R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,41 + ,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(9 + ,264) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '5' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Software month Connected Separate Learning Happiness Depression Belonging 1 12 9 41 38 13 14 12.0 53 2 11 9 39 32 16 18 11.0 83 3 15 9 30 35 19 11 14.0 66 4 6 9 31 33 15 12 12.0 67 5 13 9 34 37 14 16 21.0 76 6 10 9 35 29 13 18 12.0 78 7 12 9 39 31 19 14 22.0 53 8 14 9 34 36 15 14 11.0 80 9 12 9 36 35 14 15 10.0 74 10 9 9 37 38 15 15 13.0 76 11 10 9 38 31 16 17 10.0 79 12 12 9 36 34 16 19 8.0 54 13 12 9 38 35 16 10 15.0 67 14 11 9 39 38 16 16 14.0 54 15 15 9 33 37 17 18 10.0 87 16 12 9 32 33 15 14 14.0 58 17 10 9 36 32 15 14 14.0 75 18 12 9 38 38 20 17 11.0 88 19 11 9 39 38 18 14 10.0 64 20 12 9 32 32 16 16 13.0 57 21 11 9 32 33 16 18 9.5 66 22 12 9 31 31 16 11 14.0 68 23 13 9 39 38 19 14 12.0 54 24 11 9 37 39 16 12 14.0 56 25 12 9 39 32 17 17 11.0 86 26 13 9 41 32 17 9 9.0 80 27 10 9 36 35 16 16 11.0 76 28 14 9 33 37 15 14 15.0 69 29 12 9 33 33 16 15 14.0 78 30 10 9 34 33 14 11 13.0 67 31 12 9 31 31 15 16 9.0 80 32 8 9 27 32 12 13 15.0 54 33 10 9 37 31 14 17 10.0 71 34 12 9 34 37 16 15 11.0 84 35 12 9 34 30 14 14 13.0 74 36 7 9 32 33 10 16 8.0 71 37 9 9 29 31 10 9 20.0 63 38 12 9 36 33 14 15 12.0 71 39 10 9 29 31 16 17 10.0 76 40 10 9 35 33 16 13 10.0 69 41 10 9 37 32 16 15 9.0 74 42 12 9 34 33 14 16 14.0 75 43 15 9 38 32 20 16 8.0 54 44 10 9 35 33 14 12 14.0 52 45 10 9 38 28 14 15 11.0 69 46 12 9 37 35 11 11 13.0 68 47 13 9 38 39 14 15 9.0 65 48 11 9 33 34 15 15 11.0 75 49 11 9 36 38 16 17 15.0 74 50 12 9 38 32 14 13 11.0 75 51 14 9 32 38 16 16 10.0 72 52 10 9 32 30 14 14 14.0 67 53 12 9 32 33 12 11 18.0 63 54 13 9 34 38 16 12 14.0 62 55 5 9 32 32 9 12 11.0 63 56 6 9 37 35 14 15 14.5 76 57 12 9 39 34 16 16 13.0 74 58 12 9 29 34 16 15 9.0 67 59 11 9 37 36 15 12 10.0 73 60 10 9 35 34 16 12 15.0 70 61 7 9 30 28 12 8 20.0 53 62 12 9 38 34 16 13 12.0 77 63 14 9 34 35 16 11 12.0 80 64 11 9 31 35 14 14 14.0 52 65 12 9 34 31 16 15 13.0 54 66 13 10 35 37 17 10 11.0 80 67 14 10 36 35 18 11 17.0 66 68 11 10 30 27 18 12 12.0 73 69 12 10 39 40 12 15 13.0 63 70 12 10 35 37 16 15 14.0 69 71 8 10 38 36 10 14 13.0 67 72 11 10 31 38 14 16 15.0 54 73 14 10 34 39 18 15 13.0 81 74 14 10 38 41 18 15 10.0 69 75 12 10 34 27 16 13 11.0 84 76 9 10 39 30 17 12 19.0 80 77 13 10 37 37 16 17 13.0 70 78 11 10 34 31 16 13 17.0 69 79 12 10 28 31 13 15 13.0 77 80 12 10 37 27 16 13 9.0 54 81 12 10 33 36 16 15 11.0 79 82 12 10 35 37 16 15 9.0 71 83 12 10 37 33 15 16 12.0 73 84 11 10 32 34 15 15 12.0 72 85 10 10 33 31 16 14 13.0 77 86 9 10 38 39 14 15 13.0 75 87 12 10 33 34 16 14 12.0 69 88 12 10 29 32 16 13 15.0 54 89 12 10 33 33 15 7 22.0 70 90 9 10 31 36 12 17 13.0 73 91 15 10 36 32 17 13 15.0 54 92 12 10 35 41 16 15 13.0 77 93 12 10 32 28 15 14 15.0 82 94 12 10 29 30 13 13 12.5 80 95 10 10 39 36 16 16 11.0 80 96 13 10 37 35 16 12 16.0 69 97 9 10 35 31 16 14 11.0 78 98 12 10 37 34 16 17 11.0 81 99 10 10 32 36 14 15 10.0 76 100 14 10 38 36 16 17 10.0 76 101 11 10 37 35 16 12 16.0 73 102 15 10 36 37 20 16 12.0 85 103 11 10 32 28 15 11 11.0 66 104 11 10 33 39 16 15 16.0 79 105 12 10 40 32 13 9 19.0 68 106 12 10 38 35 17 16 11.0 76 107 12 10 41 39 16 15 16.0 71 108 11 10 36 35 16 10 15.0 54 109 7 10 43 42 12 10 24.0 46 110 12 10 30 34 16 15 14.0 85 111 14 10 31 33 16 11 15.0 74 112 11 10 32 41 17 13 11.0 88 113 11 10 32 33 13 14 15.0 38 114 10 10 37 34 12 18 12.0 76 115 13 10 37 32 18 16 10.0 86 116 13 10 33 40 14 14 14.0 54 117 8 10 34 40 14 14 13.0 67 118 11 10 33 35 13 14 9.0 69 119 12 10 38 36 16 14 15.0 90 120 11 10 33 37 13 12 15.0 54 121 13 10 31 27 16 14 14.0 76 122 12 10 38 39 13 15 11.0 89 123 14 10 37 38 16 15 8.0 76 124 13 10 36 31 15 15 11.0 73 125 15 10 31 33 16 13 11.0 79 126 10 10 39 32 15 17 8.0 90 127 11 10 44 39 17 17 10.0 74 128 9 10 33 36 15 19 11.0 81 129 11 10 35 33 12 15 13.0 72 130 10 10 32 33 16 13 11.0 71 131 11 10 28 32 10 9 20.0 66 132 8 10 40 37 16 15 10.0 77 133 11 10 27 30 12 15 15.0 65 134 12 10 37 38 14 15 12.0 74 135 12 10 32 29 15 16 14.0 85 136 9 10 28 22 13 11 23.0 54 137 11 10 34 35 15 14 14.0 63 138 10 10 30 35 11 11 16.0 54 139 8 10 35 34 12 15 11.0 64 140 9 10 31 35 11 13 12.0 69 141 8 10 32 34 16 15 10.0 54 142 9 10 30 37 15 16 14.0 84 143 15 10 30 35 17 14 12.0 86 144 11 10 31 23 16 15 12.0 77 145 8 10 40 31 10 16 11.0 89 146 13 10 32 27 18 16 12.0 76 147 12 10 36 36 13 11 13.0 60 148 12 10 32 31 16 12 11.0 75 149 9 10 35 32 13 9 19.0 73 150 7 10 38 39 10 16 12.0 85 151 13 10 42 37 15 13 17.0 79 152 9 10 34 38 16 16 9.0 71 153 6 10 35 39 16 12 12.0 72 154 8 9 38 34 14 9 19.0 69 155 8 10 33 31 10 13 18.0 78 156 15 10 36 32 17 13 15.0 54 157 6 10 32 37 13 14 14.0 69 158 9 10 33 36 15 19 11.0 81 159 11 10 34 32 16 13 9.0 84 160 8 10 32 38 12 12 18.0 84 161 8 10 34 36 13 13 16.0 69 162 10 11 27 26 13 10 24.0 66 163 8 11 31 26 12 14 14.0 81 164 14 11 38 33 17 16 20.0 82 165 10 11 34 39 15 10 18.0 72 166 8 11 24 30 10 11 23.0 54 167 11 11 30 33 14 14 12.0 78 168 12 11 26 25 11 12 14.0 74 169 12 11 34 38 13 9 16.0 82 170 12 11 27 37 16 9 18.0 73 171 5 11 37 31 12 11 20.0 55 172 12 11 36 37 16 16 12.0 72 173 10 11 41 35 12 9 12.0 78 174 7 11 29 25 9 13 17.0 59 175 12 11 36 28 12 16 13.0 72 176 11 11 32 35 15 13 9.0 78 177 8 11 37 33 12 9 16.0 68 178 9 11 30 30 12 12 18.0 69 179 10 11 31 31 14 16 10.0 67 180 9 11 38 37 12 11 14.0 74 181 12 11 36 36 16 14 11.0 54 182 6 11 35 30 11 13 9.0 67 183 15 11 31 36 19 15 11.0 70 184 12 11 38 32 15 14 10.0 80 185 12 11 22 28 8 16 11.0 89 186 12 11 32 36 16 13 19.0 76 187 11 11 36 34 17 14 14.0 74 188 7 11 39 31 12 15 12.0 87 189 7 11 28 28 11 13 14.0 54 190 5 11 32 36 11 11 21.0 61 191 12 11 32 36 14 11 13.0 38 192 12 11 38 40 16 14 10.0 75 193 3 11 32 33 12 15 15.0 69 194 11 11 35 37 16 11 16.0 62 195 10 11 32 32 13 15 14.0 72 196 12 11 37 38 15 12 12.0 70 197 9 11 34 31 16 14 19.0 79 198 12 11 33 37 16 14 15.0 87 199 9 11 33 33 14 8 19.0 62 200 12 11 26 32 16 13 13.0 77 201 12 11 30 30 16 9 17.0 69 202 10 11 24 30 14 15 12.0 69 203 9 11 34 31 11 17 11.0 75 204 12 11 34 32 12 13 14.0 54 205 8 11 33 34 15 15 11.0 72 206 11 11 34 36 15 15 13.0 74 207 11 11 35 37 16 14 12.0 85 208 12 11 35 36 16 16 15.0 52 209 10 11 36 33 11 13 14.0 70 210 10 11 34 33 15 16 12.0 84 211 12 11 34 33 12 9 17.0 64 212 12 11 41 44 12 16 11.0 84 213 11 11 32 39 15 11 18.0 87 214 8 11 30 32 15 10 13.0 79 215 12 11 35 35 16 11 17.0 67 216 10 11 28 25 14 15 13.0 65 217 11 11 33 35 17 17 11.0 85 218 10 11 39 34 14 14 12.0 83 219 8 11 36 35 13 8 22.0 61 220 12 11 36 39 15 15 14.0 82 221 12 11 35 33 13 11 12.0 76 222 10 11 38 36 14 16 12.0 58 223 12 11 33 32 15 10 17.0 72 224 9 11 31 32 12 15 9.0 72 225 9 11 34 36 13 9 21.0 38 226 6 11 32 36 8 16 10.0 78 227 10 11 31 32 14 19 11.0 54 228 9 11 33 34 14 12 12.0 63 229 9 11 34 33 11 8 23.0 66 230 9 11 34 35 12 11 13.0 70 231 6 11 34 30 13 14 12.0 71 232 10 11 33 38 10 9 16.0 67 233 6 11 32 34 16 15 9.0 58 234 14 11 41 33 18 13 17.0 72 235 10 11 34 32 13 16 9.0 72 236 10 11 36 31 11 11 14.0 70 237 6 11 37 30 4 12 17.0 76 238 12 11 36 27 13 13 13.0 50 239 12 11 29 31 16 10 11.0 72 240 7 11 37 30 10 11 12.0 72 241 8 11 27 32 12 12 10.0 88 242 11 11 35 35 12 8 19.0 53 243 3 11 28 28 10 12 16.0 58 244 6 11 35 33 13 12 16.0 66 245 10 11 37 31 15 15 14.0 82 246 8 11 29 35 12 11 20.0 69 247 9 11 32 35 14 13 15.0 68 248 9 11 36 32 10 14 23.0 44 249 8 11 19 21 12 10 20.0 56 250 9 11 21 20 12 12 16.0 53 251 7 11 31 34 11 15 14.0 70 252 7 11 33 32 10 13 17.0 78 253 6 11 36 34 12 13 11.0 71 254 9 11 33 32 16 13 13.0 72 255 10 11 37 33 12 12 17.0 68 256 11 11 34 33 14 12 15.0 67 257 12 11 35 37 16 9 21.0 75 258 8 11 31 32 14 9 18.0 62 259 11 11 37 34 13 15 15.0 67 260 3 11 35 30 4 10 8.0 83 261 11 11 27 30 15 14 12.0 64 262 12 11 34 38 11 15 12.0 68 263 7 11 40 36 11 7 22.0 62 264 9 11 29 32 14 14 12.0 72 Belonging_Final 1 32 2 51 3 42 4 41 5 46 6 47 7 37 8 49 9 45 10 47 11 49 12 33 13 42 14 33 15 53 16 36 17 45 18 54 19 41 20 36 21 41 22 44 23 33 24 37 25 52 26 47 27 43 28 44 29 45 30 44 31 49 32 33 33 43 34 54 35 42 36 44 37 37 38 43 39 46 40 42 41 45 42 44 43 33 44 31 45 42 46 40 47 43 48 46 49 42 50 45 51 44 52 40 53 37 54 46 55 36 56 47 57 45 58 42 59 43 60 43 61 32 62 45 63 48 64 31 65 33 66 49 67 42 68 41 69 38 70 42 71 44 72 33 73 48 74 40 75 50 76 49 77 43 78 44 79 47 80 33 81 46 82 45 83 43 84 44 85 47 86 45 87 42 88 33 89 43 90 46 91 33 92 46 93 48 94 47 95 47 96 43 97 46 98 48 99 46 100 45 101 45 102 52 103 42 104 47 105 41 106 47 107 43 108 33 109 30 110 52 111 44 112 55 113 11 114 47 115 53 116 33 117 44 118 42 119 55 120 33 121 46 122 54 123 47 124 45 125 47 126 55 127 44 128 53 129 44 130 42 131 40 132 46 133 40 134 46 135 53 136 33 137 42 138 35 139 40 140 41 141 33 142 51 143 53 144 46 145 55 146 47 147 38 148 46 149 46 150 53 151 47 152 41 153 44 154 43 155 51 156 33 157 43 158 53 159 51 160 50 161 46 162 43 163 47 164 50 165 43 166 33 167 48 168 44 169 50 170 41 171 34 172 44 173 47 174 35 175 44 176 44 177 43 178 41 179 41 180 42 181 33 182 41 183 44 184 48 185 55 186 44 187 43 188 52 189 30 190 39 191 11 192 44 193 42 194 41 195 44 196 44 197 48 198 53 199 37 200 44 201 44 202 40 203 42 204 35 205 43 206 45 207 55 208 31 209 44 210 50 211 40 212 53 213 54 214 49 215 40 216 41 217 52 218 52 219 36 220 52 221 46 222 31 223 44 224 44 225 11 226 46 227 33 228 34 229 42 230 43 231 43 232 44 233 36 234 46 235 44 236 43 237 50 238 33 239 43 240 44 241 53 242 34 243 35 244 40 245 53 246 42 247 43 248 29 249 36 250 30 251 42 252 47 253 44 254 45 255 44 256 43 257 43 258 40 259 41 260 52 261 38 262 41 263 39 264 43 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Connected Separate 4.293692 -0.258721 -0.017572 0.038646 Learning Happiness Depression Belonging 0.552864 -0.016433 -0.002358 0.021424 Belonging_Final -0.022855 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.0316 -1.0536 0.1702 1.1913 5.0730 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.293692 2.600565 1.651 0.100 . month -0.258721 0.157399 -1.644 0.101 Connected -0.017572 0.034098 -0.515 0.607 Separate 0.038646 0.034580 1.118 0.265 Learning 0.552864 0.050646 10.916 <2e-16 *** Happiness -0.016433 0.056869 -0.289 0.773 Depression -0.002358 0.041604 -0.057 0.955 Belonging 0.021424 0.037015 0.579 0.563 Belonging_Final -0.022855 0.054974 -0.416 0.678 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.827 on 255 degrees of freedom Multiple R-squared: 0.3988, Adjusted R-squared: 0.38 F-statistic: 21.15 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,] 0.995288119 0.009423763 0.004711881 [2,] 0.988628935 0.022742131 0.011371065 [3,] 0.987443776 0.025112447 0.012556224 [4,] 0.979137119 0.041725761 0.020862881 [5,] 0.969913689 0.060172623 0.030086311 [6,] 0.950353739 0.099292523 0.049646261 [7,] 0.947033187 0.105933626 0.052966813 [8,] 0.934055949 0.131888101 0.065944051 [9,] 0.902607665 0.194784669 0.097392335 [10,] 0.874512147 0.250975706 0.125487853 [11,] 0.833787476 0.332425048 0.166212524 [12,] 0.793805486 0.412389029 0.206194514 [13,] 0.758427285 0.483145430 0.241572715 [14,] 0.710404303 0.579191395 0.289595697 [15,] 0.707345393 0.585309213 0.292654607 [16,] 0.723698455 0.552603090 0.276301545 [17,] 0.729344371 0.541311258 0.270655629 [18,] 0.672418123 0.655163753 0.327581877 [19,] 0.626112961 0.747774079 0.373887039 [20,] 0.575335392 0.849329216 0.424664608 [21,] 0.591577054 0.816845891 0.408422946 [22,] 0.531400269 0.937199463 0.468599731 [23,] 0.474741501 0.949483002 0.525258499 [24,] 0.454007174 0.908014348 0.545992826 [25,] 0.441280654 0.882561309 0.558719346 [26,] 0.386553532 0.773107064 0.613446468 [27,] 0.367179839 0.734359678 0.632820161 [28,] 0.347779026 0.695558052 0.652220974 [29,] 0.322160539 0.644321079 0.677839461 [30,] 0.291593621 0.583187243 0.708406379 [31,] 0.259288938 0.518577876 0.740711062 [32,] 0.300275970 0.600551940 0.699724030 [33,] 0.258930341 0.517860682 0.741069659 [34,] 0.219371119 0.438742239 0.780628881 [35,] 0.256113420 0.512226840 0.743886580 [36,] 0.273908789 0.547817577 0.726091211 [37,] 0.233786041 0.467572082 0.766213959 [38,] 0.222178124 0.444356248 0.777821876 [39,] 0.204410331 0.408820662 0.795589669 [40,] 0.210020643 0.420041286 0.789979357 [41,] 0.177626832 0.355253665 0.822373168 [42,] 0.181403283 0.362806567 0.818596717 [43,] 0.159659132 0.319318265 0.840340868 [44,] 0.238787585 0.477575169 0.761212415 [45,] 0.519353261 0.961293477 0.480646739 [46,] 0.475544607 0.951089214 0.524455393 [47,] 0.431681150 0.863362299 0.568318850 [48,] 0.390406716 0.780813431 0.609593284 [49,] 0.390023868 0.780047736 0.609976132 [50,] 0.402732744 0.805465489 0.597267256 [51,] 0.361823983 0.723647966 0.638176017 [52,] 0.365330933 0.730661866 0.634669067 [53,] 0.325825421 0.651650841 0.674174579 [54,] 0.297906168 0.595812335 0.702093832 [55,] 0.262263968 0.524527936 0.737736032 [56,] 0.236243320 0.472486640 0.763756680 [57,] 0.220218747 0.440437494 0.779781253 [58,] 0.210157155 0.420314310 0.789842845 [59,] 0.184168063 0.368336126 0.815831937 [60,] 0.166639911 0.333279822 0.833360089 [61,] 0.143212184 0.286424369 0.856787816 [62,] 0.122692289 0.245384578 0.877307711 [63,] 0.104733317 0.209466634 0.895266683 [64,] 0.092537735 0.185075470 0.907462265 [65,] 0.119420080 0.238840159 0.880579920 [66,] 0.106752911 0.213505822 0.893247089 [67,] 0.089410454 0.178820908 0.910589546 [68,] 0.092063392 0.184126784 0.907936608 [69,] 0.084459856 0.168919713 0.915540144 [70,] 0.070945887 0.141891773 0.929054113 [71,] 0.059324096 0.118648192 0.940675904 [72,] 0.050398676 0.100797352 0.949601324 [73,] 0.041589040 0.083178080 0.958410960 [74,] 0.039570071 0.079140142 0.960429929 [75,] 0.046391709 0.092783418 0.953608291 [76,] 0.037641260 0.075282521 0.962358740 [77,] 0.030583660 0.061167320 0.969416340 [78,] 0.025663328 0.051326656 0.974336672 [79,] 0.021807731 0.043615461 0.978192269 [80,] 0.033580137 0.067160274 0.966419863 [81,] 0.028109619 0.056219238 0.971890381 [82,] 0.025008914 0.050017828 0.974991086 [83,] 0.025560481 0.051120961 0.974439519 [84,] 0.026612606 0.053225212 0.973387394 [85,] 0.023541395 0.047082791 0.976458605 [86,] 0.030355771 0.060711541 0.969644229 [87,] 0.024511605 0.049023211 0.975488395 [88,] 0.021098579 0.042197158 0.978901421 [89,] 0.023701688 0.047403375 0.976298312 [90,] 0.019847651 0.039695303 0.980152349 [91,] 0.016695317 0.033390634 0.983304683 [92,] 0.013173468 0.026346936 0.986826532 [93,] 0.011810987 0.023621975 0.988189013 [94,] 0.012620181 0.025240361 0.987379819 [95,] 0.009919131 0.019838261 0.990080869 [96,] 0.007793085 0.015586170 0.992206915 [97,] 0.006504457 0.013008915 0.993495543 [98,] 0.009465834 0.018931667 0.990534166 [99,] 0.007367106 0.014734212 0.992632894 [100,] 0.008197941 0.016395882 0.991802059 [101,] 0.008699172 0.017398343 0.991300828 [102,] 0.007100268 0.014200535 0.992899732 [103,] 0.005670647 0.011341294 0.994329353 [104,] 0.004388911 0.008777821 0.995611089 [105,] 0.004986207 0.009972413 0.995013793 [106,] 0.007449119 0.014898238 0.992550881 [107,] 0.006154547 0.012309094 0.993845453 [108,] 0.004762102 0.009524204 0.995237898 [109,] 0.003956612 0.007913223 0.996043388 [110,] 0.003725207 0.007450414 0.996274793 [111,] 0.003700956 0.007401912 0.996299044 [112,] 0.004291389 0.008582779 0.995708611 [113,] 0.004770365 0.009540731 0.995229635 [114,] 0.008568541 0.017137083 0.991431459 [115,] 0.007237095 0.014474189 0.992762905 [116,] 0.006294190 0.012588380 0.993705810 [117,] 0.006697998 0.013395997 0.993302002 [118,] 0.006612235 0.013224470 0.993387765 [119,] 0.006704054 0.013408107 0.993295946 [120,] 0.008853805 0.017707610 0.991146195 [121,] 0.017741414 0.035482829 0.982258586 [122,] 0.017739206 0.035478413 0.982260794 [123,] 0.016999057 0.033998113 0.983000943 [124,] 0.015050948 0.030101897 0.984949052 [125,] 0.012361422 0.024722843 0.987638578 [126,] 0.009989885 0.019979770 0.990010115 [127,] 0.009595934 0.019191867 0.990404066 [128,] 0.008605344 0.017210687 0.991394656 [129,] 0.007371290 0.014742580 0.992628710 [130,] 0.013035202 0.026070404 0.986964798 [131,] 0.014705970 0.029411940 0.985294030 [132,] 0.021288632 0.042577265 0.978711368 [133,] 0.017174690 0.034349379 0.982825310 [134,] 0.013753256 0.027506512 0.986246744 [135,] 0.011643513 0.023287025 0.988356487 [136,] 0.014874539 0.029749077 0.985125461 [137,] 0.013210557 0.026421114 0.986789443 [138,] 0.011189736 0.022379473 0.988810264 [139,] 0.009866584 0.019733169 0.990133416 [140,] 0.012208032 0.024416065 0.987791968 [141,] 0.014430356 0.028860711 0.985569644 [142,] 0.064967118 0.129934237 0.935032882 [143,] 0.065110366 0.130220731 0.934889634 [144,] 0.055983468 0.111966936 0.944016532 [145,] 0.106070044 0.212140089 0.893929956 [146,] 0.147699772 0.295399543 0.852300228 [147,] 0.138783287 0.277566574 0.861216713 [148,] 0.124655039 0.249310078 0.875344961 [149,] 0.114722413 0.229444825 0.885277587 [150,] 0.104785642 0.209571285 0.895214358 [151,] 0.090790423 0.181580846 0.909209577 [152,] 0.081633509 0.163267019 0.918366491 [153,] 0.086501494 0.173002988 0.913498506 [154,] 0.078275682 0.156551365 0.921724318 [155,] 0.066428788 0.132857576 0.933571212 [156,] 0.056390556 0.112781113 0.943609444 [157,] 0.093537063 0.187074127 0.906462937 [158,] 0.094392226 0.188784452 0.905607774 [159,] 0.082005263 0.164010526 0.917994737 [160,] 0.143740430 0.287480860 0.856259570 [161,] 0.124507842 0.249015685 0.875492158 [162,] 0.109399888 0.218799775 0.890600112 [163,] 0.093782679 0.187565359 0.906217321 [164,] 0.129414737 0.258829474 0.870585263 [165,] 0.111975807 0.223951614 0.888024193 [166,] 0.100498109 0.200996219 0.899501891 [167,] 0.085930270 0.171860540 0.914069730 [168,] 0.072329895 0.144659791 0.927670105 [169,] 0.060450041 0.120900083 0.939549959 [170,] 0.050370531 0.100741061 0.949629469 [171,] 0.057231259 0.114462518 0.942768741 [172,] 0.057436152 0.114872305 0.942563848 [173,] 0.053156215 0.106312431 0.946843785 [174,] 0.215285124 0.430570249 0.784714876 [175,] 0.197781733 0.395563466 0.802218267 [176,] 0.176640089 0.353280177 0.823359911 [177,] 0.175793968 0.351587937 0.824206032 [178,] 0.162464375 0.324928750 0.837535625 [179,] 0.240843875 0.481687750 0.759156125 [180,] 0.245853260 0.491706521 0.754146740 [181,] 0.216573598 0.433147197 0.783426402 [182,] 0.580722911 0.838554178 0.419277089 [183,] 0.548982205 0.902035590 0.451017795 [184,] 0.511899023 0.976201953 0.488100977 [185,] 0.480157965 0.960315930 0.519842035 [186,] 0.492110529 0.984221059 0.507889471 [187,] 0.456077706 0.912155412 0.543922294 [188,] 0.428647641 0.857295282 0.571352359 [189,] 0.429815637 0.859631273 0.570184363 [190,] 0.404188183 0.808376365 0.595811817 [191,] 0.382750820 0.765501640 0.617249180 [192,] 0.361352390 0.722704781 0.638647610 [193,] 0.414483041 0.828966081 0.585516959 [194,] 0.444379548 0.888759096 0.555620452 [195,] 0.401835197 0.803670394 0.598164803 [196,] 0.361709479 0.723418959 0.638290521 [197,] 0.323588293 0.647176586 0.676411707 [198,] 0.306288631 0.612577263 0.693711369 [199,] 0.269288550 0.538577099 0.730711450 [200,] 0.333280824 0.666561648 0.666719176 [201,] 0.350849700 0.701699400 0.649150300 [202,] 0.309713533 0.619427066 0.690286467 [203,] 0.327624922 0.655249844 0.672375078 [204,] 0.293004219 0.586008439 0.706995781 [205,] 0.257811753 0.515623505 0.742188247 [206,] 0.223142737 0.446285473 0.776857263 [207,] 0.188551105 0.377102210 0.811448895 [208,] 0.186130097 0.372260195 0.813869903 [209,] 0.164180990 0.328361981 0.835819010 [210,] 0.203466465 0.406932930 0.796533535 [211,] 0.169860604 0.339721208 0.830139396 [212,] 0.162483953 0.324967907 0.837516047 [213,] 0.139149791 0.278299582 0.860850209 [214,] 0.113435176 0.226870351 0.886564824 [215,] 0.091434032 0.182868065 0.908565968 [216,] 0.072441373 0.144882747 0.927558627 [217,] 0.057051659 0.114103318 0.942948341 [218,] 0.042970878 0.085941757 0.957029122 [219,] 0.032217735 0.064435470 0.967782265 [220,] 0.047009874 0.094019748 0.952990126 [221,] 0.057923991 0.115847982 0.942076009 [222,] 0.225139617 0.450279235 0.774860383 [223,] 0.198299432 0.396598863 0.801700568 [224,] 0.158049872 0.316099744 0.841950128 [225,] 0.156670805 0.313341609 0.843329195 [226,] 0.188508404 0.377016808 0.811491596 [227,] 0.203717564 0.407435128 0.796282436 [228,] 0.194767188 0.389534377 0.805232812 [229,] 0.154996906 0.309993812 0.845003094 [230,] 0.122273697 0.244547395 0.877726303 [231,] 0.154417277 0.308834555 0.845582723 [232,] 0.405514984 0.811029968 0.594485016 [233,] 0.596306813 0.807386373 0.403693187 [234,] 0.512311836 0.975376328 0.487688164 [235,] 0.437211638 0.874423275 0.562788362 [236,] 0.359998505 0.719997009 0.640001495 [237,] 0.295332148 0.590664296 0.704667852 [238,] 0.211514200 0.423028399 0.788485800 [239,] 0.160220574 0.320441148 0.839779426 [240,] 0.237560797 0.475121595 0.762439203 [241,] 0.410921366 0.821842732 0.589078634 > postscript(file="/var/fisher/rcomp/tmp/15yog1353259629.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/2kron1353259629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3qvzh1353259629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4vdjm1353259629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5th3m1353259629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 1.95373453 -0.65321729 1.46465336 -5.26158782 2.19782671 0.06908159 7 8 9 10 11 12 -0.99021228 2.61002967 1.28788139 -2.35341159 -1.61094980 0.43602844 13 14 15 16 17 18 0.22832568 -0.70098850 2.45291039 0.87210533 -1.17746714 -2.16911010 19 20 21 22 23 24 -1.88041299 0.40981872 -0.68275419 0.29826569 -0.39716344 -0.79193847 25 26 27 28 29 30 -0.26393045 0.64930106 -1.88761298 2.68463021 0.13046745 -0.60151642 31 32 33 34 35 36 0.77869335 -1.51546225 -0.48853427 0.06353245 1.36804528 -1.44052638 37 38 39 40 41 42 0.50872353 1.38845173 -1.77339425 -1.75243897 -1.68669410 1.31161701 43 44 45 46 47 48 1.28771025 -0.54091241 -0.36553830 2.91965650 2.31318583 -0.27526364 49 50 51 52 53 54 -0.95769304 1.38703354 2.03235055 -0.56048503 2.40656877 1.27114382 55 56 57 58 59 60 -2.91912314 -4.68107113 0.29702427 0.17683600 -0.35964185 -1.79429512 61 62 63 64 65 66 -2.37996456 0.16352370 2.02601634 0.34437180 0.46288270 0.67621668 67 68 69 70 71 72 1.38874723 -1.57569605 2.59456685 0.39399623 -0.12768981 0.52524083 73 74 75 76 77 78 1.07109043 1.13125632 0.58443025 -2.93123759 1.46107963 -0.37177923 79 80 81 82 83 84 2.10198731 0.88660634 0.26760588 0.40792209 1.08546643 -0.01319548 85 86 87 88 89 90 -1.48517658 -1.58718678 0.45364084 0.56694684 0.95513548 -0.38995740 91 92 93 94 95 96 3.13708870 0.15708320 1.08650727 2.05988969 -1.60909618 1.48470623 97 98 99 100 101 102 -2.49902743 0.45091605 -0.58232541 2.42739092 -0.55527856 1.09759911 103 104 105 106 107 108 0.23342424 -0.81368616 2.24544417 -0.05519149 0.40686169 -0.47528482 109 110 111 112 113 114 -2.28729650 0.30784447 2.35351028 -1.51604601 1.09601177 0.76542732 115 116 117 118 119 120 0.41084559 2.44786862 -2.56402050 1.06651186 0.31850211 1.08616402 121 122 123 124 125 126 1.63519099 1.86672387 2.34065353 2.17210542 3.33838926 -0.92368605 127 128 129 130 131 132 -1.11598592 -1.99666058 1.73911835 -1.58692387 2.71551989 -3.60754536 133 134 135 136 137 138 1.77774150 1.47580757 1.12837246 -0.41956943 0.11868989 1.24810339 139 140 141 142 143 144 -1.22427504 0.10488264 -3.43655474 -2.24022859 2.69661574 -0.21993139 145 146 147 148 149 150 -0.09108109 0.59803922 2.14211032 0.47966095 -0.83526071 -1.39305268 151 152 153 154 155 156 1.94411351 -2.72328356 -5.75587293 -2.65429174 -0.10263671 3.13708870 157 158 159 160 161 162 -3.99370619 -1.99666058 -0.59066281 -1.66429130 -1.86306656 0.62438526 163 164 165 166 167 168 -0.94024877 2.24207003 -1.00343087 0.11828463 0.74833622 3.61193523 169 170 171 172 173 174 2.06554203 0.31442492 -3.80329397 0.66344466 0.86504731 -0.09045122 175 176 177 178 179 180 3.22507593 0.03839609 -0.99569887 -0.01588422 -0.05297410 -0.25595973 181 182 183 184 185 186 0.80108826 -2.33710352 1.97969328 1.32713252 5.07300589 0.51331663 187 188 189 190 191 192 -0.86733224 -1.99545281 -1.34392214 -3.54343144 1.63191431 0.48079671 193 194 195 196 197 198 -6.03159970 -0.28118691 0.43326301 1.17235043 -2.21472558 0.46927652 199 200 201 202 203 204 -1.18966195 0.52689421 0.78956716 -0.21475246 0.52859177 3.16833739 205 206 207 208 209 210 -2.76211556 0.18574350 -0.41409655 0.82295266 1.58061563 -0.78420509 211 212 213 214 215 216 2.97107243 2.63849093 -0.09209294 -2.82782373 0.66849046 0.15967621 217 218 219 220 221 222 -0.94643725 -0.14785799 -1.65572977 1.09590303 2.33690032 -0.15421954 223 224 225 226 227 228 1.27001741 -0.04323692 -0.79407607 -1.03283416 0.05570487 -1.26907366 229 230 231 232 233 234 0.52451633 -0.14276484 -3.47688084 1.89078760 -5.19732188 1.80836588 235 236 237 238 239 240 0.47304875 1.60218728 1.58340551 2.88147564 0.64850531 -0.81343915 241 242 243 244 245 246 -1.29755069 1.99816661 -4.77419403 -3.56012635 -0.55449950 -1.21554909 247 248 249 250 251 252 -1.20320765 1.42397529 -0.72527896 0.29908541 -1.55873619 -0.97634037 253 254 255 256 257 258 -3.03939352 -2.22012629 1.07881332 0.91422022 0.46494085 -2.10352063 259 260 261 262 263 264 1.48474372 -1.61008602 0.33007583 3.35467126 -1.48765030 -1.21632261 > postscript(file="/var/fisher/rcomp/tmp/6gx471353259629.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 1.95373453 NA 1 -0.65321729 1.95373453 2 1.46465336 -0.65321729 3 -5.26158782 1.46465336 4 2.19782671 -5.26158782 5 0.06908159 2.19782671 6 -0.99021228 0.06908159 7 2.61002967 -0.99021228 8 1.28788139 2.61002967 9 -2.35341159 1.28788139 10 -1.61094980 -2.35341159 11 0.43602844 -1.61094980 12 0.22832568 0.43602844 13 -0.70098850 0.22832568 14 2.45291039 -0.70098850 15 0.87210533 2.45291039 16 -1.17746714 0.87210533 17 -2.16911010 -1.17746714 18 -1.88041299 -2.16911010 19 0.40981872 -1.88041299 20 -0.68275419 0.40981872 21 0.29826569 -0.68275419 22 -0.39716344 0.29826569 23 -0.79193847 -0.39716344 24 -0.26393045 -0.79193847 25 0.64930106 -0.26393045 26 -1.88761298 0.64930106 27 2.68463021 -1.88761298 28 0.13046745 2.68463021 29 -0.60151642 0.13046745 30 0.77869335 -0.60151642 31 -1.51546225 0.77869335 32 -0.48853427 -1.51546225 33 0.06353245 -0.48853427 34 1.36804528 0.06353245 35 -1.44052638 1.36804528 36 0.50872353 -1.44052638 37 1.38845173 0.50872353 38 -1.77339425 1.38845173 39 -1.75243897 -1.77339425 40 -1.68669410 -1.75243897 41 1.31161701 -1.68669410 42 1.28771025 1.31161701 43 -0.54091241 1.28771025 44 -0.36553830 -0.54091241 45 2.91965650 -0.36553830 46 2.31318583 2.91965650 47 -0.27526364 2.31318583 48 -0.95769304 -0.27526364 49 1.38703354 -0.95769304 50 2.03235055 1.38703354 51 -0.56048503 2.03235055 52 2.40656877 -0.56048503 53 1.27114382 2.40656877 54 -2.91912314 1.27114382 55 -4.68107113 -2.91912314 56 0.29702427 -4.68107113 57 0.17683600 0.29702427 58 -0.35964185 0.17683600 59 -1.79429512 -0.35964185 60 -2.37996456 -1.79429512 61 0.16352370 -2.37996456 62 2.02601634 0.16352370 63 0.34437180 2.02601634 64 0.46288270 0.34437180 65 0.67621668 0.46288270 66 1.38874723 0.67621668 67 -1.57569605 1.38874723 68 2.59456685 -1.57569605 69 0.39399623 2.59456685 70 -0.12768981 0.39399623 71 0.52524083 -0.12768981 72 1.07109043 0.52524083 73 1.13125632 1.07109043 74 0.58443025 1.13125632 75 -2.93123759 0.58443025 76 1.46107963 -2.93123759 77 -0.37177923 1.46107963 78 2.10198731 -0.37177923 79 0.88660634 2.10198731 80 0.26760588 0.88660634 81 0.40792209 0.26760588 82 1.08546643 0.40792209 83 -0.01319548 1.08546643 84 -1.48517658 -0.01319548 85 -1.58718678 -1.48517658 86 0.45364084 -1.58718678 87 0.56694684 0.45364084 88 0.95513548 0.56694684 89 -0.38995740 0.95513548 90 3.13708870 -0.38995740 91 0.15708320 3.13708870 92 1.08650727 0.15708320 93 2.05988969 1.08650727 94 -1.60909618 2.05988969 95 1.48470623 -1.60909618 96 -2.49902743 1.48470623 97 0.45091605 -2.49902743 98 -0.58232541 0.45091605 99 2.42739092 -0.58232541 100 -0.55527856 2.42739092 101 1.09759911 -0.55527856 102 0.23342424 1.09759911 103 -0.81368616 0.23342424 104 2.24544417 -0.81368616 105 -0.05519149 2.24544417 106 0.40686169 -0.05519149 107 -0.47528482 0.40686169 108 -2.28729650 -0.47528482 109 0.30784447 -2.28729650 110 2.35351028 0.30784447 111 -1.51604601 2.35351028 112 1.09601177 -1.51604601 113 0.76542732 1.09601177 114 0.41084559 0.76542732 115 2.44786862 0.41084559 116 -2.56402050 2.44786862 117 1.06651186 -2.56402050 118 0.31850211 1.06651186 119 1.08616402 0.31850211 120 1.63519099 1.08616402 121 1.86672387 1.63519099 122 2.34065353 1.86672387 123 2.17210542 2.34065353 124 3.33838926 2.17210542 125 -0.92368605 3.33838926 126 -1.11598592 -0.92368605 127 -1.99666058 -1.11598592 128 1.73911835 -1.99666058 129 -1.58692387 1.73911835 130 2.71551989 -1.58692387 131 -3.60754536 2.71551989 132 1.77774150 -3.60754536 133 1.47580757 1.77774150 134 1.12837246 1.47580757 135 -0.41956943 1.12837246 136 0.11868989 -0.41956943 137 1.24810339 0.11868989 138 -1.22427504 1.24810339 139 0.10488264 -1.22427504 140 -3.43655474 0.10488264 141 -2.24022859 -3.43655474 142 2.69661574 -2.24022859 143 -0.21993139 2.69661574 144 -0.09108109 -0.21993139 145 0.59803922 -0.09108109 146 2.14211032 0.59803922 147 0.47966095 2.14211032 148 -0.83526071 0.47966095 149 -1.39305268 -0.83526071 150 1.94411351 -1.39305268 151 -2.72328356 1.94411351 152 -5.75587293 -2.72328356 153 -2.65429174 -5.75587293 154 -0.10263671 -2.65429174 155 3.13708870 -0.10263671 156 -3.99370619 3.13708870 157 -1.99666058 -3.99370619 158 -0.59066281 -1.99666058 159 -1.66429130 -0.59066281 160 -1.86306656 -1.66429130 161 0.62438526 -1.86306656 162 -0.94024877 0.62438526 163 2.24207003 -0.94024877 164 -1.00343087 2.24207003 165 0.11828463 -1.00343087 166 0.74833622 0.11828463 167 3.61193523 0.74833622 168 2.06554203 3.61193523 169 0.31442492 2.06554203 170 -3.80329397 0.31442492 171 0.66344466 -3.80329397 172 0.86504731 0.66344466 173 -0.09045122 0.86504731 174 3.22507593 -0.09045122 175 0.03839609 3.22507593 176 -0.99569887 0.03839609 177 -0.01588422 -0.99569887 178 -0.05297410 -0.01588422 179 -0.25595973 -0.05297410 180 0.80108826 -0.25595973 181 -2.33710352 0.80108826 182 1.97969328 -2.33710352 183 1.32713252 1.97969328 184 5.07300589 1.32713252 185 0.51331663 5.07300589 186 -0.86733224 0.51331663 187 -1.99545281 -0.86733224 188 -1.34392214 -1.99545281 189 -3.54343144 -1.34392214 190 1.63191431 -3.54343144 191 0.48079671 1.63191431 192 -6.03159970 0.48079671 193 -0.28118691 -6.03159970 194 0.43326301 -0.28118691 195 1.17235043 0.43326301 196 -2.21472558 1.17235043 197 0.46927652 -2.21472558 198 -1.18966195 0.46927652 199 0.52689421 -1.18966195 200 0.78956716 0.52689421 201 -0.21475246 0.78956716 202 0.52859177 -0.21475246 203 3.16833739 0.52859177 204 -2.76211556 3.16833739 205 0.18574350 -2.76211556 206 -0.41409655 0.18574350 207 0.82295266 -0.41409655 208 1.58061563 0.82295266 209 -0.78420509 1.58061563 210 2.97107243 -0.78420509 211 2.63849093 2.97107243 212 -0.09209294 2.63849093 213 -2.82782373 -0.09209294 214 0.66849046 -2.82782373 215 0.15967621 0.66849046 216 -0.94643725 0.15967621 217 -0.14785799 -0.94643725 218 -1.65572977 -0.14785799 219 1.09590303 -1.65572977 220 2.33690032 1.09590303 221 -0.15421954 2.33690032 222 1.27001741 -0.15421954 223 -0.04323692 1.27001741 224 -0.79407607 -0.04323692 225 -1.03283416 -0.79407607 226 0.05570487 -1.03283416 227 -1.26907366 0.05570487 228 0.52451633 -1.26907366 229 -0.14276484 0.52451633 230 -3.47688084 -0.14276484 231 1.89078760 -3.47688084 232 -5.19732188 1.89078760 233 1.80836588 -5.19732188 234 0.47304875 1.80836588 235 1.60218728 0.47304875 236 1.58340551 1.60218728 237 2.88147564 1.58340551 238 0.64850531 2.88147564 239 -0.81343915 0.64850531 240 -1.29755069 -0.81343915 241 1.99816661 -1.29755069 242 -4.77419403 1.99816661 243 -3.56012635 -4.77419403 244 -0.55449950 -3.56012635 245 -1.21554909 -0.55449950 246 -1.20320765 -1.21554909 247 1.42397529 -1.20320765 248 -0.72527896 1.42397529 249 0.29908541 -0.72527896 250 -1.55873619 0.29908541 251 -0.97634037 -1.55873619 252 -3.03939352 -0.97634037 253 -2.22012629 -3.03939352 254 1.07881332 -2.22012629 255 0.91422022 1.07881332 256 0.46494085 0.91422022 257 -2.10352063 0.46494085 258 1.48474372 -2.10352063 259 -1.61008602 1.48474372 260 0.33007583 -1.61008602 261 3.35467126 0.33007583 262 -1.48765030 3.35467126 263 -1.21632261 -1.48765030 264 NA -1.21632261 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.65321729 1.95373453 [2,] 1.46465336 -0.65321729 [3,] -5.26158782 1.46465336 [4,] 2.19782671 -5.26158782 [5,] 0.06908159 2.19782671 [6,] -0.99021228 0.06908159 [7,] 2.61002967 -0.99021228 [8,] 1.28788139 2.61002967 [9,] -2.35341159 1.28788139 [10,] -1.61094980 -2.35341159 [11,] 0.43602844 -1.61094980 [12,] 0.22832568 0.43602844 [13,] -0.70098850 0.22832568 [14,] 2.45291039 -0.70098850 [15,] 0.87210533 2.45291039 [16,] -1.17746714 0.87210533 [17,] -2.16911010 -1.17746714 [18,] -1.88041299 -2.16911010 [19,] 0.40981872 -1.88041299 [20,] -0.68275419 0.40981872 [21,] 0.29826569 -0.68275419 [22,] -0.39716344 0.29826569 [23,] -0.79193847 -0.39716344 [24,] -0.26393045 -0.79193847 [25,] 0.64930106 -0.26393045 [26,] -1.88761298 0.64930106 [27,] 2.68463021 -1.88761298 [28,] 0.13046745 2.68463021 [29,] -0.60151642 0.13046745 [30,] 0.77869335 -0.60151642 [31,] -1.51546225 0.77869335 [32,] -0.48853427 -1.51546225 [33,] 0.06353245 -0.48853427 [34,] 1.36804528 0.06353245 [35,] -1.44052638 1.36804528 [36,] 0.50872353 -1.44052638 [37,] 1.38845173 0.50872353 [38,] -1.77339425 1.38845173 [39,] -1.75243897 -1.77339425 [40,] -1.68669410 -1.75243897 [41,] 1.31161701 -1.68669410 [42,] 1.28771025 1.31161701 [43,] -0.54091241 1.28771025 [44,] -0.36553830 -0.54091241 [45,] 2.91965650 -0.36553830 [46,] 2.31318583 2.91965650 [47,] -0.27526364 2.31318583 [48,] -0.95769304 -0.27526364 [49,] 1.38703354 -0.95769304 [50,] 2.03235055 1.38703354 [51,] -0.56048503 2.03235055 [52,] 2.40656877 -0.56048503 [53,] 1.27114382 2.40656877 [54,] -2.91912314 1.27114382 [55,] -4.68107113 -2.91912314 [56,] 0.29702427 -4.68107113 [57,] 0.17683600 0.29702427 [58,] -0.35964185 0.17683600 [59,] -1.79429512 -0.35964185 [60,] -2.37996456 -1.79429512 [61,] 0.16352370 -2.37996456 [62,] 2.02601634 0.16352370 [63,] 0.34437180 2.02601634 [64,] 0.46288270 0.34437180 [65,] 0.67621668 0.46288270 [66,] 1.38874723 0.67621668 [67,] -1.57569605 1.38874723 [68,] 2.59456685 -1.57569605 [69,] 0.39399623 2.59456685 [70,] -0.12768981 0.39399623 [71,] 0.52524083 -0.12768981 [72,] 1.07109043 0.52524083 [73,] 1.13125632 1.07109043 [74,] 0.58443025 1.13125632 [75,] -2.93123759 0.58443025 [76,] 1.46107963 -2.93123759 [77,] -0.37177923 1.46107963 [78,] 2.10198731 -0.37177923 [79,] 0.88660634 2.10198731 [80,] 0.26760588 0.88660634 [81,] 0.40792209 0.26760588 [82,] 1.08546643 0.40792209 [83,] -0.01319548 1.08546643 [84,] -1.48517658 -0.01319548 [85,] -1.58718678 -1.48517658 [86,] 0.45364084 -1.58718678 [87,] 0.56694684 0.45364084 [88,] 0.95513548 0.56694684 [89,] -0.38995740 0.95513548 [90,] 3.13708870 -0.38995740 [91,] 0.15708320 3.13708870 [92,] 1.08650727 0.15708320 [93,] 2.05988969 1.08650727 [94,] -1.60909618 2.05988969 [95,] 1.48470623 -1.60909618 [96,] -2.49902743 1.48470623 [97,] 0.45091605 -2.49902743 [98,] -0.58232541 0.45091605 [99,] 2.42739092 -0.58232541 [100,] -0.55527856 2.42739092 [101,] 1.09759911 -0.55527856 [102,] 0.23342424 1.09759911 [103,] -0.81368616 0.23342424 [104,] 2.24544417 -0.81368616 [105,] -0.05519149 2.24544417 [106,] 0.40686169 -0.05519149 [107,] -0.47528482 0.40686169 [108,] -2.28729650 -0.47528482 [109,] 0.30784447 -2.28729650 [110,] 2.35351028 0.30784447 [111,] -1.51604601 2.35351028 [112,] 1.09601177 -1.51604601 [113,] 0.76542732 1.09601177 [114,] 0.41084559 0.76542732 [115,] 2.44786862 0.41084559 [116,] -2.56402050 2.44786862 [117,] 1.06651186 -2.56402050 [118,] 0.31850211 1.06651186 [119,] 1.08616402 0.31850211 [120,] 1.63519099 1.08616402 [121,] 1.86672387 1.63519099 [122,] 2.34065353 1.86672387 [123,] 2.17210542 2.34065353 [124,] 3.33838926 2.17210542 [125,] -0.92368605 3.33838926 [126,] -1.11598592 -0.92368605 [127,] -1.99666058 -1.11598592 [128,] 1.73911835 -1.99666058 [129,] -1.58692387 1.73911835 [130,] 2.71551989 -1.58692387 [131,] -3.60754536 2.71551989 [132,] 1.77774150 -3.60754536 [133,] 1.47580757 1.77774150 [134,] 1.12837246 1.47580757 [135,] -0.41956943 1.12837246 [136,] 0.11868989 -0.41956943 [137,] 1.24810339 0.11868989 [138,] -1.22427504 1.24810339 [139,] 0.10488264 -1.22427504 [140,] -3.43655474 0.10488264 [141,] -2.24022859 -3.43655474 [142,] 2.69661574 -2.24022859 [143,] -0.21993139 2.69661574 [144,] -0.09108109 -0.21993139 [145,] 0.59803922 -0.09108109 [146,] 2.14211032 0.59803922 [147,] 0.47966095 2.14211032 [148,] -0.83526071 0.47966095 [149,] -1.39305268 -0.83526071 [150,] 1.94411351 -1.39305268 [151,] -2.72328356 1.94411351 [152,] -5.75587293 -2.72328356 [153,] -2.65429174 -5.75587293 [154,] -0.10263671 -2.65429174 [155,] 3.13708870 -0.10263671 [156,] -3.99370619 3.13708870 [157,] -1.99666058 -3.99370619 [158,] -0.59066281 -1.99666058 [159,] -1.66429130 -0.59066281 [160,] -1.86306656 -1.66429130 [161,] 0.62438526 -1.86306656 [162,] -0.94024877 0.62438526 [163,] 2.24207003 -0.94024877 [164,] -1.00343087 2.24207003 [165,] 0.11828463 -1.00343087 [166,] 0.74833622 0.11828463 [167,] 3.61193523 0.74833622 [168,] 2.06554203 3.61193523 [169,] 0.31442492 2.06554203 [170,] -3.80329397 0.31442492 [171,] 0.66344466 -3.80329397 [172,] 0.86504731 0.66344466 [173,] -0.09045122 0.86504731 [174,] 3.22507593 -0.09045122 [175,] 0.03839609 3.22507593 [176,] -0.99569887 0.03839609 [177,] -0.01588422 -0.99569887 [178,] -0.05297410 -0.01588422 [179,] -0.25595973 -0.05297410 [180,] 0.80108826 -0.25595973 [181,] -2.33710352 0.80108826 [182,] 1.97969328 -2.33710352 [183,] 1.32713252 1.97969328 [184,] 5.07300589 1.32713252 [185,] 0.51331663 5.07300589 [186,] -0.86733224 0.51331663 [187,] -1.99545281 -0.86733224 [188,] -1.34392214 -1.99545281 [189,] -3.54343144 -1.34392214 [190,] 1.63191431 -3.54343144 [191,] 0.48079671 1.63191431 [192,] -6.03159970 0.48079671 [193,] -0.28118691 -6.03159970 [194,] 0.43326301 -0.28118691 [195,] 1.17235043 0.43326301 [196,] -2.21472558 1.17235043 [197,] 0.46927652 -2.21472558 [198,] -1.18966195 0.46927652 [199,] 0.52689421 -1.18966195 [200,] 0.78956716 0.52689421 [201,] -0.21475246 0.78956716 [202,] 0.52859177 -0.21475246 [203,] 3.16833739 0.52859177 [204,] -2.76211556 3.16833739 [205,] 0.18574350 -2.76211556 [206,] -0.41409655 0.18574350 [207,] 0.82295266 -0.41409655 [208,] 1.58061563 0.82295266 [209,] -0.78420509 1.58061563 [210,] 2.97107243 -0.78420509 [211,] 2.63849093 2.97107243 [212,] -0.09209294 2.63849093 [213,] -2.82782373 -0.09209294 [214,] 0.66849046 -2.82782373 [215,] 0.15967621 0.66849046 [216,] -0.94643725 0.15967621 [217,] -0.14785799 -0.94643725 [218,] -1.65572977 -0.14785799 [219,] 1.09590303 -1.65572977 [220,] 2.33690032 1.09590303 [221,] -0.15421954 2.33690032 [222,] 1.27001741 -0.15421954 [223,] -0.04323692 1.27001741 [224,] -0.79407607 -0.04323692 [225,] -1.03283416 -0.79407607 [226,] 0.05570487 -1.03283416 [227,] -1.26907366 0.05570487 [228,] 0.52451633 -1.26907366 [229,] -0.14276484 0.52451633 [230,] -3.47688084 -0.14276484 [231,] 1.89078760 -3.47688084 [232,] -5.19732188 1.89078760 [233,] 1.80836588 -5.19732188 [234,] 0.47304875 1.80836588 [235,] 1.60218728 0.47304875 [236,] 1.58340551 1.60218728 [237,] 2.88147564 1.58340551 [238,] 0.64850531 2.88147564 [239,] -0.81343915 0.64850531 [240,] -1.29755069 -0.81343915 [241,] 1.99816661 -1.29755069 [242,] -4.77419403 1.99816661 [243,] -3.56012635 -4.77419403 [244,] -0.55449950 -3.56012635 [245,] -1.21554909 -0.55449950 [246,] -1.20320765 -1.21554909 [247,] 1.42397529 -1.20320765 [248,] -0.72527896 1.42397529 [249,] 0.29908541 -0.72527896 [250,] -1.55873619 0.29908541 [251,] -0.97634037 -1.55873619 [252,] -3.03939352 -0.97634037 [253,] -2.22012629 -3.03939352 [254,] 1.07881332 -2.22012629 [255,] 0.91422022 1.07881332 [256,] 0.46494085 0.91422022 [257,] -2.10352063 0.46494085 [258,] 1.48474372 -2.10352063 [259,] -1.61008602 1.48474372 [260,] 0.33007583 -1.61008602 [261,] 3.35467126 0.33007583 [262,] -1.48765030 3.35467126 [263,] -1.21632261 -1.48765030 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.65321729 1.95373453 2 1.46465336 -0.65321729 3 -5.26158782 1.46465336 4 2.19782671 -5.26158782 5 0.06908159 2.19782671 6 -0.99021228 0.06908159 7 2.61002967 -0.99021228 8 1.28788139 2.61002967 9 -2.35341159 1.28788139 10 -1.61094980 -2.35341159 11 0.43602844 -1.61094980 12 0.22832568 0.43602844 13 -0.70098850 0.22832568 14 2.45291039 -0.70098850 15 0.87210533 2.45291039 16 -1.17746714 0.87210533 17 -2.16911010 -1.17746714 18 -1.88041299 -2.16911010 19 0.40981872 -1.88041299 20 -0.68275419 0.40981872 21 0.29826569 -0.68275419 22 -0.39716344 0.29826569 23 -0.79193847 -0.39716344 24 -0.26393045 -0.79193847 25 0.64930106 -0.26393045 26 -1.88761298 0.64930106 27 2.68463021 -1.88761298 28 0.13046745 2.68463021 29 -0.60151642 0.13046745 30 0.77869335 -0.60151642 31 -1.51546225 0.77869335 32 -0.48853427 -1.51546225 33 0.06353245 -0.48853427 34 1.36804528 0.06353245 35 -1.44052638 1.36804528 36 0.50872353 -1.44052638 37 1.38845173 0.50872353 38 -1.77339425 1.38845173 39 -1.75243897 -1.77339425 40 -1.68669410 -1.75243897 41 1.31161701 -1.68669410 42 1.28771025 1.31161701 43 -0.54091241 1.28771025 44 -0.36553830 -0.54091241 45 2.91965650 -0.36553830 46 2.31318583 2.91965650 47 -0.27526364 2.31318583 48 -0.95769304 -0.27526364 49 1.38703354 -0.95769304 50 2.03235055 1.38703354 51 -0.56048503 2.03235055 52 2.40656877 -0.56048503 53 1.27114382 2.40656877 54 -2.91912314 1.27114382 55 -4.68107113 -2.91912314 56 0.29702427 -4.68107113 57 0.17683600 0.29702427 58 -0.35964185 0.17683600 59 -1.79429512 -0.35964185 60 -2.37996456 -1.79429512 61 0.16352370 -2.37996456 62 2.02601634 0.16352370 63 0.34437180 2.02601634 64 0.46288270 0.34437180 65 0.67621668 0.46288270 66 1.38874723 0.67621668 67 -1.57569605 1.38874723 68 2.59456685 -1.57569605 69 0.39399623 2.59456685 70 -0.12768981 0.39399623 71 0.52524083 -0.12768981 72 1.07109043 0.52524083 73 1.13125632 1.07109043 74 0.58443025 1.13125632 75 -2.93123759 0.58443025 76 1.46107963 -2.93123759 77 -0.37177923 1.46107963 78 2.10198731 -0.37177923 79 0.88660634 2.10198731 80 0.26760588 0.88660634 81 0.40792209 0.26760588 82 1.08546643 0.40792209 83 -0.01319548 1.08546643 84 -1.48517658 -0.01319548 85 -1.58718678 -1.48517658 86 0.45364084 -1.58718678 87 0.56694684 0.45364084 88 0.95513548 0.56694684 89 -0.38995740 0.95513548 90 3.13708870 -0.38995740 91 0.15708320 3.13708870 92 1.08650727 0.15708320 93 2.05988969 1.08650727 94 -1.60909618 2.05988969 95 1.48470623 -1.60909618 96 -2.49902743 1.48470623 97 0.45091605 -2.49902743 98 -0.58232541 0.45091605 99 2.42739092 -0.58232541 100 -0.55527856 2.42739092 101 1.09759911 -0.55527856 102 0.23342424 1.09759911 103 -0.81368616 0.23342424 104 2.24544417 -0.81368616 105 -0.05519149 2.24544417 106 0.40686169 -0.05519149 107 -0.47528482 0.40686169 108 -2.28729650 -0.47528482 109 0.30784447 -2.28729650 110 2.35351028 0.30784447 111 -1.51604601 2.35351028 112 1.09601177 -1.51604601 113 0.76542732 1.09601177 114 0.41084559 0.76542732 115 2.44786862 0.41084559 116 -2.56402050 2.44786862 117 1.06651186 -2.56402050 118 0.31850211 1.06651186 119 1.08616402 0.31850211 120 1.63519099 1.08616402 121 1.86672387 1.63519099 122 2.34065353 1.86672387 123 2.17210542 2.34065353 124 3.33838926 2.17210542 125 -0.92368605 3.33838926 126 -1.11598592 -0.92368605 127 -1.99666058 -1.11598592 128 1.73911835 -1.99666058 129 -1.58692387 1.73911835 130 2.71551989 -1.58692387 131 -3.60754536 2.71551989 132 1.77774150 -3.60754536 133 1.47580757 1.77774150 134 1.12837246 1.47580757 135 -0.41956943 1.12837246 136 0.11868989 -0.41956943 137 1.24810339 0.11868989 138 -1.22427504 1.24810339 139 0.10488264 -1.22427504 140 -3.43655474 0.10488264 141 -2.24022859 -3.43655474 142 2.69661574 -2.24022859 143 -0.21993139 2.69661574 144 -0.09108109 -0.21993139 145 0.59803922 -0.09108109 146 2.14211032 0.59803922 147 0.47966095 2.14211032 148 -0.83526071 0.47966095 149 -1.39305268 -0.83526071 150 1.94411351 -1.39305268 151 -2.72328356 1.94411351 152 -5.75587293 -2.72328356 153 -2.65429174 -5.75587293 154 -0.10263671 -2.65429174 155 3.13708870 -0.10263671 156 -3.99370619 3.13708870 157 -1.99666058 -3.99370619 158 -0.59066281 -1.99666058 159 -1.66429130 -0.59066281 160 -1.86306656 -1.66429130 161 0.62438526 -1.86306656 162 -0.94024877 0.62438526 163 2.24207003 -0.94024877 164 -1.00343087 2.24207003 165 0.11828463 -1.00343087 166 0.74833622 0.11828463 167 3.61193523 0.74833622 168 2.06554203 3.61193523 169 0.31442492 2.06554203 170 -3.80329397 0.31442492 171 0.66344466 -3.80329397 172 0.86504731 0.66344466 173 -0.09045122 0.86504731 174 3.22507593 -0.09045122 175 0.03839609 3.22507593 176 -0.99569887 0.03839609 177 -0.01588422 -0.99569887 178 -0.05297410 -0.01588422 179 -0.25595973 -0.05297410 180 0.80108826 -0.25595973 181 -2.33710352 0.80108826 182 1.97969328 -2.33710352 183 1.32713252 1.97969328 184 5.07300589 1.32713252 185 0.51331663 5.07300589 186 -0.86733224 0.51331663 187 -1.99545281 -0.86733224 188 -1.34392214 -1.99545281 189 -3.54343144 -1.34392214 190 1.63191431 -3.54343144 191 0.48079671 1.63191431 192 -6.03159970 0.48079671 193 -0.28118691 -6.03159970 194 0.43326301 -0.28118691 195 1.17235043 0.43326301 196 -2.21472558 1.17235043 197 0.46927652 -2.21472558 198 -1.18966195 0.46927652 199 0.52689421 -1.18966195 200 0.78956716 0.52689421 201 -0.21475246 0.78956716 202 0.52859177 -0.21475246 203 3.16833739 0.52859177 204 -2.76211556 3.16833739 205 0.18574350 -2.76211556 206 -0.41409655 0.18574350 207 0.82295266 -0.41409655 208 1.58061563 0.82295266 209 -0.78420509 1.58061563 210 2.97107243 -0.78420509 211 2.63849093 2.97107243 212 -0.09209294 2.63849093 213 -2.82782373 -0.09209294 214 0.66849046 -2.82782373 215 0.15967621 0.66849046 216 -0.94643725 0.15967621 217 -0.14785799 -0.94643725 218 -1.65572977 -0.14785799 219 1.09590303 -1.65572977 220 2.33690032 1.09590303 221 -0.15421954 2.33690032 222 1.27001741 -0.15421954 223 -0.04323692 1.27001741 224 -0.79407607 -0.04323692 225 -1.03283416 -0.79407607 226 0.05570487 -1.03283416 227 -1.26907366 0.05570487 228 0.52451633 -1.26907366 229 -0.14276484 0.52451633 230 -3.47688084 -0.14276484 231 1.89078760 -3.47688084 232 -5.19732188 1.89078760 233 1.80836588 -5.19732188 234 0.47304875 1.80836588 235 1.60218728 0.47304875 236 1.58340551 1.60218728 237 2.88147564 1.58340551 238 0.64850531 2.88147564 239 -0.81343915 0.64850531 240 -1.29755069 -0.81343915 241 1.99816661 -1.29755069 242 -4.77419403 1.99816661 243 -3.56012635 -4.77419403 244 -0.55449950 -3.56012635 245 -1.21554909 -0.55449950 246 -1.20320765 -1.21554909 247 1.42397529 -1.20320765 248 -0.72527896 1.42397529 249 0.29908541 -0.72527896 250 -1.55873619 0.29908541 251 -0.97634037 -1.55873619 252 -3.03939352 -0.97634037 253 -2.22012629 -3.03939352 254 1.07881332 -2.22012629 255 0.91422022 1.07881332 256 0.46494085 0.91422022 257 -2.10352063 0.46494085 258 1.48474372 -2.10352063 259 -1.61008602 1.48474372 260 0.33007583 -1.61008602 261 3.35467126 0.33007583 262 -1.48765030 3.35467126 263 -1.21632261 -1.48765030 > 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/7l5gd1353259629.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/8fwwg1353259629.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/9z5m51353259629.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/10avf01353259629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11t32z1353259629.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12f2i71353259629.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13dwjv1353259629.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14dbwx1353259629.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/155tsg1353259629.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/16m81t1353259629.tab") + } > > try(system("convert tmp/15yog1353259629.ps tmp/15yog1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/2kron1353259629.ps tmp/2kron1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/3qvzh1353259629.ps tmp/3qvzh1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/4vdjm1353259629.ps tmp/4vdjm1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/5th3m1353259629.ps tmp/5th3m1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/6gx471353259629.ps tmp/6gx471353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/7l5gd1353259629.ps tmp/7l5gd1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/8fwwg1353259629.ps tmp/8fwwg1353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/9z5m51353259629.ps tmp/9z5m51353259629.png",intern=TRUE)) character(0) > try(system("convert tmp/10avf01353259629.ps tmp/10avf01353259629.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.973 1.299 13.285