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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,19 + ,19 + ,11 + ,11 + ,81 + ,81 + ,33 + ,33 + ,36 + ,36 + ,1 + ,159 + ,159 + ,16 + ,11 + ,11 + ,13 + ,13 + ,9 + ,9 + ,84 + ,84 + ,34 + ,34 + ,32 + ,32 + ,1 + ,160 + ,160 + ,12 + ,8 + ,8 + ,12 + ,12 + ,18 + ,18 + ,84 + ,84 + ,32 + ,32 + ,38 + ,38 + ,1 + ,161 + ,161 + ,13 + ,8 + ,8 + ,13 + ,13 + ,16 + ,16 + ,69 + ,69 + ,34 + ,34 + ,36 + ,36 + ,0 + ,162 + ,0 + ,13 + ,10 + ,0 + ,10 + ,0 + ,24 + ,0 + ,66 + ,0 + ,27 + ,0 + ,26 + ,0) + ,dim=c(16 + ,162) + ,dimnames=list(c('Pop' + ,'t' + ,'Pop_t' + ,'Learning' + ,'Software' + ,'Software_p' + ,'Happiness' + ,'Happiness_p' + ,'Depression' + ,'Depression_p' + ,'Belonging' + ,'Belonging_p' + ,'Connected' + ,'Connected_p' + ,'Separate' + ,'Separate_p') + ,1:162)) > y <- array(NA,dim=c(16,162),dimnames=list(c('Pop','t','Pop_t','Learning','Software','Software_p','Happiness','Happiness_p','Depression','Depression_p','Belonging','Belonging_p','Connected','Connected_p','Separate','Separate_p'),1:162)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > 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 Pop_t Pop t Learning Software Software_p Happiness Happiness_p Depression 1 1 1 1 13 12 12 14 14 12.0 2 2 1 2 16 11 11 18 18 11.0 3 3 1 3 19 15 15 11 11 14.0 4 4 1 4 15 6 6 12 12 12.0 5 5 1 5 14 13 13 16 16 21.0 6 6 1 6 13 10 10 18 18 12.0 7 7 1 7 19 12 12 14 14 22.0 8 8 1 8 15 14 14 14 14 11.0 9 9 1 9 14 12 12 15 15 10.0 10 10 1 10 15 9 9 15 15 13.0 11 11 1 11 16 10 10 17 17 10.0 12 12 1 12 16 12 12 19 19 8.0 13 13 1 13 16 12 12 10 10 15.0 14 14 1 14 16 11 11 16 16 14.0 15 15 1 15 17 15 15 18 18 10.0 16 16 1 16 15 12 12 14 14 14.0 17 17 1 17 15 10 10 14 14 14.0 18 18 1 18 20 12 12 17 17 11.0 19 19 1 19 18 11 11 14 14 10.0 20 20 1 20 16 12 12 16 16 13.0 21 21 1 21 16 11 11 18 18 9.5 22 22 1 22 16 12 12 11 11 14.0 23 23 1 23 19 13 13 14 14 12.0 24 24 1 24 16 11 11 12 12 14.0 25 25 1 25 17 12 12 17 17 11.0 26 26 1 26 17 13 13 9 9 9.0 27 27 1 27 16 10 10 16 16 11.0 28 28 1 28 15 14 14 14 14 15.0 29 29 1 29 16 12 12 15 15 14.0 30 30 1 30 14 10 10 11 11 13.0 31 31 1 31 15 12 12 16 16 9.0 32 32 1 32 12 8 8 13 13 15.0 33 33 1 33 14 10 10 17 17 10.0 34 34 1 34 16 12 12 15 15 11.0 35 35 1 35 14 12 12 14 14 13.0 36 36 1 36 10 7 7 16 16 8.0 37 37 1 37 10 9 9 9 9 20.0 38 38 1 38 14 12 12 15 15 12.0 39 39 1 39 16 10 10 17 17 10.0 40 40 1 40 16 10 10 13 13 10.0 41 41 1 41 16 10 10 15 15 9.0 42 42 1 42 14 12 12 16 16 14.0 43 43 1 43 20 15 15 16 16 8.0 44 44 1 44 14 10 10 12 12 14.0 45 45 1 45 14 10 10 15 15 11.0 46 46 1 46 11 12 12 11 11 13.0 47 47 1 47 14 13 13 15 15 9.0 48 48 1 48 15 11 11 15 15 11.0 49 49 1 49 16 11 11 17 17 15.0 50 50 1 50 14 12 12 13 13 11.0 51 51 1 51 16 14 14 16 16 10.0 52 52 1 52 14 10 10 14 14 14.0 53 53 1 53 12 12 12 11 11 18.0 54 54 1 54 16 13 13 12 12 14.0 55 55 1 55 9 5 5 12 12 11.0 56 56 1 56 14 6 6 15 15 14.5 57 57 1 57 16 12 12 16 16 13.0 58 58 1 58 16 12 12 15 15 9.0 59 59 1 59 15 11 11 12 12 10.0 60 60 1 60 16 10 10 12 12 15.0 61 61 1 61 12 7 7 8 8 20.0 62 62 1 62 16 12 12 13 13 12.0 63 63 1 63 16 14 14 11 11 12.0 64 64 1 64 14 11 11 14 14 14.0 65 65 1 65 16 12 12 15 15 13.0 66 66 1 66 17 13 13 10 10 11.0 67 67 1 67 18 14 14 11 11 17.0 68 68 1 68 18 11 11 12 12 12.0 69 69 1 69 12 12 12 15 15 13.0 70 70 1 70 16 12 12 15 15 14.0 71 71 1 71 10 8 8 14 14 13.0 72 72 1 72 14 11 11 16 16 15.0 73 73 1 73 18 14 14 15 15 13.0 74 74 1 74 18 14 14 15 15 10.0 75 75 1 75 16 12 12 13 13 11.0 76 76 1 76 17 9 9 12 12 19.0 77 77 1 77 16 13 13 17 17 13.0 78 78 1 78 16 11 11 13 13 17.0 79 79 1 79 13 12 12 15 15 13.0 80 80 1 80 16 12 12 13 13 9.0 81 81 1 81 16 12 12 15 15 11.0 82 82 1 82 16 12 12 15 15 9.0 83 83 1 83 15 12 12 16 16 12.0 84 84 1 84 15 11 11 15 15 12.0 85 85 1 85 16 10 10 14 14 13.0 86 86 1 86 14 9 9 15 15 13.0 87 87 1 87 16 12 12 14 14 12.0 88 88 1 88 16 12 12 13 13 15.0 89 89 1 89 15 12 12 7 7 22.0 90 90 1 90 12 9 9 17 17 13.0 91 91 1 91 17 15 15 13 13 15.0 92 92 1 92 16 12 12 15 15 13.0 93 93 1 93 15 12 12 14 14 15.0 94 94 1 94 13 12 12 13 13 12.5 95 95 1 95 16 10 10 16 16 11.0 96 96 1 96 16 13 13 12 12 16.0 97 97 1 97 16 9 9 14 14 11.0 98 98 1 98 16 12 12 17 17 11.0 99 99 1 99 14 10 10 15 15 10.0 100 100 1 100 16 14 14 17 17 10.0 101 101 1 101 16 11 11 12 12 16.0 102 102 1 102 20 15 15 16 16 12.0 103 103 1 103 15 11 11 11 11 11.0 104 104 1 104 16 11 11 15 15 16.0 105 105 1 105 13 12 12 9 9 19.0 106 106 1 106 17 12 12 16 16 11.0 107 107 1 107 16 12 12 15 15 16.0 108 108 1 108 16 11 11 10 10 15.0 109 109 1 109 12 7 7 10 10 24.0 110 110 1 110 16 12 12 15 15 14.0 111 111 1 111 16 14 14 11 11 15.0 112 112 1 112 17 11 11 13 13 11.0 113 113 1 113 13 11 11 14 14 15.0 114 114 1 114 12 10 10 18 18 12.0 115 115 1 115 18 13 13 16 16 10.0 116 116 1 116 14 13 13 14 14 14.0 117 117 1 117 14 8 8 14 14 13.0 118 118 1 118 13 11 11 14 14 9.0 119 119 1 119 16 12 12 14 14 15.0 120 120 1 120 13 11 11 12 12 15.0 121 121 1 121 16 13 13 14 14 14.0 122 122 1 122 13 12 12 15 15 11.0 123 123 1 123 16 14 14 15 15 8.0 124 124 1 124 15 13 13 15 15 11.0 125 125 1 125 16 15 15 13 13 11.0 126 126 1 126 15 10 10 17 17 8.0 127 127 1 127 17 11 11 17 17 10.0 128 128 1 128 15 9 9 19 19 11.0 129 129 1 129 12 11 11 15 15 13.0 130 130 1 130 16 10 10 13 13 11.0 131 131 1 131 10 11 11 9 9 20.0 132 132 1 132 16 8 8 15 15 10.0 133 133 1 133 12 11 11 15 15 15.0 134 134 1 134 14 12 12 15 15 12.0 135 135 1 135 15 12 12 16 16 14.0 136 136 1 136 13 9 9 11 11 23.0 137 137 1 137 15 11 11 14 14 14.0 138 138 1 138 11 10 10 11 11 16.0 139 139 1 139 12 8 8 15 15 11.0 140 140 1 140 11 9 9 13 13 12.0 141 141 1 141 16 8 8 15 15 10.0 142 142 1 142 15 9 9 16 16 14.0 143 143 1 143 17 15 15 14 14 12.0 144 144 1 144 16 11 11 15 15 12.0 145 145 1 145 10 8 8 16 16 11.0 146 146 1 146 18 13 13 16 16 12.0 147 147 1 147 13 12 12 11 11 13.0 148 148 1 148 16 12 12 12 12 11.0 149 149 1 149 13 9 9 9 9 19.0 150 150 1 150 10 7 7 16 16 12.0 151 151 1 151 15 13 13 13 13 17.0 152 152 1 152 16 9 9 16 16 9.0 153 153 1 153 16 6 6 12 12 12.0 154 154 1 154 14 8 8 9 9 19.0 155 155 1 155 10 8 8 13 13 18.0 156 156 1 156 17 15 15 13 13 15.0 157 157 1 157 13 6 6 14 14 14.0 158 158 1 158 15 9 9 19 19 11.0 159 159 1 159 16 11 11 13 13 9.0 160 160 1 160 12 8 8 12 12 18.0 161 161 1 161 13 8 8 13 13 16.0 162 0 0 162 13 10 0 10 0 24.0 Depression_p Belonging Belonging_p Connected Connected_p Separate 1 12.0 53 53 41 41 38 2 11.0 83 83 39 39 32 3 14.0 66 66 30 30 35 4 12.0 67 67 31 31 33 5 21.0 76 76 34 34 37 6 12.0 78 78 35 35 29 7 22.0 53 53 39 39 31 8 11.0 80 80 34 34 36 9 10.0 74 74 36 36 35 10 13.0 76 76 37 37 38 11 10.0 79 79 38 38 31 12 8.0 54 54 36 36 34 13 15.0 67 67 38 38 35 14 14.0 54 54 39 39 38 15 10.0 87 87 33 33 37 16 14.0 58 58 32 32 33 17 14.0 75 75 36 36 32 18 11.0 88 88 38 38 38 19 10.0 64 64 39 39 38 20 13.0 57 57 32 32 32 21 9.5 66 66 32 32 33 22 14.0 68 68 31 31 31 23 12.0 54 54 39 39 38 24 14.0 56 56 37 37 39 25 11.0 86 86 39 39 32 26 9.0 80 80 41 41 32 27 11.0 76 76 36 36 35 28 15.0 69 69 33 33 37 29 14.0 78 78 33 33 33 30 13.0 67 67 34 34 33 31 9.0 80 80 31 31 31 32 15.0 54 54 27 27 32 33 10.0 71 71 37 37 31 34 11.0 84 84 34 34 37 35 13.0 74 74 34 34 30 36 8.0 71 71 32 32 33 37 20.0 63 63 29 29 31 38 12.0 71 71 36 36 33 39 10.0 76 76 29 29 31 40 10.0 69 69 35 35 33 41 9.0 74 74 37 37 32 42 14.0 75 75 34 34 33 43 8.0 54 54 38 38 32 44 14.0 52 52 35 35 33 45 11.0 69 69 38 38 28 46 13.0 68 68 37 37 35 47 9.0 65 65 38 38 39 48 11.0 75 75 33 33 34 49 15.0 74 74 36 36 38 50 11.0 75 75 38 38 32 51 10.0 72 72 32 32 38 52 14.0 67 67 32 32 30 53 18.0 63 63 32 32 33 54 14.0 62 62 34 34 38 55 11.0 63 63 32 32 32 56 14.5 76 76 37 37 35 57 13.0 74 74 39 39 34 58 9.0 67 67 29 29 34 59 10.0 73 73 37 37 36 60 15.0 70 70 35 35 34 61 20.0 53 53 30 30 28 62 12.0 77 77 38 38 34 63 12.0 80 80 34 34 35 64 14.0 52 52 31 31 35 65 13.0 54 54 34 34 31 66 11.0 80 80 35 35 37 67 17.0 66 66 36 36 35 68 12.0 73 73 30 30 27 69 13.0 63 63 39 39 40 70 14.0 69 69 35 35 37 71 13.0 67 67 38 38 36 72 15.0 54 54 31 31 38 73 13.0 81 81 34 34 39 74 10.0 69 69 38 38 41 75 11.0 84 84 34 34 27 76 19.0 80 80 39 39 30 77 13.0 70 70 37 37 37 78 17.0 69 69 34 34 31 79 13.0 77 77 28 28 31 80 9.0 54 54 37 37 27 81 11.0 79 79 33 33 36 82 9.0 71 71 35 35 37 83 12.0 73 73 37 37 33 84 12.0 72 72 32 32 34 85 13.0 77 77 33 33 31 86 13.0 75 75 38 38 39 87 12.0 69 69 33 33 34 88 15.0 54 54 29 29 32 89 22.0 70 70 33 33 33 90 13.0 73 73 31 31 36 91 15.0 54 54 36 36 32 92 13.0 77 77 35 35 41 93 15.0 82 82 32 32 28 94 12.5 80 80 29 29 30 95 11.0 80 80 39 39 36 96 16.0 69 69 37 37 35 97 11.0 78 78 35 35 31 98 11.0 81 81 37 37 34 99 10.0 76 76 32 32 36 100 10.0 76 76 38 38 36 101 16.0 73 73 37 37 35 102 12.0 85 85 36 36 37 103 11.0 66 66 32 32 28 104 16.0 79 79 33 33 39 105 19.0 68 68 40 40 32 106 11.0 76 76 38 38 35 107 16.0 71 71 41 41 39 108 15.0 54 54 36 36 35 109 24.0 46 46 43 43 42 110 14.0 85 85 30 30 34 111 15.0 74 74 31 31 33 112 11.0 88 88 32 32 41 113 15.0 38 38 32 32 33 114 12.0 76 76 37 37 34 115 10.0 86 86 37 37 32 116 14.0 54 54 33 33 40 117 13.0 67 67 34 34 40 118 9.0 69 69 33 33 35 119 15.0 90 90 38 38 36 120 15.0 54 54 33 33 37 121 14.0 76 76 31 31 27 122 11.0 89 89 38 38 39 123 8.0 76 76 37 37 38 124 11.0 73 73 36 36 31 125 11.0 79 79 31 31 33 126 8.0 90 90 39 39 32 127 10.0 74 74 44 44 39 128 11.0 81 81 33 33 36 129 13.0 72 72 35 35 33 130 11.0 71 71 32 32 33 131 20.0 66 66 28 28 32 132 10.0 77 77 40 40 37 133 15.0 65 65 27 27 30 134 12.0 74 74 37 37 38 135 14.0 85 85 32 32 29 136 23.0 54 54 28 28 22 137 14.0 63 63 34 34 35 138 16.0 54 54 30 30 35 139 11.0 64 64 35 35 34 140 12.0 69 69 31 31 35 141 10.0 54 54 32 32 34 142 14.0 84 84 30 30 37 143 12.0 86 86 30 30 35 144 12.0 77 77 31 31 23 145 11.0 89 89 40 40 31 146 12.0 76 76 32 32 27 147 13.0 60 60 36 36 36 148 11.0 75 75 32 32 31 149 19.0 73 73 35 35 32 150 12.0 85 85 38 38 39 151 17.0 79 79 42 42 37 152 9.0 71 71 34 34 38 153 12.0 72 72 35 35 39 154 19.0 69 69 38 38 34 155 18.0 78 78 33 33 31 156 15.0 54 54 36 36 32 157 14.0 69 69 32 32 37 158 11.0 81 81 33 33 36 159 9.0 84 84 34 34 32 160 18.0 84 84 32 32 38 161 16.0 69 69 34 34 36 162 0.0 66 0 27 0 26 Separate_p 1 38 2 32 3 35 4 33 5 37 6 29 7 31 8 36 9 35 10 38 11 31 12 34 13 35 14 38 15 37 16 33 17 32 18 38 19 38 20 32 21 33 22 31 23 38 24 39 25 32 26 32 27 35 28 37 29 33 30 33 31 31 32 32 33 31 34 37 35 30 36 33 37 31 38 33 39 31 40 33 41 32 42 33 43 32 44 33 45 28 46 35 47 39 48 34 49 38 50 32 51 38 52 30 53 33 54 38 55 32 56 35 57 34 58 34 59 36 60 34 61 28 62 34 63 35 64 35 65 31 66 37 67 35 68 27 69 40 70 37 71 36 72 38 73 39 74 41 75 27 76 30 77 37 78 31 79 31 80 27 81 36 82 37 83 33 84 34 85 31 86 39 87 34 88 32 89 33 90 36 91 32 92 41 93 28 94 30 95 36 96 35 97 31 98 34 99 36 100 36 101 35 102 37 103 28 104 39 105 32 106 35 107 39 108 35 109 42 110 34 111 33 112 41 113 33 114 34 115 32 116 40 117 40 118 35 119 36 120 37 121 27 122 39 123 38 124 31 125 33 126 32 127 39 128 36 129 33 130 33 131 32 132 37 133 30 134 38 135 29 136 22 137 35 138 35 139 34 140 35 141 34 142 37 143 35 144 23 145 31 146 27 147 36 148 31 149 32 150 39 151 37 152 38 153 39 154 34 155 31 156 32 157 37 158 36 159 32 160 38 161 36 162 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop t Learning Software -1.620e+02 1.620e+02 1.000e+00 -2.209e-15 1.395e-15 Software_p Happiness Happiness_p Depression Depression_p NA -3.860e-16 NA -7.012e-16 NA Belonging Belonging_p Connected Connected_p Separate -3.673e-16 NA 1.061e-15 NA 5.106e-16 Separate_p NA > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.665e-14 -7.540e-15 -2.460e-15 3.470e-15 3.210e-13 Coefficients: (6 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -1.620e+02 4.924e-14 -3.290e+15 <2e-16 *** Pop 1.620e+02 2.929e-14 5.531e+15 <2e-16 *** t 1.000e+00 4.965e-17 2.014e+16 <2e-16 *** Learning -2.209e-15 1.296e-15 -1.705e+00 0.0903 . Software 1.395e-15 1.269e-15 1.099e+00 0.2736 Software_p NA NA NA NA Happiness -3.860e-16 1.146e-15 -3.370e-01 0.7368 Happiness_p NA NA NA NA Depression -7.012e-16 8.554e-16 -8.200e-01 0.4137 Depression_p NA NA NA NA Belonging -3.673e-16 2.333e-16 -1.574e+00 0.1175 Belonging_p NA NA NA NA Connected 1.061e-15 7.042e-16 1.507e+00 0.1339 Connected_p NA NA NA NA Separate 5.106e-16 6.621e-16 7.710e-01 0.4418 Separate_p NA NA NA NA --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.752e-14 on 152 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 5.198e+31 on 9 and 152 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,] 1.169398e-02 2.338796e-02 9.883060e-01 [2,] 2.854857e-06 5.709714e-06 9.999971e-01 [3,] 1.970182e-07 3.940364e-07 9.999998e-01 [4,] 9.763896e-12 1.952779e-11 1.000000e+00 [5,] 8.370647e-04 1.674129e-03 9.991629e-01 [6,] 1.307528e-08 2.615057e-08 1.000000e+00 [7,] 2.066148e-06 4.132297e-06 9.999979e-01 [8,] 6.588628e-08 1.317726e-07 9.999999e-01 [9,] 1.679595e-11 3.359191e-11 1.000000e+00 [10,] 3.587090e-16 7.174180e-16 1.000000e+00 [11,] 1.539182e-11 3.078364e-11 1.000000e+00 [12,] 5.932188e-21 1.186438e-20 1.000000e+00 [13,] 1.000035e-13 2.000070e-13 1.000000e+00 [14,] 1.593969e-19 3.187938e-19 1.000000e+00 [15,] 2.023713e-05 4.047425e-05 9.999798e-01 [16,] 4.173365e-15 8.346730e-15 1.000000e+00 [17,] 6.231652e-13 1.246330e-12 1.000000e+00 [18,] 7.225553e-25 1.445111e-24 1.000000e+00 [19,] 3.906516e-19 7.813032e-19 1.000000e+00 [20,] 9.213792e-18 1.842758e-17 1.000000e+00 [21,] 6.583697e-27 1.316739e-26 1.000000e+00 [22,] 4.534401e-13 9.068801e-13 1.000000e+00 [23,] 1.135880e-26 2.271761e-26 1.000000e+00 [24,] 9.625918e-31 1.925184e-30 1.000000e+00 [25,] 1.574112e-28 3.148224e-28 1.000000e+00 [26,] 7.796088e-09 1.559218e-08 1.000000e+00 [27,] 7.624272e-21 1.524854e-20 1.000000e+00 [28,] 4.379669e-11 8.759338e-11 1.000000e+00 [29,] 1.474909e-31 2.949818e-31 1.000000e+00 [30,] 1.595307e-19 3.190615e-19 1.000000e+00 [31,] 4.807572e-50 9.615144e-50 1.000000e+00 [32,] 1.861651e-31 3.723302e-31 1.000000e+00 [33,] 2.654552e-12 5.309104e-12 1.000000e+00 [34,] 2.922294e-16 5.844588e-16 1.000000e+00 [35,] 1.289685e-07 2.579369e-07 9.999999e-01 [36,] 6.795416e-34 1.359083e-33 1.000000e+00 [37,] 4.050259e-31 8.100519e-31 1.000000e+00 [38,] 1.409848e-27 2.819697e-27 1.000000e+00 [39,] 1.191567e-35 2.383134e-35 1.000000e+00 [40,] 2.693455e-19 5.386911e-19 1.000000e+00 [41,] 4.575045e-36 9.150090e-36 1.000000e+00 [42,] 2.591652e-08 5.183304e-08 1.000000e+00 [43,] 1.136507e-39 2.273014e-39 1.000000e+00 [44,] 2.441165e-12 4.882331e-12 1.000000e+00 [45,] 1.331013e-53 2.662027e-53 1.000000e+00 [46,] 1.806710e-36 3.613421e-36 1.000000e+00 [47,] 1.724973e-23 3.449946e-23 1.000000e+00 [48,] 2.532692e-26 5.065384e-26 1.000000e+00 [49,] 3.799705e-17 7.599410e-17 1.000000e+00 [50,] 2.827708e-10 5.655416e-10 1.000000e+00 [51,] 3.824962e-26 7.649925e-26 1.000000e+00 [52,] 2.623096e-09 5.246193e-09 1.000000e+00 [53,] 5.035813e-25 1.007163e-24 1.000000e+00 [54,] 3.909746e-53 7.819491e-53 1.000000e+00 [55,] 3.937641e-72 7.875282e-72 1.000000e+00 [56,] 1.611263e-03 3.222525e-03 9.983887e-01 [57,] 5.714935e-51 1.142987e-50 1.000000e+00 [58,] 2.220642e-42 4.441284e-42 1.000000e+00 [59,] 7.895098e-60 1.579020e-59 1.000000e+00 [60,] 1.310568e-23 2.621136e-23 1.000000e+00 [61,] 6.753175e-18 1.350635e-17 1.000000e+00 [62,] 8.000564e-08 1.600113e-07 9.999999e-01 [63,] 7.176632e-50 1.435326e-49 1.000000e+00 [64,] 1.075115e-40 2.150230e-40 1.000000e+00 [65,] 1.477341e-23 2.954682e-23 1.000000e+00 [66,] 1.783859e-60 3.567718e-60 1.000000e+00 [67,] 8.681306e-47 1.736261e-46 1.000000e+00 [68,] 9.999873e-01 2.547878e-05 1.273939e-05 [69,] 2.157624e-03 4.315248e-03 9.978424e-01 [70,] 5.245921e-30 1.049184e-29 1.000000e+00 [71,] 9.713275e-45 1.942655e-44 1.000000e+00 [72,] 5.195039e-11 1.039008e-10 1.000000e+00 [73,] 4.160335e-57 8.320671e-57 1.000000e+00 [74,] 4.838251e-14 9.676503e-14 1.000000e+00 [75,] 7.440949e-40 1.488190e-39 1.000000e+00 [76,] 4.609531e-03 9.219063e-03 9.953905e-01 [77,] 1.962259e-13 3.924517e-13 1.000000e+00 [78,] 4.263179e-34 8.526357e-34 1.000000e+00 [79,] 5.248183e-27 1.049637e-26 1.000000e+00 [80,] 3.029159e-17 6.058317e-17 1.000000e+00 [81,] 2.750847e-78 5.501694e-78 1.000000e+00 [82,] 1.093253e-16 2.186505e-16 1.000000e+00 [83,] 6.392368e-52 1.278474e-51 1.000000e+00 [84,] 2.115041e-98 4.230082e-98 1.000000e+00 [85,] 1.591545e-80 3.183091e-80 1.000000e+00 [86,] 2.129092e-60 4.258184e-60 1.000000e+00 [87,] 5.528357e-50 1.105671e-49 1.000000e+00 [88,] 5.032161e-64 1.006432e-63 1.000000e+00 [89,] 3.160486e-91 6.320972e-91 1.000000e+00 [90,] 6.180952e-04 1.236190e-03 9.993819e-01 [91,] 7.820171e-77 1.564034e-76 1.000000e+00 [92,] 9.319483e-01 1.361034e-01 6.805168e-02 [93,] 2.041970e-75 4.083940e-75 1.000000e+00 [94,] 3.774146e-35 7.548293e-35 1.000000e+00 [95,] 8.822126e-43 1.764425e-42 1.000000e+00 [96,] 8.854727e-83 1.770945e-82 1.000000e+00 [97,] 1.062258e-35 2.124517e-35 1.000000e+00 [98,] 1.160561e-20 2.321121e-20 1.000000e+00 [99,] 1.541821e-35 3.083642e-35 1.000000e+00 [100,] 6.606612e-11 1.321322e-10 1.000000e+00 [101,] 1.696582e-13 3.393164e-13 1.000000e+00 [102,] 2.832876e-06 5.665752e-06 9.999972e-01 [103,] 9.999787e-01 4.256458e-05 2.128229e-05 [104,] 6.090560e-44 1.218112e-43 1.000000e+00 [105,] 1.624059e-51 3.248118e-51 1.000000e+00 [106,] 1.497408e-71 2.994816e-71 1.000000e+00 [107,] 1.535446e-02 3.070892e-02 9.846455e-01 [108,] 2.522668e-77 5.045337e-77 1.000000e+00 [109,] 8.424812e-46 1.684962e-45 1.000000e+00 [110,] 1.091424e-40 2.182848e-40 1.000000e+00 [111,] 2.620221e-42 5.240442e-42 1.000000e+00 [112,] 3.623441e-17 7.246882e-17 1.000000e+00 [113,] 7.660731e-21 1.532146e-20 1.000000e+00 [114,] 8.393583e-10 1.678717e-09 1.000000e+00 [115,] 1.316883e-36 2.633767e-36 1.000000e+00 [116,] 1.153473e-02 2.306946e-02 9.884653e-01 [117,] 9.607007e-01 7.859855e-02 3.929927e-02 [118,] 5.302215e-37 1.060443e-36 1.000000e+00 [119,] 3.957808e-60 7.915616e-60 1.000000e+00 [120,] 7.899068e-72 1.579814e-71 1.000000e+00 [121,] 6.759560e-31 1.351912e-30 1.000000e+00 [122,] 4.051349e-53 8.102697e-53 1.000000e+00 [123,] 1.369212e-17 2.738424e-17 1.000000e+00 [124,] 5.893335e-03 1.178667e-02 9.941067e-01 [125,] 4.759629e-02 9.519257e-02 9.524037e-01 > postscript(file="/var/fisher/rcomp/tmp/1baro1352143991.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/2jkic1352143991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3abk21352143991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/40r7r1352143991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5r27n1352143991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 162 Frequency = 1 1 2 3 4 5 3.209705e-13 -2.664906e-14 3.139191e-14 3.237039e-15 -9.501440e-15 6 7 8 9 10 -7.822451e-15 -4.858033e-15 -1.004267e-14 -1.210853e-14 -3.992188e-15 11 12 13 14 15 5.913571e-16 -1.812394e-14 -1.306522e-14 -1.593910e-14 -8.603870e-16 16 17 18 19 20 -1.408277e-14 -7.301151e-15 1.081532e-15 -1.219707e-14 -5.729516e-15 21 22 23 24 25 -1.740373e-15 -6.567199e-15 -1.729871e-14 -1.667936e-14 -2.084499e-15 26 27 28 29 30 -1.081044e-14 -9.460935e-16 -1.381277e-14 -2.296105e-15 -9.790924e-15 31 32 33 34 35 -7.842748e-16 -3.120059e-15 -5.822918e-15 -6.072344e-15 -8.088232e-15 36 37 38 39 40 -1.171712e-14 -6.046018e-15 -1.280512e-14 5.538938e-15 -4.478875e-15 41 42 43 44 45 -6.795286e-15 -5.585850e-15 -1.068686e-14 -1.088546e-14 -4.734953e-15 46 47 48 49 50 -1.569078e-14 -1.853452e-14 8.813235e-16 2.557808e-15 -8.905755e-15 51 52 53 54 55 -3.135560e-15 -3.036062e-16 -7.383928e-15 -5.919455e-15 -9.387660e-15 56 57 58 59 60 3.296234e-15 -2.180911e-15 2.103823e-15 -8.813461e-15 -2.257691e-16 61 62 63 64 65 6.906050e-16 -4.424646e-15 -3.713737e-15 -7.007868e-15 -5.267767e-15 66 67 68 69 70 -1.900789e-15 -1.350067e-15 1.217287e-14 -2.001937e-14 -3.064518e-15 71 72 73 74 75 -1.522106e-14 -6.302510e-15 3.179012e-15 -8.888881e-15 6.545471e-15 76 77 78 79 80 1.001485e-14 -5.671453e-15 4.462119e-15 4.103980e-15 -8.341115e-15 81 82 83 84 85 2.101823e-15 -4.412142e-15 -4.928058e-15 1.990248e-15 7.653135e-15 86 87 88 89 90 -3.627478e-15 6.120558e-16 1.521674e-15 4.509826e-15 7.430073e-16 91 92 93 94 95 -5.748414e-15 -5.437170e-17 1.189419e-14 6.200157e-15 3.532189e-15 96 97 98 99 100 -2.705959e-15 9.090184e-15 4.995932e-15 1.729924e-15 -3.540559e-15 101 102 103 104 105 4.873395e-15 8.708696e-15 3.953148e-15 1.079137e-14 -7.945752e-15 106 107 108 109 110 2.995764e-15 -5.766359e-15 -8.508677e-15 -1.791561e-14 1.255637e-14 111 112 113 114 115 1.724678e-15 7.562668e-15 -1.088330e-14 -2.633617e-15 2.259334e-15 116 117 118 119 120 -1.465867e-14 -3.191505e-15 -6.555280e-15 6.264994e-15 -6.687907e-15 121 122 123 124 125 5.832025e-15 -8.417156e-15 -8.403952e-15 -2.096462e-15 4.521381e-15 126 127 128 129 130 6.269384e-15 -1.076042e-14 6.339662e-15 -5.633334e-15 6.724818e-15 131 132 133 134 135 1.241407e-15 4.220808e-15 9.671014e-16 -8.020590e-15 1.140035e-14 136 137 138 139 140 1.345946e-14 1.722418e-15 -3.055896e-15 -7.840423e-15 -4.466640e-15 141 142 143 144 145 2.768616e-15 1.545081e-14 1.256191e-14 1.138142e-14 -5.754521e-15 146 147 148 149 150 1.288925e-14 -1.123050e-14 8.173313e-15 -9.504897e-16 3.204271e-16 151 152 153 154 155 -6.774885e-15 1.126589e-14 8.101935e-15 -3.789897e-15 8.024865e-15 156 157 158 159 160 -7.587869e-15 1.268431e-14 9.900350e-15 9.828260e-16 1.097856e-14 161 162 -1.142179e-15 -7.902368e-28 > postscript(file="/var/fisher/rcomp/tmp/6cqhw1352143991.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 3.209705e-13 NA 1 -2.664906e-14 3.209705e-13 2 3.139191e-14 -2.664906e-14 3 3.237039e-15 3.139191e-14 4 -9.501440e-15 3.237039e-15 5 -7.822451e-15 -9.501440e-15 6 -4.858033e-15 -7.822451e-15 7 -1.004267e-14 -4.858033e-15 8 -1.210853e-14 -1.004267e-14 9 -3.992188e-15 -1.210853e-14 10 5.913571e-16 -3.992188e-15 11 -1.812394e-14 5.913571e-16 12 -1.306522e-14 -1.812394e-14 13 -1.593910e-14 -1.306522e-14 14 -8.603870e-16 -1.593910e-14 15 -1.408277e-14 -8.603870e-16 16 -7.301151e-15 -1.408277e-14 17 1.081532e-15 -7.301151e-15 18 -1.219707e-14 1.081532e-15 19 -5.729516e-15 -1.219707e-14 20 -1.740373e-15 -5.729516e-15 21 -6.567199e-15 -1.740373e-15 22 -1.729871e-14 -6.567199e-15 23 -1.667936e-14 -1.729871e-14 24 -2.084499e-15 -1.667936e-14 25 -1.081044e-14 -2.084499e-15 26 -9.460935e-16 -1.081044e-14 27 -1.381277e-14 -9.460935e-16 28 -2.296105e-15 -1.381277e-14 29 -9.790924e-15 -2.296105e-15 30 -7.842748e-16 -9.790924e-15 31 -3.120059e-15 -7.842748e-16 32 -5.822918e-15 -3.120059e-15 33 -6.072344e-15 -5.822918e-15 34 -8.088232e-15 -6.072344e-15 35 -1.171712e-14 -8.088232e-15 36 -6.046018e-15 -1.171712e-14 37 -1.280512e-14 -6.046018e-15 38 5.538938e-15 -1.280512e-14 39 -4.478875e-15 5.538938e-15 40 -6.795286e-15 -4.478875e-15 41 -5.585850e-15 -6.795286e-15 42 -1.068686e-14 -5.585850e-15 43 -1.088546e-14 -1.068686e-14 44 -4.734953e-15 -1.088546e-14 45 -1.569078e-14 -4.734953e-15 46 -1.853452e-14 -1.569078e-14 47 8.813235e-16 -1.853452e-14 48 2.557808e-15 8.813235e-16 49 -8.905755e-15 2.557808e-15 50 -3.135560e-15 -8.905755e-15 51 -3.036062e-16 -3.135560e-15 52 -7.383928e-15 -3.036062e-16 53 -5.919455e-15 -7.383928e-15 54 -9.387660e-15 -5.919455e-15 55 3.296234e-15 -9.387660e-15 56 -2.180911e-15 3.296234e-15 57 2.103823e-15 -2.180911e-15 58 -8.813461e-15 2.103823e-15 59 -2.257691e-16 -8.813461e-15 60 6.906050e-16 -2.257691e-16 61 -4.424646e-15 6.906050e-16 62 -3.713737e-15 -4.424646e-15 63 -7.007868e-15 -3.713737e-15 64 -5.267767e-15 -7.007868e-15 65 -1.900789e-15 -5.267767e-15 66 -1.350067e-15 -1.900789e-15 67 1.217287e-14 -1.350067e-15 68 -2.001937e-14 1.217287e-14 69 -3.064518e-15 -2.001937e-14 70 -1.522106e-14 -3.064518e-15 71 -6.302510e-15 -1.522106e-14 72 3.179012e-15 -6.302510e-15 73 -8.888881e-15 3.179012e-15 74 6.545471e-15 -8.888881e-15 75 1.001485e-14 6.545471e-15 76 -5.671453e-15 1.001485e-14 77 4.462119e-15 -5.671453e-15 78 4.103980e-15 4.462119e-15 79 -8.341115e-15 4.103980e-15 80 2.101823e-15 -8.341115e-15 81 -4.412142e-15 2.101823e-15 82 -4.928058e-15 -4.412142e-15 83 1.990248e-15 -4.928058e-15 84 7.653135e-15 1.990248e-15 85 -3.627478e-15 7.653135e-15 86 6.120558e-16 -3.627478e-15 87 1.521674e-15 6.120558e-16 88 4.509826e-15 1.521674e-15 89 7.430073e-16 4.509826e-15 90 -5.748414e-15 7.430073e-16 91 -5.437170e-17 -5.748414e-15 92 1.189419e-14 -5.437170e-17 93 6.200157e-15 1.189419e-14 94 3.532189e-15 6.200157e-15 95 -2.705959e-15 3.532189e-15 96 9.090184e-15 -2.705959e-15 97 4.995932e-15 9.090184e-15 98 1.729924e-15 4.995932e-15 99 -3.540559e-15 1.729924e-15 100 4.873395e-15 -3.540559e-15 101 8.708696e-15 4.873395e-15 102 3.953148e-15 8.708696e-15 103 1.079137e-14 3.953148e-15 104 -7.945752e-15 1.079137e-14 105 2.995764e-15 -7.945752e-15 106 -5.766359e-15 2.995764e-15 107 -8.508677e-15 -5.766359e-15 108 -1.791561e-14 -8.508677e-15 109 1.255637e-14 -1.791561e-14 110 1.724678e-15 1.255637e-14 111 7.562668e-15 1.724678e-15 112 -1.088330e-14 7.562668e-15 113 -2.633617e-15 -1.088330e-14 114 2.259334e-15 -2.633617e-15 115 -1.465867e-14 2.259334e-15 116 -3.191505e-15 -1.465867e-14 117 -6.555280e-15 -3.191505e-15 118 6.264994e-15 -6.555280e-15 119 -6.687907e-15 6.264994e-15 120 5.832025e-15 -6.687907e-15 121 -8.417156e-15 5.832025e-15 122 -8.403952e-15 -8.417156e-15 123 -2.096462e-15 -8.403952e-15 124 4.521381e-15 -2.096462e-15 125 6.269384e-15 4.521381e-15 126 -1.076042e-14 6.269384e-15 127 6.339662e-15 -1.076042e-14 128 -5.633334e-15 6.339662e-15 129 6.724818e-15 -5.633334e-15 130 1.241407e-15 6.724818e-15 131 4.220808e-15 1.241407e-15 132 9.671014e-16 4.220808e-15 133 -8.020590e-15 9.671014e-16 134 1.140035e-14 -8.020590e-15 135 1.345946e-14 1.140035e-14 136 1.722418e-15 1.345946e-14 137 -3.055896e-15 1.722418e-15 138 -7.840423e-15 -3.055896e-15 139 -4.466640e-15 -7.840423e-15 140 2.768616e-15 -4.466640e-15 141 1.545081e-14 2.768616e-15 142 1.256191e-14 1.545081e-14 143 1.138142e-14 1.256191e-14 144 -5.754521e-15 1.138142e-14 145 1.288925e-14 -5.754521e-15 146 -1.123050e-14 1.288925e-14 147 8.173313e-15 -1.123050e-14 148 -9.504897e-16 8.173313e-15 149 3.204271e-16 -9.504897e-16 150 -6.774885e-15 3.204271e-16 151 1.126589e-14 -6.774885e-15 152 8.101935e-15 1.126589e-14 153 -3.789897e-15 8.101935e-15 154 8.024865e-15 -3.789897e-15 155 -7.587869e-15 8.024865e-15 156 1.268431e-14 -7.587869e-15 157 9.900350e-15 1.268431e-14 158 9.828260e-16 9.900350e-15 159 1.097856e-14 9.828260e-16 160 -1.142179e-15 1.097856e-14 161 -7.902368e-28 -1.142179e-15 162 NA -7.902368e-28 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.664906e-14 3.209705e-13 [2,] 3.139191e-14 -2.664906e-14 [3,] 3.237039e-15 3.139191e-14 [4,] -9.501440e-15 3.237039e-15 [5,] -7.822451e-15 -9.501440e-15 [6,] -4.858033e-15 -7.822451e-15 [7,] -1.004267e-14 -4.858033e-15 [8,] -1.210853e-14 -1.004267e-14 [9,] -3.992188e-15 -1.210853e-14 [10,] 5.913571e-16 -3.992188e-15 [11,] -1.812394e-14 5.913571e-16 [12,] -1.306522e-14 -1.812394e-14 [13,] -1.593910e-14 -1.306522e-14 [14,] -8.603870e-16 -1.593910e-14 [15,] -1.408277e-14 -8.603870e-16 [16,] -7.301151e-15 -1.408277e-14 [17,] 1.081532e-15 -7.301151e-15 [18,] -1.219707e-14 1.081532e-15 [19,] -5.729516e-15 -1.219707e-14 [20,] -1.740373e-15 -5.729516e-15 [21,] -6.567199e-15 -1.740373e-15 [22,] -1.729871e-14 -6.567199e-15 [23,] -1.667936e-14 -1.729871e-14 [24,] -2.084499e-15 -1.667936e-14 [25,] -1.081044e-14 -2.084499e-15 [26,] -9.460935e-16 -1.081044e-14 [27,] -1.381277e-14 -9.460935e-16 [28,] -2.296105e-15 -1.381277e-14 [29,] -9.790924e-15 -2.296105e-15 [30,] -7.842748e-16 -9.790924e-15 [31,] -3.120059e-15 -7.842748e-16 [32,] -5.822918e-15 -3.120059e-15 [33,] -6.072344e-15 -5.822918e-15 [34,] -8.088232e-15 -6.072344e-15 [35,] -1.171712e-14 -8.088232e-15 [36,] -6.046018e-15 -1.171712e-14 [37,] -1.280512e-14 -6.046018e-15 [38,] 5.538938e-15 -1.280512e-14 [39,] -4.478875e-15 5.538938e-15 [40,] -6.795286e-15 -4.478875e-15 [41,] -5.585850e-15 -6.795286e-15 [42,] -1.068686e-14 -5.585850e-15 [43,] -1.088546e-14 -1.068686e-14 [44,] -4.734953e-15 -1.088546e-14 [45,] -1.569078e-14 -4.734953e-15 [46,] -1.853452e-14 -1.569078e-14 [47,] 8.813235e-16 -1.853452e-14 [48,] 2.557808e-15 8.813235e-16 [49,] -8.905755e-15 2.557808e-15 [50,] -3.135560e-15 -8.905755e-15 [51,] -3.036062e-16 -3.135560e-15 [52,] -7.383928e-15 -3.036062e-16 [53,] -5.919455e-15 -7.383928e-15 [54,] -9.387660e-15 -5.919455e-15 [55,] 3.296234e-15 -9.387660e-15 [56,] -2.180911e-15 3.296234e-15 [57,] 2.103823e-15 -2.180911e-15 [58,] -8.813461e-15 2.103823e-15 [59,] -2.257691e-16 -8.813461e-15 [60,] 6.906050e-16 -2.257691e-16 [61,] -4.424646e-15 6.906050e-16 [62,] -3.713737e-15 -4.424646e-15 [63,] -7.007868e-15 -3.713737e-15 [64,] -5.267767e-15 -7.007868e-15 [65,] -1.900789e-15 -5.267767e-15 [66,] -1.350067e-15 -1.900789e-15 [67,] 1.217287e-14 -1.350067e-15 [68,] -2.001937e-14 1.217287e-14 [69,] -3.064518e-15 -2.001937e-14 [70,] -1.522106e-14 -3.064518e-15 [71,] -6.302510e-15 -1.522106e-14 [72,] 3.179012e-15 -6.302510e-15 [73,] -8.888881e-15 3.179012e-15 [74,] 6.545471e-15 -8.888881e-15 [75,] 1.001485e-14 6.545471e-15 [76,] -5.671453e-15 1.001485e-14 [77,] 4.462119e-15 -5.671453e-15 [78,] 4.103980e-15 4.462119e-15 [79,] -8.341115e-15 4.103980e-15 [80,] 2.101823e-15 -8.341115e-15 [81,] -4.412142e-15 2.101823e-15 [82,] -4.928058e-15 -4.412142e-15 [83,] 1.990248e-15 -4.928058e-15 [84,] 7.653135e-15 1.990248e-15 [85,] -3.627478e-15 7.653135e-15 [86,] 6.120558e-16 -3.627478e-15 [87,] 1.521674e-15 6.120558e-16 [88,] 4.509826e-15 1.521674e-15 [89,] 7.430073e-16 4.509826e-15 [90,] -5.748414e-15 7.430073e-16 [91,] -5.437170e-17 -5.748414e-15 [92,] 1.189419e-14 -5.437170e-17 [93,] 6.200157e-15 1.189419e-14 [94,] 3.532189e-15 6.200157e-15 [95,] -2.705959e-15 3.532189e-15 [96,] 9.090184e-15 -2.705959e-15 [97,] 4.995932e-15 9.090184e-15 [98,] 1.729924e-15 4.995932e-15 [99,] -3.540559e-15 1.729924e-15 [100,] 4.873395e-15 -3.540559e-15 [101,] 8.708696e-15 4.873395e-15 [102,] 3.953148e-15 8.708696e-15 [103,] 1.079137e-14 3.953148e-15 [104,] -7.945752e-15 1.079137e-14 [105,] 2.995764e-15 -7.945752e-15 [106,] -5.766359e-15 2.995764e-15 [107,] -8.508677e-15 -5.766359e-15 [108,] -1.791561e-14 -8.508677e-15 [109,] 1.255637e-14 -1.791561e-14 [110,] 1.724678e-15 1.255637e-14 [111,] 7.562668e-15 1.724678e-15 [112,] -1.088330e-14 7.562668e-15 [113,] -2.633617e-15 -1.088330e-14 [114,] 2.259334e-15 -2.633617e-15 [115,] -1.465867e-14 2.259334e-15 [116,] -3.191505e-15 -1.465867e-14 [117,] -6.555280e-15 -3.191505e-15 [118,] 6.264994e-15 -6.555280e-15 [119,] -6.687907e-15 6.264994e-15 [120,] 5.832025e-15 -6.687907e-15 [121,] -8.417156e-15 5.832025e-15 [122,] -8.403952e-15 -8.417156e-15 [123,] -2.096462e-15 -8.403952e-15 [124,] 4.521381e-15 -2.096462e-15 [125,] 6.269384e-15 4.521381e-15 [126,] -1.076042e-14 6.269384e-15 [127,] 6.339662e-15 -1.076042e-14 [128,] -5.633334e-15 6.339662e-15 [129,] 6.724818e-15 -5.633334e-15 [130,] 1.241407e-15 6.724818e-15 [131,] 4.220808e-15 1.241407e-15 [132,] 9.671014e-16 4.220808e-15 [133,] -8.020590e-15 9.671014e-16 [134,] 1.140035e-14 -8.020590e-15 [135,] 1.345946e-14 1.140035e-14 [136,] 1.722418e-15 1.345946e-14 [137,] -3.055896e-15 1.722418e-15 [138,] -7.840423e-15 -3.055896e-15 [139,] -4.466640e-15 -7.840423e-15 [140,] 2.768616e-15 -4.466640e-15 [141,] 1.545081e-14 2.768616e-15 [142,] 1.256191e-14 1.545081e-14 [143,] 1.138142e-14 1.256191e-14 [144,] -5.754521e-15 1.138142e-14 [145,] 1.288925e-14 -5.754521e-15 [146,] -1.123050e-14 1.288925e-14 [147,] 8.173313e-15 -1.123050e-14 [148,] -9.504897e-16 8.173313e-15 [149,] 3.204271e-16 -9.504897e-16 [150,] -6.774885e-15 3.204271e-16 [151,] 1.126589e-14 -6.774885e-15 [152,] 8.101935e-15 1.126589e-14 [153,] -3.789897e-15 8.101935e-15 [154,] 8.024865e-15 -3.789897e-15 [155,] -7.587869e-15 8.024865e-15 [156,] 1.268431e-14 -7.587869e-15 [157,] 9.900350e-15 1.268431e-14 [158,] 9.828260e-16 9.900350e-15 [159,] 1.097856e-14 9.828260e-16 [160,] -1.142179e-15 1.097856e-14 [161,] -7.902368e-28 -1.142179e-15 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.664906e-14 3.209705e-13 2 3.139191e-14 -2.664906e-14 3 3.237039e-15 3.139191e-14 4 -9.501440e-15 3.237039e-15 5 -7.822451e-15 -9.501440e-15 6 -4.858033e-15 -7.822451e-15 7 -1.004267e-14 -4.858033e-15 8 -1.210853e-14 -1.004267e-14 9 -3.992188e-15 -1.210853e-14 10 5.913571e-16 -3.992188e-15 11 -1.812394e-14 5.913571e-16 12 -1.306522e-14 -1.812394e-14 13 -1.593910e-14 -1.306522e-14 14 -8.603870e-16 -1.593910e-14 15 -1.408277e-14 -8.603870e-16 16 -7.301151e-15 -1.408277e-14 17 1.081532e-15 -7.301151e-15 18 -1.219707e-14 1.081532e-15 19 -5.729516e-15 -1.219707e-14 20 -1.740373e-15 -5.729516e-15 21 -6.567199e-15 -1.740373e-15 22 -1.729871e-14 -6.567199e-15 23 -1.667936e-14 -1.729871e-14 24 -2.084499e-15 -1.667936e-14 25 -1.081044e-14 -2.084499e-15 26 -9.460935e-16 -1.081044e-14 27 -1.381277e-14 -9.460935e-16 28 -2.296105e-15 -1.381277e-14 29 -9.790924e-15 -2.296105e-15 30 -7.842748e-16 -9.790924e-15 31 -3.120059e-15 -7.842748e-16 32 -5.822918e-15 -3.120059e-15 33 -6.072344e-15 -5.822918e-15 34 -8.088232e-15 -6.072344e-15 35 -1.171712e-14 -8.088232e-15 36 -6.046018e-15 -1.171712e-14 37 -1.280512e-14 -6.046018e-15 38 5.538938e-15 -1.280512e-14 39 -4.478875e-15 5.538938e-15 40 -6.795286e-15 -4.478875e-15 41 -5.585850e-15 -6.795286e-15 42 -1.068686e-14 -5.585850e-15 43 -1.088546e-14 -1.068686e-14 44 -4.734953e-15 -1.088546e-14 45 -1.569078e-14 -4.734953e-15 46 -1.853452e-14 -1.569078e-14 47 8.813235e-16 -1.853452e-14 48 2.557808e-15 8.813235e-16 49 -8.905755e-15 2.557808e-15 50 -3.135560e-15 -8.905755e-15 51 -3.036062e-16 -3.135560e-15 52 -7.383928e-15 -3.036062e-16 53 -5.919455e-15 -7.383928e-15 54 -9.387660e-15 -5.919455e-15 55 3.296234e-15 -9.387660e-15 56 -2.180911e-15 3.296234e-15 57 2.103823e-15 -2.180911e-15 58 -8.813461e-15 2.103823e-15 59 -2.257691e-16 -8.813461e-15 60 6.906050e-16 -2.257691e-16 61 -4.424646e-15 6.906050e-16 62 -3.713737e-15 -4.424646e-15 63 -7.007868e-15 -3.713737e-15 64 -5.267767e-15 -7.007868e-15 65 -1.900789e-15 -5.267767e-15 66 -1.350067e-15 -1.900789e-15 67 1.217287e-14 -1.350067e-15 68 -2.001937e-14 1.217287e-14 69 -3.064518e-15 -2.001937e-14 70 -1.522106e-14 -3.064518e-15 71 -6.302510e-15 -1.522106e-14 72 3.179012e-15 -6.302510e-15 73 -8.888881e-15 3.179012e-15 74 6.545471e-15 -8.888881e-15 75 1.001485e-14 6.545471e-15 76 -5.671453e-15 1.001485e-14 77 4.462119e-15 -5.671453e-15 78 4.103980e-15 4.462119e-15 79 -8.341115e-15 4.103980e-15 80 2.101823e-15 -8.341115e-15 81 -4.412142e-15 2.101823e-15 82 -4.928058e-15 -4.412142e-15 83 1.990248e-15 -4.928058e-15 84 7.653135e-15 1.990248e-15 85 -3.627478e-15 7.653135e-15 86 6.120558e-16 -3.627478e-15 87 1.521674e-15 6.120558e-16 88 4.509826e-15 1.521674e-15 89 7.430073e-16 4.509826e-15 90 -5.748414e-15 7.430073e-16 91 -5.437170e-17 -5.748414e-15 92 1.189419e-14 -5.437170e-17 93 6.200157e-15 1.189419e-14 94 3.532189e-15 6.200157e-15 95 -2.705959e-15 3.532189e-15 96 9.090184e-15 -2.705959e-15 97 4.995932e-15 9.090184e-15 98 1.729924e-15 4.995932e-15 99 -3.540559e-15 1.729924e-15 100 4.873395e-15 -3.540559e-15 101 8.708696e-15 4.873395e-15 102 3.953148e-15 8.708696e-15 103 1.079137e-14 3.953148e-15 104 -7.945752e-15 1.079137e-14 105 2.995764e-15 -7.945752e-15 106 -5.766359e-15 2.995764e-15 107 -8.508677e-15 -5.766359e-15 108 -1.791561e-14 -8.508677e-15 109 1.255637e-14 -1.791561e-14 110 1.724678e-15 1.255637e-14 111 7.562668e-15 1.724678e-15 112 -1.088330e-14 7.562668e-15 113 -2.633617e-15 -1.088330e-14 114 2.259334e-15 -2.633617e-15 115 -1.465867e-14 2.259334e-15 116 -3.191505e-15 -1.465867e-14 117 -6.555280e-15 -3.191505e-15 118 6.264994e-15 -6.555280e-15 119 -6.687907e-15 6.264994e-15 120 5.832025e-15 -6.687907e-15 121 -8.417156e-15 5.832025e-15 122 -8.403952e-15 -8.417156e-15 123 -2.096462e-15 -8.403952e-15 124 4.521381e-15 -2.096462e-15 125 6.269384e-15 4.521381e-15 126 -1.076042e-14 6.269384e-15 127 6.339662e-15 -1.076042e-14 128 -5.633334e-15 6.339662e-15 129 6.724818e-15 -5.633334e-15 130 1.241407e-15 6.724818e-15 131 4.220808e-15 1.241407e-15 132 9.671014e-16 4.220808e-15 133 -8.020590e-15 9.671014e-16 134 1.140035e-14 -8.020590e-15 135 1.345946e-14 1.140035e-14 136 1.722418e-15 1.345946e-14 137 -3.055896e-15 1.722418e-15 138 -7.840423e-15 -3.055896e-15 139 -4.466640e-15 -7.840423e-15 140 2.768616e-15 -4.466640e-15 141 1.545081e-14 2.768616e-15 142 1.256191e-14 1.545081e-14 143 1.138142e-14 1.256191e-14 144 -5.754521e-15 1.138142e-14 145 1.288925e-14 -5.754521e-15 146 -1.123050e-14 1.288925e-14 147 8.173313e-15 -1.123050e-14 148 -9.504897e-16 8.173313e-15 149 3.204271e-16 -9.504897e-16 150 -6.774885e-15 3.204271e-16 151 1.126589e-14 -6.774885e-15 152 8.101935e-15 1.126589e-14 153 -3.789897e-15 8.101935e-15 154 8.024865e-15 -3.789897e-15 155 -7.587869e-15 8.024865e-15 156 1.268431e-14 -7.587869e-15 157 9.900350e-15 1.268431e-14 158 9.828260e-16 9.900350e-15 159 1.097856e-14 9.828260e-16 160 -1.142179e-15 1.097856e-14 161 -7.902368e-28 -1.142179e-15 > 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/7b3hv1352143991.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/8mdxc1352143991.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/93nci1352143991.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') Warning messages: 1: Not plotting observations with leverage one: 162 2: Not plotting observations with leverage one: 162 > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10hbf11352143991.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='') + } + } Error: subscript out of bounds Execution halted