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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,0 + ,260 + ,0 + ,35 + ,0 + ,30 + ,0 + ,4 + ,3 + ,0 + ,10 + ,0 + ,8 + ,0 + ,83 + ,0 + ,0 + ,261 + ,0 + ,27 + ,0 + ,30 + ,0 + ,15 + ,11 + ,0 + ,14 + ,0 + ,12 + ,0 + ,64 + ,0 + ,0 + ,262 + ,0 + ,34 + ,0 + ,38 + ,0 + ,11 + ,12 + ,0 + ,15 + ,0 + ,12 + ,0 + ,68 + ,0 + ,0 + ,263 + ,0 + ,40 + ,0 + ,36 + ,0 + ,11 + ,7 + ,0 + ,7 + ,0 + ,22 + ,0 + ,62 + ,0 + ,0 + ,264 + ,0 + ,29 + ,0 + ,32 + ,0 + ,14 + ,9 + ,0 + ,14 + ,0 + ,12 + ,0 + ,72 + ,0) + ,dim=c(16 + ,264) + ,dimnames=list(c('Pop' + ,'t' + ,'Pop_t' + ,'Connected' + ,'Connected_p' + ,'Separate' + ,'Separate_p' + ,'Learning' + ,'Software' + ,'Software_p' + ,'Happiness' + ,'Happiness_p' + ,'Depression' + ,'Depression_p' + ,'Belonging' + ,'Belonging_p') + ,1:264)) > y <- array(NA,dim=c(16,264),dimnames=list(c('Pop','t','Pop_t','Connected','Connected_p','Separate','Separate_p','Learning','Software','Software_p','Happiness','Happiness_p','Depression','Depression_p','Belonging','Belonging_p'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '8' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Learning Pop t Pop_t Connected Connected_p Separate Separate_p Software 1 13 1 1 1 41 41 38 38 12 2 16 1 2 2 39 39 32 32 11 3 19 1 3 3 30 30 35 35 15 4 15 1 4 4 31 31 33 33 6 5 14 1 5 5 34 34 37 37 13 6 13 1 6 6 35 35 29 29 10 7 19 1 7 7 39 39 31 31 12 8 15 1 8 8 34 34 36 36 14 9 14 1 9 9 36 36 35 35 12 10 15 1 10 10 37 37 38 38 9 11 16 1 11 11 38 38 31 31 10 12 16 1 12 12 36 36 34 34 12 13 16 1 13 13 38 38 35 35 12 14 16 1 14 14 39 39 38 38 11 15 17 1 15 15 33 33 37 37 15 16 15 1 16 16 32 32 33 33 12 17 15 1 17 17 36 36 32 32 10 18 20 1 18 18 38 38 38 38 12 19 18 1 19 19 39 39 38 38 11 20 16 1 20 20 32 32 32 32 12 21 16 1 21 21 32 32 33 33 11 22 16 1 22 22 31 31 31 31 12 23 19 1 23 23 39 39 38 38 13 24 16 1 24 24 37 37 39 39 11 25 17 1 25 25 39 39 32 32 12 26 17 1 26 26 41 41 32 32 13 27 16 1 27 27 36 36 35 35 10 28 15 1 28 28 33 33 37 37 14 29 16 1 29 29 33 33 33 33 12 30 14 1 30 30 34 34 33 33 10 31 15 1 31 31 31 31 31 31 12 32 12 1 32 32 27 27 32 32 8 33 14 1 33 33 37 37 31 31 10 34 16 1 34 34 34 34 37 37 12 35 14 1 35 35 34 34 30 30 12 36 10 1 36 36 32 32 33 33 7 37 10 1 37 37 29 29 31 31 9 38 14 1 38 38 36 36 33 33 12 39 16 1 39 39 29 29 31 31 10 40 16 1 40 40 35 35 33 33 10 41 16 1 41 41 37 37 32 32 10 42 14 1 42 42 34 34 33 33 12 43 20 1 43 43 38 38 32 32 15 44 14 1 44 44 35 35 33 33 10 45 14 1 45 45 38 38 28 28 10 46 11 1 46 46 37 37 35 35 12 47 14 1 47 47 38 38 39 39 13 48 15 1 48 48 33 33 34 34 11 49 16 1 49 49 36 36 38 38 11 50 14 1 50 50 38 38 32 32 12 51 16 1 51 51 32 32 38 38 14 52 14 1 52 52 32 32 30 30 10 53 12 1 53 53 32 32 33 33 12 54 16 1 54 54 34 34 38 38 13 55 9 1 55 55 32 32 32 32 5 56 14 1 56 56 37 37 35 35 6 57 16 1 57 57 39 39 34 34 12 58 16 1 58 58 29 29 34 34 12 59 15 1 59 59 37 37 36 36 11 60 16 1 60 60 35 35 34 34 10 61 12 1 61 61 30 30 28 28 7 62 16 1 62 62 38 38 34 34 12 63 16 1 63 63 34 34 35 35 14 64 14 1 64 64 31 31 35 35 11 65 16 1 65 65 34 34 31 31 12 66 17 1 66 66 35 35 37 37 13 67 18 1 67 67 36 36 35 35 14 68 18 1 68 68 30 30 27 27 11 69 12 1 69 69 39 39 40 40 12 70 16 1 70 70 35 35 37 37 12 71 10 1 71 71 38 38 36 36 8 72 14 1 72 72 31 31 38 38 11 73 18 1 73 73 34 34 39 39 14 74 18 1 74 74 38 38 41 41 14 75 16 1 75 75 34 34 27 27 12 76 17 1 76 76 39 39 30 30 9 77 16 1 77 77 37 37 37 37 13 78 16 1 78 78 34 34 31 31 11 79 13 1 79 79 28 28 31 31 12 80 16 1 80 80 37 37 27 27 12 81 16 1 81 81 33 33 36 36 12 82 16 1 82 82 35 35 37 37 12 83 15 1 83 83 37 37 33 33 12 84 15 1 84 84 32 32 34 34 11 85 16 1 85 85 33 33 31 31 10 86 14 1 86 86 38 38 39 39 9 87 16 1 87 87 33 33 34 34 12 88 16 1 88 88 29 29 32 32 12 89 15 1 89 89 33 33 33 33 12 90 12 1 90 90 31 31 36 36 9 91 17 1 91 91 36 36 32 32 15 92 16 1 92 92 35 35 41 41 12 93 15 1 93 93 32 32 28 28 12 94 13 1 94 94 29 29 30 30 12 95 16 1 95 95 39 39 36 36 10 96 16 1 96 96 37 37 35 35 13 97 16 1 97 97 35 35 31 31 9 98 16 1 98 98 37 37 34 34 12 99 14 1 99 99 32 32 36 36 10 100 16 1 100 100 38 38 36 36 14 101 16 1 101 101 37 37 35 35 11 102 20 1 102 102 36 36 37 37 15 103 15 1 103 103 32 32 28 28 11 104 16 1 104 104 33 33 39 39 11 105 13 1 105 105 40 40 32 32 12 106 17 1 106 106 38 38 35 35 12 107 16 1 107 107 41 41 39 39 12 108 16 1 108 108 36 36 35 35 11 109 12 1 109 109 43 43 42 42 7 110 16 1 110 110 30 30 34 34 12 111 16 1 111 111 31 31 33 33 14 112 17 1 112 112 32 32 41 41 11 113 13 1 113 113 32 32 33 33 11 114 12 1 114 114 37 37 34 34 10 115 18 1 115 115 37 37 32 32 13 116 14 1 116 116 33 33 40 40 13 117 14 1 117 117 34 34 40 40 8 118 13 1 118 118 33 33 35 35 11 119 16 1 119 119 38 38 36 36 12 120 13 1 120 120 33 33 37 37 11 121 16 1 121 121 31 31 27 27 13 122 13 1 122 122 38 38 39 39 12 123 16 1 123 123 37 37 38 38 14 124 15 1 124 124 36 36 31 31 13 125 16 1 125 125 31 31 33 33 15 126 15 1 126 126 39 39 32 32 10 127 17 1 127 127 44 44 39 39 11 128 15 1 128 128 33 33 36 36 9 129 12 1 129 129 35 35 33 33 11 130 16 1 130 130 32 32 33 33 10 131 10 1 131 131 28 28 32 32 11 132 16 1 132 132 40 40 37 37 8 133 12 1 133 133 27 27 30 30 11 134 14 1 134 134 37 37 38 38 12 135 15 1 135 135 32 32 29 29 12 136 13 1 136 136 28 28 22 22 9 137 15 1 137 137 34 34 35 35 11 138 11 1 138 138 30 30 35 35 10 139 12 1 139 139 35 35 34 34 8 140 11 1 140 140 31 31 35 35 9 141 16 1 141 141 32 32 34 34 8 142 15 1 142 142 30 30 37 37 9 143 17 1 143 143 30 30 35 35 15 144 16 1 144 144 31 31 23 23 11 145 10 1 145 145 40 40 31 31 8 146 18 1 146 146 32 32 27 27 13 147 13 1 147 147 36 36 36 36 12 148 16 1 148 148 32 32 31 31 12 149 13 1 149 149 35 35 32 32 9 150 10 1 150 150 38 38 39 39 7 151 15 1 151 151 42 42 37 37 13 152 16 1 152 152 34 34 38 38 9 153 16 1 153 153 35 35 39 39 6 154 14 1 154 154 38 38 34 34 8 155 10 1 155 155 33 33 31 31 8 156 17 1 156 156 36 36 32 32 15 157 13 1 157 157 32 32 37 37 6 158 15 1 158 158 33 33 36 36 9 159 16 1 159 159 34 34 32 32 11 160 12 1 160 160 32 32 38 38 8 161 13 1 161 161 34 34 36 36 8 162 13 0 162 0 27 0 26 0 10 163 12 0 163 0 31 0 26 0 8 164 17 0 164 0 38 0 33 0 14 165 15 0 165 0 34 0 39 0 10 166 10 0 166 0 24 0 30 0 8 167 14 0 167 0 30 0 33 0 11 168 11 0 168 0 26 0 25 0 12 169 13 0 169 0 34 0 38 0 12 170 16 0 170 0 27 0 37 0 12 171 12 0 171 0 37 0 31 0 5 172 16 0 172 0 36 0 37 0 12 173 12 0 173 0 41 0 35 0 10 174 9 0 174 0 29 0 25 0 7 175 12 0 175 0 36 0 28 0 12 176 15 0 176 0 32 0 35 0 11 177 12 0 177 0 37 0 33 0 8 178 12 0 178 0 30 0 30 0 9 179 14 0 179 0 31 0 31 0 10 180 12 0 180 0 38 0 37 0 9 181 16 0 181 0 36 0 36 0 12 182 11 0 182 0 35 0 30 0 6 183 19 0 183 0 31 0 36 0 15 184 15 0 184 0 38 0 32 0 12 185 8 0 185 0 22 0 28 0 12 186 16 0 186 0 32 0 36 0 12 187 17 0 187 0 36 0 34 0 11 188 12 0 188 0 39 0 31 0 7 189 11 0 189 0 28 0 28 0 7 190 11 0 190 0 32 0 36 0 5 191 14 0 191 0 32 0 36 0 12 192 16 0 192 0 38 0 40 0 12 193 12 0 193 0 32 0 33 0 3 194 16 0 194 0 35 0 37 0 11 195 13 0 195 0 32 0 32 0 10 196 15 0 196 0 37 0 38 0 12 197 16 0 197 0 34 0 31 0 9 198 16 0 198 0 33 0 37 0 12 199 14 0 199 0 33 0 33 0 9 200 16 0 200 0 26 0 32 0 12 201 16 0 201 0 30 0 30 0 12 202 14 0 202 0 24 0 30 0 10 203 11 0 203 0 34 0 31 0 9 204 12 0 204 0 34 0 32 0 12 205 15 0 205 0 33 0 34 0 8 206 15 0 206 0 34 0 36 0 11 207 16 0 207 0 35 0 37 0 11 208 16 0 208 0 35 0 36 0 12 209 11 0 209 0 36 0 33 0 10 210 15 0 210 0 34 0 33 0 10 211 12 0 211 0 34 0 33 0 12 212 12 0 212 0 41 0 44 0 12 213 15 0 213 0 32 0 39 0 11 214 15 0 214 0 30 0 32 0 8 215 16 0 215 0 35 0 35 0 12 216 14 0 216 0 28 0 25 0 10 217 17 0 217 0 33 0 35 0 11 218 14 0 218 0 39 0 34 0 10 219 13 0 219 0 36 0 35 0 8 220 15 0 220 0 36 0 39 0 12 221 13 0 221 0 35 0 33 0 12 222 14 0 222 0 38 0 36 0 10 223 15 0 223 0 33 0 32 0 12 224 12 0 224 0 31 0 32 0 9 225 13 0 225 0 34 0 36 0 9 226 8 0 226 0 32 0 36 0 6 227 14 0 227 0 31 0 32 0 10 228 14 0 228 0 33 0 34 0 9 229 11 0 229 0 34 0 33 0 9 230 12 0 230 0 34 0 35 0 9 231 13 0 231 0 34 0 30 0 6 232 10 0 232 0 33 0 38 0 10 233 16 0 233 0 32 0 34 0 6 234 18 0 234 0 41 0 33 0 14 235 13 0 235 0 34 0 32 0 10 236 11 0 236 0 36 0 31 0 10 237 4 0 237 0 37 0 30 0 6 238 13 0 238 0 36 0 27 0 12 239 16 0 239 0 29 0 31 0 12 240 10 0 240 0 37 0 30 0 7 241 12 0 241 0 27 0 32 0 8 242 12 0 242 0 35 0 35 0 11 243 10 0 243 0 28 0 28 0 3 244 13 0 244 0 35 0 33 0 6 245 15 0 245 0 37 0 31 0 10 246 12 0 246 0 29 0 35 0 8 247 14 0 247 0 32 0 35 0 9 248 10 0 248 0 36 0 32 0 9 249 12 0 249 0 19 0 21 0 8 250 12 0 250 0 21 0 20 0 9 251 11 0 251 0 31 0 34 0 7 252 10 0 252 0 33 0 32 0 7 253 12 0 253 0 36 0 34 0 6 254 16 0 254 0 33 0 32 0 9 255 12 0 255 0 37 0 33 0 10 256 14 0 256 0 34 0 33 0 11 257 16 0 257 0 35 0 37 0 12 258 14 0 258 0 31 0 32 0 8 259 13 0 259 0 37 0 34 0 11 260 4 0 260 0 35 0 30 0 3 261 15 0 261 0 27 0 30 0 11 262 11 0 262 0 34 0 38 0 12 263 11 0 263 0 40 0 36 0 7 264 14 0 264 0 29 0 32 0 9 Software_p Happiness Happiness_p Depression Depression_p Belonging 1 12 14 14 12.0 12.0 53 2 11 18 18 11.0 11.0 83 3 15 11 11 14.0 14.0 66 4 6 12 12 12.0 12.0 67 5 13 16 16 21.0 21.0 76 6 10 18 18 12.0 12.0 78 7 12 14 14 22.0 22.0 53 8 14 14 14 11.0 11.0 80 9 12 15 15 10.0 10.0 74 10 9 15 15 13.0 13.0 76 11 10 17 17 10.0 10.0 79 12 12 19 19 8.0 8.0 54 13 12 10 10 15.0 15.0 67 14 11 16 16 14.0 14.0 54 15 15 18 18 10.0 10.0 87 16 12 14 14 14.0 14.0 58 17 10 14 14 14.0 14.0 75 18 12 17 17 11.0 11.0 88 19 11 14 14 10.0 10.0 64 20 12 16 16 13.0 13.0 57 21 11 18 18 9.5 9.5 66 22 12 11 11 14.0 14.0 68 23 13 14 14 12.0 12.0 54 24 11 12 12 14.0 14.0 56 25 12 17 17 11.0 11.0 86 26 13 9 9 9.0 9.0 80 27 10 16 16 11.0 11.0 76 28 14 14 14 15.0 15.0 69 29 12 15 15 14.0 14.0 78 30 10 11 11 13.0 13.0 67 31 12 16 16 9.0 9.0 80 32 8 13 13 15.0 15.0 54 33 10 17 17 10.0 10.0 71 34 12 15 15 11.0 11.0 84 35 12 14 14 13.0 13.0 74 36 7 16 16 8.0 8.0 71 37 9 9 9 20.0 20.0 63 38 12 15 15 12.0 12.0 71 39 10 17 17 10.0 10.0 76 40 10 13 13 10.0 10.0 69 41 10 15 15 9.0 9.0 74 42 12 16 16 14.0 14.0 75 43 15 16 16 8.0 8.0 54 44 10 12 12 14.0 14.0 52 45 10 15 15 11.0 11.0 69 46 12 11 11 13.0 13.0 68 47 13 15 15 9.0 9.0 65 48 11 15 15 11.0 11.0 75 49 11 17 17 15.0 15.0 74 50 12 13 13 11.0 11.0 75 51 14 16 16 10.0 10.0 72 52 10 14 14 14.0 14.0 67 53 12 11 11 18.0 18.0 63 54 13 12 12 14.0 14.0 62 55 5 12 12 11.0 11.0 63 56 6 15 15 14.5 14.5 76 57 12 16 16 13.0 13.0 74 58 12 15 15 9.0 9.0 67 59 11 12 12 10.0 10.0 73 60 10 12 12 15.0 15.0 70 61 7 8 8 20.0 20.0 53 62 12 13 13 12.0 12.0 77 63 14 11 11 12.0 12.0 80 64 11 14 14 14.0 14.0 52 65 12 15 15 13.0 13.0 54 66 13 10 10 11.0 11.0 80 67 14 11 11 17.0 17.0 66 68 11 12 12 12.0 12.0 73 69 12 15 15 13.0 13.0 63 70 12 15 15 14.0 14.0 69 71 8 14 14 13.0 13.0 67 72 11 16 16 15.0 15.0 54 73 14 15 15 13.0 13.0 81 74 14 15 15 10.0 10.0 69 75 12 13 13 11.0 11.0 84 76 9 12 12 19.0 19.0 80 77 13 17 17 13.0 13.0 70 78 11 13 13 17.0 17.0 69 79 12 15 15 13.0 13.0 77 80 12 13 13 9.0 9.0 54 81 12 15 15 11.0 11.0 79 82 12 15 15 9.0 9.0 71 83 12 16 16 12.0 12.0 73 84 11 15 15 12.0 12.0 72 85 10 14 14 13.0 13.0 77 86 9 15 15 13.0 13.0 75 87 12 14 14 12.0 12.0 69 88 12 13 13 15.0 15.0 54 89 12 7 7 22.0 22.0 70 90 9 17 17 13.0 13.0 73 91 15 13 13 15.0 15.0 54 92 12 15 15 13.0 13.0 77 93 12 14 14 15.0 15.0 82 94 12 13 13 12.5 12.5 80 95 10 16 16 11.0 11.0 80 96 13 12 12 16.0 16.0 69 97 9 14 14 11.0 11.0 78 98 12 17 17 11.0 11.0 81 99 10 15 15 10.0 10.0 76 100 14 17 17 10.0 10.0 76 101 11 12 12 16.0 16.0 73 102 15 16 16 12.0 12.0 85 103 11 11 11 11.0 11.0 66 104 11 15 15 16.0 16.0 79 105 12 9 9 19.0 19.0 68 106 12 16 16 11.0 11.0 76 107 12 15 15 16.0 16.0 71 108 11 10 10 15.0 15.0 54 109 7 10 10 24.0 24.0 46 110 12 15 15 14.0 14.0 85 111 14 11 11 15.0 15.0 74 112 11 13 13 11.0 11.0 88 113 11 14 14 15.0 15.0 38 114 10 18 18 12.0 12.0 76 115 13 16 16 10.0 10.0 86 116 13 14 14 14.0 14.0 54 117 8 14 14 13.0 13.0 67 118 11 14 14 9.0 9.0 69 119 12 14 14 15.0 15.0 90 120 11 12 12 15.0 15.0 54 121 13 14 14 14.0 14.0 76 122 12 15 15 11.0 11.0 89 123 14 15 15 8.0 8.0 76 124 13 15 15 11.0 11.0 73 125 15 13 13 11.0 11.0 79 126 10 17 17 8.0 8.0 90 127 11 17 17 10.0 10.0 74 128 9 19 19 11.0 11.0 81 129 11 15 15 13.0 13.0 72 130 10 13 13 11.0 11.0 71 131 11 9 9 20.0 20.0 66 132 8 15 15 10.0 10.0 77 133 11 15 15 15.0 15.0 65 134 12 15 15 12.0 12.0 74 135 12 16 16 14.0 14.0 85 136 9 11 11 23.0 23.0 54 137 11 14 14 14.0 14.0 63 138 10 11 11 16.0 16.0 54 139 8 15 15 11.0 11.0 64 140 9 13 13 12.0 12.0 69 141 8 15 15 10.0 10.0 54 142 9 16 16 14.0 14.0 84 143 15 14 14 12.0 12.0 86 144 11 15 15 12.0 12.0 77 145 8 16 16 11.0 11.0 89 146 13 16 16 12.0 12.0 76 147 12 11 11 13.0 13.0 60 148 12 12 12 11.0 11.0 75 149 9 9 9 19.0 19.0 73 150 7 16 16 12.0 12.0 85 151 13 13 13 17.0 17.0 79 152 9 16 16 9.0 9.0 71 153 6 12 12 12.0 12.0 72 154 8 9 9 19.0 19.0 69 155 8 13 13 18.0 18.0 78 156 15 13 13 15.0 15.0 54 157 6 14 14 14.0 14.0 69 158 9 19 19 11.0 11.0 81 159 11 13 13 9.0 9.0 84 160 8 12 12 18.0 18.0 84 161 8 13 13 16.0 16.0 69 162 0 10 0 24.0 0.0 66 163 0 14 0 14.0 0.0 81 164 0 16 0 20.0 0.0 82 165 0 10 0 18.0 0.0 72 166 0 11 0 23.0 0.0 54 167 0 14 0 12.0 0.0 78 168 0 12 0 14.0 0.0 74 169 0 9 0 16.0 0.0 82 170 0 9 0 18.0 0.0 73 171 0 11 0 20.0 0.0 55 172 0 16 0 12.0 0.0 72 173 0 9 0 12.0 0.0 78 174 0 13 0 17.0 0.0 59 175 0 16 0 13.0 0.0 72 176 0 13 0 9.0 0.0 78 177 0 9 0 16.0 0.0 68 178 0 12 0 18.0 0.0 69 179 0 16 0 10.0 0.0 67 180 0 11 0 14.0 0.0 74 181 0 14 0 11.0 0.0 54 182 0 13 0 9.0 0.0 67 183 0 15 0 11.0 0.0 70 184 0 14 0 10.0 0.0 80 185 0 16 0 11.0 0.0 89 186 0 13 0 19.0 0.0 76 187 0 14 0 14.0 0.0 74 188 0 15 0 12.0 0.0 87 189 0 13 0 14.0 0.0 54 190 0 11 0 21.0 0.0 61 191 0 11 0 13.0 0.0 38 192 0 14 0 10.0 0.0 75 193 0 15 0 15.0 0.0 69 194 0 11 0 16.0 0.0 62 195 0 15 0 14.0 0.0 72 196 0 12 0 12.0 0.0 70 197 0 14 0 19.0 0.0 79 198 0 14 0 15.0 0.0 87 199 0 8 0 19.0 0.0 62 200 0 13 0 13.0 0.0 77 201 0 9 0 17.0 0.0 69 202 0 15 0 12.0 0.0 69 203 0 17 0 11.0 0.0 75 204 0 13 0 14.0 0.0 54 205 0 15 0 11.0 0.0 72 206 0 15 0 13.0 0.0 74 207 0 14 0 12.0 0.0 85 208 0 16 0 15.0 0.0 52 209 0 13 0 14.0 0.0 70 210 0 16 0 12.0 0.0 84 211 0 9 0 17.0 0.0 64 212 0 16 0 11.0 0.0 84 213 0 11 0 18.0 0.0 87 214 0 10 0 13.0 0.0 79 215 0 11 0 17.0 0.0 67 216 0 15 0 13.0 0.0 65 217 0 17 0 11.0 0.0 85 218 0 14 0 12.0 0.0 83 219 0 8 0 22.0 0.0 61 220 0 15 0 14.0 0.0 82 221 0 11 0 12.0 0.0 76 222 0 16 0 12.0 0.0 58 223 0 10 0 17.0 0.0 72 224 0 15 0 9.0 0.0 72 225 0 9 0 21.0 0.0 38 226 0 16 0 10.0 0.0 78 227 0 19 0 11.0 0.0 54 228 0 12 0 12.0 0.0 63 229 0 8 0 23.0 0.0 66 230 0 11 0 13.0 0.0 70 231 0 14 0 12.0 0.0 71 232 0 9 0 16.0 0.0 67 233 0 15 0 9.0 0.0 58 234 0 13 0 17.0 0.0 72 235 0 16 0 9.0 0.0 72 236 0 11 0 14.0 0.0 70 237 0 12 0 17.0 0.0 76 238 0 13 0 13.0 0.0 50 239 0 10 0 11.0 0.0 72 240 0 11 0 12.0 0.0 72 241 0 12 0 10.0 0.0 88 242 0 8 0 19.0 0.0 53 243 0 12 0 16.0 0.0 58 244 0 12 0 16.0 0.0 66 245 0 15 0 14.0 0.0 82 246 0 11 0 20.0 0.0 69 247 0 13 0 15.0 0.0 68 248 0 14 0 23.0 0.0 44 249 0 10 0 20.0 0.0 56 250 0 12 0 16.0 0.0 53 251 0 15 0 14.0 0.0 70 252 0 13 0 17.0 0.0 78 253 0 13 0 11.0 0.0 71 254 0 13 0 13.0 0.0 72 255 0 12 0 17.0 0.0 68 256 0 12 0 15.0 0.0 67 257 0 9 0 21.0 0.0 75 258 0 9 0 18.0 0.0 62 259 0 15 0 15.0 0.0 67 260 0 10 0 8.0 0.0 83 261 0 14 0 12.0 0.0 64 262 0 15 0 12.0 0.0 68 263 0 7 0 22.0 0.0 62 264 0 14 0 12.0 0.0 72 Belonging_p 1 53 2 83 3 66 4 67 5 76 6 78 7 53 8 80 9 74 10 76 11 79 12 54 13 67 14 54 15 87 16 58 17 75 18 88 19 64 20 57 21 66 22 68 23 54 24 56 25 86 26 80 27 76 28 69 29 78 30 67 31 80 32 54 33 71 34 84 35 74 36 71 37 63 38 71 39 76 40 69 41 74 42 75 43 54 44 52 45 69 46 68 47 65 48 75 49 74 50 75 51 72 52 67 53 63 54 62 55 63 56 76 57 74 58 67 59 73 60 70 61 53 62 77 63 80 64 52 65 54 66 80 67 66 68 73 69 63 70 69 71 67 72 54 73 81 74 69 75 84 76 80 77 70 78 69 79 77 80 54 81 79 82 71 83 73 84 72 85 77 86 75 87 69 88 54 89 70 90 73 91 54 92 77 93 82 94 80 95 80 96 69 97 78 98 81 99 76 100 76 101 73 102 85 103 66 104 79 105 68 106 76 107 71 108 54 109 46 110 85 111 74 112 88 113 38 114 76 115 86 116 54 117 67 118 69 119 90 120 54 121 76 122 89 123 76 124 73 125 79 126 90 127 74 128 81 129 72 130 71 131 66 132 77 133 65 134 74 135 85 136 54 137 63 138 54 139 64 140 69 141 54 142 84 143 86 144 77 145 89 146 76 147 60 148 75 149 73 150 85 151 79 152 71 153 72 154 69 155 78 156 54 157 69 158 81 159 84 160 84 161 69 162 0 163 0 164 0 165 0 166 0 167 0 168 0 169 0 170 0 171 0 172 0 173 0 174 0 175 0 176 0 177 0 178 0 179 0 180 0 181 0 182 0 183 0 184 0 185 0 186 0 187 0 188 0 189 0 190 0 191 0 192 0 193 0 194 0 195 0 196 0 197 0 198 0 199 0 200 0 201 0 202 0 203 0 204 0 205 0 206 0 207 0 208 0 209 0 210 0 211 0 212 0 213 0 214 0 215 0 216 0 217 0 218 0 219 0 220 0 221 0 222 0 223 0 224 0 225 0 226 0 227 0 228 0 229 0 230 0 231 0 232 0 233 0 234 0 235 0 236 0 237 0 238 0 239 0 240 0 241 0 242 0 243 0 244 0 245 0 246 0 247 0 248 0 249 0 250 0 251 0 252 0 253 0 254 0 255 0 256 0 257 0 258 0 259 0 260 0 261 0 262 0 263 0 264 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop t Pop_t Connected 4.0221319 2.5122087 -0.0041633 0.0001722 -0.0555568 Connected_p Separate Separate_p Software Software_p 0.1428762 0.1570048 -0.1710505 0.5616887 -0.0423405 Happiness Happiness_p Depression Depression_p Belonging 0.1295590 -0.0990921 0.0223866 -0.1059047 -0.0094106 Belonging_p 0.0247480 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.6477 -1.0599 0.2827 1.1002 4.4184 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.0221319 3.3275782 1.209 0.22792 Pop 2.5122087 4.2401553 0.592 0.55407 t -0.0041633 0.0063718 -0.653 0.51411 Pop_t 0.0001722 0.0071811 0.024 0.98089 Connected -0.0555568 0.0522639 -1.063 0.28881 Connected_p 0.1428762 0.0700299 2.040 0.04239 * Separate 0.1570048 0.0597976 2.626 0.00919 ** Separate_p -0.1710505 0.0744738 -2.297 0.02246 * Software 0.5616887 0.0822318 6.831 6.49e-11 *** Software_p -0.0423405 0.1094055 -0.387 0.69908 Happiness 0.1295590 0.0893820 1.449 0.14846 Happiness_p -0.0990921 0.1178677 -0.841 0.40132 Depression 0.0223866 0.0622067 0.360 0.71925 Depression_p -0.1059047 0.0843116 -1.256 0.21026 Belonging -0.0094106 0.0190539 -0.494 0.62182 Belonging_p 0.0247480 0.0246210 1.005 0.31580 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.846 on 248 degrees of freedom Multiple R-squared: 0.4674, Adjusted R-squared: 0.4352 F-statistic: 14.51 on 15 and 248 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,] 9.983424e-01 0.0033151781 0.001657589 [2,] 9.959061e-01 0.0081877651 0.004093883 [3,] 9.908263e-01 0.0183474359 0.009173718 [4,] 9.849251e-01 0.0301498246 0.015074912 [5,] 9.773700e-01 0.0452599793 0.022629990 [6,] 9.685848e-01 0.0628304245 0.031415212 [7,] 9.504942e-01 0.0990115571 0.049505779 [8,] 9.300801e-01 0.1398398923 0.069919946 [9,] 9.013017e-01 0.1973966873 0.098698344 [10,] 9.129317e-01 0.1741365359 0.087068268 [11,] 8.806837e-01 0.2386325970 0.119316298 [12,] 8.772129e-01 0.2455742306 0.122787115 [13,] 8.436563e-01 0.3126873020 0.156343651 [14,] 8.323305e-01 0.3353390839 0.167669542 [15,] 8.122311e-01 0.3755377613 0.187768881 [16,] 7.624886e-01 0.4750228451 0.237511423 [17,] 7.444202e-01 0.5111596196 0.255579810 [18,] 8.132881e-01 0.3734237135 0.186711857 [19,] 8.531152e-01 0.2937695119 0.146884756 [20,] 8.343443e-01 0.3313113909 0.165655695 [21,] 8.560053e-01 0.2879893325 0.143994666 [22,] 8.394663e-01 0.3210673627 0.160533681 [23,] 8.111489e-01 0.3777021897 0.188851095 [24,] 7.849083e-01 0.4301833255 0.215091663 [25,] 7.912692e-01 0.4174616986 0.208730849 [26,] 7.511931e-01 0.4976137622 0.248806881 [27,] 7.146455e-01 0.5707089376 0.285354469 [28,] 8.660350e-01 0.2679299610 0.133964981 [29,] 8.718187e-01 0.2563626896 0.128181345 [30,] 8.477742e-01 0.3044516400 0.152225820 [31,] 8.380646e-01 0.3238708426 0.161935421 [32,] 8.217975e-01 0.3564050241 0.178202512 [33,] 7.876072e-01 0.4247856014 0.212392801 [34,] 7.533089e-01 0.4933821027 0.246691051 [35,] 7.520813e-01 0.4958374560 0.247918728 [36,] 7.243457e-01 0.5513086281 0.275654314 [37,] 7.298814e-01 0.5402371542 0.270118577 [38,] 7.371446e-01 0.5257107761 0.262855388 [39,] 6.992661e-01 0.6014677379 0.300733869 [40,] 6.824616e-01 0.6350767437 0.317538372 [41,] 6.434533e-01 0.7130934045 0.356546702 [42,] 6.640826e-01 0.6718348863 0.335917443 [43,] 6.349177e-01 0.7301645461 0.365082273 [44,] 5.956362e-01 0.8087276158 0.404363808 [45,] 5.557220e-01 0.8885560130 0.444278007 [46,] 5.103929e-01 0.9792141653 0.489607083 [47,] 4.758416e-01 0.9516831231 0.524158438 [48,] 4.576166e-01 0.9152331713 0.542383414 [49,] 4.603214e-01 0.9206427125 0.539678644 [50,] 5.827819e-01 0.8344361632 0.417218082 [51,] 6.845653e-01 0.6308694257 0.315434713 [52,] 6.531385e-01 0.6937229921 0.346861496 [53,] 7.264777e-01 0.5470445562 0.273522278 [54,] 6.903255e-01 0.6193490901 0.309674545 [55,] 6.843136e-01 0.6313727290 0.315686364 [56,] 6.632904e-01 0.6734192147 0.336709607 [57,] 6.246817e-01 0.7506365417 0.375318271 [58,] 6.814790e-01 0.6370420091 0.318521005 [59,] 6.433647e-01 0.7132706318 0.356635316 [60,] 6.218991e-01 0.7562018021 0.378100901 [61,] 6.264638e-01 0.7470724135 0.373536207 [62,] 5.865563e-01 0.8268873492 0.413443675 [63,] 5.507548e-01 0.8984904764 0.449245238 [64,] 5.131696e-01 0.9736608036 0.486830402 [65,] 4.808343e-01 0.9616686430 0.519165678 [66,] 4.420128e-01 0.8840256889 0.557987156 [67,] 4.304254e-01 0.8608507954 0.569574602 [68,] 3.914703e-01 0.7829405551 0.608529722 [69,] 3.576461e-01 0.7152922338 0.642353883 [70,] 3.398866e-01 0.6797732985 0.660113351 [71,] 3.073320e-01 0.6146640924 0.692667954 [72,] 2.945789e-01 0.5891578182 0.705421091 [73,] 2.632403e-01 0.5264805255 0.736759737 [74,] 2.359743e-01 0.4719485879 0.764025706 [75,] 2.087683e-01 0.4175365510 0.791231724 [76,] 2.181971e-01 0.4363941163 0.781802942 [77,] 1.976039e-01 0.3952078379 0.802396081 [78,] 1.719765e-01 0.3439529685 0.828023516 [79,] 1.706978e-01 0.3413956489 0.829302176 [80,] 1.468973e-01 0.2937946884 0.853102656 [81,] 1.273083e-01 0.2546166502 0.872691675 [82,] 1.149094e-01 0.2298188711 0.885090564 [83,] 1.035147e-01 0.2070294546 0.896485273 [84,] 1.221563e-01 0.2443126268 0.877843687 [85,] 1.037424e-01 0.2074848874 0.896257556 [86,] 9.814799e-02 0.1962959770 0.901852011 [87,] 1.118713e-01 0.2237426870 0.888128656 [88,] 1.002148e-01 0.2004295274 0.899785236 [89,] 8.769065e-02 0.1753813027 0.912309349 [90,] 8.438410e-02 0.1687681902 0.915615905 [91,] 7.707971e-02 0.1541594210 0.922920289 [92,] 6.831905e-02 0.1366381095 0.931680945 [93,] 5.817858e-02 0.1163571620 0.941821419 [94,] 6.678473e-02 0.1335694518 0.933215274 [95,] 5.740816e-02 0.1148163135 0.942591843 [96,] 6.916809e-02 0.1383361899 0.930831905 [97,] 6.691465e-02 0.1338293085 0.933085346 [98,] 5.911112e-02 0.1182222382 0.940888881 [99,] 5.458951e-02 0.1091790117 0.945410494 [100,] 5.340056e-02 0.1068011224 0.946599439 [101,] 5.188456e-02 0.1037691149 0.948115443 [102,] 4.433770e-02 0.0886754005 0.955662300 [103,] 3.846792e-02 0.0769358364 0.961532082 [104,] 4.628136e-02 0.0925627111 0.953718644 [105,] 3.892564e-02 0.0778512787 0.961074361 [106,] 3.348914e-02 0.0669782860 0.966510857 [107,] 2.774466e-02 0.0554893228 0.972255339 [108,] 2.229561e-02 0.0445912235 0.977704388 [109,] 2.168490e-02 0.0433698058 0.978315097 [110,] 2.015087e-02 0.0403017394 0.979849130 [111,] 2.342960e-02 0.0468591962 0.976570402 [112,] 2.510549e-02 0.0502109717 0.974894514 [113,] 3.414939e-02 0.0682987742 0.965850613 [114,] 4.552772e-02 0.0910554389 0.954472281 [115,] 4.554187e-02 0.0910837333 0.954458133 [116,] 4.011021e-02 0.0802204274 0.959889786 [117,] 3.338554e-02 0.0667710787 0.966614461 [118,] 2.911144e-02 0.0582228788 0.970888561 [119,] 2.559560e-02 0.0511911913 0.974404404 [120,] 2.783574e-02 0.0556714795 0.972164260 [121,] 2.392766e-02 0.0478553158 0.976072342 [122,] 3.184330e-02 0.0636866075 0.968156696 [123,] 4.065572e-02 0.0813114365 0.959344282 [124,] 3.883549e-02 0.0776709847 0.961164508 [125,] 3.195130e-02 0.0639026013 0.968048699 [126,] 2.826672e-02 0.0565334479 0.971733276 [127,] 4.557875e-02 0.0911574946 0.954421253 [128,] 5.508200e-02 0.1101640030 0.944917999 [129,] 6.836063e-02 0.1367212522 0.931639374 [130,] 5.871908e-02 0.1174381680 0.941280916 [131,] 4.932245e-02 0.0986449017 0.950677549 [132,] 6.595039e-02 0.1319007769 0.934049612 [133,] 5.928356e-02 0.1185671204 0.940716440 [134,] 6.073440e-02 0.1214688098 0.939265595 [135,] 7.992991e-02 0.1598598162 0.920070092 [136,] 6.972811e-02 0.1394562234 0.930271888 [137,] 6.789971e-02 0.1357994146 0.932100293 [138,] 5.698172e-02 0.1139634397 0.943018280 [139,] 4.965500e-02 0.0993099994 0.950345000 [140,] 4.218398e-02 0.0843679674 0.957816016 [141,] 3.516994e-02 0.0703398721 0.964830064 [142,] 2.858423e-02 0.0571684632 0.971415768 [143,] 2.300562e-02 0.0460112476 0.976994376 [144,] 1.833276e-02 0.0366655149 0.981667243 [145,] 1.457156e-02 0.0291431170 0.985428442 [146,] 1.194192e-02 0.0238838417 0.988058079 [147,] 9.530738e-03 0.0190614765 0.990469262 [148,] 9.651050e-03 0.0193021008 0.990348950 [149,] 7.481607e-03 0.0149632143 0.992518393 [150,] 8.028105e-03 0.0160562094 0.991971895 [151,] 7.340082e-03 0.0146801641 0.992659918 [152,] 5.956849e-03 0.0119136975 0.994043151 [153,] 6.979487e-03 0.0139589739 0.993020513 [154,] 5.408013e-03 0.0108160258 0.994591987 [155,] 4.288396e-03 0.0085767914 0.995711604 [156,] 4.236450e-03 0.0084729002 0.995763550 [157,] 4.231209e-03 0.0084624183 0.995768791 [158,] 3.502176e-03 0.0070043519 0.996497824 [159,] 2.619412e-03 0.0052388233 0.997380588 [160,] 2.105809e-03 0.0042116178 0.997894191 [161,] 1.616453e-03 0.0032329058 0.998383547 [162,] 1.391319e-03 0.0027826387 0.998608681 [163,] 1.094176e-03 0.0021883523 0.998905824 [164,] 7.935067e-04 0.0015870135 0.999206493 [165,] 7.847055e-04 0.0015694109 0.999215295 [166,] 5.798807e-04 0.0011597614 0.999420119 [167,] 1.682086e-02 0.0336417282 0.983179136 [168,] 1.437744e-02 0.0287548765 0.985622562 [169,] 1.698939e-02 0.0339787731 0.983010613 [170,] 1.337776e-02 0.0267555195 0.986622240 [171,] 1.131635e-02 0.0226327030 0.988683648 [172,] 8.964567e-03 0.0179291335 0.991035433 [173,] 7.904797e-03 0.0158095933 0.992095203 [174,] 6.102130e-03 0.0122042604 0.993897870 [175,] 5.608552e-03 0.0112171047 0.994391448 [176,] 4.873788e-03 0.0097475761 0.995126212 [177,] 3.964897e-03 0.0079297934 0.996035103 [178,] 2.897804e-03 0.0057956084 0.997102196 [179,] 3.971742e-03 0.0079434849 0.996028258 [180,] 2.898204e-03 0.0057964077 0.997101796 [181,] 2.446589e-03 0.0048931772 0.997553411 [182,] 2.053784e-03 0.0041075688 0.997946216 [183,] 1.811492e-03 0.0036229841 0.998188508 [184,] 1.389441e-03 0.0027788822 0.998610559 [185,] 1.663396e-03 0.0033267918 0.998336604 [186,] 2.645812e-03 0.0052916232 0.997354188 [187,] 2.640137e-03 0.0052802730 0.997359863 [188,] 1.880584e-03 0.0037611677 0.998119416 [189,] 1.521576e-03 0.0030431527 0.998478424 [190,] 1.078572e-03 0.0021571436 0.998921428 [191,] 1.378861e-03 0.0027577229 0.998621139 [192,] 1.036224e-03 0.0020724486 0.998963776 [193,] 1.323843e-03 0.0026476857 0.998676157 [194,] 4.032498e-03 0.0080649950 0.995967502 [195,] 2.803907e-03 0.0056078137 0.997196093 [196,] 3.704304e-03 0.0074086085 0.996295696 [197,] 2.829671e-03 0.0056593411 0.997170329 [198,] 2.030202e-03 0.0040604040 0.997969798 [199,] 2.165603e-03 0.0043312066 0.997834397 [200,] 1.679637e-03 0.0033592741 0.998320363 [201,] 1.463459e-03 0.0029269184 0.998536541 [202,] 1.007588e-03 0.0020151762 0.998992412 [203,] 7.379952e-04 0.0014759903 0.999262005 [204,] 4.748724e-04 0.0009497448 0.999525128 [205,] 3.234869e-04 0.0006469738 0.999676513 [206,] 2.187256e-04 0.0004374512 0.999781274 [207,] 1.329043e-04 0.0002658087 0.999867096 [208,] 3.450013e-04 0.0006900026 0.999654999 [209,] 2.401043e-04 0.0004802087 0.999759896 [210,] 1.499246e-04 0.0002998492 0.999850075 [211,] 9.767003e-05 0.0001953401 0.999902330 [212,] 5.844210e-05 0.0001168842 0.999941558 [213,] 6.141390e-05 0.0001228278 0.999938586 [214,] 2.817367e-04 0.0005634735 0.999718263 [215,] 1.400861e-03 0.0028017224 0.998599139 [216,] 2.289741e-03 0.0045794819 0.997710259 [217,] 1.337848e-03 0.0026756953 0.998662152 [218,] 8.735973e-04 0.0017471947 0.999126403 [219,] 1.918091e-02 0.0383618280 0.980819086 [220,] 1.165371e-02 0.0233074224 0.988346289 [221,] 7.944134e-03 0.0158882683 0.992055866 [222,] 4.536145e-03 0.0090722896 0.995463855 [223,] 3.125833e-03 0.0062516668 0.996874167 [224,] 5.117903e-03 0.0102358054 0.994882097 [225,] 2.930621e-03 0.0058612429 0.997069379 [226,] 2.124768e-03 0.0042495361 0.997875232 [227,] 1.113815e-03 0.0022276296 0.998886185 > postscript(file="/var/wessaorg/rcomp/tmp/1cyj81352154925.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/2zjm91352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/32tix1352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4avm71352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5b1ho1352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -3.046083853 -0.097885472 2.381281244 2.731155412 -1.614307618 -2.095227848 7 8 9 10 11 12 3.889361672 -1.971324220 -2.139282147 0.597450973 0.538954384 -0.123511784 13 14 15 16 17 18 0.379329329 0.790552310 -0.674116582 -0.180222203 0.238407564 3.571997802 19 20 21 22 23 24 2.383996153 0.692581919 0.738684169 0.840979686 2.681673323 1.110340647 25 26 27 28 29 30 0.459016248 -0.062256701 0.993630452 -1.287354597 0.447129194 -0.390442523 31 32 33 34 35 36 -0.877073040 -0.441086324 -1.163223672 0.093370145 -1.650082732 -3.265086391 37 38 39 40 41 42 -2.727741818 -1.838583996 1.482591400 1.219986431 0.814154553 -1.572760963 43 44 45 46 47 48 2.330837638 -0.138775670 -1.069660746 -4.614485627 -2.570906971 -0.148188267 49 50 51 52 53 54 0.938503281 -2.063309004 -0.618730355 -0.178019628 -2.683765704 0.347265324 55 56 57 58 59 60 -2.669484673 1.422225656 -0.003626575 0.677314469 -0.386915103 1.746573703 61 62 63 64 65 66 0.461124336 0.065518931 -0.590941349 -0.261866865 0.759975261 0.828101104 67 68 69 70 71 72 1.882696936 3.300864888 -3.672281623 0.630343924 -3.586652412 -0.195891331 73 74 75 76 77 78 1.451465642 1.067764404 0.177481194 3.105017756 -0.195494635 1.496153743 79 80 81 82 83 84 -2.012991571 0.228562094 0.430911605 0.229972159 -0.807445923 0.212340342 85 86 87 88 89 90 1.643521380 -0.157162591 0.694124829 1.530382878 0.721172556 -1.602358824 91 92 93 94 95 96 0.373075636 0.568114210 -0.227714771 -2.081327575 0.955762767 0.270470473 97 98 99 100 101 102 1.853750511 0.029782633 -0.408738965 -1.066991022 1.267773121 2.669794486 103 104 105 106 107 108 0.334270075 1.501782303 -2.121064767 1.095591200 0.418550755 1.651855036 109 110 111 112 113 114 0.094684451 0.939050063 0.177075686 2.154429571 -0.883473804 -2.737925680 115 116 117 118 119 120 1.420453657 -1.228111477 1.002398217 -1.899311535 0.341804495 -1.071136018 121 122 123 124 125 126 0.446467035 -2.953286739 -0.965887394 -1.156982748 -0.758089866 -0.411090666 127 128 129 130 131 132 1.147708624 1.023995324 -2.800546536 1.893986019 -3.335922783 2.061816729 133 134 135 136 137 138 -1.853766898 -1.518542121 -0.236507084 0.955939989 0.598813891 -2.132096531 139 140 141 142 143 144 -1.232882644 -2.317151286 3.106912918 1.651817307 0.374850487 1.307935338 145 146 147 148 149 150 -4.101570283 2.230955010 -1.987321910 0.868155244 -0.027501747 -3.130394560 151 152 153 154 155 156 -1.018847217 2.138291381 3.984137915 1.339284535 -2.605686135 0.632496425 157 158 159 160 161 162 1.386083240 1.143727995 0.935273542 -0.461648177 0.372169536 0.241572893 163 164 165 166 167 168 0.438130834 0.977998836 0.792681890 -2.633186375 -0.368331427 -2.715343737 169 170 171 172 173 174 -1.888599055 0.754202065 1.714500846 0.480536123 -0.836752333 -2.053128439 175 176 177 178 179 180 -2.116317591 0.662960929 0.211407502 -0.688041152 0.295021024 -1.068134703 181 182 183 184 185 186 0.787123339 0.344561643 1.853610710 0.805809359 -6.647726530 0.743212520 187 188 189 190 191 192 2.808854280 0.735010043 -0.497144058 -0.235128060 -1.200137894 0.536023918 193 194 195 196 197 198 2.063122417 1.542401986 -0.652748953 -0.021578718 3.268885220 0.755228294 199 200 201 202 203 204 1.525018472 1.239906700 2.133711339 0.262490053 -1.953361859 -2.537816964 205 206 207 208 209 210 2.320968306 0.355660628 1.513838673 0.476488803 -2.288943941 1.391950854 211 212 213 214 215 216 -2.120496256 -4.038868305 0.331316763 3.174673096 1.406817956 1.267997862 217 218 219 220 221 222 2.392076776 0.795743770 0.946062926 -0.454744585 -1.057563970 -0.051553752 223 224 225 226 227 228 0.976636997 -0.913949293 -0.182381939 -3.888500612 -0.195549171 1.136629250 229 230 231 232 233 234 -1.346430686 -0.783445263 2.333928414 -3.699652462 4.418384561 2.797828284 235 236 237 238 239 240 -0.392730636 -1.603408206 -6.280183329 -0.515384322 2.112346369 -0.625532740 241 242 243 244 245 246 0.013148320 -1.706930392 1.096855527 2.095111636 2.084309672 -0.599045499 247 248 249 250 251 252 0.853503967 -2.983598495 1.063163587 0.575953092 -1.122927656 -1.426397800 253 254 255 256 257 258 1.060560372 3.491633848 -0.998299111 0.312867479 1.512522153 2.271058486 259 260 261 262 263 264 -1.053654052 -4.084003559 1.195610175 -4.320972188 -0.104872489 1.203866722 > postscript(file="/var/wessaorg/rcomp/tmp/6sdrn1352154925.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 -3.046083853 NA 1 -0.097885472 -3.046083853 2 2.381281244 -0.097885472 3 2.731155412 2.381281244 4 -1.614307618 2.731155412 5 -2.095227848 -1.614307618 6 3.889361672 -2.095227848 7 -1.971324220 3.889361672 8 -2.139282147 -1.971324220 9 0.597450973 -2.139282147 10 0.538954384 0.597450973 11 -0.123511784 0.538954384 12 0.379329329 -0.123511784 13 0.790552310 0.379329329 14 -0.674116582 0.790552310 15 -0.180222203 -0.674116582 16 0.238407564 -0.180222203 17 3.571997802 0.238407564 18 2.383996153 3.571997802 19 0.692581919 2.383996153 20 0.738684169 0.692581919 21 0.840979686 0.738684169 22 2.681673323 0.840979686 23 1.110340647 2.681673323 24 0.459016248 1.110340647 25 -0.062256701 0.459016248 26 0.993630452 -0.062256701 27 -1.287354597 0.993630452 28 0.447129194 -1.287354597 29 -0.390442523 0.447129194 30 -0.877073040 -0.390442523 31 -0.441086324 -0.877073040 32 -1.163223672 -0.441086324 33 0.093370145 -1.163223672 34 -1.650082732 0.093370145 35 -3.265086391 -1.650082732 36 -2.727741818 -3.265086391 37 -1.838583996 -2.727741818 38 1.482591400 -1.838583996 39 1.219986431 1.482591400 40 0.814154553 1.219986431 41 -1.572760963 0.814154553 42 2.330837638 -1.572760963 43 -0.138775670 2.330837638 44 -1.069660746 -0.138775670 45 -4.614485627 -1.069660746 46 -2.570906971 -4.614485627 47 -0.148188267 -2.570906971 48 0.938503281 -0.148188267 49 -2.063309004 0.938503281 50 -0.618730355 -2.063309004 51 -0.178019628 -0.618730355 52 -2.683765704 -0.178019628 53 0.347265324 -2.683765704 54 -2.669484673 0.347265324 55 1.422225656 -2.669484673 56 -0.003626575 1.422225656 57 0.677314469 -0.003626575 58 -0.386915103 0.677314469 59 1.746573703 -0.386915103 60 0.461124336 1.746573703 61 0.065518931 0.461124336 62 -0.590941349 0.065518931 63 -0.261866865 -0.590941349 64 0.759975261 -0.261866865 65 0.828101104 0.759975261 66 1.882696936 0.828101104 67 3.300864888 1.882696936 68 -3.672281623 3.300864888 69 0.630343924 -3.672281623 70 -3.586652412 0.630343924 71 -0.195891331 -3.586652412 72 1.451465642 -0.195891331 73 1.067764404 1.451465642 74 0.177481194 1.067764404 75 3.105017756 0.177481194 76 -0.195494635 3.105017756 77 1.496153743 -0.195494635 78 -2.012991571 1.496153743 79 0.228562094 -2.012991571 80 0.430911605 0.228562094 81 0.229972159 0.430911605 82 -0.807445923 0.229972159 83 0.212340342 -0.807445923 84 1.643521380 0.212340342 85 -0.157162591 1.643521380 86 0.694124829 -0.157162591 87 1.530382878 0.694124829 88 0.721172556 1.530382878 89 -1.602358824 0.721172556 90 0.373075636 -1.602358824 91 0.568114210 0.373075636 92 -0.227714771 0.568114210 93 -2.081327575 -0.227714771 94 0.955762767 -2.081327575 95 0.270470473 0.955762767 96 1.853750511 0.270470473 97 0.029782633 1.853750511 98 -0.408738965 0.029782633 99 -1.066991022 -0.408738965 100 1.267773121 -1.066991022 101 2.669794486 1.267773121 102 0.334270075 2.669794486 103 1.501782303 0.334270075 104 -2.121064767 1.501782303 105 1.095591200 -2.121064767 106 0.418550755 1.095591200 107 1.651855036 0.418550755 108 0.094684451 1.651855036 109 0.939050063 0.094684451 110 0.177075686 0.939050063 111 2.154429571 0.177075686 112 -0.883473804 2.154429571 113 -2.737925680 -0.883473804 114 1.420453657 -2.737925680 115 -1.228111477 1.420453657 116 1.002398217 -1.228111477 117 -1.899311535 1.002398217 118 0.341804495 -1.899311535 119 -1.071136018 0.341804495 120 0.446467035 -1.071136018 121 -2.953286739 0.446467035 122 -0.965887394 -2.953286739 123 -1.156982748 -0.965887394 124 -0.758089866 -1.156982748 125 -0.411090666 -0.758089866 126 1.147708624 -0.411090666 127 1.023995324 1.147708624 128 -2.800546536 1.023995324 129 1.893986019 -2.800546536 130 -3.335922783 1.893986019 131 2.061816729 -3.335922783 132 -1.853766898 2.061816729 133 -1.518542121 -1.853766898 134 -0.236507084 -1.518542121 135 0.955939989 -0.236507084 136 0.598813891 0.955939989 137 -2.132096531 0.598813891 138 -1.232882644 -2.132096531 139 -2.317151286 -1.232882644 140 3.106912918 -2.317151286 141 1.651817307 3.106912918 142 0.374850487 1.651817307 143 1.307935338 0.374850487 144 -4.101570283 1.307935338 145 2.230955010 -4.101570283 146 -1.987321910 2.230955010 147 0.868155244 -1.987321910 148 -0.027501747 0.868155244 149 -3.130394560 -0.027501747 150 -1.018847217 -3.130394560 151 2.138291381 -1.018847217 152 3.984137915 2.138291381 153 1.339284535 3.984137915 154 -2.605686135 1.339284535 155 0.632496425 -2.605686135 156 1.386083240 0.632496425 157 1.143727995 1.386083240 158 0.935273542 1.143727995 159 -0.461648177 0.935273542 160 0.372169536 -0.461648177 161 0.241572893 0.372169536 162 0.438130834 0.241572893 163 0.977998836 0.438130834 164 0.792681890 0.977998836 165 -2.633186375 0.792681890 166 -0.368331427 -2.633186375 167 -2.715343737 -0.368331427 168 -1.888599055 -2.715343737 169 0.754202065 -1.888599055 170 1.714500846 0.754202065 171 0.480536123 1.714500846 172 -0.836752333 0.480536123 173 -2.053128439 -0.836752333 174 -2.116317591 -2.053128439 175 0.662960929 -2.116317591 176 0.211407502 0.662960929 177 -0.688041152 0.211407502 178 0.295021024 -0.688041152 179 -1.068134703 0.295021024 180 0.787123339 -1.068134703 181 0.344561643 0.787123339 182 1.853610710 0.344561643 183 0.805809359 1.853610710 184 -6.647726530 0.805809359 185 0.743212520 -6.647726530 186 2.808854280 0.743212520 187 0.735010043 2.808854280 188 -0.497144058 0.735010043 189 -0.235128060 -0.497144058 190 -1.200137894 -0.235128060 191 0.536023918 -1.200137894 192 2.063122417 0.536023918 193 1.542401986 2.063122417 194 -0.652748953 1.542401986 195 -0.021578718 -0.652748953 196 3.268885220 -0.021578718 197 0.755228294 3.268885220 198 1.525018472 0.755228294 199 1.239906700 1.525018472 200 2.133711339 1.239906700 201 0.262490053 2.133711339 202 -1.953361859 0.262490053 203 -2.537816964 -1.953361859 204 2.320968306 -2.537816964 205 0.355660628 2.320968306 206 1.513838673 0.355660628 207 0.476488803 1.513838673 208 -2.288943941 0.476488803 209 1.391950854 -2.288943941 210 -2.120496256 1.391950854 211 -4.038868305 -2.120496256 212 0.331316763 -4.038868305 213 3.174673096 0.331316763 214 1.406817956 3.174673096 215 1.267997862 1.406817956 216 2.392076776 1.267997862 217 0.795743770 2.392076776 218 0.946062926 0.795743770 219 -0.454744585 0.946062926 220 -1.057563970 -0.454744585 221 -0.051553752 -1.057563970 222 0.976636997 -0.051553752 223 -0.913949293 0.976636997 224 -0.182381939 -0.913949293 225 -3.888500612 -0.182381939 226 -0.195549171 -3.888500612 227 1.136629250 -0.195549171 228 -1.346430686 1.136629250 229 -0.783445263 -1.346430686 230 2.333928414 -0.783445263 231 -3.699652462 2.333928414 232 4.418384561 -3.699652462 233 2.797828284 4.418384561 234 -0.392730636 2.797828284 235 -1.603408206 -0.392730636 236 -6.280183329 -1.603408206 237 -0.515384322 -6.280183329 238 2.112346369 -0.515384322 239 -0.625532740 2.112346369 240 0.013148320 -0.625532740 241 -1.706930392 0.013148320 242 1.096855527 -1.706930392 243 2.095111636 1.096855527 244 2.084309672 2.095111636 245 -0.599045499 2.084309672 246 0.853503967 -0.599045499 247 -2.983598495 0.853503967 248 1.063163587 -2.983598495 249 0.575953092 1.063163587 250 -1.122927656 0.575953092 251 -1.426397800 -1.122927656 252 1.060560372 -1.426397800 253 3.491633848 1.060560372 254 -0.998299111 3.491633848 255 0.312867479 -0.998299111 256 1.512522153 0.312867479 257 2.271058486 1.512522153 258 -1.053654052 2.271058486 259 -4.084003559 -1.053654052 260 1.195610175 -4.084003559 261 -4.320972188 1.195610175 262 -0.104872489 -4.320972188 263 1.203866722 -0.104872489 264 NA 1.203866722 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.097885472 -3.046083853 [2,] 2.381281244 -0.097885472 [3,] 2.731155412 2.381281244 [4,] -1.614307618 2.731155412 [5,] -2.095227848 -1.614307618 [6,] 3.889361672 -2.095227848 [7,] -1.971324220 3.889361672 [8,] -2.139282147 -1.971324220 [9,] 0.597450973 -2.139282147 [10,] 0.538954384 0.597450973 [11,] -0.123511784 0.538954384 [12,] 0.379329329 -0.123511784 [13,] 0.790552310 0.379329329 [14,] -0.674116582 0.790552310 [15,] -0.180222203 -0.674116582 [16,] 0.238407564 -0.180222203 [17,] 3.571997802 0.238407564 [18,] 2.383996153 3.571997802 [19,] 0.692581919 2.383996153 [20,] 0.738684169 0.692581919 [21,] 0.840979686 0.738684169 [22,] 2.681673323 0.840979686 [23,] 1.110340647 2.681673323 [24,] 0.459016248 1.110340647 [25,] -0.062256701 0.459016248 [26,] 0.993630452 -0.062256701 [27,] -1.287354597 0.993630452 [28,] 0.447129194 -1.287354597 [29,] -0.390442523 0.447129194 [30,] -0.877073040 -0.390442523 [31,] -0.441086324 -0.877073040 [32,] -1.163223672 -0.441086324 [33,] 0.093370145 -1.163223672 [34,] -1.650082732 0.093370145 [35,] -3.265086391 -1.650082732 [36,] -2.727741818 -3.265086391 [37,] -1.838583996 -2.727741818 [38,] 1.482591400 -1.838583996 [39,] 1.219986431 1.482591400 [40,] 0.814154553 1.219986431 [41,] -1.572760963 0.814154553 [42,] 2.330837638 -1.572760963 [43,] -0.138775670 2.330837638 [44,] -1.069660746 -0.138775670 [45,] -4.614485627 -1.069660746 [46,] -2.570906971 -4.614485627 [47,] -0.148188267 -2.570906971 [48,] 0.938503281 -0.148188267 [49,] -2.063309004 0.938503281 [50,] -0.618730355 -2.063309004 [51,] -0.178019628 -0.618730355 [52,] -2.683765704 -0.178019628 [53,] 0.347265324 -2.683765704 [54,] -2.669484673 0.347265324 [55,] 1.422225656 -2.669484673 [56,] -0.003626575 1.422225656 [57,] 0.677314469 -0.003626575 [58,] -0.386915103 0.677314469 [59,] 1.746573703 -0.386915103 [60,] 0.461124336 1.746573703 [61,] 0.065518931 0.461124336 [62,] -0.590941349 0.065518931 [63,] -0.261866865 -0.590941349 [64,] 0.759975261 -0.261866865 [65,] 0.828101104 0.759975261 [66,] 1.882696936 0.828101104 [67,] 3.300864888 1.882696936 [68,] -3.672281623 3.300864888 [69,] 0.630343924 -3.672281623 [70,] -3.586652412 0.630343924 [71,] -0.195891331 -3.586652412 [72,] 1.451465642 -0.195891331 [73,] 1.067764404 1.451465642 [74,] 0.177481194 1.067764404 [75,] 3.105017756 0.177481194 [76,] -0.195494635 3.105017756 [77,] 1.496153743 -0.195494635 [78,] -2.012991571 1.496153743 [79,] 0.228562094 -2.012991571 [80,] 0.430911605 0.228562094 [81,] 0.229972159 0.430911605 [82,] -0.807445923 0.229972159 [83,] 0.212340342 -0.807445923 [84,] 1.643521380 0.212340342 [85,] -0.157162591 1.643521380 [86,] 0.694124829 -0.157162591 [87,] 1.530382878 0.694124829 [88,] 0.721172556 1.530382878 [89,] -1.602358824 0.721172556 [90,] 0.373075636 -1.602358824 [91,] 0.568114210 0.373075636 [92,] -0.227714771 0.568114210 [93,] -2.081327575 -0.227714771 [94,] 0.955762767 -2.081327575 [95,] 0.270470473 0.955762767 [96,] 1.853750511 0.270470473 [97,] 0.029782633 1.853750511 [98,] -0.408738965 0.029782633 [99,] -1.066991022 -0.408738965 [100,] 1.267773121 -1.066991022 [101,] 2.669794486 1.267773121 [102,] 0.334270075 2.669794486 [103,] 1.501782303 0.334270075 [104,] -2.121064767 1.501782303 [105,] 1.095591200 -2.121064767 [106,] 0.418550755 1.095591200 [107,] 1.651855036 0.418550755 [108,] 0.094684451 1.651855036 [109,] 0.939050063 0.094684451 [110,] 0.177075686 0.939050063 [111,] 2.154429571 0.177075686 [112,] -0.883473804 2.154429571 [113,] -2.737925680 -0.883473804 [114,] 1.420453657 -2.737925680 [115,] -1.228111477 1.420453657 [116,] 1.002398217 -1.228111477 [117,] -1.899311535 1.002398217 [118,] 0.341804495 -1.899311535 [119,] -1.071136018 0.341804495 [120,] 0.446467035 -1.071136018 [121,] -2.953286739 0.446467035 [122,] -0.965887394 -2.953286739 [123,] -1.156982748 -0.965887394 [124,] -0.758089866 -1.156982748 [125,] -0.411090666 -0.758089866 [126,] 1.147708624 -0.411090666 [127,] 1.023995324 1.147708624 [128,] -2.800546536 1.023995324 [129,] 1.893986019 -2.800546536 [130,] -3.335922783 1.893986019 [131,] 2.061816729 -3.335922783 [132,] -1.853766898 2.061816729 [133,] -1.518542121 -1.853766898 [134,] -0.236507084 -1.518542121 [135,] 0.955939989 -0.236507084 [136,] 0.598813891 0.955939989 [137,] -2.132096531 0.598813891 [138,] -1.232882644 -2.132096531 [139,] -2.317151286 -1.232882644 [140,] 3.106912918 -2.317151286 [141,] 1.651817307 3.106912918 [142,] 0.374850487 1.651817307 [143,] 1.307935338 0.374850487 [144,] -4.101570283 1.307935338 [145,] 2.230955010 -4.101570283 [146,] -1.987321910 2.230955010 [147,] 0.868155244 -1.987321910 [148,] -0.027501747 0.868155244 [149,] -3.130394560 -0.027501747 [150,] -1.018847217 -3.130394560 [151,] 2.138291381 -1.018847217 [152,] 3.984137915 2.138291381 [153,] 1.339284535 3.984137915 [154,] -2.605686135 1.339284535 [155,] 0.632496425 -2.605686135 [156,] 1.386083240 0.632496425 [157,] 1.143727995 1.386083240 [158,] 0.935273542 1.143727995 [159,] -0.461648177 0.935273542 [160,] 0.372169536 -0.461648177 [161,] 0.241572893 0.372169536 [162,] 0.438130834 0.241572893 [163,] 0.977998836 0.438130834 [164,] 0.792681890 0.977998836 [165,] -2.633186375 0.792681890 [166,] -0.368331427 -2.633186375 [167,] -2.715343737 -0.368331427 [168,] -1.888599055 -2.715343737 [169,] 0.754202065 -1.888599055 [170,] 1.714500846 0.754202065 [171,] 0.480536123 1.714500846 [172,] -0.836752333 0.480536123 [173,] -2.053128439 -0.836752333 [174,] -2.116317591 -2.053128439 [175,] 0.662960929 -2.116317591 [176,] 0.211407502 0.662960929 [177,] -0.688041152 0.211407502 [178,] 0.295021024 -0.688041152 [179,] -1.068134703 0.295021024 [180,] 0.787123339 -1.068134703 [181,] 0.344561643 0.787123339 [182,] 1.853610710 0.344561643 [183,] 0.805809359 1.853610710 [184,] -6.647726530 0.805809359 [185,] 0.743212520 -6.647726530 [186,] 2.808854280 0.743212520 [187,] 0.735010043 2.808854280 [188,] -0.497144058 0.735010043 [189,] -0.235128060 -0.497144058 [190,] -1.200137894 -0.235128060 [191,] 0.536023918 -1.200137894 [192,] 2.063122417 0.536023918 [193,] 1.542401986 2.063122417 [194,] -0.652748953 1.542401986 [195,] -0.021578718 -0.652748953 [196,] 3.268885220 -0.021578718 [197,] 0.755228294 3.268885220 [198,] 1.525018472 0.755228294 [199,] 1.239906700 1.525018472 [200,] 2.133711339 1.239906700 [201,] 0.262490053 2.133711339 [202,] -1.953361859 0.262490053 [203,] -2.537816964 -1.953361859 [204,] 2.320968306 -2.537816964 [205,] 0.355660628 2.320968306 [206,] 1.513838673 0.355660628 [207,] 0.476488803 1.513838673 [208,] -2.288943941 0.476488803 [209,] 1.391950854 -2.288943941 [210,] -2.120496256 1.391950854 [211,] -4.038868305 -2.120496256 [212,] 0.331316763 -4.038868305 [213,] 3.174673096 0.331316763 [214,] 1.406817956 3.174673096 [215,] 1.267997862 1.406817956 [216,] 2.392076776 1.267997862 [217,] 0.795743770 2.392076776 [218,] 0.946062926 0.795743770 [219,] -0.454744585 0.946062926 [220,] -1.057563970 -0.454744585 [221,] -0.051553752 -1.057563970 [222,] 0.976636997 -0.051553752 [223,] -0.913949293 0.976636997 [224,] -0.182381939 -0.913949293 [225,] -3.888500612 -0.182381939 [226,] -0.195549171 -3.888500612 [227,] 1.136629250 -0.195549171 [228,] -1.346430686 1.136629250 [229,] -0.783445263 -1.346430686 [230,] 2.333928414 -0.783445263 [231,] -3.699652462 2.333928414 [232,] 4.418384561 -3.699652462 [233,] 2.797828284 4.418384561 [234,] -0.392730636 2.797828284 [235,] -1.603408206 -0.392730636 [236,] -6.280183329 -1.603408206 [237,] -0.515384322 -6.280183329 [238,] 2.112346369 -0.515384322 [239,] -0.625532740 2.112346369 [240,] 0.013148320 -0.625532740 [241,] -1.706930392 0.013148320 [242,] 1.096855527 -1.706930392 [243,] 2.095111636 1.096855527 [244,] 2.084309672 2.095111636 [245,] -0.599045499 2.084309672 [246,] 0.853503967 -0.599045499 [247,] -2.983598495 0.853503967 [248,] 1.063163587 -2.983598495 [249,] 0.575953092 1.063163587 [250,] -1.122927656 0.575953092 [251,] -1.426397800 -1.122927656 [252,] 1.060560372 -1.426397800 [253,] 3.491633848 1.060560372 [254,] -0.998299111 3.491633848 [255,] 0.312867479 -0.998299111 [256,] 1.512522153 0.312867479 [257,] 2.271058486 1.512522153 [258,] -1.053654052 2.271058486 [259,] -4.084003559 -1.053654052 [260,] 1.195610175 -4.084003559 [261,] -4.320972188 1.195610175 [262,] -0.104872489 -4.320972188 [263,] 1.203866722 -0.104872489 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.097885472 -3.046083853 2 2.381281244 -0.097885472 3 2.731155412 2.381281244 4 -1.614307618 2.731155412 5 -2.095227848 -1.614307618 6 3.889361672 -2.095227848 7 -1.971324220 3.889361672 8 -2.139282147 -1.971324220 9 0.597450973 -2.139282147 10 0.538954384 0.597450973 11 -0.123511784 0.538954384 12 0.379329329 -0.123511784 13 0.790552310 0.379329329 14 -0.674116582 0.790552310 15 -0.180222203 -0.674116582 16 0.238407564 -0.180222203 17 3.571997802 0.238407564 18 2.383996153 3.571997802 19 0.692581919 2.383996153 20 0.738684169 0.692581919 21 0.840979686 0.738684169 22 2.681673323 0.840979686 23 1.110340647 2.681673323 24 0.459016248 1.110340647 25 -0.062256701 0.459016248 26 0.993630452 -0.062256701 27 -1.287354597 0.993630452 28 0.447129194 -1.287354597 29 -0.390442523 0.447129194 30 -0.877073040 -0.390442523 31 -0.441086324 -0.877073040 32 -1.163223672 -0.441086324 33 0.093370145 -1.163223672 34 -1.650082732 0.093370145 35 -3.265086391 -1.650082732 36 -2.727741818 -3.265086391 37 -1.838583996 -2.727741818 38 1.482591400 -1.838583996 39 1.219986431 1.482591400 40 0.814154553 1.219986431 41 -1.572760963 0.814154553 42 2.330837638 -1.572760963 43 -0.138775670 2.330837638 44 -1.069660746 -0.138775670 45 -4.614485627 -1.069660746 46 -2.570906971 -4.614485627 47 -0.148188267 -2.570906971 48 0.938503281 -0.148188267 49 -2.063309004 0.938503281 50 -0.618730355 -2.063309004 51 -0.178019628 -0.618730355 52 -2.683765704 -0.178019628 53 0.347265324 -2.683765704 54 -2.669484673 0.347265324 55 1.422225656 -2.669484673 56 -0.003626575 1.422225656 57 0.677314469 -0.003626575 58 -0.386915103 0.677314469 59 1.746573703 -0.386915103 60 0.461124336 1.746573703 61 0.065518931 0.461124336 62 -0.590941349 0.065518931 63 -0.261866865 -0.590941349 64 0.759975261 -0.261866865 65 0.828101104 0.759975261 66 1.882696936 0.828101104 67 3.300864888 1.882696936 68 -3.672281623 3.300864888 69 0.630343924 -3.672281623 70 -3.586652412 0.630343924 71 -0.195891331 -3.586652412 72 1.451465642 -0.195891331 73 1.067764404 1.451465642 74 0.177481194 1.067764404 75 3.105017756 0.177481194 76 -0.195494635 3.105017756 77 1.496153743 -0.195494635 78 -2.012991571 1.496153743 79 0.228562094 -2.012991571 80 0.430911605 0.228562094 81 0.229972159 0.430911605 82 -0.807445923 0.229972159 83 0.212340342 -0.807445923 84 1.643521380 0.212340342 85 -0.157162591 1.643521380 86 0.694124829 -0.157162591 87 1.530382878 0.694124829 88 0.721172556 1.530382878 89 -1.602358824 0.721172556 90 0.373075636 -1.602358824 91 0.568114210 0.373075636 92 -0.227714771 0.568114210 93 -2.081327575 -0.227714771 94 0.955762767 -2.081327575 95 0.270470473 0.955762767 96 1.853750511 0.270470473 97 0.029782633 1.853750511 98 -0.408738965 0.029782633 99 -1.066991022 -0.408738965 100 1.267773121 -1.066991022 101 2.669794486 1.267773121 102 0.334270075 2.669794486 103 1.501782303 0.334270075 104 -2.121064767 1.501782303 105 1.095591200 -2.121064767 106 0.418550755 1.095591200 107 1.651855036 0.418550755 108 0.094684451 1.651855036 109 0.939050063 0.094684451 110 0.177075686 0.939050063 111 2.154429571 0.177075686 112 -0.883473804 2.154429571 113 -2.737925680 -0.883473804 114 1.420453657 -2.737925680 115 -1.228111477 1.420453657 116 1.002398217 -1.228111477 117 -1.899311535 1.002398217 118 0.341804495 -1.899311535 119 -1.071136018 0.341804495 120 0.446467035 -1.071136018 121 -2.953286739 0.446467035 122 -0.965887394 -2.953286739 123 -1.156982748 -0.965887394 124 -0.758089866 -1.156982748 125 -0.411090666 -0.758089866 126 1.147708624 -0.411090666 127 1.023995324 1.147708624 128 -2.800546536 1.023995324 129 1.893986019 -2.800546536 130 -3.335922783 1.893986019 131 2.061816729 -3.335922783 132 -1.853766898 2.061816729 133 -1.518542121 -1.853766898 134 -0.236507084 -1.518542121 135 0.955939989 -0.236507084 136 0.598813891 0.955939989 137 -2.132096531 0.598813891 138 -1.232882644 -2.132096531 139 -2.317151286 -1.232882644 140 3.106912918 -2.317151286 141 1.651817307 3.106912918 142 0.374850487 1.651817307 143 1.307935338 0.374850487 144 -4.101570283 1.307935338 145 2.230955010 -4.101570283 146 -1.987321910 2.230955010 147 0.868155244 -1.987321910 148 -0.027501747 0.868155244 149 -3.130394560 -0.027501747 150 -1.018847217 -3.130394560 151 2.138291381 -1.018847217 152 3.984137915 2.138291381 153 1.339284535 3.984137915 154 -2.605686135 1.339284535 155 0.632496425 -2.605686135 156 1.386083240 0.632496425 157 1.143727995 1.386083240 158 0.935273542 1.143727995 159 -0.461648177 0.935273542 160 0.372169536 -0.461648177 161 0.241572893 0.372169536 162 0.438130834 0.241572893 163 0.977998836 0.438130834 164 0.792681890 0.977998836 165 -2.633186375 0.792681890 166 -0.368331427 -2.633186375 167 -2.715343737 -0.368331427 168 -1.888599055 -2.715343737 169 0.754202065 -1.888599055 170 1.714500846 0.754202065 171 0.480536123 1.714500846 172 -0.836752333 0.480536123 173 -2.053128439 -0.836752333 174 -2.116317591 -2.053128439 175 0.662960929 -2.116317591 176 0.211407502 0.662960929 177 -0.688041152 0.211407502 178 0.295021024 -0.688041152 179 -1.068134703 0.295021024 180 0.787123339 -1.068134703 181 0.344561643 0.787123339 182 1.853610710 0.344561643 183 0.805809359 1.853610710 184 -6.647726530 0.805809359 185 0.743212520 -6.647726530 186 2.808854280 0.743212520 187 0.735010043 2.808854280 188 -0.497144058 0.735010043 189 -0.235128060 -0.497144058 190 -1.200137894 -0.235128060 191 0.536023918 -1.200137894 192 2.063122417 0.536023918 193 1.542401986 2.063122417 194 -0.652748953 1.542401986 195 -0.021578718 -0.652748953 196 3.268885220 -0.021578718 197 0.755228294 3.268885220 198 1.525018472 0.755228294 199 1.239906700 1.525018472 200 2.133711339 1.239906700 201 0.262490053 2.133711339 202 -1.953361859 0.262490053 203 -2.537816964 -1.953361859 204 2.320968306 -2.537816964 205 0.355660628 2.320968306 206 1.513838673 0.355660628 207 0.476488803 1.513838673 208 -2.288943941 0.476488803 209 1.391950854 -2.288943941 210 -2.120496256 1.391950854 211 -4.038868305 -2.120496256 212 0.331316763 -4.038868305 213 3.174673096 0.331316763 214 1.406817956 3.174673096 215 1.267997862 1.406817956 216 2.392076776 1.267997862 217 0.795743770 2.392076776 218 0.946062926 0.795743770 219 -0.454744585 0.946062926 220 -1.057563970 -0.454744585 221 -0.051553752 -1.057563970 222 0.976636997 -0.051553752 223 -0.913949293 0.976636997 224 -0.182381939 -0.913949293 225 -3.888500612 -0.182381939 226 -0.195549171 -3.888500612 227 1.136629250 -0.195549171 228 -1.346430686 1.136629250 229 -0.783445263 -1.346430686 230 2.333928414 -0.783445263 231 -3.699652462 2.333928414 232 4.418384561 -3.699652462 233 2.797828284 4.418384561 234 -0.392730636 2.797828284 235 -1.603408206 -0.392730636 236 -6.280183329 -1.603408206 237 -0.515384322 -6.280183329 238 2.112346369 -0.515384322 239 -0.625532740 2.112346369 240 0.013148320 -0.625532740 241 -1.706930392 0.013148320 242 1.096855527 -1.706930392 243 2.095111636 1.096855527 244 2.084309672 2.095111636 245 -0.599045499 2.084309672 246 0.853503967 -0.599045499 247 -2.983598495 0.853503967 248 1.063163587 -2.983598495 249 0.575953092 1.063163587 250 -1.122927656 0.575953092 251 -1.426397800 -1.122927656 252 1.060560372 -1.426397800 253 3.491633848 1.060560372 254 -0.998299111 3.491633848 255 0.312867479 -0.998299111 256 1.512522153 0.312867479 257 2.271058486 1.512522153 258 -1.053654052 2.271058486 259 -4.084003559 -1.053654052 260 1.195610175 -4.084003559 261 -4.320972188 1.195610175 262 -0.104872489 -4.320972188 263 1.203866722 -0.104872489 > 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/7urlz1352154925.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/8j4si1352154925.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/9we7r1352154925.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/100y6f1352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1180d31352154925.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12x2mf1352154925.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13rza01352154925.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/143hm11352154925.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15lw851352154925.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16ggqp1352154925.tab") + } > > try(system("convert tmp/1cyj81352154925.ps tmp/1cyj81352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/2zjm91352154925.ps tmp/2zjm91352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/32tix1352154925.ps tmp/32tix1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/4avm71352154925.ps tmp/4avm71352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/5b1ho1352154925.ps tmp/5b1ho1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/6sdrn1352154925.ps tmp/6sdrn1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/7urlz1352154925.ps tmp/7urlz1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/8j4si1352154925.ps tmp/8j4si1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/9we7r1352154925.ps tmp/9we7r1352154925.png",intern=TRUE)) character(0) > try(system("convert tmp/100y6f1352154925.ps tmp/100y6f1352154925.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.235 1.190 17.428