R version 2.11.1 (2010-05-31) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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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+ ,8 + ,0 + ,76 + ,0 + ,8 + ,0 + ,9 + ,0 + ,15 + ,0 + ,12 + ,0 + ,12 + ,0 + ,16 + ,0 + ,7 + ,0) + ,dim=c(22 + ,156) + ,dimnames=list(c('Gender' + ,'trend' + ,'trend_G' + ,'Popularity' + ,'Depression' + ,'Depression_G' + ,'Belonging' + ,'Belonging_G' + ,'WeightedPopularity' + ,'WeightedPopularity_G' + ,'ParentalCriticism' + ,'ParentalCriticism_G' + ,'Happiness' + ,'Happiness_G' + ,'FindingFriends' + ,'FindingFriends_G' + ,'KnowingPeople' + ,'KnowingPeople_G' + ,'Liked' + ,'Liked_G' + ,'Celebrity' + ,'Celebrity_G') + ,1:156)) > y <- array(NA,dim=c(22,156),dimnames=list(c('Gender','trend','trend_G','Popularity','Depression','Depression_G','Belonging','Belonging_G','WeightedPopularity','WeightedPopularity_G','ParentalCriticism','ParentalCriticism_G','Happiness','Happiness_G','FindingFriends','FindingFriends_G','KnowingPeople','KnowingPeople_G','Liked','Liked_G','Celebrity','Celebrity_G'),1:156)) > 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 = '4' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > 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 Popularity Gender trend trend_G Depression Depression_G Belonging 1 15 1 132 132 10 10 77 2 12 0 24 0 20 0 63 3 15 0 135 0 16 0 73 4 12 0 95 0 10 0 76 5 14 0 122 0 8 0 90 6 8 0 144 0 14 0 67 7 11 1 23 23 19 19 69 8 15 1 42 42 15 15 70 9 4 0 105 0 23 0 54 10 13 0 68 0 9 0 54 11 19 1 139 139 12 12 76 12 10 1 51 51 14 14 75 13 15 1 7 7 13 13 76 14 6 0 52 0 11 0 80 15 7 1 138 138 11 11 89 16 14 0 41 0 10 0 73 17 16 0 125 0 12 0 74 18 16 1 152 152 18 18 78 19 14 1 106 106 12 12 76 20 15 0 26 0 10 0 69 21 14 1 33 33 15 15 74 22 12 1 136 136 15 15 82 23 9 0 48 0 12 0 77 24 12 1 156 156 9 9 84 25 14 1 114 114 11 11 75 26 12 1 75 75 15 15 54 27 14 1 91 91 16 16 79 28 10 1 146 146 17 17 79 29 14 1 82 82 12 12 69 30 16 1 102 102 11 11 88 31 10 1 96 96 13 13 57 32 8 1 109 109 9 9 69 33 12 1 2 2 11 11 86 34 11 1 113 113 9 9 65 35 8 0 123 0 20 0 66 36 13 0 29 0 8 0 54 37 11 1 104 104 12 12 85 38 12 0 6 0 10 0 79 39 16 0 62 0 11 0 84 40 16 1 64 64 13 13 70 41 13 1 50 50 13 13 54 42 14 1 108 108 13 13 70 43 5 0 70 0 15 0 54 44 14 0 154 0 12 0 69 45 13 1 31 31 13 13 68 46 16 1 101 101 13 13 68 47 14 0 149 0 9 0 71 48 15 0 149 0 9 0 71 49 15 1 3 3 14 14 66 50 11 1 111 111 9 9 67 51 15 1 69 69 9 9 71 52 16 1 116 116 15 15 54 53 13 1 28 28 10 10 76 54 11 0 67 0 13 0 77 55 12 0 32 0 8 0 71 56 12 1 88 88 15 15 69 57 10 1 92 92 13 13 73 58 8 1 97 97 24 24 46 59 9 0 87 0 11 0 66 60 12 1 78 78 13 13 77 61 14 0 137 0 12 0 77 62 12 1 76 76 22 22 70 63 11 0 34 0 11 0 86 64 14 0 103 0 15 0 38 65 7 0 14 0 7 0 66 66 16 0 46 0 14 0 75 67 16 1 127 127 19 19 80 68 11 0 15 0 10 0 64 69 16 1 58 58 9 9 80 70 13 1 134 134 12 12 86 71 11 1 129 129 16 16 54 72 13 1 39 39 13 13 74 73 14 1 63 63 11 11 88 74 15 1 143 143 12 12 85 75 10 0 38 0 11 0 63 76 15 1 60 60 13 13 81 77 11 0 118 0 13 0 81 78 11 1 45 45 10 10 74 79 6 1 80 80 11 11 80 80 11 1 21 21 9 9 80 81 12 0 141 0 13 0 60 82 13 0 124 0 15 0 65 83 12 1 37 37 14 14 62 84 8 0 147 0 14 0 63 85 9 1 153 153 11 11 89 86 10 1 133 133 10 10 76 87 16 1 117 117 11 11 81 88 15 1 71 71 12 12 72 89 14 0 155 0 14 0 84 90 12 1 112 112 14 14 76 91 12 1 4 4 21 21 76 92 10 1 19 19 14 14 78 93 12 1 121 121 13 13 72 94 8 0 98 0 11 0 81 95 16 1 150 150 12 12 72 96 11 1 10 10 12 12 78 97 12 1 145 145 11 11 79 98 9 1 49 49 14 14 52 99 14 0 55 0 13 0 67 100 15 0 22 0 13 0 74 101 8 0 54 0 12 0 73 102 12 1 140 140 14 14 69 103 10 0 5 0 12 0 67 104 16 1 89 89 12 12 76 105 17 1 47 47 12 12 77 106 8 0 59 0 18 0 63 107 9 1 65 65 11 11 84 108 8 1 110 110 15 15 90 109 11 0 73 0 13 0 75 110 16 1 93 93 11 11 76 111 13 0 142 0 11 0 75 112 5 1 43 43 22 22 53 113 15 1 13 13 10 10 87 114 15 1 94 94 11 11 78 115 12 1 77 77 15 15 54 116 12 0 86 0 14 0 58 117 16 1 40 40 11 11 80 118 12 1 128 128 10 10 74 119 10 1 17 17 14 14 56 120 12 1 126 126 14 14 82 121 4 1 130 130 11 11 64 122 11 0 11 0 15 0 67 123 16 0 36 0 11 0 75 124 7 0 61 0 10 0 69 125 9 1 35 35 10 10 72 126 14 0 120 0 16 0 71 127 11 1 16 16 12 12 54 128 10 1 84 84 14 14 68 129 6 0 20 0 15 0 54 130 14 1 25 25 10 10 71 131 11 1 12 12 12 12 53 132 11 1 57 57 15 15 54 133 9 0 27 0 12 0 71 134 16 1 30 30 11 11 69 135 7 0 81 0 10 0 30 136 8 0 44 0 20 0 53 137 10 0 90 0 19 0 68 138 14 1 66 66 17 17 69 139 9 1 8 8 8 8 54 140 13 1 151 151 17 17 66 141 13 0 85 0 11 0 79 142 12 0 53 0 13 0 67 143 11 0 99 0 9 0 74 144 10 0 148 0 10 0 86 145 12 1 56 56 13 13 63 146 14 1 83 83 16 16 69 147 11 0 74 0 12 0 73 148 13 0 1 0 14 0 69 149 14 0 119 0 11 0 71 150 13 1 72 72 13 13 77 151 16 1 131 131 15 15 74 152 13 1 100 100 14 14 82 153 12 1 9 9 14 14 54 154 9 1 107 107 14 14 54 155 14 1 79 79 10 10 80 156 15 0 115 0 8 0 76 Belonging_G WeightedPopularity WeightedPopularity_G ParentalCriticism 1 77 5 5 4 2 0 6 0 4 3 0 4 0 10 4 0 6 0 6 5 0 3 0 5 6 0 10 0 8 7 69 8 8 9 8 70 3 3 6 9 0 4 0 8 10 0 3 0 11 11 76 5 5 6 12 75 5 5 8 13 76 6 6 11 14 0 5 0 5 15 89 3 3 10 16 0 4 0 7 17 0 8 0 7 18 78 8 8 13 19 76 8 8 10 20 0 5 0 8 21 74 8 8 6 22 82 2 2 8 23 0 0 0 7 24 84 5 5 5 25 75 2 2 9 26 54 7 7 9 27 79 5 5 11 28 79 2 2 11 29 69 12 12 11 30 88 7 7 9 31 57 0 0 7 32 69 2 2 6 33 86 3 3 6 34 65 0 0 6 35 0 9 0 5 36 0 2 0 4 37 85 3 3 10 38 0 1 0 8 39 0 10 0 6 40 70 1 1 5 41 54 4 4 9 42 70 6 6 10 43 0 6 0 6 44 0 4 0 9 45 68 4 4 10 46 68 7 7 6 47 0 7 0 6 48 0 7 0 6 49 66 0 0 13 50 67 3 3 8 51 71 8 8 10 52 54 8 8 5 53 76 10 10 8 54 0 11 0 6 55 0 6 0 9 56 69 2 2 9 57 73 6 6 7 58 46 1 1 20 59 0 5 0 8 60 77 4 4 8 61 0 6 0 7 62 70 6 6 7 63 0 4 0 10 64 0 1 0 5 65 0 6 0 8 66 0 7 0 9 67 80 7 7 9 68 0 2 0 20 69 80 7 7 6 70 86 8 8 10 71 54 5 5 11 72 74 4 4 7 73 88 2 2 12 74 85 0 0 12 75 0 7 0 8 76 81 0 0 6 77 0 5 0 6 78 74 3 3 9 79 80 3 3 5 80 80 3 3 11 81 0 3 0 6 82 0 7 0 6 83 62 6 6 10 84 0 3 0 8 85 89 0 0 7 86 76 2 2 8 87 81 0 0 9 88 72 9 9 8 89 0 10 0 10 90 76 3 3 13 91 76 7 7 7 92 78 3 3 7 93 72 6 6 7 94 0 5 0 8 95 72 0 0 9 96 78 0 0 9 97 79 4 4 8 98 52 0 0 7 99 0 0 0 6 100 0 7 0 8 101 0 3 0 8 102 69 9 9 4 103 0 4 0 8 104 76 4 4 10 105 77 15 15 7 106 0 7 0 8 107 84 8 8 7 108 90 2 2 10 109 0 8 0 9 110 76 7 7 8 111 0 3 0 8 112 53 3 3 5 113 87 6 6 8 114 78 8 8 9 115 54 5 5 11 116 0 6 0 7 117 80 10 10 8 118 74 0 0 4 119 56 5 5 16 120 82 0 0 9 121 64 0 0 16 122 0 5 0 12 123 0 10 0 8 124 0 0 0 4 125 72 5 5 11 126 0 6 0 11 127 54 1 1 8 128 68 5 5 8 129 0 3 0 12 130 71 3 3 8 131 53 6 6 6 132 54 2 2 8 133 0 5 0 6 134 69 6 6 14 135 0 2 0 10 136 0 3 0 5 137 0 7 0 8 138 69 6 6 12 139 54 3 3 11 140 66 6 6 8 141 0 9 0 8 142 0 2 0 9 143 0 5 0 6 144 0 10 0 5 145 63 9 9 8 146 69 8 8 7 147 0 8 0 4 148 0 5 0 9 149 0 9 0 5 150 77 9 9 9 151 74 14 14 12 152 82 5 5 6 153 54 12 12 4 154 54 6 6 6 155 80 6 6 7 156 0 8 0 9 ParentalCriticism_G Happiness Happiness_G FindingFriends FindingFriends_G 1 4 15 15 11 11 2 0 9 0 12 0 3 0 12 0 12 0 4 0 15 0 11 0 5 0 17 0 11 0 6 0 14 0 10 0 7 9 9 9 11 11 8 6 12 12 9 9 9 0 11 0 10 0 10 0 13 0 12 0 11 6 16 16 12 12 12 8 16 16 12 12 13 11 15 15 13 13 14 0 10 0 9 0 15 10 16 16 12 12 16 0 12 0 12 0 17 0 15 0 12 0 18 13 13 13 12 12 19 10 18 18 13 13 20 0 13 0 11 0 21 6 17 17 12 12 22 8 14 14 12 12 23 0 13 0 15 0 24 5 13 13 11 11 25 9 15 15 12 12 26 9 13 13 10 10 27 11 15 15 11 11 28 11 13 13 13 13 29 11 14 14 6 6 30 9 13 13 12 12 31 7 16 16 12 12 32 6 14 14 10 10 33 6 18 18 12 12 34 6 15 15 12 12 35 0 9 0 11 0 36 0 16 0 9 0 37 10 16 16 10 10 38 0 17 0 12 0 39 0 13 0 12 0 40 5 17 17 11 11 41 9 15 15 12 12 42 10 14 14 11 11 43 0 10 0 14 0 44 0 13 0 10 0 45 10 11 11 10 10 46 6 11 11 11 11 47 0 16 0 11 0 48 0 16 0 11 0 49 13 11 11 10 10 50 8 15 15 10 10 51 10 15 15 12 12 52 5 12 12 11 11 53 8 17 17 8 8 54 0 15 0 12 0 55 0 16 0 10 0 56 9 14 14 7 7 57 7 17 17 11 11 58 20 10 10 7 7 59 0 11 0 11 0 60 8 15 15 8 8 61 0 15 0 11 0 62 7 7 7 12 12 63 0 17 0 8 0 64 0 14 0 14 0 65 0 18 0 14 0 66 0 14 0 11 0 67 9 12 12 12 12 68 0 14 0 14 0 69 6 9 9 9 9 70 10 14 14 13 13 71 11 11 11 8 8 72 7 16 16 11 11 73 12 17 17 9 9 74 12 16 16 12 12 75 0 12 0 7 0 76 6 15 15 11 11 77 0 15 0 12 0 78 9 15 15 11 11 79 5 16 16 12 12 80 11 16 16 9 9 81 0 11 0 11 0 82 0 15 0 13 0 83 10 12 12 12 12 84 0 14 0 12 0 85 7 15 15 11 11 86 8 17 17 12 12 87 9 19 19 12 12 88 8 15 15 11 11 89 0 16 0 11 0 90 13 14 14 8 8 91 7 16 16 9 9 92 7 15 15 11 11 93 7 15 15 12 12 94 0 17 0 13 0 95 9 12 12 12 12 96 9 18 18 6 6 97 8 13 13 12 12 98 7 14 14 11 11 99 0 14 0 13 0 100 0 14 0 11 0 101 0 12 0 12 0 102 4 14 14 10 10 103 0 12 0 10 0 104 10 15 15 11 11 105 7 11 11 11 11 106 0 11 0 11 0 107 7 15 15 9 9 108 10 14 14 7 7 109 0 15 0 11 0 110 8 16 16 12 12 111 0 12 0 12 0 112 5 14 14 15 15 113 8 18 18 11 11 114 9 14 14 10 10 115 11 13 13 13 13 116 0 14 0 13 0 117 8 14 14 11 11 118 4 17 17 12 12 119 16 12 12 12 12 120 9 16 16 12 12 121 16 15 15 8 8 122 0 10 0 5 0 123 0 13 0 11 0 124 0 15 0 12 0 125 11 16 16 12 12 126 0 15 0 11 0 127 8 14 14 12 12 128 8 11 11 10 10 129 0 13 0 7 0 130 8 17 17 12 12 131 6 14 14 12 12 132 8 16 16 9 9 133 0 15 0 11 0 134 14 12 12 12 12 135 0 16 0 12 0 136 0 8 0 11 0 137 0 9 0 11 0 138 12 13 13 12 12 139 11 19 19 12 12 140 8 11 11 11 11 141 0 15 0 12 0 142 0 11 0 12 0 143 0 15 0 8 0 144 0 16 0 15 0 145 8 15 15 11 11 146 7 12 12 11 11 147 0 16 0 6 0 148 0 15 0 13 0 149 0 13 0 12 0 150 9 14 14 12 12 151 12 11 11 12 12 152 6 15 15 12 12 153 4 16 16 12 12 154 6 14 14 10 10 155 7 13 13 12 12 156 0 15 0 12 0 KnowingPeople KnowingPeople_G Liked Liked_G Celebrity Celebrity_G 1 12 12 13 13 6 6 2 7 0 11 0 4 0 3 13 0 14 0 6 0 4 11 0 12 0 5 0 5 16 0 12 0 5 0 6 10 0 6 0 4 0 7 15 15 10 10 5 5 8 5 5 11 11 3 3 9 4 0 10 0 2 0 10 7 0 12 0 5 0 11 15 15 15 15 6 6 12 5 5 13 13 6 6 13 16 16 18 18 8 8 14 15 0 11 0 6 0 15 13 13 12 12 3 3 16 13 0 13 0 6 0 17 15 0 14 0 6 0 18 15 15 16 16 7 7 19 10 10 16 16 8 8 20 17 0 16 0 6 0 21 14 14 15 15 7 7 22 9 9 13 13 4 4 23 6 0 8 0 4 0 24 11 11 14 14 2 2 25 13 13 15 15 6 6 26 12 12 13 13 6 6 27 10 10 16 16 6 6 28 4 4 13 13 6 6 29 13 13 12 12 6 6 30 15 15 15 15 7 7 31 8 8 11 11 4 4 32 10 10 14 14 3 3 33 8 8 13 13 5 5 34 7 7 13 13 6 6 35 9 0 12 0 4 0 36 14 0 14 0 6 0 37 5 5 13 13 3 3 38 7 0 12 0 3 0 39 16 0 14 0 6 0 40 14 14 15 15 6 6 41 16 16 16 16 6 6 42 15 15 15 15 8 8 43 4 0 5 0 2 0 44 12 0 15 0 6 0 45 8 8 8 8 4 4 46 17 17 16 16 7 7 47 15 0 16 0 6 0 48 16 0 14 0 6 0 49 12 12 16 16 6 6 50 12 12 14 14 5 5 51 13 13 13 13 6 6 52 14 14 14 14 6 6 53 14 14 14 14 5 5 54 15 0 12 0 6 0 55 14 0 13 0 7 0 56 11 11 15 15 5 5 57 13 13 15 15 6 6 58 4 4 13 13 6 6 59 8 0 10 0 4 0 60 13 13 13 13 5 5 61 15 0 14 0 6 0 62 15 15 13 13 6 6 63 8 0 13 0 4 0 64 17 0 18 0 6 0 65 12 0 12 0 4 0 66 13 0 14 0 7 0 67 14 14 16 16 8 8 68 7 0 13 0 6 0 69 16 16 16 16 6 6 70 11 11 15 15 6 6 71 10 10 14 14 5 5 72 14 14 13 13 6 6 73 19 19 12 12 6 6 74 14 14 16 16 4 4 75 8 0 9 0 5 0 76 15 15 15 15 8 8 77 8 0 16 0 6 0 78 8 8 12 12 6 6 79 6 6 11 11 2 2 80 7 7 13 13 2 2 81 16 0 13 0 4 0 82 15 0 14 0 6 0 83 10 10 15 15 6 6 84 8 0 14 0 5 0 85 9 9 12 12 4 4 86 8 8 16 16 4 4 87 14 14 14 14 6 6 88 14 14 13 13 5 5 89 14 0 12 0 6 0 90 15 15 13 13 7 7 91 7 7 12 12 6 6 92 7 7 9 9 4 4 93 12 12 13 13 4 4 94 7 0 10 0 3 0 95 12 12 15 15 8 8 96 6 6 9 9 4 4 97 10 10 13 13 4 4 98 12 12 13 13 5 5 99 13 0 13 0 5 0 100 14 0 15 0 7 0 101 8 0 13 0 4 0 102 14 14 14 14 5 5 103 10 0 11 0 5 0 104 14 14 15 15 8 8 105 15 15 14 14 5 5 106 10 0 15 0 2 0 107 6 6 12 12 5 5 108 9 9 15 15 4 4 109 11 0 14 0 5 0 110 16 16 16 16 7 7 111 14 0 14 0 6 0 112 8 8 12 12 3 3 113 16 16 11 11 5 5 114 16 16 13 13 6 6 115 14 14 12 12 5 5 116 12 0 12 0 6 0 117 16 16 16 16 7 7 118 15 15 13 13 6 6 119 11 11 12 12 6 6 120 6 6 14 14 5 5 121 6 6 4 4 4 4 122 16 0 14 0 6 0 123 16 0 15 0 6 0 124 8 0 12 0 3 0 125 11 11 11 11 4 4 126 12 0 12 0 4 0 127 13 13 11 11 4 4 128 11 11 12 12 5 5 129 9 0 11 0 4 0 130 15 15 13 13 6 6 131 11 11 12 12 6 6 132 12 12 12 12 4 4 133 15 0 15 0 7 0 134 8 8 14 14 4 4 135 7 0 12 0 4 0 136 10 0 12 0 4 0 137 9 0 12 0 4 0 138 13 13 13 13 5 5 139 11 11 11 11 4 4 140 12 12 13 13 7 7 141 5 0 12 0 3 0 142 12 0 14 0 5 0 143 14 0 15 0 5 0 144 15 0 15 0 6 0 145 14 14 13 13 5 5 146 13 13 16 16 6 6 147 14 0 17 0 6 0 148 14 0 13 0 3 0 149 15 0 14 0 6 0 150 13 13 13 13 5 5 151 14 14 16 16 8 8 152 11 11 13 13 6 6 153 14 14 14 14 4 4 154 11 11 13 13 3 3 155 8 8 14 14 4 4 156 12 0 16 0 7 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender trend -5.634184 9.184925 0.002303 trend_G Depression Depression_G -0.009272 -0.021567 -0.121607 Belonging Belonging_G WeightedPopularity 0.048530 0.005099 0.033130 WeightedPopularity_G ParentalCriticism ParentalCriticism_G 0.044696 0.171942 -0.217764 Happiness Happiness_G FindingFriends -0.007898 -0.189090 0.259336 FindingFriends_G KnowingPeople KnowingPeople_G -0.264147 0.264170 -0.060189 Liked Liked_G Celebrity 0.290831 0.056980 0.527765 Celebrity_G 0.102235 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.73943 -1.18111 -0.02254 1.29856 5.84829 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -5.634184 4.276467 -1.317 0.1899 Gender 9.184925 5.486919 1.674 0.0965 . trend 0.002303 0.006202 0.371 0.7110 trend_G -0.009272 0.008071 -1.149 0.2527 Depression -0.021567 0.118609 -0.182 0.8560 Depression_G -0.121607 0.145592 -0.835 0.4051 Belonging 0.048530 0.028317 1.714 0.0889 . Belonging_G 0.005099 0.036379 0.140 0.8887 WeightedPopularity 0.033130 0.114090 0.290 0.7720 WeightedPopularity_G 0.044696 0.135552 0.330 0.7421 ParentalCriticism 0.171942 0.113319 1.517 0.1315 ParentalCriticism_G -0.217764 0.140323 -1.552 0.1230 Happiness -0.007898 0.158552 -0.050 0.9603 Happiness_G -0.189090 0.195098 -0.969 0.3342 FindingFriends 0.259336 0.140909 1.840 0.0679 . FindingFriends_G -0.264147 0.194901 -1.355 0.1776 KnowingPeople 0.264170 0.116816 2.261 0.0253 * KnowingPeople_G -0.060189 0.141009 -0.427 0.6702 Liked 0.290831 0.154827 1.878 0.0625 . Liked_G 0.056980 0.201886 0.282 0.7782 Celebrity 0.527765 0.315830 1.671 0.0970 . Celebrity_G 0.102235 0.369213 0.277 0.7823 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.028 on 134 degrees of freedom Multiple R-squared: 0.5878, Adjusted R-squared: 0.5232 F-statistic: 9.1 on 21 and 134 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.6213375 0.75732509 0.378662546 [2,] 0.8270475 0.34590508 0.172952538 [3,] 0.7976679 0.40466424 0.202332121 [4,] 0.7425207 0.51495851 0.257479256 [5,] 0.6474002 0.70519960 0.352599802 [6,] 0.5549262 0.89014769 0.445073847 [7,] 0.5086389 0.98272217 0.491361083 [8,] 0.8321709 0.33565828 0.167829140 [9,] 0.7816417 0.43671667 0.218358336 [10,] 0.7253755 0.54924906 0.274624528 [11,] 0.8321294 0.33574113 0.167870564 [12,] 0.8411049 0.31779027 0.158895134 [13,] 0.8212607 0.35747864 0.178739322 [14,] 0.7992706 0.40145888 0.200729439 [15,] 0.7546185 0.49076292 0.245381461 [16,] 0.7457054 0.50858910 0.254294552 [17,] 0.6964131 0.60717382 0.303586911 [18,] 0.6736987 0.65260266 0.326301328 [19,] 0.6103490 0.77930191 0.389650954 [20,] 0.5700899 0.85982018 0.429910089 [21,] 0.8303470 0.33930609 0.169653043 [22,] 0.7976900 0.40462006 0.202310031 [23,] 0.8074940 0.38501204 0.192506021 [24,] 0.7735312 0.45293759 0.226468795 [25,] 0.7307089 0.53858210 0.269291051 [26,] 0.7053799 0.58924012 0.294620062 [27,] 0.7945080 0.41098403 0.205492013 [28,] 0.8178710 0.36425794 0.182128969 [29,] 0.7869537 0.42609253 0.213046264 [30,] 0.8842151 0.23156987 0.115784935 [31,] 0.8690610 0.26187797 0.130938986 [32,] 0.8608852 0.27822964 0.139114821 [33,] 0.9169188 0.16616230 0.083081151 [34,] 0.8982418 0.20351637 0.101758185 [35,] 0.8716524 0.25669525 0.128347625 [36,] 0.8459541 0.30809175 0.154045874 [37,] 0.8122209 0.37555813 0.187779065 [38,] 0.8361623 0.32767550 0.163837748 [39,] 0.8186271 0.36274587 0.181372937 [40,] 0.8390479 0.32190412 0.160952060 [41,] 0.9430808 0.11383846 0.056919228 [42,] 0.9454605 0.10907904 0.054539518 [43,] 0.9293842 0.14123155 0.070615776 [44,] 0.9237468 0.15250633 0.076253164 [45,] 0.9082550 0.18348998 0.091744988 [46,] 0.8923172 0.21536563 0.107682816 [47,] 0.8714119 0.25717615 0.128588076 [48,] 0.8426132 0.31477361 0.157386807 [49,] 0.8231062 0.35378761 0.176893807 [50,] 0.8411035 0.31779292 0.158896459 [51,] 0.8272168 0.34556632 0.172783162 [52,] 0.7994064 0.40118723 0.200593614 [53,] 0.8690055 0.26198903 0.130994513 [54,] 0.8468581 0.30628373 0.153141865 [55,] 0.8718764 0.25624723 0.128123615 [56,] 0.8497539 0.30049213 0.150246065 [57,] 0.8198463 0.36030733 0.180153664 [58,] 0.7847397 0.43052055 0.215260273 [59,] 0.7536701 0.49265980 0.246329900 [60,] 0.7695119 0.46097628 0.230488142 [61,] 0.7661535 0.46769298 0.233846490 [62,] 0.7469072 0.50618562 0.253092811 [63,] 0.7734587 0.45308253 0.226541264 [64,] 0.7669596 0.46608085 0.233040426 [65,] 0.7261016 0.54779688 0.273898441 [66,] 0.7069091 0.58618188 0.293090942 [67,] 0.6845336 0.63093290 0.315466449 [68,] 0.6399408 0.72011835 0.360059174 [69,] 0.5917436 0.81651280 0.408256399 [70,] 0.5843830 0.83123403 0.415617017 [71,] 0.5575092 0.88498162 0.442490811 [72,] 0.6727847 0.65443059 0.327215293 [73,] 0.6258641 0.74827173 0.374135865 [74,] 0.6084241 0.78315175 0.391575874 [75,] 0.7166190 0.56676191 0.283380953 [76,] 0.7364828 0.52703439 0.263517193 [77,] 0.7337693 0.53246135 0.266230677 [78,] 0.6902883 0.61942348 0.309711740 [79,] 0.6384511 0.72309778 0.361548891 [80,] 0.5964722 0.80705551 0.403527756 [81,] 0.5639283 0.87214335 0.436071674 [82,] 0.5937397 0.81252050 0.406260250 [83,] 0.5707166 0.85856681 0.429283404 [84,] 0.7864741 0.42705178 0.213525892 [85,] 0.7566370 0.48672593 0.243362963 [86,] 0.7035755 0.59284890 0.296424452 [87,] 0.6603687 0.67926268 0.339631340 [88,] 0.7063898 0.58722040 0.293610200 [89,] 0.6803383 0.63932338 0.319661690 [90,] 0.6445936 0.71081279 0.355406396 [91,] 0.5800631 0.83987375 0.419936873 [92,] 0.6824735 0.63505308 0.317526542 [93,] 0.6165276 0.76694481 0.383472407 [94,] 0.5483202 0.90335965 0.451679826 [95,] 0.6224108 0.75517846 0.377589231 [96,] 0.5885216 0.82295676 0.411478378 [97,] 0.5561395 0.88772094 0.443860472 [98,] 0.5154940 0.96901210 0.484506048 [99,] 0.4560981 0.91219613 0.543901936 [100,] 0.3869656 0.77393129 0.613034357 [101,] 0.3695361 0.73907213 0.630463933 [102,] 0.9764545 0.04709093 0.023545466 [103,] 0.9536947 0.09261060 0.046305302 [104,] 0.9738941 0.05221185 0.026105925 [105,] 0.9485500 0.10290001 0.051450003 [106,] 0.9837064 0.03258722 0.016293610 [107,] 0.9919807 0.01603855 0.008019274 > postscript(file="/var/www/rcomp/tmp/1ip6q1290266691.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/59sc31290266691.ps",horizontal=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 = 156 Frequency = 1 1 2 3 4 5 6 1.724004160 3.865973772 1.584002053 0.943446042 1.124982351 -0.334418588 7 8 9 10 11 12 -1.442799048 5.848291323 -1.767286366 3.072951697 5.098636320 -1.347554967 13 14 15 16 17 18 -2.226374172 -5.739427090 -4.068347585 1.477761343 2.350894997 1.459515437 19 20 21 22 23 24 -0.370639018 0.839415761 -0.565779574 1.296134066 0.031942223 0.400691284 25 26 27 28 29 30 0.416791120 -0.049207024 0.875350932 -0.481746874 0.789707831 -0.185078507 31 32 33 34 35 36 1.475172519 -3.076946132 -0.232425625 -0.402536491 -1.338805147 2.221017932 37 38 39 40 41 42 1.326823736 2.693282677 1.814849625 2.702567930 -0.732059847 -1.205770744 43 44 45 46 47 48 -0.172673423 1.319942676 3.307321304 -0.125146206 0.267148653 1.584640817 49 50 51 52 53 54 0.952978354 -1.412906754 1.305856413 3.027446421 -0.899189815 -1.985324274 55 56 57 58 59 60 -1.099398634 -0.053006078 -3.356697856 -0.180846598 0.028329905 -0.550087808 61 62 63 64 65 66 0.503263013 -1.696154966 0.821955132 0.570898556 -4.283999332 2.468716696 67 68 69 70 71 72 0.592744686 -2.112426114 -1.210661749 -1.095926523 0.004128384 -0.329365033 73 74 75 76 77 78 -0.299236984 2.424079402 2.028828547 -0.649534887 -1.412260900 -1.172891222 79 80 81 82 83 84 -2.813301652 0.850089073 0.662554202 -0.199596072 -1.237604650 -2.760045956 85 86 87 88 89 90 -1.883676620 -1.367356647 3.203356556 1.947423788 0.370615302 -1.674362136 91 92 93 94 95 96 0.997443958 0.419871904 0.669463781 -1.404302268 1.480397420 2.166818687 97 98 99 100 101 102 0.390423971 -1.981375297 2.390082993 1.167897443 -2.271476637 -0.857395747 103 104 105 106 107 108 0.143661042 0.753496139 1.659507186 -1.603847385 -2.879064376 -3.942118602 109 110 111 112 113 114 -1.056399832 0.389099526 -0.524172821 -2.813568776 1.564554899 0.526643823 115 116 117 118 119 120 0.796306111 0.433532798 -0.827024207 -0.967012827 -1.862994730 1.312855427 121 122 123 124 125 126 -1.910829133 -1.230750729 1.936123328 -1.507166195 -2.001663764 2.661801319 127 128 129 130 131 132 0.489498593 -1.982821625 -2.272094223 0.425903094 -1.165367934 1.362626283 133 134 135 136 137 138 -4.565724894 4.107751094 -1.014176902 -0.599249458 0.169009091 2.020917203 139 140 141 142 143 144 -0.764229837 0.136056537 3.780398060 0.021584711 -0.844140830 -5.110890237 145 146 147 148 149 150 -0.531272544 -0.263782042 -1.011690712 1.556746657 0.783694642 -0.112952636 151 152 153 154 155 156 -0.234603933 0.105989925 -0.165747567 -0.738052711 1.592361478 0.529798964 > postscript(file="/var/www/rcomp/tmp/69sc31290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 1.724004160 NA 1 3.865973772 1.724004160 2 1.584002053 3.865973772 3 0.943446042 1.584002053 4 1.124982351 0.943446042 5 -0.334418588 1.124982351 6 -1.442799048 -0.334418588 7 5.848291323 -1.442799048 8 -1.767286366 5.848291323 9 3.072951697 -1.767286366 10 5.098636320 3.072951697 11 -1.347554967 5.098636320 12 -2.226374172 -1.347554967 13 -5.739427090 -2.226374172 14 -4.068347585 -5.739427090 15 1.477761343 -4.068347585 16 2.350894997 1.477761343 17 1.459515437 2.350894997 18 -0.370639018 1.459515437 19 0.839415761 -0.370639018 20 -0.565779574 0.839415761 21 1.296134066 -0.565779574 22 0.031942223 1.296134066 23 0.400691284 0.031942223 24 0.416791120 0.400691284 25 -0.049207024 0.416791120 26 0.875350932 -0.049207024 27 -0.481746874 0.875350932 28 0.789707831 -0.481746874 29 -0.185078507 0.789707831 30 1.475172519 -0.185078507 31 -3.076946132 1.475172519 32 -0.232425625 -3.076946132 33 -0.402536491 -0.232425625 34 -1.338805147 -0.402536491 35 2.221017932 -1.338805147 36 1.326823736 2.221017932 37 2.693282677 1.326823736 38 1.814849625 2.693282677 39 2.702567930 1.814849625 40 -0.732059847 2.702567930 41 -1.205770744 -0.732059847 42 -0.172673423 -1.205770744 43 1.319942676 -0.172673423 44 3.307321304 1.319942676 45 -0.125146206 3.307321304 46 0.267148653 -0.125146206 47 1.584640817 0.267148653 48 0.952978354 1.584640817 49 -1.412906754 0.952978354 50 1.305856413 -1.412906754 51 3.027446421 1.305856413 52 -0.899189815 3.027446421 53 -1.985324274 -0.899189815 54 -1.099398634 -1.985324274 55 -0.053006078 -1.099398634 56 -3.356697856 -0.053006078 57 -0.180846598 -3.356697856 58 0.028329905 -0.180846598 59 -0.550087808 0.028329905 60 0.503263013 -0.550087808 61 -1.696154966 0.503263013 62 0.821955132 -1.696154966 63 0.570898556 0.821955132 64 -4.283999332 0.570898556 65 2.468716696 -4.283999332 66 0.592744686 2.468716696 67 -2.112426114 0.592744686 68 -1.210661749 -2.112426114 69 -1.095926523 -1.210661749 70 0.004128384 -1.095926523 71 -0.329365033 0.004128384 72 -0.299236984 -0.329365033 73 2.424079402 -0.299236984 74 2.028828547 2.424079402 75 -0.649534887 2.028828547 76 -1.412260900 -0.649534887 77 -1.172891222 -1.412260900 78 -2.813301652 -1.172891222 79 0.850089073 -2.813301652 80 0.662554202 0.850089073 81 -0.199596072 0.662554202 82 -1.237604650 -0.199596072 83 -2.760045956 -1.237604650 84 -1.883676620 -2.760045956 85 -1.367356647 -1.883676620 86 3.203356556 -1.367356647 87 1.947423788 3.203356556 88 0.370615302 1.947423788 89 -1.674362136 0.370615302 90 0.997443958 -1.674362136 91 0.419871904 0.997443958 92 0.669463781 0.419871904 93 -1.404302268 0.669463781 94 1.480397420 -1.404302268 95 2.166818687 1.480397420 96 0.390423971 2.166818687 97 -1.981375297 0.390423971 98 2.390082993 -1.981375297 99 1.167897443 2.390082993 100 -2.271476637 1.167897443 101 -0.857395747 -2.271476637 102 0.143661042 -0.857395747 103 0.753496139 0.143661042 104 1.659507186 0.753496139 105 -1.603847385 1.659507186 106 -2.879064376 -1.603847385 107 -3.942118602 -2.879064376 108 -1.056399832 -3.942118602 109 0.389099526 -1.056399832 110 -0.524172821 0.389099526 111 -2.813568776 -0.524172821 112 1.564554899 -2.813568776 113 0.526643823 1.564554899 114 0.796306111 0.526643823 115 0.433532798 0.796306111 116 -0.827024207 0.433532798 117 -0.967012827 -0.827024207 118 -1.862994730 -0.967012827 119 1.312855427 -1.862994730 120 -1.910829133 1.312855427 121 -1.230750729 -1.910829133 122 1.936123328 -1.230750729 123 -1.507166195 1.936123328 124 -2.001663764 -1.507166195 125 2.661801319 -2.001663764 126 0.489498593 2.661801319 127 -1.982821625 0.489498593 128 -2.272094223 -1.982821625 129 0.425903094 -2.272094223 130 -1.165367934 0.425903094 131 1.362626283 -1.165367934 132 -4.565724894 1.362626283 133 4.107751094 -4.565724894 134 -1.014176902 4.107751094 135 -0.599249458 -1.014176902 136 0.169009091 -0.599249458 137 2.020917203 0.169009091 138 -0.764229837 2.020917203 139 0.136056537 -0.764229837 140 3.780398060 0.136056537 141 0.021584711 3.780398060 142 -0.844140830 0.021584711 143 -5.110890237 -0.844140830 144 -0.531272544 -5.110890237 145 -0.263782042 -0.531272544 146 -1.011690712 -0.263782042 147 1.556746657 -1.011690712 148 0.783694642 1.556746657 149 -0.112952636 0.783694642 150 -0.234603933 -0.112952636 151 0.105989925 -0.234603933 152 -0.165747567 0.105989925 153 -0.738052711 -0.165747567 154 1.592361478 -0.738052711 155 0.529798964 1.592361478 156 NA 0.529798964 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 3.865973772 1.724004160 [2,] 1.584002053 3.865973772 [3,] 0.943446042 1.584002053 [4,] 1.124982351 0.943446042 [5,] -0.334418588 1.124982351 [6,] -1.442799048 -0.334418588 [7,] 5.848291323 -1.442799048 [8,] -1.767286366 5.848291323 [9,] 3.072951697 -1.767286366 [10,] 5.098636320 3.072951697 [11,] -1.347554967 5.098636320 [12,] -2.226374172 -1.347554967 [13,] -5.739427090 -2.226374172 [14,] -4.068347585 -5.739427090 [15,] 1.477761343 -4.068347585 [16,] 2.350894997 1.477761343 [17,] 1.459515437 2.350894997 [18,] -0.370639018 1.459515437 [19,] 0.839415761 -0.370639018 [20,] -0.565779574 0.839415761 [21,] 1.296134066 -0.565779574 [22,] 0.031942223 1.296134066 [23,] 0.400691284 0.031942223 [24,] 0.416791120 0.400691284 [25,] -0.049207024 0.416791120 [26,] 0.875350932 -0.049207024 [27,] -0.481746874 0.875350932 [28,] 0.789707831 -0.481746874 [29,] -0.185078507 0.789707831 [30,] 1.475172519 -0.185078507 [31,] -3.076946132 1.475172519 [32,] -0.232425625 -3.076946132 [33,] -0.402536491 -0.232425625 [34,] -1.338805147 -0.402536491 [35,] 2.221017932 -1.338805147 [36,] 1.326823736 2.221017932 [37,] 2.693282677 1.326823736 [38,] 1.814849625 2.693282677 [39,] 2.702567930 1.814849625 [40,] -0.732059847 2.702567930 [41,] -1.205770744 -0.732059847 [42,] -0.172673423 -1.205770744 [43,] 1.319942676 -0.172673423 [44,] 3.307321304 1.319942676 [45,] -0.125146206 3.307321304 [46,] 0.267148653 -0.125146206 [47,] 1.584640817 0.267148653 [48,] 0.952978354 1.584640817 [49,] -1.412906754 0.952978354 [50,] 1.305856413 -1.412906754 [51,] 3.027446421 1.305856413 [52,] -0.899189815 3.027446421 [53,] -1.985324274 -0.899189815 [54,] -1.099398634 -1.985324274 [55,] -0.053006078 -1.099398634 [56,] -3.356697856 -0.053006078 [57,] -0.180846598 -3.356697856 [58,] 0.028329905 -0.180846598 [59,] -0.550087808 0.028329905 [60,] 0.503263013 -0.550087808 [61,] -1.696154966 0.503263013 [62,] 0.821955132 -1.696154966 [63,] 0.570898556 0.821955132 [64,] -4.283999332 0.570898556 [65,] 2.468716696 -4.283999332 [66,] 0.592744686 2.468716696 [67,] -2.112426114 0.592744686 [68,] -1.210661749 -2.112426114 [69,] -1.095926523 -1.210661749 [70,] 0.004128384 -1.095926523 [71,] -0.329365033 0.004128384 [72,] -0.299236984 -0.329365033 [73,] 2.424079402 -0.299236984 [74,] 2.028828547 2.424079402 [75,] -0.649534887 2.028828547 [76,] -1.412260900 -0.649534887 [77,] -1.172891222 -1.412260900 [78,] -2.813301652 -1.172891222 [79,] 0.850089073 -2.813301652 [80,] 0.662554202 0.850089073 [81,] -0.199596072 0.662554202 [82,] -1.237604650 -0.199596072 [83,] -2.760045956 -1.237604650 [84,] -1.883676620 -2.760045956 [85,] -1.367356647 -1.883676620 [86,] 3.203356556 -1.367356647 [87,] 1.947423788 3.203356556 [88,] 0.370615302 1.947423788 [89,] -1.674362136 0.370615302 [90,] 0.997443958 -1.674362136 [91,] 0.419871904 0.997443958 [92,] 0.669463781 0.419871904 [93,] -1.404302268 0.669463781 [94,] 1.480397420 -1.404302268 [95,] 2.166818687 1.480397420 [96,] 0.390423971 2.166818687 [97,] -1.981375297 0.390423971 [98,] 2.390082993 -1.981375297 [99,] 1.167897443 2.390082993 [100,] -2.271476637 1.167897443 [101,] -0.857395747 -2.271476637 [102,] 0.143661042 -0.857395747 [103,] 0.753496139 0.143661042 [104,] 1.659507186 0.753496139 [105,] -1.603847385 1.659507186 [106,] -2.879064376 -1.603847385 [107,] -3.942118602 -2.879064376 [108,] -1.056399832 -3.942118602 [109,] 0.389099526 -1.056399832 [110,] -0.524172821 0.389099526 [111,] -2.813568776 -0.524172821 [112,] 1.564554899 -2.813568776 [113,] 0.526643823 1.564554899 [114,] 0.796306111 0.526643823 [115,] 0.433532798 0.796306111 [116,] -0.827024207 0.433532798 [117,] -0.967012827 -0.827024207 [118,] -1.862994730 -0.967012827 [119,] 1.312855427 -1.862994730 [120,] -1.910829133 1.312855427 [121,] -1.230750729 -1.910829133 [122,] 1.936123328 -1.230750729 [123,] -1.507166195 1.936123328 [124,] -2.001663764 -1.507166195 [125,] 2.661801319 -2.001663764 [126,] 0.489498593 2.661801319 [127,] -1.982821625 0.489498593 [128,] -2.272094223 -1.982821625 [129,] 0.425903094 -2.272094223 [130,] -1.165367934 0.425903094 [131,] 1.362626283 -1.165367934 [132,] -4.565724894 1.362626283 [133,] 4.107751094 -4.565724894 [134,] -1.014176902 4.107751094 [135,] -0.599249458 -1.014176902 [136,] 0.169009091 -0.599249458 [137,] 2.020917203 0.169009091 [138,] -0.764229837 2.020917203 [139,] 0.136056537 -0.764229837 [140,] 3.780398060 0.136056537 [141,] 0.021584711 3.780398060 [142,] -0.844140830 0.021584711 [143,] -5.110890237 -0.844140830 [144,] -0.531272544 -5.110890237 [145,] -0.263782042 -0.531272544 [146,] -1.011690712 -0.263782042 [147,] 1.556746657 -1.011690712 [148,] 0.783694642 1.556746657 [149,] -0.112952636 0.783694642 [150,] -0.234603933 -0.112952636 [151,] 0.105989925 -0.234603933 [152,] -0.165747567 0.105989925 [153,] -0.738052711 -0.165747567 [154,] 1.592361478 -0.738052711 [155,] 0.529798964 1.592361478 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 3.865973772 1.724004160 2 1.584002053 3.865973772 3 0.943446042 1.584002053 4 1.124982351 0.943446042 5 -0.334418588 1.124982351 6 -1.442799048 -0.334418588 7 5.848291323 -1.442799048 8 -1.767286366 5.848291323 9 3.072951697 -1.767286366 10 5.098636320 3.072951697 11 -1.347554967 5.098636320 12 -2.226374172 -1.347554967 13 -5.739427090 -2.226374172 14 -4.068347585 -5.739427090 15 1.477761343 -4.068347585 16 2.350894997 1.477761343 17 1.459515437 2.350894997 18 -0.370639018 1.459515437 19 0.839415761 -0.370639018 20 -0.565779574 0.839415761 21 1.296134066 -0.565779574 22 0.031942223 1.296134066 23 0.400691284 0.031942223 24 0.416791120 0.400691284 25 -0.049207024 0.416791120 26 0.875350932 -0.049207024 27 -0.481746874 0.875350932 28 0.789707831 -0.481746874 29 -0.185078507 0.789707831 30 1.475172519 -0.185078507 31 -3.076946132 1.475172519 32 -0.232425625 -3.076946132 33 -0.402536491 -0.232425625 34 -1.338805147 -0.402536491 35 2.221017932 -1.338805147 36 1.326823736 2.221017932 37 2.693282677 1.326823736 38 1.814849625 2.693282677 39 2.702567930 1.814849625 40 -0.732059847 2.702567930 41 -1.205770744 -0.732059847 42 -0.172673423 -1.205770744 43 1.319942676 -0.172673423 44 3.307321304 1.319942676 45 -0.125146206 3.307321304 46 0.267148653 -0.125146206 47 1.584640817 0.267148653 48 0.952978354 1.584640817 49 -1.412906754 0.952978354 50 1.305856413 -1.412906754 51 3.027446421 1.305856413 52 -0.899189815 3.027446421 53 -1.985324274 -0.899189815 54 -1.099398634 -1.985324274 55 -0.053006078 -1.099398634 56 -3.356697856 -0.053006078 57 -0.180846598 -3.356697856 58 0.028329905 -0.180846598 59 -0.550087808 0.028329905 60 0.503263013 -0.550087808 61 -1.696154966 0.503263013 62 0.821955132 -1.696154966 63 0.570898556 0.821955132 64 -4.283999332 0.570898556 65 2.468716696 -4.283999332 66 0.592744686 2.468716696 67 -2.112426114 0.592744686 68 -1.210661749 -2.112426114 69 -1.095926523 -1.210661749 70 0.004128384 -1.095926523 71 -0.329365033 0.004128384 72 -0.299236984 -0.329365033 73 2.424079402 -0.299236984 74 2.028828547 2.424079402 75 -0.649534887 2.028828547 76 -1.412260900 -0.649534887 77 -1.172891222 -1.412260900 78 -2.813301652 -1.172891222 79 0.850089073 -2.813301652 80 0.662554202 0.850089073 81 -0.199596072 0.662554202 82 -1.237604650 -0.199596072 83 -2.760045956 -1.237604650 84 -1.883676620 -2.760045956 85 -1.367356647 -1.883676620 86 3.203356556 -1.367356647 87 1.947423788 3.203356556 88 0.370615302 1.947423788 89 -1.674362136 0.370615302 90 0.997443958 -1.674362136 91 0.419871904 0.997443958 92 0.669463781 0.419871904 93 -1.404302268 0.669463781 94 1.480397420 -1.404302268 95 2.166818687 1.480397420 96 0.390423971 2.166818687 97 -1.981375297 0.390423971 98 2.390082993 -1.981375297 99 1.167897443 2.390082993 100 -2.271476637 1.167897443 101 -0.857395747 -2.271476637 102 0.143661042 -0.857395747 103 0.753496139 0.143661042 104 1.659507186 0.753496139 105 -1.603847385 1.659507186 106 -2.879064376 -1.603847385 107 -3.942118602 -2.879064376 108 -1.056399832 -3.942118602 109 0.389099526 -1.056399832 110 -0.524172821 0.389099526 111 -2.813568776 -0.524172821 112 1.564554899 -2.813568776 113 0.526643823 1.564554899 114 0.796306111 0.526643823 115 0.433532798 0.796306111 116 -0.827024207 0.433532798 117 -0.967012827 -0.827024207 118 -1.862994730 -0.967012827 119 1.312855427 -1.862994730 120 -1.910829133 1.312855427 121 -1.230750729 -1.910829133 122 1.936123328 -1.230750729 123 -1.507166195 1.936123328 124 -2.001663764 -1.507166195 125 2.661801319 -2.001663764 126 0.489498593 2.661801319 127 -1.982821625 0.489498593 128 -2.272094223 -1.982821625 129 0.425903094 -2.272094223 130 -1.165367934 0.425903094 131 1.362626283 -1.165367934 132 -4.565724894 1.362626283 133 4.107751094 -4.565724894 134 -1.014176902 4.107751094 135 -0.599249458 -1.014176902 136 0.169009091 -0.599249458 137 2.020917203 0.169009091 138 -0.764229837 2.020917203 139 0.136056537 -0.764229837 140 3.780398060 0.136056537 141 0.021584711 3.780398060 142 -0.844140830 0.021584711 143 -5.110890237 -0.844140830 144 -0.531272544 -5.110890237 145 -0.263782042 -0.531272544 146 -1.011690712 -0.263782042 147 1.556746657 -1.011690712 148 0.783694642 1.556746657 149 -0.112952636 0.783694642 150 -0.234603933 -0.112952636 151 0.105989925 -0.234603933 152 -0.165747567 0.105989925 153 -0.738052711 -0.165747567 154 1.592361478 -0.738052711 155 0.529798964 1.592361478 > 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/www/rcomp/tmp/7wzmh1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8wzmh1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9pq321290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10pq321290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/rcomp/tmp/11sqj81290266691.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/www/rcomp/tmp/12w90e1290266691.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/www/rcomp/tmp/13ksx71290266691.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/www/rcomp/tmp/14d1ws1290266691.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/www/rcomp/tmp/15hkdy1290266691.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/www/rcomp/tmp/16dba71290266691.tab") + } > try(system("convert tmp/1ip6q1290266691.ps tmp/1ip6q1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/2by5b1290266691.ps tmp/2by5b1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/3by5b1290266691.ps tmp/3by5b1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/4by5b1290266691.ps tmp/4by5b1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/59sc31290266691.ps tmp/59sc31290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/69sc31290266691.ps tmp/69sc31290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/7wzmh1290266691.ps tmp/7wzmh1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/8wzmh1290266691.ps tmp/8wzmh1290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/9pq321290266691.ps tmp/9pq321290266691.png",intern=TRUE)) character(0) > try(system("convert tmp/10pq321290266691.ps tmp/10pq321290266691.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.280 2.310 10.693