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Type 'q()' to quit R. > x <- array(list(4 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,4 + ,2 + ,3 + ,1 + ,1 + ,2 + ,3 + ,3 + ,2 + ,2 + ,1 + ,2 + ,2 + ,5 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,2 + ,1 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,1 + ,1 + ,2 + ,3 + ,4 + ,4 + ,2 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,1 + ,2 + ,2 + ,2 + ,2 + ,3 + ,2 + ,2 + ,1 + ,3 + ,1 + ,2 + ,4 + ,3 + ,4 + ,3 + ,4 + ,3 + ,4 + ,4 + ,1 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,3 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,2 + ,1 + ,3 + ,3 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,1 + ,3 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,1 + ,1 + ,1 + ,2 + ,2 + ,2 + ,3 + ,1 + ,4 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,1 + ,2 + ,1 + ,2 + ,3 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,1 + ,1 + ,1 + ,2 + ,4 + ,5 + ,4 + ,2 + ,3 + ,2 + ,4 + ,3 + ,1 + ,3 + ,2 + ,2 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,4 + ,4 + ,3 + ,4 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,5 + ,5 + ,5 + ,4 + ,3 + ,3 + ,4 + ,4 + ,2 + ,1 + ,1 + ,2 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,2 + ,2 + ,1 + ,2 + ,3 + ,3 + ,3 + ,4 + ,1 + ,1 + ,1 + ,1 + ,4 + ,3 + ,4 + ,3 + ,4 + ,2 + ,4 + ,3 + ,4 + ,3 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,2 + ,2 + ,1 + ,3 + ,1 + ,1 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,3 + ,5 + ,1 + ,3 + ,2 + ,1 + ,1 + ,1 + ,2 + ,3 + ,3 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,3 + ,2 + ,2 + ,3 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,2 + ,1 + ,1 + ,1 + ,1 + ,1 + ,2 + ,2 + ,2 + ,4 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,5 + ,3 + ,4 + ,4 + ,4 + ,5 + ,4 + ,3 + ,5 + ,1 + ,NA + ,2 + ,2 + ,1 + ,1 + ,1 + ,1 + ,2 + ,3 + ,2 + ,3 + ,4 + ,2 + ,2 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,2 + ,2 + ,4 + ,2 + ,1 + ,2 + ,4 + ,3 + ,2 + ,3 + ,5 + ,2 + ,4 + ,4 + ,1 + ,2 + ,2 + ,2 + ,4 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,3 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,1 + ,1 + ,2 + ,2 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,2 + ,2 + ,3 + ,1 + ,1 + ,1 + ,3 + ,4 + ,4 + ,2 + ,4 + ,3 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,2 + ,3 + ,3 + ,2 + ,3 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,2 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,2 + ,2 + ,3 + ,2 + ,2 + ,3 + ,1 + ,1 + ,1 + ,1 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,NA + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3) + ,dim=c(4 + ,156) + ,dimnames=list(c('Q1' + ,'Q2' + ,'Q3' + ,'Q4 ') + ,1:156)) > y <- array(NA,dim=c(4,156),dimnames=list(c('Q1','Q2','Q3','Q4 '),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 = 'Linear Trend' > par2 = 'Include Monthly 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 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 Q4\r Q1 Q2 Q3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 3 4 4 3 1 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 0 1 0 0 0 0 0 0 0 0 0 2 3 4 2 4 2 0 0 1 0 0 0 0 0 0 0 0 3 4 1 2 3 1 0 0 0 1 0 0 0 0 0 0 0 4 5 2 2 3 3 0 0 0 0 1 0 0 0 0 0 0 5 6 2 2 1 2 0 0 0 0 0 1 0 0 0 0 0 6 7 3 5 4 4 0 0 0 0 0 0 1 0 0 0 0 7 8 2 4 3 2 0 0 0 0 0 0 0 1 0 0 0 8 9 2 4 4 4 0 0 0 0 0 0 0 0 1 0 0 9 10 2 2 1 1 0 0 0 0 0 0 0 0 0 1 0 10 11 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1 11 12 3 2 3 3 0 0 0 0 0 0 0 0 0 0 0 12 13 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 13 14 2 4 4 2 0 1 0 0 0 0 0 0 0 0 0 14 15 3 1 1 2 0 0 1 0 0 0 0 0 0 0 0 15 16 3 4 4 2 0 0 0 1 0 0 0 0 0 0 0 16 17 3 3 2 3 0 0 0 0 1 0 0 0 0 0 0 17 18 4 4 4 3 0 0 0 0 0 1 0 0 0 0 0 18 19 2 1 2 2 0 0 0 0 0 0 1 0 0 0 0 19 20 2 2 3 2 0 0 0 0 0 0 0 1 0 0 0 20 21 2 1 3 1 0 0 0 0 0 0 0 0 1 0 0 21 22 3 4 3 4 0 0 0 0 0 0 0 0 0 1 0 22 23 4 4 3 4 0 0 0 0 0 0 0 0 0 0 1 23 24 2 1 2 2 0 0 0 0 0 0 0 0 0 0 0 24 25 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 25 26 4 5 4 4 0 1 0 0 0 0 0 0 0 0 0 26 27 3 4 4 4 0 0 1 0 0 0 0 0 0 0 0 27 28 3 4 4 3 0 0 0 1 0 0 0 0 0 0 0 28 29 3 4 4 3 0 0 0 0 1 0 0 0 0 0 0 29 30 2 2 2 2 0 0 0 0 0 1 0 0 0 0 0 30 31 2 2 2 2 0 0 0 0 0 0 1 0 0 0 0 31 32 4 4 4 2 0 0 0 0 0 0 0 1 0 0 0 32 33 3 4 3 4 0 0 0 0 0 0 0 0 1 0 0 33 34 3 2 2 1 0 0 0 0 0 0 0 0 0 1 0 34 35 2 3 2 4 0 0 0 0 0 0 0 0 0 0 1 35 36 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 36 37 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 37 38 2 2 2 2 0 1 0 0 0 0 0 0 0 0 0 38 39 3 4 4 3 0 0 1 0 0 0 0 0 0 0 0 39 40 4 4 4 4 0 0 0 1 0 0 0 0 0 0 0 40 41 4 3 3 3 0 0 0 0 1 0 0 0 0 0 0 41 42 2 1 1 1 0 0 0 0 0 1 0 0 0 0 0 42 43 1 2 2 3 0 0 0 0 0 0 1 0 0 0 0 43 44 2 4 2 2 0 0 0 0 0 0 0 1 0 0 0 44 45 3 2 2 1 0 0 0 0 0 0 0 0 1 0 0 45 46 3 3 4 3 0 0 0 0 0 0 0 0 0 1 0 46 47 4 4 3 4 0 0 0 0 0 0 0 0 0 0 1 47 48 2 1 2 1 0 0 0 0 0 0 0 0 0 0 0 48 49 3 3 2 4 1 0 0 0 0 0 0 0 0 0 0 49 50 4 4 4 4 0 1 0 0 0 0 0 0 0 0 0 50 51 2 1 1 1 0 0 1 0 0 0 0 0 0 0 0 51 52 2 4 5 4 0 0 0 1 0 0 0 0 0 0 0 52 53 3 3 2 4 0 0 0 0 1 0 0 0 0 0 0 53 54 2 1 3 2 0 0 0 0 0 1 0 0 0 0 0 54 55 4 1 4 4 0 0 0 0 0 0 1 0 0 0 0 55 56 3 4 4 3 0 0 0 0 0 0 0 1 0 0 0 56 57 3 4 3 2 0 0 0 0 0 0 0 0 1 0 0 57 58 4 4 4 4 0 0 0 0 0 0 0 0 0 1 0 58 59 4 2 2 2 0 0 0 0 0 0 0 0 0 0 1 59 60 4 4 3 4 0 0 0 0 0 0 0 0 0 0 0 60 61 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 61 62 4 4 4 4 0 1 0 0 0 0 0 0 0 0 0 62 63 4 5 5 5 0 0 1 0 0 0 0 0 0 0 0 63 64 4 3 3 4 0 0 0 1 0 0 0 0 0 0 0 64 65 2 2 1 1 0 0 0 0 1 0 0 0 0 0 0 65 66 3 4 3 3 0 0 0 0 0 1 0 0 0 0 0 66 67 3 4 4 4 0 0 0 0 0 0 1 0 0 0 0 67 68 2 2 2 1 0 0 0 0 0 0 0 1 0 0 0 68 69 4 3 3 3 0 0 0 0 0 0 0 0 1 0 0 69 70 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 70 71 3 4 3 4 0 0 0 0 0 0 0 0 0 0 1 71 72 3 4 2 4 0 0 0 0 0 0 0 0 0 0 0 72 73 2 4 3 2 1 0 0 0 0 0 0 0 0 0 0 73 74 2 4 4 4 0 1 0 0 0 0 0 0 0 0 0 74 75 3 3 3 3 0 0 1 0 0 0 0 0 0 0 0 75 76 3 4 4 4 0 0 0 1 0 0 0 0 0 0 0 76 77 3 3 4 4 0 0 0 0 1 0 0 0 0 0 0 77 78 3 3 3 4 0 0 0 0 0 1 0 0 0 0 0 78 79 3 2 2 1 0 0 0 0 0 0 1 0 0 0 0 79 80 2 1 1 2 0 0 0 0 0 0 0 1 0 0 0 80 81 2 2 2 1 0 0 0 0 0 0 0 0 1 0 0 81 82 3 4 3 3 0 0 0 0 0 0 0 0 0 1 0 82 83 3 3 4 3 0 0 0 0 0 0 0 0 0 0 1 83 84 2 5 1 3 0 0 0 0 0 0 0 0 0 0 0 84 85 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 85 86 3 3 3 3 0 1 0 0 0 0 0 0 0 0 0 86 87 2 2 2 2 0 0 1 0 0 0 0 0 0 0 0 87 88 3 3 2 3 0 0 0 1 0 0 0 0 0 0 0 88 89 3 4 3 4 0 0 0 0 1 0 0 0 0 0 0 89 90 2 3 2 2 0 0 0 0 0 1 0 0 0 0 0 90 91 3 3 2 2 0 0 0 0 0 0 1 0 0 0 0 91 92 3 4 3 3 0 0 0 0 0 0 0 1 0 0 0 92 93 4 4 4 4 0 0 0 0 0 0 0 0 1 0 0 93 94 4 4 4 4 0 0 0 0 0 0 0 0 0 1 0 94 95 3 2 2 4 0 0 0 0 0 0 0 0 0 0 1 95 96 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 96 97 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 97 98 2 1 2 2 0 1 0 0 0 0 0 0 0 0 0 98 99 3 4 3 4 0 0 1 0 0 0 0 0 0 0 0 99 100 3 2 3 3 0 0 0 1 0 0 0 0 0 0 0 100 101 5 4 4 4 0 0 0 0 1 0 0 0 0 0 0 101 102 4 3 4 4 0 0 0 0 0 1 0 0 0 0 0 102 103 5 5 4 3 0 0 0 0 0 0 1 0 0 0 0 103 104 2 1 NA 2 0 0 0 0 0 0 0 1 0 0 0 104 105 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 105 106 3 2 3 2 0 0 0 0 0 0 0 0 0 1 0 106 107 3 4 2 2 0 0 0 0 0 0 0 0 0 0 1 107 108 4 4 3 4 0 0 0 0 0 0 0 0 0 0 0 108 109 2 3 3 2 1 0 0 0 0 0 0 0 0 0 0 109 110 2 4 2 1 0 1 0 0 0 0 0 0 0 0 0 110 111 3 4 3 2 0 0 1 0 0 0 0 0 0 0 0 111 112 4 5 2 4 0 0 0 1 0 0 0 0 0 0 0 112 113 2 1 2 2 0 0 0 0 1 0 0 0 0 0 0 113 114 3 4 3 3 0 0 0 0 0 1 0 0 0 0 0 114 115 3 4 2 3 0 0 0 0 0 0 1 0 0 0 0 115 116 4 4 3 3 0 0 0 0 0 0 0 1 0 0 0 116 117 4 2 4 4 0 0 0 0 0 0 0 0 1 0 0 117 118 2 2 2 2 0 0 0 0 0 0 0 0 0 1 0 118 119 3 4 4 4 0 0 0 0 0 0 0 0 0 0 1 119 120 2 3 3 4 0 0 0 0 0 0 0 0 0 0 0 120 121 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 121 122 4 4 4 4 0 1 0 0 0 0 0 0 0 0 0 122 123 2 2 2 3 0 0 1 0 0 0 0 0 0 0 0 123 124 4 4 3 4 0 0 0 1 0 0 0 0 0 0 0 124 125 4 4 4 3 0 0 0 0 1 0 0 0 0 0 0 125 126 2 1 1 2 0 0 0 0 0 1 0 0 0 0 0 126 127 3 4 4 3 0 0 0 0 0 0 1 0 0 0 0 127 128 3 4 4 4 0 0 0 0 0 0 0 1 0 0 0 128 129 3 3 2 2 0 0 0 0 0 0 0 0 1 0 0 129 130 3 1 1 1 0 0 0 0 0 0 0 0 0 1 0 130 131 4 4 4 2 0 0 0 0 0 0 0 0 0 0 1 131 132 3 3 2 4 0 0 0 0 0 0 0 0 0 0 0 132 133 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 133 134 3 3 3 2 0 1 0 0 0 0 0 0 0 0 0 134 135 3 4 3 3 0 0 1 0 0 0 0 0 0 0 0 135 136 3 2 2 2 0 0 0 1 0 0 0 0 0 0 0 136 137 4 4 3 4 0 0 0 0 1 0 0 0 0 0 0 137 138 3 4 3 3 0 0 0 0 0 1 0 0 0 0 0 138 139 4 3 4 4 0 0 0 0 0 0 1 0 0 0 0 139 140 4 4 3 3 0 0 0 0 0 0 0 1 0 0 0 140 141 4 4 4 4 0 0 0 0 0 0 0 0 1 0 0 141 142 3 4 3 4 0 0 0 0 0 0 0 0 0 1 0 142 143 3 4 4 3 0 0 0 0 0 0 0 0 0 0 1 143 144 2 3 3 2 0 0 0 0 0 0 0 0 0 0 0 144 145 3 3 2 2 1 0 0 0 0 0 0 0 0 0 0 145 146 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 146 147 2 2 2 2 0 0 1 0 0 0 0 0 0 0 0 147 148 4 4 4 3 0 0 0 1 0 0 0 0 0 0 0 148 149 4 4 4 4 0 0 0 0 1 0 0 0 0 0 0 149 150 3 3 3 3 0 0 0 0 0 1 0 0 0 0 0 150 151 3 3 3 3 0 0 0 0 0 0 1 0 0 0 0 151 152 4 4 3 4 0 0 0 0 0 0 0 1 0 0 0 152 153 2 3 2 2 0 0 0 0 0 0 0 0 1 0 0 153 154 4 4 NA 4 0 0 0 0 0 0 0 0 0 1 0 154 155 3 4 4 3 0 0 0 0 0 0 0 0 0 0 1 155 156 3 4 2 2 0 0 0 0 0 0 0 0 0 0 0 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Q1 Q2 Q3 M1 M2 0.865046 0.145400 0.252561 0.220272 -0.077694 -0.135577 M3 M4 M5 M6 M7 M8 0.071660 0.097698 0.278052 0.056742 0.133278 0.087319 M9 M10 M11 t 0.121227 0.207612 0.230803 0.002075 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.79614 -0.46164 -0.02281 0.42775 1.54526 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.865046 0.261534 3.308 0.00120 ** Q1 0.145400 0.067585 2.151 0.03319 * Q2 0.252561 0.076826 3.287 0.00128 ** Q3 0.220272 0.072113 3.055 0.00271 ** M1 -0.077694 0.258216 -0.301 0.76395 M2 -0.135577 0.259051 -0.523 0.60156 M3 0.071660 0.256919 0.279 0.78072 M4 0.097698 0.259071 0.377 0.70667 M5 0.278052 0.255952 1.086 0.27922 M6 0.056742 0.255813 0.222 0.82479 M7 0.133278 0.257550 0.517 0.60565 M8 0.087319 0.265127 0.329 0.74239 M9 0.121227 0.259243 0.468 0.64079 M10 0.207612 0.261986 0.792 0.42946 M11 0.230803 0.256484 0.900 0.36976 t 0.002075 0.001186 1.750 0.08233 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6464 on 138 degrees of freedom (2 observations deleted due to missingness) Multiple R-squared: 0.4921, Adjusted R-squared: 0.4369 F-statistic: 8.913 on 15 and 138 DF, p-value: 3.716e-14 > 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,] 0.6619287 0.67614251 0.338071255 [2,] 0.5318880 0.93622399 0.468111993 [3,] 0.5567617 0.88647655 0.443238273 [4,] 0.4713865 0.94277307 0.528613465 [5,] 0.3682480 0.73649596 0.631752022 [6,] 0.3937281 0.78745628 0.606271861 [7,] 0.4587933 0.91758655 0.541206723 [8,] 0.4706585 0.94131696 0.529341521 [9,] 0.7687370 0.46252597 0.231262987 [10,] 0.7164158 0.56716835 0.283584176 [11,] 0.6602666 0.67946681 0.339733405 [12,] 0.6685651 0.66286975 0.331434875 [13,] 0.6003142 0.79937167 0.399685834 [14,] 0.7220684 0.55586328 0.277931639 [15,] 0.6831019 0.63379625 0.316898127 [16,] 0.6483972 0.70320567 0.351602837 [17,] 0.8007364 0.39852714 0.199263570 [18,] 0.7614387 0.47712269 0.238561344 [19,] 0.7234908 0.55301838 0.276509192 [20,] 0.6663121 0.66737589 0.333687947 [21,] 0.7258917 0.54821664 0.274108320 [22,] 0.7576906 0.48461871 0.242309353 [23,] 0.7905610 0.41887793 0.209438963 [24,] 0.7515556 0.49688875 0.248444376 [25,] 0.8766683 0.24666339 0.123331696 [26,] 0.8645930 0.27081399 0.135406996 [27,] 0.8934814 0.21303729 0.106518647 [28,] 0.8792460 0.24150793 0.120753963 [29,] 0.8581803 0.28363933 0.141819667 [30,] 0.8448241 0.31035183 0.155175917 [31,] 0.8108781 0.37824382 0.189121908 [32,] 0.8091183 0.38176340 0.190881702 [33,] 0.7894474 0.42110520 0.210552602 [34,] 0.9364733 0.12705331 0.063526655 [35,] 0.9180384 0.16392320 0.081961598 [36,] 0.9055943 0.18881144 0.094405720 [37,] 0.9498869 0.10022611 0.050113057 [38,] 0.9396106 0.12077874 0.060389368 [39,] 0.9224027 0.15519458 0.077597288 [40,] 0.9043002 0.19139954 0.095699768 [41,] 0.9604097 0.07918062 0.039590310 [42,] 0.9645278 0.07094447 0.035472234 [43,] 0.9608449 0.07831023 0.039155113 [44,] 0.9604030 0.07919398 0.039596989 [45,] 0.9536337 0.09273266 0.046366329 [46,] 0.9688676 0.06226475 0.031132373 [47,] 0.9607023 0.07859533 0.039297665 [48,] 0.9497644 0.10047126 0.050235631 [49,] 0.9502474 0.09950529 0.049752643 [50,] 0.9369516 0.12609671 0.063048356 [51,] 0.9571810 0.08563810 0.042819048 [52,] 0.9703611 0.05927778 0.029638892 [53,] 0.9705235 0.05895291 0.029476454 [54,] 0.9636376 0.07272470 0.036362351 [55,] 0.9688681 0.06226374 0.031131869 [56,] 0.9908791 0.01824186 0.009120929 [57,] 0.9882942 0.02341155 0.011705775 [58,] 0.9911029 0.01779417 0.008897085 [59,] 0.9920183 0.01596347 0.007981735 [60,] 0.9889056 0.02218872 0.011094359 [61,] 0.9907907 0.01841863 0.009209313 [62,] 0.9871268 0.02574634 0.012873172 [63,] 0.9828220 0.03435592 0.017177962 [64,] 0.9790315 0.04193691 0.020968455 [65,] 0.9754518 0.04909632 0.024548161 [66,] 0.9763681 0.04726380 0.023631898 [67,] 0.9745653 0.05086934 0.025434669 [68,] 0.9662302 0.06753969 0.033769847 [69,] 0.9591471 0.08170586 0.040852932 [70,] 0.9512896 0.09742077 0.048710387 [71,] 0.9591517 0.08169668 0.040848341 [72,] 0.9564600 0.08707999 0.043539993 [73,] 0.9485321 0.10293590 0.051467949 [74,] 0.9446079 0.11078417 0.055392086 [75,] 0.9305403 0.13891946 0.069459731 [76,] 0.9138480 0.17230391 0.086151956 [77,] 0.9010329 0.19793414 0.098967068 [78,] 0.8809775 0.23804506 0.119022531 [79,] 0.8726235 0.25475298 0.127376491 [80,] 0.8427813 0.31443749 0.157218743 [81,] 0.8160329 0.36793412 0.183967061 [82,] 0.7879615 0.42407696 0.212038478 [83,] 0.8333134 0.33337317 0.166686587 [84,] 0.8199926 0.36001473 0.180007364 [85,] 0.8953653 0.20926931 0.104634656 [86,] 0.9138810 0.17223808 0.086119041 [87,] 0.8909152 0.21816957 0.109084784 [88,] 0.8664528 0.26709437 0.133547183 [89,] 0.8982397 0.20352066 0.101760331 [90,] 0.9067950 0.18641007 0.093205035 [91,] 0.9015131 0.19697378 0.098486892 [92,] 0.8732556 0.25348887 0.126744437 [93,] 0.8543596 0.29128081 0.145640404 [94,] 0.8500270 0.29994591 0.149972956 [95,] 0.8131732 0.37365350 0.186826750 [96,] 0.7676903 0.46461935 0.232309675 [97,] 0.7446205 0.51075900 0.255379501 [98,] 0.7933970 0.41320607 0.206603037 [99,] 0.7976394 0.40472126 0.202360629 [100,] 0.7875746 0.42485085 0.212425423 [101,] 0.8314597 0.33708052 0.168540260 [102,] 0.7802467 0.43950653 0.219753265 [103,] 0.7746186 0.45076276 0.225381379 [104,] 0.7298926 0.54021486 0.270107430 [105,] 0.6699756 0.66004887 0.330024433 [106,] 0.5952584 0.80948316 0.404741578 [107,] 0.5146822 0.97063557 0.485317784 [108,] 0.5807327 0.83853454 0.419267271 [109,] 0.8022737 0.39545262 0.197726312 [110,] 0.7305355 0.53892892 0.269464460 [111,] 0.8911091 0.21778180 0.108890901 [112,] 0.9596638 0.08067235 0.040336173 [113,] 0.9295010 0.14099808 0.070499038 [114,] 0.9194604 0.16107923 0.080539615 [115,] 0.9066794 0.18664114 0.093320572 [116,] 0.8421076 0.31578482 0.157892409 [117,] 0.7525111 0.49497788 0.247488941 [118,] 0.5565768 0.88684633 0.443423165 [119,] 0.5918122 0.81637554 0.408187768 > postscript(file="/var/www/html/freestat/rcomp/tmp/1tdzz1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) Warning message: In x[, 1] - mysum$resid : longer object length is not a multiple of shorter object length > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2tdzz1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/34myk1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/44myk1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/54myk1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 154 Frequency = 1 1 2 3 4 5 6 -0.042088001 0.029914043 1.315480218 -1.239799392 -0.862772828 0.081856780 7 8 9 10 11 12 -0.631182520 -0.748792655 -1.477881307 0.142959239 0.408393205 0.400754461 13 14 15 16 17 18 0.712740419 -0.790907441 1.193664485 -0.028332132 0.219489134 0.788201747 19 20 21 22 23 24 -0.128814576 -0.482891822 -0.153202114 -0.338679451 0.636055173 -0.005911162 25 26 27 28 29 30 -0.312158760 0.598248570 -0.465662956 -0.273503713 -0.455932344 -0.220502724 31 32 33 34 35 36 -0.299113761 0.948847841 -0.275118518 0.840599735 -0.990882853 0.587322543 37 38 39 40 41 42 0.721720420 -0.044783493 -0.270289732 0.481324706 0.917129630 0.372831652 43 44 45 46 47 48 -1.544285342 -0.570929045 0.904160669 -0.275366547 0.586256816 0.164562882 49 50 51 52 53 54 0.288565182 0.693850218 0.339239351 -1.796135619 -0.075480804 -0.377462222 55 56 57 58 59 60 0.850820790 -0.321222919 0.115627929 0.334061866 1.545263600 0.790085332 61 62 63 64 65 66 -0.150389187 0.668951040 -0.158594047 0.829487502 -0.041601624 -0.058833821 67 68 69 70 71 72 -0.610278407 -0.109654988 1.015856354 -0.836136648 -0.463541542 0.017747300 73 74 75 76 77 78 -0.718649524 -1.355948139 0.052973884 -0.593372830 -0.630401455 -0.158605396 79 80 81 82 83 84 0.821561926 0.043134584 -0.170536868 -0.242902943 -0.375329459 -0.679718336 85 86 87 88 89 90 0.418046011 0.237386237 -0.353691740 0.252522693 -0.548139493 -0.490398623 91 92 93 94 95 96 0.430990339 -0.143359309 0.347824442 0.259364330 0.030021260 -0.300706241 97 98 99 100 101 102 -0.606853168 -0.023879381 -0.362496882 0.120462374 1.174400181 0.539035100 103 105 106 107 108 109 1.389896453 -0.822374072 0.218371115 0.154866873 0.690488617 -0.647947054 110 111 112 113 114 115 -0.264706176 0.053148744 0.691651921 -0.468631881 -0.158430536 0.015519573 116 117 118 119 120 121 0.806842334 0.588826097 -0.553966918 -0.815699403 -1.189010555 0.106881365 122 123 124 125 126 127 0.544455146 -0.648661678 0.559591602 0.344874226 -0.021735001 -0.514501898 128 129 130 131 132 133 -0.690890393 0.364194009 1.039367459 0.599946223 0.038651413 -0.299784259 134 135 136 137 138 139 0.358061924 -0.216922016 0.518598386 0.352263792 -0.208228893 0.385726527 140 141 142 143 144 145 0.757043977 0.248227727 -0.587671238 -0.645225358 -0.798264108 0.529916556 146 147 148 149 150 151 -0.650642547 -0.478187633 0.477504500 0.074803467 -0.087728066 -0.166339103 152 153 155 156 0.511872396 -0.685604348 -0.670124537 0.283997853 > postscript(file="/var/www/html/freestat/rcomp/tmp/6evgm1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.042088001 NA 1 0.029914043 -0.042088001 2 1.315480218 0.029914043 3 -1.239799392 1.315480218 4 -0.862772828 -1.239799392 5 0.081856780 -0.862772828 6 -0.631182520 0.081856780 7 -0.748792655 -0.631182520 8 -1.477881307 -0.748792655 9 0.142959239 -1.477881307 10 0.408393205 0.142959239 11 0.400754461 0.408393205 12 0.712740419 0.400754461 13 -0.790907441 0.712740419 14 1.193664485 -0.790907441 15 -0.028332132 1.193664485 16 0.219489134 -0.028332132 17 0.788201747 0.219489134 18 -0.128814576 0.788201747 19 -0.482891822 -0.128814576 20 -0.153202114 -0.482891822 21 -0.338679451 -0.153202114 22 0.636055173 -0.338679451 23 -0.005911162 0.636055173 24 -0.312158760 -0.005911162 25 0.598248570 -0.312158760 26 -0.465662956 0.598248570 27 -0.273503713 -0.465662956 28 -0.455932344 -0.273503713 29 -0.220502724 -0.455932344 30 -0.299113761 -0.220502724 31 0.948847841 -0.299113761 32 -0.275118518 0.948847841 33 0.840599735 -0.275118518 34 -0.990882853 0.840599735 35 0.587322543 -0.990882853 36 0.721720420 0.587322543 37 -0.044783493 0.721720420 38 -0.270289732 -0.044783493 39 0.481324706 -0.270289732 40 0.917129630 0.481324706 41 0.372831652 0.917129630 42 -1.544285342 0.372831652 43 -0.570929045 -1.544285342 44 0.904160669 -0.570929045 45 -0.275366547 0.904160669 46 0.586256816 -0.275366547 47 0.164562882 0.586256816 48 0.288565182 0.164562882 49 0.693850218 0.288565182 50 0.339239351 0.693850218 51 -1.796135619 0.339239351 52 -0.075480804 -1.796135619 53 -0.377462222 -0.075480804 54 0.850820790 -0.377462222 55 -0.321222919 0.850820790 56 0.115627929 -0.321222919 57 0.334061866 0.115627929 58 1.545263600 0.334061866 59 0.790085332 1.545263600 60 -0.150389187 0.790085332 61 0.668951040 -0.150389187 62 -0.158594047 0.668951040 63 0.829487502 -0.158594047 64 -0.041601624 0.829487502 65 -0.058833821 -0.041601624 66 -0.610278407 -0.058833821 67 -0.109654988 -0.610278407 68 1.015856354 -0.109654988 69 -0.836136648 1.015856354 70 -0.463541542 -0.836136648 71 0.017747300 -0.463541542 72 -0.718649524 0.017747300 73 -1.355948139 -0.718649524 74 0.052973884 -1.355948139 75 -0.593372830 0.052973884 76 -0.630401455 -0.593372830 77 -0.158605396 -0.630401455 78 0.821561926 -0.158605396 79 0.043134584 0.821561926 80 -0.170536868 0.043134584 81 -0.242902943 -0.170536868 82 -0.375329459 -0.242902943 83 -0.679718336 -0.375329459 84 0.418046011 -0.679718336 85 0.237386237 0.418046011 86 -0.353691740 0.237386237 87 0.252522693 -0.353691740 88 -0.548139493 0.252522693 89 -0.490398623 -0.548139493 90 0.430990339 -0.490398623 91 -0.143359309 0.430990339 92 0.347824442 -0.143359309 93 0.259364330 0.347824442 94 0.030021260 0.259364330 95 -0.300706241 0.030021260 96 -0.606853168 -0.300706241 97 -0.023879381 -0.606853168 98 -0.362496882 -0.023879381 99 0.120462374 -0.362496882 100 1.174400181 0.120462374 101 0.539035100 1.174400181 102 1.389896453 0.539035100 103 -0.822374072 1.389896453 104 0.218371115 -0.822374072 105 0.154866873 0.218371115 106 0.690488617 0.154866873 107 -0.647947054 0.690488617 108 -0.264706176 -0.647947054 109 0.053148744 -0.264706176 110 0.691651921 0.053148744 111 -0.468631881 0.691651921 112 -0.158430536 -0.468631881 113 0.015519573 -0.158430536 114 0.806842334 0.015519573 115 0.588826097 0.806842334 116 -0.553966918 0.588826097 117 -0.815699403 -0.553966918 118 -1.189010555 -0.815699403 119 0.106881365 -1.189010555 120 0.544455146 0.106881365 121 -0.648661678 0.544455146 122 0.559591602 -0.648661678 123 0.344874226 0.559591602 124 -0.021735001 0.344874226 125 -0.514501898 -0.021735001 126 -0.690890393 -0.514501898 127 0.364194009 -0.690890393 128 1.039367459 0.364194009 129 0.599946223 1.039367459 130 0.038651413 0.599946223 131 -0.299784259 0.038651413 132 0.358061924 -0.299784259 133 -0.216922016 0.358061924 134 0.518598386 -0.216922016 135 0.352263792 0.518598386 136 -0.208228893 0.352263792 137 0.385726527 -0.208228893 138 0.757043977 0.385726527 139 0.248227727 0.757043977 140 -0.587671238 0.248227727 141 -0.645225358 -0.587671238 142 -0.798264108 -0.645225358 143 0.529916556 -0.798264108 144 -0.650642547 0.529916556 145 -0.478187633 -0.650642547 146 0.477504500 -0.478187633 147 0.074803467 0.477504500 148 -0.087728066 0.074803467 149 -0.166339103 -0.087728066 150 0.511872396 -0.166339103 151 -0.685604348 0.511872396 152 -0.670124537 -0.685604348 153 0.283997853 -0.670124537 154 NA 0.283997853 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.029914043 -0.042088001 [2,] 1.315480218 0.029914043 [3,] -1.239799392 1.315480218 [4,] -0.862772828 -1.239799392 [5,] 0.081856780 -0.862772828 [6,] -0.631182520 0.081856780 [7,] -0.748792655 -0.631182520 [8,] -1.477881307 -0.748792655 [9,] 0.142959239 -1.477881307 [10,] 0.408393205 0.142959239 [11,] 0.400754461 0.408393205 [12,] 0.712740419 0.400754461 [13,] -0.790907441 0.712740419 [14,] 1.193664485 -0.790907441 [15,] -0.028332132 1.193664485 [16,] 0.219489134 -0.028332132 [17,] 0.788201747 0.219489134 [18,] -0.128814576 0.788201747 [19,] -0.482891822 -0.128814576 [20,] -0.153202114 -0.482891822 [21,] -0.338679451 -0.153202114 [22,] 0.636055173 -0.338679451 [23,] -0.005911162 0.636055173 [24,] -0.312158760 -0.005911162 [25,] 0.598248570 -0.312158760 [26,] -0.465662956 0.598248570 [27,] -0.273503713 -0.465662956 [28,] -0.455932344 -0.273503713 [29,] -0.220502724 -0.455932344 [30,] -0.299113761 -0.220502724 [31,] 0.948847841 -0.299113761 [32,] -0.275118518 0.948847841 [33,] 0.840599735 -0.275118518 [34,] -0.990882853 0.840599735 [35,] 0.587322543 -0.990882853 [36,] 0.721720420 0.587322543 [37,] -0.044783493 0.721720420 [38,] -0.270289732 -0.044783493 [39,] 0.481324706 -0.270289732 [40,] 0.917129630 0.481324706 [41,] 0.372831652 0.917129630 [42,] -1.544285342 0.372831652 [43,] -0.570929045 -1.544285342 [44,] 0.904160669 -0.570929045 [45,] -0.275366547 0.904160669 [46,] 0.586256816 -0.275366547 [47,] 0.164562882 0.586256816 [48,] 0.288565182 0.164562882 [49,] 0.693850218 0.288565182 [50,] 0.339239351 0.693850218 [51,] -1.796135619 0.339239351 [52,] -0.075480804 -1.796135619 [53,] -0.377462222 -0.075480804 [54,] 0.850820790 -0.377462222 [55,] -0.321222919 0.850820790 [56,] 0.115627929 -0.321222919 [57,] 0.334061866 0.115627929 [58,] 1.545263600 0.334061866 [59,] 0.790085332 1.545263600 [60,] -0.150389187 0.790085332 [61,] 0.668951040 -0.150389187 [62,] -0.158594047 0.668951040 [63,] 0.829487502 -0.158594047 [64,] -0.041601624 0.829487502 [65,] -0.058833821 -0.041601624 [66,] -0.610278407 -0.058833821 [67,] -0.109654988 -0.610278407 [68,] 1.015856354 -0.109654988 [69,] -0.836136648 1.015856354 [70,] -0.463541542 -0.836136648 [71,] 0.017747300 -0.463541542 [72,] -0.718649524 0.017747300 [73,] -1.355948139 -0.718649524 [74,] 0.052973884 -1.355948139 [75,] -0.593372830 0.052973884 [76,] -0.630401455 -0.593372830 [77,] -0.158605396 -0.630401455 [78,] 0.821561926 -0.158605396 [79,] 0.043134584 0.821561926 [80,] -0.170536868 0.043134584 [81,] -0.242902943 -0.170536868 [82,] -0.375329459 -0.242902943 [83,] -0.679718336 -0.375329459 [84,] 0.418046011 -0.679718336 [85,] 0.237386237 0.418046011 [86,] -0.353691740 0.237386237 [87,] 0.252522693 -0.353691740 [88,] -0.548139493 0.252522693 [89,] -0.490398623 -0.548139493 [90,] 0.430990339 -0.490398623 [91,] -0.143359309 0.430990339 [92,] 0.347824442 -0.143359309 [93,] 0.259364330 0.347824442 [94,] 0.030021260 0.259364330 [95,] -0.300706241 0.030021260 [96,] -0.606853168 -0.300706241 [97,] -0.023879381 -0.606853168 [98,] -0.362496882 -0.023879381 [99,] 0.120462374 -0.362496882 [100,] 1.174400181 0.120462374 [101,] 0.539035100 1.174400181 [102,] 1.389896453 0.539035100 [103,] -0.822374072 1.389896453 [104,] 0.218371115 -0.822374072 [105,] 0.154866873 0.218371115 [106,] 0.690488617 0.154866873 [107,] -0.647947054 0.690488617 [108,] -0.264706176 -0.647947054 [109,] 0.053148744 -0.264706176 [110,] 0.691651921 0.053148744 [111,] -0.468631881 0.691651921 [112,] -0.158430536 -0.468631881 [113,] 0.015519573 -0.158430536 [114,] 0.806842334 0.015519573 [115,] 0.588826097 0.806842334 [116,] -0.553966918 0.588826097 [117,] -0.815699403 -0.553966918 [118,] -1.189010555 -0.815699403 [119,] 0.106881365 -1.189010555 [120,] 0.544455146 0.106881365 [121,] -0.648661678 0.544455146 [122,] 0.559591602 -0.648661678 [123,] 0.344874226 0.559591602 [124,] -0.021735001 0.344874226 [125,] -0.514501898 -0.021735001 [126,] -0.690890393 -0.514501898 [127,] 0.364194009 -0.690890393 [128,] 1.039367459 0.364194009 [129,] 0.599946223 1.039367459 [130,] 0.038651413 0.599946223 [131,] -0.299784259 0.038651413 [132,] 0.358061924 -0.299784259 [133,] -0.216922016 0.358061924 [134,] 0.518598386 -0.216922016 [135,] 0.352263792 0.518598386 [136,] -0.208228893 0.352263792 [137,] 0.385726527 -0.208228893 [138,] 0.757043977 0.385726527 [139,] 0.248227727 0.757043977 [140,] -0.587671238 0.248227727 [141,] -0.645225358 -0.587671238 [142,] -0.798264108 -0.645225358 [143,] 0.529916556 -0.798264108 [144,] -0.650642547 0.529916556 [145,] -0.478187633 -0.650642547 [146,] 0.477504500 -0.478187633 [147,] 0.074803467 0.477504500 [148,] -0.087728066 0.074803467 [149,] -0.166339103 -0.087728066 [150,] 0.511872396 -0.166339103 [151,] -0.685604348 0.511872396 [152,] -0.670124537 -0.685604348 [153,] 0.283997853 -0.670124537 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.029914043 -0.042088001 2 1.315480218 0.029914043 3 -1.239799392 1.315480218 4 -0.862772828 -1.239799392 5 0.081856780 -0.862772828 6 -0.631182520 0.081856780 7 -0.748792655 -0.631182520 8 -1.477881307 -0.748792655 9 0.142959239 -1.477881307 10 0.408393205 0.142959239 11 0.400754461 0.408393205 12 0.712740419 0.400754461 13 -0.790907441 0.712740419 14 1.193664485 -0.790907441 15 -0.028332132 1.193664485 16 0.219489134 -0.028332132 17 0.788201747 0.219489134 18 -0.128814576 0.788201747 19 -0.482891822 -0.128814576 20 -0.153202114 -0.482891822 21 -0.338679451 -0.153202114 22 0.636055173 -0.338679451 23 -0.005911162 0.636055173 24 -0.312158760 -0.005911162 25 0.598248570 -0.312158760 26 -0.465662956 0.598248570 27 -0.273503713 -0.465662956 28 -0.455932344 -0.273503713 29 -0.220502724 -0.455932344 30 -0.299113761 -0.220502724 31 0.948847841 -0.299113761 32 -0.275118518 0.948847841 33 0.840599735 -0.275118518 34 -0.990882853 0.840599735 35 0.587322543 -0.990882853 36 0.721720420 0.587322543 37 -0.044783493 0.721720420 38 -0.270289732 -0.044783493 39 0.481324706 -0.270289732 40 0.917129630 0.481324706 41 0.372831652 0.917129630 42 -1.544285342 0.372831652 43 -0.570929045 -1.544285342 44 0.904160669 -0.570929045 45 -0.275366547 0.904160669 46 0.586256816 -0.275366547 47 0.164562882 0.586256816 48 0.288565182 0.164562882 49 0.693850218 0.288565182 50 0.339239351 0.693850218 51 -1.796135619 0.339239351 52 -0.075480804 -1.796135619 53 -0.377462222 -0.075480804 54 0.850820790 -0.377462222 55 -0.321222919 0.850820790 56 0.115627929 -0.321222919 57 0.334061866 0.115627929 58 1.545263600 0.334061866 59 0.790085332 1.545263600 60 -0.150389187 0.790085332 61 0.668951040 -0.150389187 62 -0.158594047 0.668951040 63 0.829487502 -0.158594047 64 -0.041601624 0.829487502 65 -0.058833821 -0.041601624 66 -0.610278407 -0.058833821 67 -0.109654988 -0.610278407 68 1.015856354 -0.109654988 69 -0.836136648 1.015856354 70 -0.463541542 -0.836136648 71 0.017747300 -0.463541542 72 -0.718649524 0.017747300 73 -1.355948139 -0.718649524 74 0.052973884 -1.355948139 75 -0.593372830 0.052973884 76 -0.630401455 -0.593372830 77 -0.158605396 -0.630401455 78 0.821561926 -0.158605396 79 0.043134584 0.821561926 80 -0.170536868 0.043134584 81 -0.242902943 -0.170536868 82 -0.375329459 -0.242902943 83 -0.679718336 -0.375329459 84 0.418046011 -0.679718336 85 0.237386237 0.418046011 86 -0.353691740 0.237386237 87 0.252522693 -0.353691740 88 -0.548139493 0.252522693 89 -0.490398623 -0.548139493 90 0.430990339 -0.490398623 91 -0.143359309 0.430990339 92 0.347824442 -0.143359309 93 0.259364330 0.347824442 94 0.030021260 0.259364330 95 -0.300706241 0.030021260 96 -0.606853168 -0.300706241 97 -0.023879381 -0.606853168 98 -0.362496882 -0.023879381 99 0.120462374 -0.362496882 100 1.174400181 0.120462374 101 0.539035100 1.174400181 102 1.389896453 0.539035100 103 -0.822374072 1.389896453 104 0.218371115 -0.822374072 105 0.154866873 0.218371115 106 0.690488617 0.154866873 107 -0.647947054 0.690488617 108 -0.264706176 -0.647947054 109 0.053148744 -0.264706176 110 0.691651921 0.053148744 111 -0.468631881 0.691651921 112 -0.158430536 -0.468631881 113 0.015519573 -0.158430536 114 0.806842334 0.015519573 115 0.588826097 0.806842334 116 -0.553966918 0.588826097 117 -0.815699403 -0.553966918 118 -1.189010555 -0.815699403 119 0.106881365 -1.189010555 120 0.544455146 0.106881365 121 -0.648661678 0.544455146 122 0.559591602 -0.648661678 123 0.344874226 0.559591602 124 -0.021735001 0.344874226 125 -0.514501898 -0.021735001 126 -0.690890393 -0.514501898 127 0.364194009 -0.690890393 128 1.039367459 0.364194009 129 0.599946223 1.039367459 130 0.038651413 0.599946223 131 -0.299784259 0.038651413 132 0.358061924 -0.299784259 133 -0.216922016 0.358061924 134 0.518598386 -0.216922016 135 0.352263792 0.518598386 136 -0.208228893 0.352263792 137 0.385726527 -0.208228893 138 0.757043977 0.385726527 139 0.248227727 0.757043977 140 -0.587671238 0.248227727 141 -0.645225358 -0.587671238 142 -0.798264108 -0.645225358 143 0.529916556 -0.798264108 144 -0.650642547 0.529916556 145 -0.478187633 -0.650642547 146 0.477504500 -0.478187633 147 0.074803467 0.477504500 148 -0.087728066 0.074803467 149 -0.166339103 -0.087728066 150 0.511872396 -0.166339103 151 -0.685604348 0.511872396 152 -0.670124537 -0.685604348 153 0.283997853 -0.670124537 > 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/freestat/rcomp/tmp/77nfp1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/87nfp1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/97nfp1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10ieea1291202429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/113wvg1291202429.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/12z7ez1291202430.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/136qbb1291202430.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/freestat/rcomp/tmp/14ghse1291202430.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/freestat/rcomp/tmp/152i921291202430.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/freestat/rcomp/tmp/16yaoa1291202430.tab") + } > > try(system("convert tmp/1tdzz1291202429.ps tmp/1tdzz1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/2tdzz1291202429.ps tmp/2tdzz1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/34myk1291202429.ps tmp/34myk1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/44myk1291202429.ps tmp/44myk1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/54myk1291202429.ps tmp/54myk1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/6evgm1291202429.ps tmp/6evgm1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/77nfp1291202429.ps tmp/77nfp1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/87nfp1291202429.ps tmp/87nfp1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/97nfp1291202429.ps tmp/97nfp1291202429.png",intern=TRUE)) character(0) > try(system("convert tmp/10ieea1291202429.ps tmp/10ieea1291202429.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.810 2.747 6.536