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Type 'q()' to quit R. > x <- array(list(70.5 + ,0 + ,71.3 + ,0 + ,71.4 + ,0 + ,70.1 + ,0 + ,69.4 + ,0 + ,69.8 + ,0 + ,69.8 + ,0 + ,70.7 + ,0 + ,69.4 + ,0 + ,69.8 + ,0 + ,69.3 + ,0 + ,72.9 + ,0 + ,70.0 + ,0 + ,64.4 + ,0 + ,63.5 + ,0 + ,69.8 + ,0 + ,69.9 + ,0 + ,69.3 + ,0 + ,69.7 + ,0 + ,69.8 + ,0 + ,70.2 + ,0 + ,69.8 + ,0 + ,70.7 + ,0 + ,71.4 + ,0 + ,70.3 + ,0 + ,70.9 + ,0 + ,70.6 + ,0 + ,69.0 + ,0 + ,71.0 + ,0 + ,74.7 + ,0 + ,77.5 + ,0 + ,78.6 + ,0 + ,75.3 + ,0 + ,72.1 + ,0 + ,73.8 + ,0 + ,73.7 + ,0 + ,75.2 + ,0 + ,75.2 + ,0 + ,74.5 + ,0 + ,74.4 + ,0 + ,75.4 + ,0 + ,73.7 + ,0 + ,74.3 + ,0 + ,75.0 + ,0 + ,75.8 + ,0 + ,76.7 + ,0 + ,76.8 + ,0 + ,76.8 + ,0 + ,76.4 + ,0 + ,76.4 + ,0 + ,77.2 + ,0 + ,77.2 + ,0 + ,77.4 + ,0 + ,78.1 + ,0 + ,78.5 + ,0 + ,77.9 + ,0 + ,78.6 + ,0 + ,79.8 + ,0 + ,80.3 + ,0 + ,80.8 + ,0 + ,80.5 + ,0 + ,79.4 + ,0 + ,79.3 + ,0 + ,79.6 + ,0 + ,79.2 + ,0 + ,79.1 + ,0 + ,79.8 + ,0 + ,80.0 + ,0 + ,80.5 + ,0 + ,80.4 + ,0 + ,81.1 + ,0 + ,82.2 + ,0 + ,81.5 + ,0 + ,84.2 + ,0 + ,84.3 + ,0 + ,83.3 + ,0 + ,84.2 + ,0 + ,84.9 + ,0 + ,85.0 + ,0 + ,85.3 + ,0 + ,85.4 + ,0 + ,85.8 + ,0 + ,85.2 + ,0 + ,86.4 + ,0 + ,88.2 + ,0 + ,88.3 + ,0 + ,88.0 + ,0 + ,87.8 + ,0 + ,87.4 + ,0 + ,87.4 + ,0 + ,88.0 + ,0 + ,88.0 + ,0 + ,89.9 + ,0 + ,88.4 + ,0 + ,89.7 + ,0 + ,89.9 + ,0 + ,90.5 + ,0 + ,90.7 + ,0 + ,89.5 + ,0 + ,91.2 + ,0 + ,91.2 + ,0 + ,89.8 + ,0 + ,89.6 + ,0 + ,92.3 + ,0 + ,90.1 + ,0 + ,92.9 + ,0 + ,93.3 + ,0 + ,93.5 + ,0 + ,93.4 + ,0 + ,93.6 + ,0 + ,93.7 + ,0 + ,93.6 + ,0 + ,93.0 + ,0 + ,94.1 + ,0 + ,95.7 + ,0 + ,95.6 + ,0 + ,97.2 + ,0 + ,98.1 + ,0 + ,98.8 + ,0 + ,95.3 + ,0 + ,95.3 + ,0 + ,96.7 + ,0 + ,99.2 + ,0 + ,99.0 + ,0 + ,100.9 + ,0 + ,100.1 + ,0 + ,100.4 + ,0 + ,100.5 + ,0 + ,102.6 + ,0 + ,101.8 + ,0 + ,102.6 + ,0 + ,101.0 + ,0 + ,101.6 + ,0 + ,100.6 + ,0 + ,100.4 + ,0 + ,100.7 + ,0 + ,100.6 + ,0 + ,100.3 + ,0 + ,101.4 + ,0 + ,103.2 + ,0 + ,79.2 + ,1 + ,83.4 + ,1 + ,86.5 + ,1 + ,91.3 + ,1 + ,91.5 + ,1 + ,93.1 + ,1 + ,93.1 + ,1 + ,93.3 + ,1 + ,94.4 + ,1 + ,94.4 + ,1 + ,94.1 + ,1 + ,95.3 + ,1 + ,93.8 + ,1 + ,96.3 + ,1 + ,96.0 + ,1 + ,97.6 + ,1 + ,96.8 + ,1 + ,95.0 + ,1 + ,93.7 + ,1 + ,91.0 + ,1 + ,92.2 + ,1 + ,93.6 + ,1 + ,97.2 + ,1 + ,97.1 + ,1 + ,98.2 + ,1 + ,98.3 + ,1 + ,99.8 + ,1 + ,100.5 + ,1 + ,99.2 + ,1 + ,101.0 + ,1 + ,102.1 + ,1 + ,102.8 + ,1 + ,102.5 + ,1 + ,104.2 + ,1 + ,104.3 + ,1 + ,105.3 + ,1 + ,105.1 + ,1 + ,107.4 + ,1 + ,106.4 + ,1 + ,106.4 + ,1 + ,107.9 + ,1 + ,107.8 + ,1 + ,108.3 + ,1 + ,108.3 + ,1 + ,109.2 + ,1 + ,109.3 + ,1 + ,109.3 + ,1 + ,109.6 + ,1 + ,111.1 + ,1 + ,109.0 + ,1 + ,109.8 + ,1 + ,108.8 + ,1 + ,110.9 + ,1 + ,110.2 + ,1 + ,111.3 + ,1 + ,111.6 + ,1 + ,112.3 + ,1 + ,111.2 + ,1 + ,111.7 + ,1 + ,111.7 + ,1 + ,112.7 + ,1 + ,113.2 + ,1 + ,113.0 + ,1 + ,114.2 + ,1 + ,114.0 + ,1 + ,111.7 + ,1 + ,114.2 + ,1 + ,114.7 + ,1 + ,116.5 + ,1 + ,116.2 + ,1 + ,116.2 + ,1 + ,117.4 + ,1 + ,117.4 + ,1 + ,118.2 + ,1 + ,116.4 + ,1 + ,117.3 + ,1 + ,117.1 + ,1 + ,116.5 + ,1 + ,117.4 + ,1 + ,118.2 + ,1 + ,118.4 + ,1 + ,116.9 + ,1 + ,116.3 + ,1 + ,116.8 + ,1 + ,114.9 + ,1) + ,dim=c(2 + ,225) + ,dimnames=list(c('Y' + ,'D') + ,1:225)) > y <- array(NA,dim=c(2,225),dimnames=list(c('Y','D'),1:225)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'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 Attaching package: 'zoo' The following object(s) are masked from package:base : 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 Y D M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 70.5 0 1 0 0 0 0 0 0 0 0 0 0 1 2 71.3 0 0 1 0 0 0 0 0 0 0 0 0 2 3 71.4 0 0 0 1 0 0 0 0 0 0 0 0 3 4 70.1 0 0 0 0 1 0 0 0 0 0 0 0 4 5 69.4 0 0 0 0 0 1 0 0 0 0 0 0 5 6 69.8 0 0 0 0 0 0 1 0 0 0 0 0 6 7 69.8 0 0 0 0 0 0 0 1 0 0 0 0 7 8 70.7 0 0 0 0 0 0 0 0 1 0 0 0 8 9 69.4 0 0 0 0 0 0 0 0 0 1 0 0 9 10 69.8 0 0 0 0 0 0 0 0 0 0 1 0 10 11 69.3 0 0 0 0 0 0 0 0 0 0 0 1 11 12 72.9 0 0 0 0 0 0 0 0 0 0 0 0 12 13 70.0 0 1 0 0 0 0 0 0 0 0 0 0 13 14 64.4 0 0 1 0 0 0 0 0 0 0 0 0 14 15 63.5 0 0 0 1 0 0 0 0 0 0 0 0 15 16 69.8 0 0 0 0 1 0 0 0 0 0 0 0 16 17 69.9 0 0 0 0 0 1 0 0 0 0 0 0 17 18 69.3 0 0 0 0 0 0 1 0 0 0 0 0 18 19 69.7 0 0 0 0 0 0 0 1 0 0 0 0 19 20 69.8 0 0 0 0 0 0 0 0 1 0 0 0 20 21 70.2 0 0 0 0 0 0 0 0 0 1 0 0 21 22 69.8 0 0 0 0 0 0 0 0 0 0 1 0 22 23 70.7 0 0 0 0 0 0 0 0 0 0 0 1 23 24 71.4 0 0 0 0 0 0 0 0 0 0 0 0 24 25 70.3 0 1 0 0 0 0 0 0 0 0 0 0 25 26 70.9 0 0 1 0 0 0 0 0 0 0 0 0 26 27 70.6 0 0 0 1 0 0 0 0 0 0 0 0 27 28 69.0 0 0 0 0 1 0 0 0 0 0 0 0 28 29 71.0 0 0 0 0 0 1 0 0 0 0 0 0 29 30 74.7 0 0 0 0 0 0 1 0 0 0 0 0 30 31 77.5 0 0 0 0 0 0 0 1 0 0 0 0 31 32 78.6 0 0 0 0 0 0 0 0 1 0 0 0 32 33 75.3 0 0 0 0 0 0 0 0 0 1 0 0 33 34 72.1 0 0 0 0 0 0 0 0 0 0 1 0 34 35 73.8 0 0 0 0 0 0 0 0 0 0 0 1 35 36 73.7 0 0 0 0 0 0 0 0 0 0 0 0 36 37 75.2 0 1 0 0 0 0 0 0 0 0 0 0 37 38 75.2 0 0 1 0 0 0 0 0 0 0 0 0 38 39 74.5 0 0 0 1 0 0 0 0 0 0 0 0 39 40 74.4 0 0 0 0 1 0 0 0 0 0 0 0 40 41 75.4 0 0 0 0 0 1 0 0 0 0 0 0 41 42 73.7 0 0 0 0 0 0 1 0 0 0 0 0 42 43 74.3 0 0 0 0 0 0 0 1 0 0 0 0 43 44 75.0 0 0 0 0 0 0 0 0 1 0 0 0 44 45 75.8 0 0 0 0 0 0 0 0 0 1 0 0 45 46 76.7 0 0 0 0 0 0 0 0 0 0 1 0 46 47 76.8 0 0 0 0 0 0 0 0 0 0 0 1 47 48 76.8 0 0 0 0 0 0 0 0 0 0 0 0 48 49 76.4 0 1 0 0 0 0 0 0 0 0 0 0 49 50 76.4 0 0 1 0 0 0 0 0 0 0 0 0 50 51 77.2 0 0 0 1 0 0 0 0 0 0 0 0 51 52 77.2 0 0 0 0 1 0 0 0 0 0 0 0 52 53 77.4 0 0 0 0 0 1 0 0 0 0 0 0 53 54 78.1 0 0 0 0 0 0 1 0 0 0 0 0 54 55 78.5 0 0 0 0 0 0 0 1 0 0 0 0 55 56 77.9 0 0 0 0 0 0 0 0 1 0 0 0 56 57 78.6 0 0 0 0 0 0 0 0 0 1 0 0 57 58 79.8 0 0 0 0 0 0 0 0 0 0 1 0 58 59 80.3 0 0 0 0 0 0 0 0 0 0 0 1 59 60 80.8 0 0 0 0 0 0 0 0 0 0 0 0 60 61 80.5 0 1 0 0 0 0 0 0 0 0 0 0 61 62 79.4 0 0 1 0 0 0 0 0 0 0 0 0 62 63 79.3 0 0 0 1 0 0 0 0 0 0 0 0 63 64 79.6 0 0 0 0 1 0 0 0 0 0 0 0 64 65 79.2 0 0 0 0 0 1 0 0 0 0 0 0 65 66 79.1 0 0 0 0 0 0 1 0 0 0 0 0 66 67 79.8 0 0 0 0 0 0 0 1 0 0 0 0 67 68 80.0 0 0 0 0 0 0 0 0 1 0 0 0 68 69 80.5 0 0 0 0 0 0 0 0 0 1 0 0 69 70 80.4 0 0 0 0 0 0 0 0 0 0 1 0 70 71 81.1 0 0 0 0 0 0 0 0 0 0 0 1 71 72 82.2 0 0 0 0 0 0 0 0 0 0 0 0 72 73 81.5 0 1 0 0 0 0 0 0 0 0 0 0 73 74 84.2 0 0 1 0 0 0 0 0 0 0 0 0 74 75 84.3 0 0 0 1 0 0 0 0 0 0 0 0 75 76 83.3 0 0 0 0 1 0 0 0 0 0 0 0 76 77 84.2 0 0 0 0 0 1 0 0 0 0 0 0 77 78 84.9 0 0 0 0 0 0 1 0 0 0 0 0 78 79 85.0 0 0 0 0 0 0 0 1 0 0 0 0 79 80 85.3 0 0 0 0 0 0 0 0 1 0 0 0 80 81 85.4 0 0 0 0 0 0 0 0 0 1 0 0 81 82 85.8 0 0 0 0 0 0 0 0 0 0 1 0 82 83 85.2 0 0 0 0 0 0 0 0 0 0 0 1 83 84 86.4 0 0 0 0 0 0 0 0 0 0 0 0 84 85 88.2 0 1 0 0 0 0 0 0 0 0 0 0 85 86 88.3 0 0 1 0 0 0 0 0 0 0 0 0 86 87 88.0 0 0 0 1 0 0 0 0 0 0 0 0 87 88 87.8 0 0 0 0 1 0 0 0 0 0 0 0 88 89 87.4 0 0 0 0 0 1 0 0 0 0 0 0 89 90 87.4 0 0 0 0 0 0 1 0 0 0 0 0 90 91 88.0 0 0 0 0 0 0 0 1 0 0 0 0 91 92 88.0 0 0 0 0 0 0 0 0 1 0 0 0 92 93 89.9 0 0 0 0 0 0 0 0 0 1 0 0 93 94 88.4 0 0 0 0 0 0 0 0 0 0 1 0 94 95 89.7 0 0 0 0 0 0 0 0 0 0 0 1 95 96 89.9 0 0 0 0 0 0 0 0 0 0 0 0 96 97 90.5 0 1 0 0 0 0 0 0 0 0 0 0 97 98 90.7 0 0 1 0 0 0 0 0 0 0 0 0 98 99 89.5 0 0 0 1 0 0 0 0 0 0 0 0 99 100 91.2 0 0 0 0 1 0 0 0 0 0 0 0 100 101 91.2 0 0 0 0 0 1 0 0 0 0 0 0 101 102 89.8 0 0 0 0 0 0 1 0 0 0 0 0 102 103 89.6 0 0 0 0 0 0 0 1 0 0 0 0 103 104 92.3 0 0 0 0 0 0 0 0 1 0 0 0 104 105 90.1 0 0 0 0 0 0 0 0 0 1 0 0 105 106 92.9 0 0 0 0 0 0 0 0 0 0 1 0 106 107 93.3 0 0 0 0 0 0 0 0 0 0 0 1 107 108 93.5 0 0 0 0 0 0 0 0 0 0 0 0 108 109 93.4 0 1 0 0 0 0 0 0 0 0 0 0 109 110 93.6 0 0 1 0 0 0 0 0 0 0 0 0 110 111 93.7 0 0 0 1 0 0 0 0 0 0 0 0 111 112 93.6 0 0 0 0 1 0 0 0 0 0 0 0 112 113 93.0 0 0 0 0 0 1 0 0 0 0 0 0 113 114 94.1 0 0 0 0 0 0 1 0 0 0 0 0 114 115 95.7 0 0 0 0 0 0 0 1 0 0 0 0 115 116 95.6 0 0 0 0 0 0 0 0 1 0 0 0 116 117 97.2 0 0 0 0 0 0 0 0 0 1 0 0 117 118 98.1 0 0 0 0 0 0 0 0 0 0 1 0 118 119 98.8 0 0 0 0 0 0 0 0 0 0 0 1 119 120 95.3 0 0 0 0 0 0 0 0 0 0 0 0 120 121 95.3 0 1 0 0 0 0 0 0 0 0 0 0 121 122 96.7 0 0 1 0 0 0 0 0 0 0 0 0 122 123 99.2 0 0 0 1 0 0 0 0 0 0 0 0 123 124 99.0 0 0 0 0 1 0 0 0 0 0 0 0 124 125 100.9 0 0 0 0 0 1 0 0 0 0 0 0 125 126 100.1 0 0 0 0 0 0 1 0 0 0 0 0 126 127 100.4 0 0 0 0 0 0 0 1 0 0 0 0 127 128 100.5 0 0 0 0 0 0 0 0 1 0 0 0 128 129 102.6 0 0 0 0 0 0 0 0 0 1 0 0 129 130 101.8 0 0 0 0 0 0 0 0 0 0 1 0 130 131 102.6 0 0 0 0 0 0 0 0 0 0 0 1 131 132 101.0 0 0 0 0 0 0 0 0 0 0 0 0 132 133 101.6 0 1 0 0 0 0 0 0 0 0 0 0 133 134 100.6 0 0 1 0 0 0 0 0 0 0 0 0 134 135 100.4 0 0 0 1 0 0 0 0 0 0 0 0 135 136 100.7 0 0 0 0 1 0 0 0 0 0 0 0 136 137 100.6 0 0 0 0 0 1 0 0 0 0 0 0 137 138 100.3 0 0 0 0 0 0 1 0 0 0 0 0 138 139 101.4 0 0 0 0 0 0 0 1 0 0 0 0 139 140 103.2 0 0 0 0 0 0 0 0 1 0 0 0 140 141 79.2 1 0 0 0 0 0 0 0 0 1 0 0 141 142 83.4 1 0 0 0 0 0 0 0 0 0 1 0 142 143 86.5 1 0 0 0 0 0 0 0 0 0 0 1 143 144 91.3 1 0 0 0 0 0 0 0 0 0 0 0 144 145 91.5 1 1 0 0 0 0 0 0 0 0 0 0 145 146 93.1 1 0 1 0 0 0 0 0 0 0 0 0 146 147 93.1 1 0 0 1 0 0 0 0 0 0 0 0 147 148 93.3 1 0 0 0 1 0 0 0 0 0 0 0 148 149 94.4 1 0 0 0 0 1 0 0 0 0 0 0 149 150 94.4 1 0 0 0 0 0 1 0 0 0 0 0 150 151 94.1 1 0 0 0 0 0 0 1 0 0 0 0 151 152 95.3 1 0 0 0 0 0 0 0 1 0 0 0 152 153 93.8 1 0 0 0 0 0 0 0 0 1 0 0 153 154 96.3 1 0 0 0 0 0 0 0 0 0 1 0 154 155 96.0 1 0 0 0 0 0 0 0 0 0 0 1 155 156 97.6 1 0 0 0 0 0 0 0 0 0 0 0 156 157 96.8 1 1 0 0 0 0 0 0 0 0 0 0 157 158 95.0 1 0 1 0 0 0 0 0 0 0 0 0 158 159 93.7 1 0 0 1 0 0 0 0 0 0 0 0 159 160 91.0 1 0 0 0 1 0 0 0 0 0 0 0 160 161 92.2 1 0 0 0 0 1 0 0 0 0 0 0 161 162 93.6 1 0 0 0 0 0 1 0 0 0 0 0 162 163 97.2 1 0 0 0 0 0 0 1 0 0 0 0 163 164 97.1 1 0 0 0 0 0 0 0 1 0 0 0 164 165 98.2 1 0 0 0 0 0 0 0 0 1 0 0 165 166 98.3 1 0 0 0 0 0 0 0 0 0 1 0 166 167 99.8 1 0 0 0 0 0 0 0 0 0 0 1 167 168 100.5 1 0 0 0 0 0 0 0 0 0 0 0 168 169 99.2 1 1 0 0 0 0 0 0 0 0 0 0 169 170 101.0 1 0 1 0 0 0 0 0 0 0 0 0 170 171 102.1 1 0 0 1 0 0 0 0 0 0 0 0 171 172 102.8 1 0 0 0 1 0 0 0 0 0 0 0 172 173 102.5 1 0 0 0 0 1 0 0 0 0 0 0 173 174 104.2 1 0 0 0 0 0 1 0 0 0 0 0 174 175 104.3 1 0 0 0 0 0 0 1 0 0 0 0 175 176 105.3 1 0 0 0 0 0 0 0 1 0 0 0 176 177 105.1 1 0 0 0 0 0 0 0 0 1 0 0 177 178 107.4 1 0 0 0 0 0 0 0 0 0 1 0 178 179 106.4 1 0 0 0 0 0 0 0 0 0 0 1 179 180 106.4 1 0 0 0 0 0 0 0 0 0 0 0 180 181 107.9 1 1 0 0 0 0 0 0 0 0 0 0 181 182 107.8 1 0 1 0 0 0 0 0 0 0 0 0 182 183 108.3 1 0 0 1 0 0 0 0 0 0 0 0 183 184 108.3 1 0 0 0 1 0 0 0 0 0 0 0 184 185 109.2 1 0 0 0 0 1 0 0 0 0 0 0 185 186 109.3 1 0 0 0 0 0 1 0 0 0 0 0 186 187 109.3 1 0 0 0 0 0 0 1 0 0 0 0 187 188 109.6 1 0 0 0 0 0 0 0 1 0 0 0 188 189 111.1 1 0 0 0 0 0 0 0 0 1 0 0 189 190 109.0 1 0 0 0 0 0 0 0 0 0 1 0 190 191 109.8 1 0 0 0 0 0 0 0 0 0 0 1 191 192 108.8 1 0 0 0 0 0 0 0 0 0 0 0 192 193 110.9 1 1 0 0 0 0 0 0 0 0 0 0 193 194 110.2 1 0 1 0 0 0 0 0 0 0 0 0 194 195 111.3 1 0 0 1 0 0 0 0 0 0 0 0 195 196 111.6 1 0 0 0 1 0 0 0 0 0 0 0 196 197 112.3 1 0 0 0 0 1 0 0 0 0 0 0 197 198 111.2 1 0 0 0 0 0 1 0 0 0 0 0 198 199 111.7 1 0 0 0 0 0 0 1 0 0 0 0 199 200 111.7 1 0 0 0 0 0 0 0 1 0 0 0 200 201 112.7 1 0 0 0 0 0 0 0 0 1 0 0 201 202 113.2 1 0 0 0 0 0 0 0 0 0 1 0 202 203 113.0 1 0 0 0 0 0 0 0 0 0 0 1 203 204 114.2 1 0 0 0 0 0 0 0 0 0 0 0 204 205 114.0 1 1 0 0 0 0 0 0 0 0 0 0 205 206 111.7 1 0 1 0 0 0 0 0 0 0 0 0 206 207 114.2 1 0 0 1 0 0 0 0 0 0 0 0 207 208 114.7 1 0 0 0 1 0 0 0 0 0 0 0 208 209 116.5 1 0 0 0 0 1 0 0 0 0 0 0 209 210 116.2 1 0 0 0 0 0 1 0 0 0 0 0 210 211 116.2 1 0 0 0 0 0 0 1 0 0 0 0 211 212 117.4 1 0 0 0 0 0 0 0 1 0 0 0 212 213 117.4 1 0 0 0 0 0 0 0 0 1 0 0 213 214 118.2 1 0 0 0 0 0 0 0 0 0 1 0 214 215 116.4 1 0 0 0 0 0 0 0 0 0 0 1 215 216 117.3 1 0 0 0 0 0 0 0 0 0 0 0 216 217 117.1 1 1 0 0 0 0 0 0 0 0 0 0 217 218 116.5 1 0 1 0 0 0 0 0 0 0 0 0 218 219 117.4 1 0 0 1 0 0 0 0 0 0 0 0 219 220 118.2 1 0 0 0 1 0 0 0 0 0 0 0 220 221 118.4 1 0 0 0 0 1 0 0 0 0 0 0 221 222 116.9 1 0 0 0 0 0 1 0 0 0 0 0 222 223 116.3 1 0 0 0 0 0 0 1 0 0 0 0 223 224 116.8 1 0 0 0 0 0 0 0 1 0 0 0 224 225 114.9 1 0 0 0 0 0 0 0 0 1 0 0 225 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) D M1 M2 M3 M4 63.93216 -9.40780 0.08121 -0.39904 -0.43192 -0.50691 M5 M6 M7 M8 M9 M10 -0.28716 -0.46215 -0.12661 0.18788 -0.79197 -0.47283 M11 t -0.30308 0.28025 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -14.0478 -1.4244 -0.2504 1.8210 7.2064 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 63.932161 0.760024 84.119 <2e-16 *** D -9.407799 0.698854 -13.462 <2e-16 *** M1 0.081214 0.906293 0.090 0.929 M2 -0.399037 0.906227 -0.440 0.660 M3 -0.431920 0.906191 -0.477 0.634 M4 -0.506908 0.906185 -0.559 0.576 M5 -0.287160 0.906209 -0.317 0.752 M6 -0.462148 0.906264 -0.510 0.611 M7 -0.126610 0.906348 -0.140 0.889 M8 0.187876 0.906462 0.207 0.836 M9 -0.791965 0.906238 -0.874 0.383 M10 -0.472831 0.918374 -0.515 0.607 M11 -0.303082 0.918329 -0.330 0.742 t 0.280251 0.005217 53.718 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.755 on 211 degrees of freedom Multiple R-squared: 0.9676, Adjusted R-squared: 0.9656 F-statistic: 485.2 on 13 and 211 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,] 0.860536203 2.789276e-01 1.394638e-01 [2,] 0.796737519 4.065250e-01 2.032625e-01 [3,] 0.731345256 5.373095e-01 2.686547e-01 [4,] 0.634777594 7.304448e-01 3.652224e-01 [5,] 0.590795034 8.184099e-01 4.092050e-01 [6,] 0.508800465 9.823991e-01 4.911995e-01 [7,] 0.477944558 9.558891e-01 5.220554e-01 [8,] 0.386470398 7.729408e-01 6.135296e-01 [9,] 0.333952273 6.679045e-01 6.660477e-01 [10,] 0.428610306 8.572206e-01 5.713897e-01 [11,] 0.470206731 9.404135e-01 5.297933e-01 [12,] 0.392847690 7.856954e-01 6.071523e-01 [13,] 0.339985049 6.799701e-01 6.600150e-01 [14,] 0.496143279 9.922866e-01 5.038567e-01 [15,] 0.800187120 3.996258e-01 1.998129e-01 [16,] 0.947444354 1.051113e-01 5.255565e-02 [17,] 0.960343916 7.931217e-02 3.965608e-02 [18,] 0.945708546 1.085829e-01 5.429145e-02 [19,] 0.935650469 1.286991e-01 6.434953e-02 [20,] 0.917674260 1.646515e-01 8.232574e-02 [21,] 0.912428768 1.751425e-01 8.757123e-02 [22,] 0.921611979 1.567760e-01 7.838802e-02 [23,] 0.918777368 1.624453e-01 8.122263e-02 [24,] 0.903420696 1.931586e-01 9.657930e-02 [25,] 0.892515357 2.149693e-01 1.074846e-01 [26,] 0.872077630 2.558447e-01 1.279224e-01 [27,] 0.853590953 2.928181e-01 1.464090e-01 [28,] 0.833215671 3.335687e-01 1.667843e-01 [29,] 0.811318199 3.773636e-01 1.886818e-01 [30,] 0.812430502 3.751390e-01 1.875695e-01 [31,] 0.799445185 4.011096e-01 2.005548e-01 [32,] 0.769791092 4.604178e-01 2.302089e-01 [33,] 0.733926938 5.321461e-01 2.660731e-01 [34,] 0.703221918 5.935562e-01 2.967781e-01 [35,] 0.696408568 6.071829e-01 3.035914e-01 [36,] 0.670876150 6.582477e-01 3.291239e-01 [37,] 0.636999214 7.260016e-01 3.630008e-01 [38,] 0.612569766 7.748605e-01 3.874302e-01 [39,] 0.582106381 8.357872e-01 4.178936e-01 [40,] 0.543413323 9.131734e-01 4.565867e-01 [41,] 0.515401149 9.691977e-01 4.845989e-01 [42,] 0.523763858 9.524723e-01 4.762361e-01 [43,] 0.530029350 9.399413e-01 4.699706e-01 [44,] 0.523533635 9.529327e-01 4.764664e-01 [45,] 0.506657870 9.866843e-01 4.933421e-01 [46,] 0.473137590 9.462752e-01 5.268624e-01 [47,] 0.437287050 8.745741e-01 5.627129e-01 [48,] 0.399598701 7.991974e-01 6.004013e-01 [49,] 0.356353790 7.127076e-01 6.436462e-01 [50,] 0.318438374 6.368767e-01 6.815616e-01 [51,] 0.283947265 5.678945e-01 7.160527e-01 [52,] 0.252683072 5.053661e-01 7.473169e-01 [53,] 0.220224747 4.404495e-01 7.797753e-01 [54,] 0.187901674 3.758033e-01 8.120983e-01 [55,] 0.159072364 3.181447e-01 8.409276e-01 [56,] 0.134854961 2.697099e-01 8.651450e-01 [57,] 0.111923391 2.238468e-01 8.880766e-01 [58,] 0.135956982 2.719140e-01 8.640430e-01 [59,] 0.163211930 3.264239e-01 8.367881e-01 [60,] 0.151138453 3.022769e-01 8.488615e-01 [61,] 0.147750769 2.955015e-01 8.522492e-01 [62,] 0.153148450 3.062969e-01 8.468516e-01 [63,] 0.144487902 2.889758e-01 8.555121e-01 [64,] 0.132631696 2.652634e-01 8.673683e-01 [65,] 0.129650579 2.593012e-01 8.703494e-01 [66,] 0.128673387 2.573468e-01 8.713266e-01 [67,] 0.111700264 2.234005e-01 8.882997e-01 [68,] 0.099860363 1.997207e-01 9.001396e-01 [69,] 0.120592236 2.411845e-01 8.794078e-01 [70,] 0.154177550 3.083551e-01 8.458225e-01 [71,] 0.176716266 3.534325e-01 8.232837e-01 [72,] 0.183315482 3.666310e-01 8.166845e-01 [73,] 0.169430610 3.388612e-01 8.305694e-01 [74,] 0.152516795 3.050336e-01 8.474832e-01 [75,] 0.136277494 2.725550e-01 8.637225e-01 [76,] 0.116826700 2.336534e-01 8.831733e-01 [77,] 0.134919891 2.698398e-01 8.650801e-01 [78,] 0.118357030 2.367141e-01 8.816430e-01 [79,] 0.112717726 2.254355e-01 8.872823e-01 [80,] 0.100031440 2.000629e-01 8.999686e-01 [81,] 0.092450396 1.849008e-01 9.075496e-01 [82,] 0.090630117 1.812602e-01 9.093699e-01 [83,] 0.076966607 1.539332e-01 9.230334e-01 [84,] 0.076823748 1.536475e-01 9.231763e-01 [85,] 0.071254497 1.425090e-01 9.287455e-01 [86,] 0.058345336 1.166907e-01 9.416547e-01 [87,] 0.048615422 9.723084e-02 9.513846e-01 [88,] 0.042984261 8.596852e-02 9.570157e-01 [89,] 0.034848075 6.969615e-02 9.651519e-01 [90,] 0.033330166 6.666033e-02 9.666698e-01 [91,] 0.031096904 6.219381e-02 9.689031e-01 [92,] 0.026615216 5.323043e-02 9.733848e-01 [93,] 0.021842845 4.368569e-02 9.781572e-01 [94,] 0.018611752 3.722350e-02 9.813882e-01 [95,] 0.016123809 3.224762e-02 9.838762e-01 [96,] 0.013114561 2.622912e-02 9.868854e-01 [97,] 0.010463589 2.092718e-02 9.895364e-01 [98,] 0.008386272 1.677254e-02 9.916137e-01 [99,] 0.007584551 1.516910e-02 9.924154e-01 [100,] 0.006136888 1.227378e-02 9.938631e-01 [101,] 0.007605720 1.521144e-02 9.923943e-01 [102,] 0.010360267 2.072053e-02 9.896397e-01 [103,] 0.014600521 2.920104e-02 9.853995e-01 [104,] 0.012076040 2.415208e-02 9.879240e-01 [105,] 0.010278621 2.055724e-02 9.897214e-01 [106,] 0.008138085 1.627617e-02 9.918619e-01 [107,] 0.009795279 1.959056e-02 9.902047e-01 [108,] 0.010128887 2.025777e-02 9.898711e-01 [109,] 0.015997071 3.199414e-02 9.840029e-01 [110,] 0.018140090 3.628018e-02 9.818599e-01 [111,] 0.018789980 3.757996e-02 9.812100e-01 [112,] 0.017591663 3.518333e-02 9.824083e-01 [113,] 0.035359545 7.071909e-02 9.646405e-01 [114,] 0.041186725 8.237345e-02 9.588133e-01 [115,] 0.053171933 1.063439e-01 9.468281e-01 [116,] 0.046038448 9.207690e-02 9.539616e-01 [117,] 0.041301272 8.260254e-02 9.586987e-01 [118,] 0.034172754 6.834551e-02 9.658272e-01 [119,] 0.027414001 5.482800e-02 9.725860e-01 [120,] 0.021861431 4.372286e-02 9.781386e-01 [121,] 0.017100091 3.420018e-02 9.828999e-01 [122,] 0.013502497 2.700499e-02 9.864975e-01 [123,] 0.010500295 2.100059e-02 9.894997e-01 [124,] 0.008358199 1.671640e-02 9.916418e-01 [125,] 0.079848964 1.596979e-01 9.201510e-01 [126,] 0.263999979 5.280000e-01 7.360000e-01 [127,] 0.399744531 7.994891e-01 6.002555e-01 [128,] 0.496532957 9.930659e-01 5.034670e-01 [129,] 0.533596265 9.328075e-01 4.664037e-01 [130,] 0.581177892 8.376442e-01 4.188221e-01 [131,] 0.596969947 8.060601e-01 4.030301e-01 [132,] 0.594836921 8.103262e-01 4.051631e-01 [133,] 0.598615824 8.027684e-01 4.013842e-01 [134,] 0.590240985 8.195180e-01 4.097590e-01 [135,] 0.560141106 8.797178e-01 4.398589e-01 [136,] 0.535462766 9.290745e-01 4.645372e-01 [137,] 0.506543001 9.869140e-01 4.934570e-01 [138,] 0.495224496 9.904490e-01 5.047755e-01 [139,] 0.465765025 9.315301e-01 5.342350e-01 [140,] 0.450704312 9.014086e-01 5.492957e-01 [141,] 0.416075312 8.321506e-01 5.839247e-01 [142,] 0.385823088 7.716462e-01 6.141769e-01 [143,] 0.435597414 8.711948e-01 5.644026e-01 [144,] 0.739744449 5.205111e-01 2.602556e-01 [145,] 0.935764947 1.284701e-01 6.423505e-02 [146,] 0.985903207 2.819359e-02 1.409679e-02 [147,] 0.988398018 2.320396e-02 1.160198e-02 [148,] 0.993529076 1.294185e-02 6.470924e-03 [149,] 0.995477099 9.045801e-03 4.522901e-03 [150,] 0.998823005 2.353989e-03 1.176995e-03 [151,] 0.999181986 1.636029e-03 8.180143e-04 [152,] 0.999351018 1.297963e-03 6.489816e-04 [153,] 0.999920234 1.595314e-04 7.976572e-05 [154,] 0.999936442 1.271166e-04 6.355828e-05 [155,] 0.999963009 7.398116e-05 3.699058e-05 [156,] 0.999977705 4.458950e-05 2.229475e-05 [157,] 0.999996824 6.352862e-06 3.176431e-06 [158,] 0.999997036 5.927957e-06 2.963979e-06 [159,] 0.999996796 6.407050e-06 3.203525e-06 [160,] 0.999995847 8.306738e-06 4.153369e-06 [161,] 0.999995879 8.242821e-06 4.121410e-06 [162,] 0.999994983 1.003358e-05 5.016789e-06 [163,] 0.999992722 1.455558e-05 7.277790e-06 [164,] 0.999990045 1.990984e-05 9.954919e-06 [165,] 0.999984671 3.065804e-05 1.532902e-05 [166,] 0.999978318 4.336379e-05 2.168190e-05 [167,] 0.999966119 6.776297e-05 3.388149e-05 [168,] 0.999948989 1.020213e-04 5.101063e-05 [169,] 0.999925775 1.484498e-04 7.422488e-05 [170,] 0.999879961 2.400784e-04 1.200392e-04 [171,] 0.999794550 4.109000e-04 2.054500e-04 [172,] 0.999635514 7.289728e-04 3.644864e-04 [173,] 0.999690654 6.186923e-04 3.093462e-04 [174,] 0.999687817 6.243652e-04 3.121826e-04 [175,] 0.999426479 1.147042e-03 5.735212e-04 [176,] 0.999544913 9.101745e-04 4.550873e-04 [177,] 0.999145296 1.709409e-03 8.547043e-04 [178,] 0.998351637 3.296726e-03 1.648363e-03 [179,] 0.996988409 6.023182e-03 3.011591e-03 [180,] 0.994841393 1.031721e-02 5.158607e-03 [181,] 0.991946340 1.610732e-02 8.053660e-03 [182,] 0.988179185 2.364163e-02 1.182082e-02 [183,] 0.979975725 4.004855e-02 2.002428e-02 [184,] 0.974344752 5.131050e-02 2.565525e-02 [185,] 0.955432237 8.913553e-02 4.456776e-02 [186,] 0.955809220 8.838156e-02 4.419078e-02 [187,] 0.932748804 1.345024e-01 6.725120e-02 [188,] 0.896462008 2.070760e-01 1.035380e-01 [189,] 0.845952466 3.080951e-01 1.540475e-01 [190,] 0.872657721 2.546846e-01 1.273423e-01 [191,] 0.841719681 3.165606e-01 1.582803e-01 [192,] 0.871842251 2.563155e-01 1.281577e-01 > postscript(file="/var/www/html/rcomp/tmp/1eq0h1227952310.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2uh3d1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/3etft1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ci4d1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/52zf31227952310.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 = 225 Frequency = 1 1 2 3 4 5 6.206373839 7.206373839 7.059005418 5.553742260 4.353742260 6 7 8 9 10 4.648479102 4.032689629 4.337952787 3.737542339 3.538156640 11 12 13 14 15 2.588156640 5.604823307 2.343358093 -3.056641907 -4.204010328 16 17 18 19 20 1.890726515 1.490726515 0.785463357 0.569673883 0.074937041 21 22 23 24 25 1.174526593 0.175140894 0.625140894 0.741807561 -0.719657652 26 27 28 29 30 0.080342348 -0.467026073 -2.272289231 -0.772289231 2.822447611 31 32 33 34 35 5.006658137 5.511921295 2.911510847 -0.887874851 0.362125149 36 37 38 39 40 -0.321208185 0.817326602 1.017326602 0.069958181 -0.235304977 41 42 43 44 45 0.264695023 -1.540568135 -1.556357609 -1.451094451 0.048495101 46 47 48 49 50 0.349109403 -0.000890597 -0.584223930 -1.345689144 -1.145689144 51 52 53 54 55 -0.593057565 -0.798320723 -1.098320723 -0.503583881 -0.719373354 56 57 58 59 60 -1.914110196 -0.514520644 0.086093657 0.136093657 0.052760324 61 62 63 64 65 -0.608704889 -1.508704889 -1.856073310 -1.761336468 -2.661336468 66 67 68 69 70 -2.866599626 -2.782389100 -3.177125942 -1.977536390 -2.676922088 71 72 73 74 75 -2.426922088 -1.910255422 -2.971720635 -0.071720635 -0.219089056 76 77 78 79 80 -1.424352214 -1.024352214 -0.429615372 -0.945404846 -1.240141688 81 82 83 84 85 -0.440552136 -0.639937834 -1.689937834 -1.073271168 0.365263619 86 87 88 89 90 0.665263619 0.117895198 -0.287367960 -1.187367960 -1.292631118 91 92 93 94 95 -1.308420591 -1.903157433 0.696432119 -1.402953580 -0.552953580 96 97 98 99 100 -0.936286913 -0.697752127 -0.297752127 -1.745120548 -0.250383705 101 102 103 104 105 -0.750383705 -2.255646863 -3.071436337 -0.966173179 -2.466583627 106 107 108 109 110 -0.265969326 -0.315969326 -0.699302659 -1.160767872 -0.760767872 111 112 113 114 115 -0.908136293 -1.213399451 -2.313399451 -1.318662609 -0.334452083 116 117 118 119 120 -1.029188925 1.270400627 1.571014929 1.821014929 -2.262318405 121 122 123 124 125 -2.623783618 -1.023783618 1.228847961 0.823584803 2.223584803 126 127 128 129 130 1.318321645 1.002532172 0.507795329 3.307384881 1.907999183 131 132 133 134 135 2.257999183 0.074665850 0.313200636 -0.486799364 -0.934167785 136 137 138 139 140 -0.839430943 -1.439430943 -1.844694101 -1.360483574 -0.155220416 141 142 143 144 145 -14.047832354 -10.447218052 -7.797218052 -3.580551386 -3.742016599 146 147 148 149 150 -1.942016599 -2.189385020 -2.194648178 -1.594648178 -1.699911336 151 152 153 154 155 -2.615700810 -2.010437652 -2.810848100 -0.910233798 -1.660233798 156 157 158 159 160 -0.643567131 -1.805032345 -3.405032345 -4.952400766 -7.857663924 161 162 163 164 165 -7.157663924 -5.862927082 -2.878716555 -3.573453397 -1.773863845 166 167 168 169 170 -2.273249544 -1.223249544 -1.106582877 -2.768048090 -0.768048090 171 172 173 174 175 0.084583489 0.579320331 -0.220679669 1.374057173 0.858267699 176 177 178 179 180 1.263530857 1.763120409 3.463734710 2.013734710 1.430401377 181 182 183 184 185 2.568936164 2.668936164 2.921567743 2.716304585 3.116304585 186 187 188 189 190 3.111041427 2.495251953 2.200515111 4.400104663 1.700718965 191 192 193 194 195 2.050718965 0.467385631 2.205920418 1.705920418 2.558551997 196 197 198 199 200 2.653288839 2.853288839 1.648025681 1.532236208 0.937499366 201 202 203 204 205 2.637088918 2.537703219 1.887703219 2.504369886 1.942904672 206 207 208 209 210 -0.157095328 2.095536251 2.390273093 3.690273093 3.285009936 211 212 213 214 215 2.669220462 3.274483620 3.974073172 4.174687473 1.924687473 216 217 218 219 220 2.241354140 1.679888927 1.279888927 1.932520506 2.527257348 221 222 223 224 225 2.227257348 0.621994190 -0.593795284 -0.688532126 -1.888942574 > postscript(file="/var/www/html/rcomp/tmp/66h3u1227952310.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 = 225 Frequency = 1 lag(myerror, k = 1) myerror 0 6.206373839 NA 1 7.206373839 6.206373839 2 7.059005418 7.206373839 3 5.553742260 7.059005418 4 4.353742260 5.553742260 5 4.648479102 4.353742260 6 4.032689629 4.648479102 7 4.337952787 4.032689629 8 3.737542339 4.337952787 9 3.538156640 3.737542339 10 2.588156640 3.538156640 11 5.604823307 2.588156640 12 2.343358093 5.604823307 13 -3.056641907 2.343358093 14 -4.204010328 -3.056641907 15 1.890726515 -4.204010328 16 1.490726515 1.890726515 17 0.785463357 1.490726515 18 0.569673883 0.785463357 19 0.074937041 0.569673883 20 1.174526593 0.074937041 21 0.175140894 1.174526593 22 0.625140894 0.175140894 23 0.741807561 0.625140894 24 -0.719657652 0.741807561 25 0.080342348 -0.719657652 26 -0.467026073 0.080342348 27 -2.272289231 -0.467026073 28 -0.772289231 -2.272289231 29 2.822447611 -0.772289231 30 5.006658137 2.822447611 31 5.511921295 5.006658137 32 2.911510847 5.511921295 33 -0.887874851 2.911510847 34 0.362125149 -0.887874851 35 -0.321208185 0.362125149 36 0.817326602 -0.321208185 37 1.017326602 0.817326602 38 0.069958181 1.017326602 39 -0.235304977 0.069958181 40 0.264695023 -0.235304977 41 -1.540568135 0.264695023 42 -1.556357609 -1.540568135 43 -1.451094451 -1.556357609 44 0.048495101 -1.451094451 45 0.349109403 0.048495101 46 -0.000890597 0.349109403 47 -0.584223930 -0.000890597 48 -1.345689144 -0.584223930 49 -1.145689144 -1.345689144 50 -0.593057565 -1.145689144 51 -0.798320723 -0.593057565 52 -1.098320723 -0.798320723 53 -0.503583881 -1.098320723 54 -0.719373354 -0.503583881 55 -1.914110196 -0.719373354 56 -0.514520644 -1.914110196 57 0.086093657 -0.514520644 58 0.136093657 0.086093657 59 0.052760324 0.136093657 60 -0.608704889 0.052760324 61 -1.508704889 -0.608704889 62 -1.856073310 -1.508704889 63 -1.761336468 -1.856073310 64 -2.661336468 -1.761336468 65 -2.866599626 -2.661336468 66 -2.782389100 -2.866599626 67 -3.177125942 -2.782389100 68 -1.977536390 -3.177125942 69 -2.676922088 -1.977536390 70 -2.426922088 -2.676922088 71 -1.910255422 -2.426922088 72 -2.971720635 -1.910255422 73 -0.071720635 -2.971720635 74 -0.219089056 -0.071720635 75 -1.424352214 -0.219089056 76 -1.024352214 -1.424352214 77 -0.429615372 -1.024352214 78 -0.945404846 -0.429615372 79 -1.240141688 -0.945404846 80 -0.440552136 -1.240141688 81 -0.639937834 -0.440552136 82 -1.689937834 -0.639937834 83 -1.073271168 -1.689937834 84 0.365263619 -1.073271168 85 0.665263619 0.365263619 86 0.117895198 0.665263619 87 -0.287367960 0.117895198 88 -1.187367960 -0.287367960 89 -1.292631118 -1.187367960 90 -1.308420591 -1.292631118 91 -1.903157433 -1.308420591 92 0.696432119 -1.903157433 93 -1.402953580 0.696432119 94 -0.552953580 -1.402953580 95 -0.936286913 -0.552953580 96 -0.697752127 -0.936286913 97 -0.297752127 -0.697752127 98 -1.745120548 -0.297752127 99 -0.250383705 -1.745120548 100 -0.750383705 -0.250383705 101 -2.255646863 -0.750383705 102 -3.071436337 -2.255646863 103 -0.966173179 -3.071436337 104 -2.466583627 -0.966173179 105 -0.265969326 -2.466583627 106 -0.315969326 -0.265969326 107 -0.699302659 -0.315969326 108 -1.160767872 -0.699302659 109 -0.760767872 -1.160767872 110 -0.908136293 -0.760767872 111 -1.213399451 -0.908136293 112 -2.313399451 -1.213399451 113 -1.318662609 -2.313399451 114 -0.334452083 -1.318662609 115 -1.029188925 -0.334452083 116 1.270400627 -1.029188925 117 1.571014929 1.270400627 118 1.821014929 1.571014929 119 -2.262318405 1.821014929 120 -2.623783618 -2.262318405 121 -1.023783618 -2.623783618 122 1.228847961 -1.023783618 123 0.823584803 1.228847961 124 2.223584803 0.823584803 125 1.318321645 2.223584803 126 1.002532172 1.318321645 127 0.507795329 1.002532172 128 3.307384881 0.507795329 129 1.907999183 3.307384881 130 2.257999183 1.907999183 131 0.074665850 2.257999183 132 0.313200636 0.074665850 133 -0.486799364 0.313200636 134 -0.934167785 -0.486799364 135 -0.839430943 -0.934167785 136 -1.439430943 -0.839430943 137 -1.844694101 -1.439430943 138 -1.360483574 -1.844694101 139 -0.155220416 -1.360483574 140 -14.047832354 -0.155220416 141 -10.447218052 -14.047832354 142 -7.797218052 -10.447218052 143 -3.580551386 -7.797218052 144 -3.742016599 -3.580551386 145 -1.942016599 -3.742016599 146 -2.189385020 -1.942016599 147 -2.194648178 -2.189385020 148 -1.594648178 -2.194648178 149 -1.699911336 -1.594648178 150 -2.615700810 -1.699911336 151 -2.010437652 -2.615700810 152 -2.810848100 -2.010437652 153 -0.910233798 -2.810848100 154 -1.660233798 -0.910233798 155 -0.643567131 -1.660233798 156 -1.805032345 -0.643567131 157 -3.405032345 -1.805032345 158 -4.952400766 -3.405032345 159 -7.857663924 -4.952400766 160 -7.157663924 -7.857663924 161 -5.862927082 -7.157663924 162 -2.878716555 -5.862927082 163 -3.573453397 -2.878716555 164 -1.773863845 -3.573453397 165 -2.273249544 -1.773863845 166 -1.223249544 -2.273249544 167 -1.106582877 -1.223249544 168 -2.768048090 -1.106582877 169 -0.768048090 -2.768048090 170 0.084583489 -0.768048090 171 0.579320331 0.084583489 172 -0.220679669 0.579320331 173 1.374057173 -0.220679669 174 0.858267699 1.374057173 175 1.263530857 0.858267699 176 1.763120409 1.263530857 177 3.463734710 1.763120409 178 2.013734710 3.463734710 179 1.430401377 2.013734710 180 2.568936164 1.430401377 181 2.668936164 2.568936164 182 2.921567743 2.668936164 183 2.716304585 2.921567743 184 3.116304585 2.716304585 185 3.111041427 3.116304585 186 2.495251953 3.111041427 187 2.200515111 2.495251953 188 4.400104663 2.200515111 189 1.700718965 4.400104663 190 2.050718965 1.700718965 191 0.467385631 2.050718965 192 2.205920418 0.467385631 193 1.705920418 2.205920418 194 2.558551997 1.705920418 195 2.653288839 2.558551997 196 2.853288839 2.653288839 197 1.648025681 2.853288839 198 1.532236208 1.648025681 199 0.937499366 1.532236208 200 2.637088918 0.937499366 201 2.537703219 2.637088918 202 1.887703219 2.537703219 203 2.504369886 1.887703219 204 1.942904672 2.504369886 205 -0.157095328 1.942904672 206 2.095536251 -0.157095328 207 2.390273093 2.095536251 208 3.690273093 2.390273093 209 3.285009936 3.690273093 210 2.669220462 3.285009936 211 3.274483620 2.669220462 212 3.974073172 3.274483620 213 4.174687473 3.974073172 214 1.924687473 4.174687473 215 2.241354140 1.924687473 216 1.679888927 2.241354140 217 1.279888927 1.679888927 218 1.932520506 1.279888927 219 2.527257348 1.932520506 220 2.227257348 2.527257348 221 0.621994190 2.227257348 222 -0.593795284 0.621994190 223 -0.688532126 -0.593795284 224 -1.888942574 -0.688532126 225 NA -1.888942574 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 7.206373839 6.206373839 [2,] 7.059005418 7.206373839 [3,] 5.553742260 7.059005418 [4,] 4.353742260 5.553742260 [5,] 4.648479102 4.353742260 [6,] 4.032689629 4.648479102 [7,] 4.337952787 4.032689629 [8,] 3.737542339 4.337952787 [9,] 3.538156640 3.737542339 [10,] 2.588156640 3.538156640 [11,] 5.604823307 2.588156640 [12,] 2.343358093 5.604823307 [13,] -3.056641907 2.343358093 [14,] -4.204010328 -3.056641907 [15,] 1.890726515 -4.204010328 [16,] 1.490726515 1.890726515 [17,] 0.785463357 1.490726515 [18,] 0.569673883 0.785463357 [19,] 0.074937041 0.569673883 [20,] 1.174526593 0.074937041 [21,] 0.175140894 1.174526593 [22,] 0.625140894 0.175140894 [23,] 0.741807561 0.625140894 [24,] -0.719657652 0.741807561 [25,] 0.080342348 -0.719657652 [26,] -0.467026073 0.080342348 [27,] -2.272289231 -0.467026073 [28,] -0.772289231 -2.272289231 [29,] 2.822447611 -0.772289231 [30,] 5.006658137 2.822447611 [31,] 5.511921295 5.006658137 [32,] 2.911510847 5.511921295 [33,] -0.887874851 2.911510847 [34,] 0.362125149 -0.887874851 [35,] -0.321208185 0.362125149 [36,] 0.817326602 -0.321208185 [37,] 1.017326602 0.817326602 [38,] 0.069958181 1.017326602 [39,] -0.235304977 0.069958181 [40,] 0.264695023 -0.235304977 [41,] -1.540568135 0.264695023 [42,] -1.556357609 -1.540568135 [43,] -1.451094451 -1.556357609 [44,] 0.048495101 -1.451094451 [45,] 0.349109403 0.048495101 [46,] -0.000890597 0.349109403 [47,] -0.584223930 -0.000890597 [48,] -1.345689144 -0.584223930 [49,] -1.145689144 -1.345689144 [50,] -0.593057565 -1.145689144 [51,] -0.798320723 -0.593057565 [52,] -1.098320723 -0.798320723 [53,] -0.503583881 -1.098320723 [54,] -0.719373354 -0.503583881 [55,] -1.914110196 -0.719373354 [56,] -0.514520644 -1.914110196 [57,] 0.086093657 -0.514520644 [58,] 0.136093657 0.086093657 [59,] 0.052760324 0.136093657 [60,] -0.608704889 0.052760324 [61,] -1.508704889 -0.608704889 [62,] -1.856073310 -1.508704889 [63,] -1.761336468 -1.856073310 [64,] -2.661336468 -1.761336468 [65,] -2.866599626 -2.661336468 [66,] -2.782389100 -2.866599626 [67,] -3.177125942 -2.782389100 [68,] -1.977536390 -3.177125942 [69,] -2.676922088 -1.977536390 [70,] -2.426922088 -2.676922088 [71,] -1.910255422 -2.426922088 [72,] -2.971720635 -1.910255422 [73,] -0.071720635 -2.971720635 [74,] -0.219089056 -0.071720635 [75,] -1.424352214 -0.219089056 [76,] -1.024352214 -1.424352214 [77,] -0.429615372 -1.024352214 [78,] -0.945404846 -0.429615372 [79,] -1.240141688 -0.945404846 [80,] -0.440552136 -1.240141688 [81,] -0.639937834 -0.440552136 [82,] -1.689937834 -0.639937834 [83,] -1.073271168 -1.689937834 [84,] 0.365263619 -1.073271168 [85,] 0.665263619 0.365263619 [86,] 0.117895198 0.665263619 [87,] -0.287367960 0.117895198 [88,] -1.187367960 -0.287367960 [89,] -1.292631118 -1.187367960 [90,] -1.308420591 -1.292631118 [91,] -1.903157433 -1.308420591 [92,] 0.696432119 -1.903157433 [93,] -1.402953580 0.696432119 [94,] -0.552953580 -1.402953580 [95,] -0.936286913 -0.552953580 [96,] -0.697752127 -0.936286913 [97,] -0.297752127 -0.697752127 [98,] -1.745120548 -0.297752127 [99,] -0.250383705 -1.745120548 [100,] -0.750383705 -0.250383705 [101,] -2.255646863 -0.750383705 [102,] -3.071436337 -2.255646863 [103,] -0.966173179 -3.071436337 [104,] -2.466583627 -0.966173179 [105,] -0.265969326 -2.466583627 [106,] -0.315969326 -0.265969326 [107,] -0.699302659 -0.315969326 [108,] -1.160767872 -0.699302659 [109,] -0.760767872 -1.160767872 [110,] -0.908136293 -0.760767872 [111,] -1.213399451 -0.908136293 [112,] -2.313399451 -1.213399451 [113,] -1.318662609 -2.313399451 [114,] -0.334452083 -1.318662609 [115,] -1.029188925 -0.334452083 [116,] 1.270400627 -1.029188925 [117,] 1.571014929 1.270400627 [118,] 1.821014929 1.571014929 [119,] -2.262318405 1.821014929 [120,] -2.623783618 -2.262318405 [121,] -1.023783618 -2.623783618 [122,] 1.228847961 -1.023783618 [123,] 0.823584803 1.228847961 [124,] 2.223584803 0.823584803 [125,] 1.318321645 2.223584803 [126,] 1.002532172 1.318321645 [127,] 0.507795329 1.002532172 [128,] 3.307384881 0.507795329 [129,] 1.907999183 3.307384881 [130,] 2.257999183 1.907999183 [131,] 0.074665850 2.257999183 [132,] 0.313200636 0.074665850 [133,] -0.486799364 0.313200636 [134,] -0.934167785 -0.486799364 [135,] -0.839430943 -0.934167785 [136,] -1.439430943 -0.839430943 [137,] -1.844694101 -1.439430943 [138,] -1.360483574 -1.844694101 [139,] -0.155220416 -1.360483574 [140,] -14.047832354 -0.155220416 [141,] -10.447218052 -14.047832354 [142,] -7.797218052 -10.447218052 [143,] -3.580551386 -7.797218052 [144,] -3.742016599 -3.580551386 [145,] -1.942016599 -3.742016599 [146,] -2.189385020 -1.942016599 [147,] -2.194648178 -2.189385020 [148,] -1.594648178 -2.194648178 [149,] -1.699911336 -1.594648178 [150,] -2.615700810 -1.699911336 [151,] -2.010437652 -2.615700810 [152,] -2.810848100 -2.010437652 [153,] -0.910233798 -2.810848100 [154,] -1.660233798 -0.910233798 [155,] -0.643567131 -1.660233798 [156,] -1.805032345 -0.643567131 [157,] -3.405032345 -1.805032345 [158,] -4.952400766 -3.405032345 [159,] -7.857663924 -4.952400766 [160,] -7.157663924 -7.857663924 [161,] -5.862927082 -7.157663924 [162,] -2.878716555 -5.862927082 [163,] -3.573453397 -2.878716555 [164,] -1.773863845 -3.573453397 [165,] -2.273249544 -1.773863845 [166,] -1.223249544 -2.273249544 [167,] -1.106582877 -1.223249544 [168,] -2.768048090 -1.106582877 [169,] -0.768048090 -2.768048090 [170,] 0.084583489 -0.768048090 [171,] 0.579320331 0.084583489 [172,] -0.220679669 0.579320331 [173,] 1.374057173 -0.220679669 [174,] 0.858267699 1.374057173 [175,] 1.263530857 0.858267699 [176,] 1.763120409 1.263530857 [177,] 3.463734710 1.763120409 [178,] 2.013734710 3.463734710 [179,] 1.430401377 2.013734710 [180,] 2.568936164 1.430401377 [181,] 2.668936164 2.568936164 [182,] 2.921567743 2.668936164 [183,] 2.716304585 2.921567743 [184,] 3.116304585 2.716304585 [185,] 3.111041427 3.116304585 [186,] 2.495251953 3.111041427 [187,] 2.200515111 2.495251953 [188,] 4.400104663 2.200515111 [189,] 1.700718965 4.400104663 [190,] 2.050718965 1.700718965 [191,] 0.467385631 2.050718965 [192,] 2.205920418 0.467385631 [193,] 1.705920418 2.205920418 [194,] 2.558551997 1.705920418 [195,] 2.653288839 2.558551997 [196,] 2.853288839 2.653288839 [197,] 1.648025681 2.853288839 [198,] 1.532236208 1.648025681 [199,] 0.937499366 1.532236208 [200,] 2.637088918 0.937499366 [201,] 2.537703219 2.637088918 [202,] 1.887703219 2.537703219 [203,] 2.504369886 1.887703219 [204,] 1.942904672 2.504369886 [205,] -0.157095328 1.942904672 [206,] 2.095536251 -0.157095328 [207,] 2.390273093 2.095536251 [208,] 3.690273093 2.390273093 [209,] 3.285009936 3.690273093 [210,] 2.669220462 3.285009936 [211,] 3.274483620 2.669220462 [212,] 3.974073172 3.274483620 [213,] 4.174687473 3.974073172 [214,] 1.924687473 4.174687473 [215,] 2.241354140 1.924687473 [216,] 1.679888927 2.241354140 [217,] 1.279888927 1.679888927 [218,] 1.932520506 1.279888927 [219,] 2.527257348 1.932520506 [220,] 2.227257348 2.527257348 [221,] 0.621994190 2.227257348 [222,] -0.593795284 0.621994190 [223,] -0.688532126 -0.593795284 [224,] -1.888942574 -0.688532126 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 7.206373839 6.206373839 2 7.059005418 7.206373839 3 5.553742260 7.059005418 4 4.353742260 5.553742260 5 4.648479102 4.353742260 6 4.032689629 4.648479102 7 4.337952787 4.032689629 8 3.737542339 4.337952787 9 3.538156640 3.737542339 10 2.588156640 3.538156640 11 5.604823307 2.588156640 12 2.343358093 5.604823307 13 -3.056641907 2.343358093 14 -4.204010328 -3.056641907 15 1.890726515 -4.204010328 16 1.490726515 1.890726515 17 0.785463357 1.490726515 18 0.569673883 0.785463357 19 0.074937041 0.569673883 20 1.174526593 0.074937041 21 0.175140894 1.174526593 22 0.625140894 0.175140894 23 0.741807561 0.625140894 24 -0.719657652 0.741807561 25 0.080342348 -0.719657652 26 -0.467026073 0.080342348 27 -2.272289231 -0.467026073 28 -0.772289231 -2.272289231 29 2.822447611 -0.772289231 30 5.006658137 2.822447611 31 5.511921295 5.006658137 32 2.911510847 5.511921295 33 -0.887874851 2.911510847 34 0.362125149 -0.887874851 35 -0.321208185 0.362125149 36 0.817326602 -0.321208185 37 1.017326602 0.817326602 38 0.069958181 1.017326602 39 -0.235304977 0.069958181 40 0.264695023 -0.235304977 41 -1.540568135 0.264695023 42 -1.556357609 -1.540568135 43 -1.451094451 -1.556357609 44 0.048495101 -1.451094451 45 0.349109403 0.048495101 46 -0.000890597 0.349109403 47 -0.584223930 -0.000890597 48 -1.345689144 -0.584223930 49 -1.145689144 -1.345689144 50 -0.593057565 -1.145689144 51 -0.798320723 -0.593057565 52 -1.098320723 -0.798320723 53 -0.503583881 -1.098320723 54 -0.719373354 -0.503583881 55 -1.914110196 -0.719373354 56 -0.514520644 -1.914110196 57 0.086093657 -0.514520644 58 0.136093657 0.086093657 59 0.052760324 0.136093657 60 -0.608704889 0.052760324 61 -1.508704889 -0.608704889 62 -1.856073310 -1.508704889 63 -1.761336468 -1.856073310 64 -2.661336468 -1.761336468 65 -2.866599626 -2.661336468 66 -2.782389100 -2.866599626 67 -3.177125942 -2.782389100 68 -1.977536390 -3.177125942 69 -2.676922088 -1.977536390 70 -2.426922088 -2.676922088 71 -1.910255422 -2.426922088 72 -2.971720635 -1.910255422 73 -0.071720635 -2.971720635 74 -0.219089056 -0.071720635 75 -1.424352214 -0.219089056 76 -1.024352214 -1.424352214 77 -0.429615372 -1.024352214 78 -0.945404846 -0.429615372 79 -1.240141688 -0.945404846 80 -0.440552136 -1.240141688 81 -0.639937834 -0.440552136 82 -1.689937834 -0.639937834 83 -1.073271168 -1.689937834 84 0.365263619 -1.073271168 85 0.665263619 0.365263619 86 0.117895198 0.665263619 87 -0.287367960 0.117895198 88 -1.187367960 -0.287367960 89 -1.292631118 -1.187367960 90 -1.308420591 -1.292631118 91 -1.903157433 -1.308420591 92 0.696432119 -1.903157433 93 -1.402953580 0.696432119 94 -0.552953580 -1.402953580 95 -0.936286913 -0.552953580 96 -0.697752127 -0.936286913 97 -0.297752127 -0.697752127 98 -1.745120548 -0.297752127 99 -0.250383705 -1.745120548 100 -0.750383705 -0.250383705 101 -2.255646863 -0.750383705 102 -3.071436337 -2.255646863 103 -0.966173179 -3.071436337 104 -2.466583627 -0.966173179 105 -0.265969326 -2.466583627 106 -0.315969326 -0.265969326 107 -0.699302659 -0.315969326 108 -1.160767872 -0.699302659 109 -0.760767872 -1.160767872 110 -0.908136293 -0.760767872 111 -1.213399451 -0.908136293 112 -2.313399451 -1.213399451 113 -1.318662609 -2.313399451 114 -0.334452083 -1.318662609 115 -1.029188925 -0.334452083 116 1.270400627 -1.029188925 117 1.571014929 1.270400627 118 1.821014929 1.571014929 119 -2.262318405 1.821014929 120 -2.623783618 -2.262318405 121 -1.023783618 -2.623783618 122 1.228847961 -1.023783618 123 0.823584803 1.228847961 124 2.223584803 0.823584803 125 1.318321645 2.223584803 126 1.002532172 1.318321645 127 0.507795329 1.002532172 128 3.307384881 0.507795329 129 1.907999183 3.307384881 130 2.257999183 1.907999183 131 0.074665850 2.257999183 132 0.313200636 0.074665850 133 -0.486799364 0.313200636 134 -0.934167785 -0.486799364 135 -0.839430943 -0.934167785 136 -1.439430943 -0.839430943 137 -1.844694101 -1.439430943 138 -1.360483574 -1.844694101 139 -0.155220416 -1.360483574 140 -14.047832354 -0.155220416 141 -10.447218052 -14.047832354 142 -7.797218052 -10.447218052 143 -3.580551386 -7.797218052 144 -3.742016599 -3.580551386 145 -1.942016599 -3.742016599 146 -2.189385020 -1.942016599 147 -2.194648178 -2.189385020 148 -1.594648178 -2.194648178 149 -1.699911336 -1.594648178 150 -2.615700810 -1.699911336 151 -2.010437652 -2.615700810 152 -2.810848100 -2.010437652 153 -0.910233798 -2.810848100 154 -1.660233798 -0.910233798 155 -0.643567131 -1.660233798 156 -1.805032345 -0.643567131 157 -3.405032345 -1.805032345 158 -4.952400766 -3.405032345 159 -7.857663924 -4.952400766 160 -7.157663924 -7.857663924 161 -5.862927082 -7.157663924 162 -2.878716555 -5.862927082 163 -3.573453397 -2.878716555 164 -1.773863845 -3.573453397 165 -2.273249544 -1.773863845 166 -1.223249544 -2.273249544 167 -1.106582877 -1.223249544 168 -2.768048090 -1.106582877 169 -0.768048090 -2.768048090 170 0.084583489 -0.768048090 171 0.579320331 0.084583489 172 -0.220679669 0.579320331 173 1.374057173 -0.220679669 174 0.858267699 1.374057173 175 1.263530857 0.858267699 176 1.763120409 1.263530857 177 3.463734710 1.763120409 178 2.013734710 3.463734710 179 1.430401377 2.013734710 180 2.568936164 1.430401377 181 2.668936164 2.568936164 182 2.921567743 2.668936164 183 2.716304585 2.921567743 184 3.116304585 2.716304585 185 3.111041427 3.116304585 186 2.495251953 3.111041427 187 2.200515111 2.495251953 188 4.400104663 2.200515111 189 1.700718965 4.400104663 190 2.050718965 1.700718965 191 0.467385631 2.050718965 192 2.205920418 0.467385631 193 1.705920418 2.205920418 194 2.558551997 1.705920418 195 2.653288839 2.558551997 196 2.853288839 2.653288839 197 1.648025681 2.853288839 198 1.532236208 1.648025681 199 0.937499366 1.532236208 200 2.637088918 0.937499366 201 2.537703219 2.637088918 202 1.887703219 2.537703219 203 2.504369886 1.887703219 204 1.942904672 2.504369886 205 -0.157095328 1.942904672 206 2.095536251 -0.157095328 207 2.390273093 2.095536251 208 3.690273093 2.390273093 209 3.285009936 3.690273093 210 2.669220462 3.285009936 211 3.274483620 2.669220462 212 3.974073172 3.274483620 213 4.174687473 3.974073172 214 1.924687473 4.174687473 215 2.241354140 1.924687473 216 1.679888927 2.241354140 217 1.279888927 1.679888927 218 1.932520506 1.279888927 219 2.527257348 1.932520506 220 2.227257348 2.527257348 221 0.621994190 2.227257348 222 -0.593795284 0.621994190 223 -0.688532126 -0.593795284 224 -1.888942574 -0.688532126 > 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/html/rcomp/tmp/7m2xk1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8lg2a1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9x8d71227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/108etj1227952310.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/html/rcomp/tmp/11maax1227952310.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/html/rcomp/tmp/12utuv1227952310.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/html/rcomp/tmp/13n7sk1227952310.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/html/rcomp/tmp/14qolp1227952310.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/html/rcomp/tmp/15twn31227952310.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/html/rcomp/tmp/16a75u1227952311.tab") + } > > system("convert tmp/1eq0h1227952310.ps tmp/1eq0h1227952310.png") > system("convert tmp/2uh3d1227952310.ps tmp/2uh3d1227952310.png") > system("convert tmp/3etft1227952310.ps tmp/3etft1227952310.png") > system("convert tmp/4ci4d1227952310.ps tmp/4ci4d1227952310.png") > system("convert tmp/52zf31227952310.ps tmp/52zf31227952310.png") > system("convert tmp/66h3u1227952310.ps tmp/66h3u1227952310.png") > system("convert tmp/7m2xk1227952310.ps tmp/7m2xk1227952310.png") > system("convert tmp/8lg2a1227952310.ps tmp/8lg2a1227952310.png") > system("convert tmp/9x8d71227952310.ps tmp/9x8d71227952310.png") > system("convert tmp/108etj1227952310.ps tmp/108etj1227952310.png") > > > proc.time() user system elapsed 5.532 1.774 6.121