0.06254549 -1.03265206 [203,] -1.81628986 0.06254549 [204,] -1.74321276 -1.81628986 [205,] 0.29832293 -1.74321276 [206,] -1.11968509 0.29832293 [207,] 1.77115938 -1.11968509 [208,] -0.70419184 1.77115938 [209,] 0.78458431 -0.70419184 [210,] -0.73552919 0.78458431 [211,] -0.68529250 -0.73552919 [212,] 3.16741560 -0.68529250 [213,] -3.04587979 3.16741560 [214,] 1.34617174 -3.04587979 [215,] -0.69352850 1.34617174 [216,] 0.39463698 -0.69352850 [217,] -0.96572126 0.39463698 [218,] 3.81862694 -0.96572126 [219,] 1.52285832 3.81862694 [220,] -3.32953057 1.52285832 [221,] -0.32042424 -3.32953057 [222,] 0.73143005 -0.32042424 [223,] -4.11301881 0.73143005 [224,] 3.70573441 -4.11301881 [225,] -2.02496054 3.70573441 [226,] -0.40793279 -2.02496054 [227,] -2.85295524 -0.40793279 [228,] 4.51661425 -2.85295524 [229,] -2.91805081 4.51661425 [230,] -1.66389889 -2.91805081 [231,] -2.05365461 -1.66389889 [232,] -4.84782480 -2.05365461 [233,] 2.85785769 -4.84782480 [234,] -3.28804823 2.85785769 [235,] -1.90046547 -3.28804823 [236,] 1.24875793 -1.90046547 [237,] -3.00800975 1.24875793 [238,] -5.16364565 -3.00800975 [239,] -3.78169082 -5.16364565 [240,] -4.14070220 -3.78169082 [241,] -0.20353747 -4.14070220 [242,] -0.17791904 -0.20353747 [243,] 0.67183370 -0.17791904 [244,] 1.41078519 0.67183370 [245,] 3.93542354 1.41078519 [246,] 0.18243489 3.93542354 [247,] 7.14318308 0.18243489 [248,] 1.90423434 7.14318308 [249,] -0.21113782 1.90423434 [250,] 0.80842072 -0.21113782 [251,] 2.94063686 0.80842072 [252,] -3.51395609 2.94063686 [253,] -1.34537926 -3.51395609 [254,] 1.38446181 -1.34537926 [255,] -0.58227712 1.38446181 [256,] 4.77813977 -0.58227712 [257,] -0.03983342 4.77813977 [258,] 1.79145657 -0.03983342 [259,] -8.38639710 1.79145657 [260,] -2.04568528 -8.38639710 [261,] -1.25529876 -2.04568528 [262,] 2.81832756 -1.25529876 [263,] -1.57858838 2.81832756 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.91381741 -1.71577203 2 -1.19667177 1.91381741 3 -2.44359290 -1.19667177 4 9.96883665 -2.44359290 5 2.47004903 9.96883665 6 7.81264756 2.47004903 7 -1.15986004 7.81264756 8 -1.81411223 -1.15986004 9 1.24876835 -1.81411223 10 -0.11628064 1.24876835 11 -2.27142971 -0.11628064 12 -0.68188953 -2.27142971 13 1.75474287 -0.68188953 14 1.12924026 1.75474287 15 0.33137825 1.12924026 16 1.72073001 0.33137825 17 1.74182029 1.72073001 18 -3.15665074 1.74182029 19 0.59225522 -3.15665074 20 -0.91360841 0.59225522 21 -1.33734910 -0.91360841 22 -1.42327959 -1.33734910 23 -1.32972691 -1.42327959 24 1.61504554 -1.32972691 25 -5.92916213 1.61504554 26 0.59660296 -5.92916213 27 1.84872684 0.59660296 28 2.85553593 1.84872684 29 -2.54306936 2.85553593 30 -1.87819723 -2.54306936 31 0.15980220 -1.87819723 32 -0.58724128 0.15980220 33 -0.58482219 -0.58724128 34 0.93556124 -0.58482219 35 -3.81145473 0.93556124 36 3.19248153 -3.81145473 37 0.02997163 3.19248153 38 -0.37305839 0.02997163 39 -3.41438576 -0.37305839 40 -2.67343716 -3.41438576 41 3.13069059 -2.67343716 42 -4.00454728 3.13069059 43 -1.18089550 -4.00454728 44 -1.06877741 -1.18089550 45 -1.84377485 -1.06877741 46 -3.87843482 -1.84377485 47 -0.84052549 -3.87843482 48 5.11096531 -0.84052549 49 -1.94923638 5.11096531 50 -1.29207618 -1.94923638 51 1.07384639 -1.29207618 52 2.75767704 1.07384639 53 -1.85381936 2.75767704 54 -3.61115421 -1.85381936 55 2.68603948 -3.61115421 56 2.06562245 2.68603948 57 -3.53125631 2.06562245 58 -3.62292921 -3.53125631 59 0.90498272 -3.62292921 60 1.85160781 0.90498272 61 -0.52602020 1.85160781 62 -1.98221752 -0.52602020 63 0.06553833 -1.98221752 64 -0.03491300 0.06553833 65 -4.18521602 -0.03491300 66 1.46798221 -4.18521602 67 -1.74508468 1.46798221 68 0.05071304 -1.74508468 69 1.50184975 0.05071304 70 -1.08120824 1.50184975 71 1.97885479 -1.08120824 72 1.55402158 1.97885479 73 -1.99198194 1.55402158 74 -1.72630704 -1.99198194 75 5.28702932 -1.72630704 76 1.92396492 5.28702932 77 2.81785438 1.92396492 78 0.64278247 2.81785438 79 -5.73711254 0.64278247 80 -0.59232971 -5.73711254 81 -3.64973209 -0.59232971 82 0.67040095 -3.64973209 83 -0.47095799 0.67040095 84 0.26184986 -0.47095799 85 0.93022803 0.26184986 86 -1.22317180 0.93022803 87 0.02376809 -1.22317180 88 3.96922170 0.02376809 89 1.51877624 3.96922170 90 0.29111756 1.51877624 91 1.13009142 0.29111756 92 2.83306782 1.13009142 93 -0.71043106 2.83306782 94 0.24544891 -0.71043106 95 1.37502778 0.24544891 96 -1.37370147 1.37502778 97 0.88894375 -1.37370147 98 -2.21893553 0.88894375 99 -0.40231111 -2.21893553 100 1.68516623 -0.40231111 101 1.42348670 1.68516623 102 -4.80602963 1.42348670 103 4.23627445 -4.80602963 104 2.40818721 4.23627445 105 -0.34437150 2.40818721 106 3.82232822 -0.34437150 107 -1.83552749 3.82232822 108 6.28953440 -1.83552749 109 2.34423479 6.28953440 110 0.17584386 2.34423479 111 -1.92457791 0.17584386 112 1.50834009 -1.92457791 113 1.69569729 1.50834009 114 -0.65394798 1.69569729 115 -0.32054733 -0.65394798 116 -0.97877461 -0.32054733 117 -4.40213263 -0.97877461 118 3.20343591 -4.40213263 119 -0.73690761 3.20343591 120 1.23348909 -0.73690761 121 -0.29960599 1.23348909 122 -4.10777853 -0.29960599 123 -1.33682857 -4.10777853 124 -2.13680633 -1.33682857 125 -1.78434125 -2.13680633 126 -0.36528312 -1.78434125 127 1.27726457 -0.36528312 128 0.44551301 1.27726457 129 -2.62137892 0.44551301 130 2.73862608 -2.62137892 131 -1.73889841 2.73862608 132 1.74976423 -1.73889841 133 -0.39238758 1.74976423 134 2.88331911 -0.39238758 135 6.48422240 2.88331911 136 -0.21005523 6.48422240 137 -0.92392385 -0.21005523 138 -2.21551737 -0.92392385 139 -2.10540952 -2.21551737 140 -3.56194858 -2.10540952 141 2.93867421 -3.56194858 142 -0.26361219 2.93867421 143 0.07390243 -0.26361219 144 0.11465108 0.07390243 145 0.57777207 0.11465108 146 -3.15507214 0.57777207 147 -3.27464576 -3.15507214 148 2.26422108 -3.27464576 149 0.70607827 2.26422108 150 4.08808953 0.70607827 151 -2.39343267 4.08808953 152 -2.41275436 -2.39343267 153 2.66750451 -2.41275436 154 3.76536111 2.66750451 155 0.29111756 3.76536111 156 0.38065609 0.29111756 157 1.27726457 0.38065609 158 -3.90440666 1.27726457 159 4.25335144 -3.90440666 160 1.30280565 4.25335144 161 6.66734364 1.30280565 162 1.17811321 6.66734364 163 8.71423779 1.17811321 164 1.88107271 8.71423779 165 5.73060078 1.88107271 166 -1.37421635 5.73060078 167 -0.99130890 -1.37421635 168 -0.40781966 -0.99130890 169 1.55136463 -0.40781966 170 3.18827269 1.55136463 171 -0.06826567 3.18827269 172 -4.43546806 -0.06826567 173 1.66224062 -4.43546806 174 0.73488020 1.66224062 175 -4.32407380 0.73488020 176 -1.50868902 -4.32407380 177 2.82080540 -1.50868902 178 -2.63435825 2.82080540 179 -1.02475878 -2.63435825 180 -3.55560489 -1.02475878 181 -5.74992634 -3.55560489 182 -2.01349024 -5.74992634 183 -2.76999678 -2.01349024 184 -0.90775887 -2.76999678 185 5.41826330 -0.90775887 186 1.10663418 5.41826330 187 0.22710060 1.10663418 188 -1.27313755 0.22710060 189 4.14502576 -1.27313755 190 -2.91809654 4.14502576 191 -2.90709178 -2.91809654 192 1.71674585 -2.90709178 193 -0.61289568 1.71674585 194 0.97687455 -0.61289568 195 -3.13493623 0.97687455 196 6.00469649 -3.13493623 197 2.45847156 6.00469649 198 0.80625174 2.45847156 199 -0.57689438 0.80625174 200 -0.44259717 -0.57689438 201 -1.03265206 -0.44259717 202 0.06254549 -1.03265206 203 -1.81628986 0.06254549 204 -1.74321276 -1.81628986 205 0.29832293 -1.74321276 206 -1.11968509 0.29832293 207 1.77115938 -1.11968509 208 -0.70419184 1.77115938 209 0.78458431 -0.70419184 210 -0.73552919 0.78458431 211 -0.68529250 -0.73552919 212 3.16741560 -0.68529250 213 -3.04587979 3.16741560 214 1.34617174 -3.04587979 215 -0.69352850 1.34617174 216 0.39463698 -0.69352850 217 -0.96572126 0.39463698 218 3.81862694 -0.96572126 219 1.52285832 3.81862694 220 -3.32953057 1.52285832 221 -0.32042424 -3.32953057 222 0.73143005 -0.32042424 223 -4.11301881 0.73143005 224 3.70573441 -4.11301881 225 -2.02496054 3.70573441 226 -0.40793279 -2.02496054 227 -2.85295524 -0.40793279 228 4.51661425 -2.85295524 229 -2.91805081 4.51661425 230 -1.66389889 -2.91805081 231 -2.05365461 -1.66389889 232 -4.84782480 -2.05365461 233 2.85785769 -4.84782480 234 -3.28804823 2.85785769 235 -1.90046547 -3.28804823 236 1.24875793 -1.90046547 237 -3.00800975 1.24875793 238 -5.16364565 -3.00800975 239 -3.78169082 -5.16364565 240 -4.14070220 -3.78169082 241 -0.20353747 -4.14070220 242 -0.17791904 -0.20353747 243 0.67183370 -0.17791904 244 1.41078519 0.67183370 245 3.93542354 1.41078519 246 0.18243489 3.93542354 247 7.14318308 0.18243489 248 1.90423434 7.14318308 249 -0.21113782 1.90423434 250 0.80842072 -0.21113782 251 2.94063686 0.80842072 252 -3.51395609 2.94063686 253 -1.34537926 -3.51395609 254 1.38446181 -1.34537926 255 -0.58227712 1.38446181 256 4.77813977 -0.58227712 257 -0.03983342 4.77813977 258 1.79145657 -0.03983342 259 -8.38639710 1.79145657 260 -2.04568528 -8.38639710 261 -1.25529876 -2.04568528 262 2.81832756 -1.25529876 263 -1.57858838 2.81832756 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7di1i1384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8l3f61384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9to6j1384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10pif81384952393.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, 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/wessaorg/rcomp/tmp/110rfs1384952393.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/wessaorg/rcomp/tmp/126fr71384952393.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/wessaorg/rcomp/tmp/13keh91384952393.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/wessaorg/rcomp/tmp/143tf51384952393.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/wessaorg/rcomp/tmp/1503kl1384952393.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/wessaorg/rcomp/tmp/16hoej1384952393.tab") + } > > try(system("convert tmp/1bxqu1384952393.ps tmp/1bxqu1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/23cq51384952393.ps tmp/23cq51384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/3vofs1384952393.ps tmp/3vofs1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/497jn1384952393.ps tmp/497jn1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/5z6sm1384952393.ps tmp/5z6sm1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/6buq71384952393.ps tmp/6buq71384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/7di1i1384952393.ps tmp/7di1i1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/8l3f61384952393.ps tmp/8l3f61384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/9to6j1384952393.ps tmp/9to6j1384952393.png",intern=TRUE)) character(0) > try(system("convert tmp/10pif81384952393.ps tmp/10pif81384952393.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 21.239 3.434 24.669