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Type 'q()' to quit R. > x <- array(list(97.7 + ,0 + ,98.3 + ,91.6 + ,104.6 + ,111.6 + ,106.3 + ,0 + ,97.7 + ,98.3 + ,91.6 + ,104.6 + ,102.3 + ,0 + ,106.3 + ,97.7 + ,98.3 + ,91.6 + ,106.6 + ,0 + ,102.3 + ,106.3 + ,97.7 + ,98.3 + ,108.1 + ,0 + ,106.6 + ,102.3 + ,106.3 + ,97.7 + ,93.8 + ,0 + ,108.1 + ,106.6 + ,102.3 + ,106.3 + ,88.2 + ,0 + ,93.8 + ,108.1 + ,106.6 + ,102.3 + ,108.9 + ,0 + ,88.2 + ,93.8 + ,108.1 + ,106.6 + ,114.2 + ,0 + ,108.9 + ,88.2 + ,93.8 + ,108.1 + ,102.5 + ,0 + ,114.2 + ,108.9 + ,88.2 + ,93.8 + ,94.2 + ,0 + ,102.5 + ,114.2 + ,108.9 + ,88.2 + ,97.4 + ,0 + ,94.2 + ,102.5 + ,114.2 + ,108.9 + ,98.5 + ,0 + ,97.4 + ,94.2 + ,102.5 + ,114.2 + ,106.5 + ,0 + ,98.5 + ,97.4 + ,94.2 + ,102.5 + ,102.9 + ,0 + ,106.5 + ,98.5 + ,97.4 + ,94.2 + ,97.1 + ,0 + ,102.9 + ,106.5 + ,98.5 + ,97.4 + ,103.7 + ,0 + ,97.1 + ,102.9 + ,106.5 + ,98.5 + ,93.4 + ,0 + ,103.7 + ,97.1 + ,102.9 + ,106.5 + ,85.8 + ,0 + ,93.4 + ,103.7 + ,97.1 + ,102.9 + ,108.6 + ,0 + ,85.8 + ,93.4 + ,103.7 + ,97.1 + ,110.2 + ,0 + ,108.6 + ,85.8 + ,93.4 + ,103.7 + ,101.2 + ,0 + ,110.2 + ,108.6 + ,85.8 + ,93.4 + ,101.2 + ,0 + ,101.2 + ,110.2 + ,108.6 + ,85.8 + ,96.9 + ,0 + ,101.2 + ,101.2 + ,110.2 + ,108.6 + ,99.4 + ,0 + ,96.9 + ,101.2 + ,101.2 + ,110.2 + ,118.7 + ,0 + ,99.4 + ,96.9 + ,101.2 + ,101.2 + ,108.0 + ,0 + ,118.7 + ,99.4 + ,96.9 + ,101.2 + ,101.2 + ,0 + ,108.0 + ,118.7 + ,99.4 + ,96.9 + ,119.9 + ,0 + ,101.2 + ,108.0 + ,118.7 + ,99.4 + ,94.8 + ,0 + ,119.9 + ,101.2 + ,108.0 + ,118.7 + ,95.3 + ,0 + ,94.8 + ,119.9 + ,101.2 + ,108.0 + ,118.0 + ,0 + ,95.3 + ,94.8 + ,119.9 + ,101.2 + ,115.9 + ,0 + ,118.0 + ,95.3 + ,94.8 + ,119.9 + ,111.4 + ,0 + ,115.9 + ,118.0 + ,95.3 + ,94.8 + ,108.2 + ,0 + ,111.4 + ,115.9 + ,118.0 + ,95.3 + ,108.8 + ,0 + ,108.2 + ,111.4 + ,115.9 + ,118.0 + ,109.5 + ,0 + ,108.8 + ,108.2 + ,111.4 + ,115.9 + ,124.8 + ,0 + ,109.5 + ,108.8 + ,108.2 + ,111.4 + ,115.3 + ,0 + ,124.8 + ,109.5 + ,108.8 + ,108.2 + ,109.5 + ,0 + ,115.3 + ,124.8 + ,109.5 + ,108.8 + ,124.2 + ,0 + ,109.5 + ,115.3 + ,124.8 + ,109.5 + ,92.9 + ,0 + ,124.2 + ,109.5 + ,115.3 + ,124.8 + ,98.4 + ,0 + ,92.9 + ,124.2 + ,109.5 + ,115.3 + ,120.9 + ,0 + ,98.4 + ,92.9 + ,124.2 + ,109.5 + ,111.7 + ,0 + ,120.9 + ,98.4 + ,92.9 + ,124.2 + ,116.1 + ,0 + ,111.7 + ,120.9 + ,98.4 + ,92.9 + ,109.4 + ,0 + ,116.1 + ,111.7 + ,120.9 + ,98.4 + ,111.7 + ,0 + ,109.4 + ,116.1 + ,111.7 + ,120.9 + ,114.3 + ,0 + ,111.7 + ,109.4 + ,116.1 + ,111.7 + ,133.7 + ,0 + ,114.3 + ,111.7 + ,109.4 + ,116.1 + ,114.3 + ,0 + ,133.7 + ,114.3 + ,111.7 + ,109.4 + ,126.5 + ,0 + ,114.3 + ,133.7 + ,114.3 + ,111.7 + ,131.0 + ,0 + ,126.5 + ,114.3 + ,133.7 + ,114.3 + ,104.0 + ,0 + ,131.0 + ,126.5 + ,114.3 + ,133.7 + ,108.9 + ,0 + ,104.0 + ,131.0 + ,126.5 + ,114.3 + ,128.5 + ,0 + ,108.9 + ,104.0 + ,131.0 + ,126.5 + ,132.4 + ,0 + ,128.5 + ,108.9 + ,104.0 + ,131.0 + ,128.0 + ,0 + ,132.4 + ,128.5 + ,108.9 + ,104.0 + ,116.4 + ,0 + ,128.0 + ,132.4 + ,128.5 + ,108.9 + ,120.9 + ,0 + ,116.4 + ,128.0 + ,132.4 + ,128.5 + ,118.6 + ,0 + ,120.9 + ,116.4 + ,128.0 + ,132.4 + ,133.1 + ,0 + ,118.6 + ,120.9 + ,116.4 + ,128.0 + ,121.1 + ,0 + ,133.1 + ,118.6 + ,120.9 + ,116.4 + ,127.6 + ,0 + ,121.1 + ,133.1 + ,118.6 + ,120.9 + ,135.4 + ,0 + ,127.6 + ,121.1 + ,133.1 + ,118.6 + ,114.9 + ,0 + ,135.4 + ,127.6 + ,121.1 + ,133.1 + ,114.3 + ,0 + ,114.9 + ,135.4 + ,127.6 + ,121.1 + ,128.9 + ,0 + ,114.3 + ,114.9 + ,135.4 + ,127.6 + ,138.9 + ,0 + ,128.9 + ,114.3 + ,114.9 + ,135.4 + ,129.4 + ,0 + ,138.9 + ,128.9 + ,114.3 + ,114.9 + ,115.0 + ,0 + ,129.4 + ,138.9 + ,128.9 + ,114.3 + ,128.0 + ,0 + ,115.0 + ,129.4 + ,138.9 + ,128.9 + ,127.0 + ,0 + ,128.0 + ,115.0 + ,129.4 + ,138.9 + ,128.8 + ,0 + ,127.0 + ,128.0 + ,115.0 + ,129.4 + ,137.9 + ,0 + ,128.8 + ,127.0 + ,128.0 + ,115.0 + ,128.4 + ,0 + ,137.9 + ,128.8 + ,127.0 + ,128.0 + ,135.9 + ,0 + ,128.4 + ,137.9 + ,128.8 + ,127.0 + ,122.2 + ,0 + ,135.9 + ,128.4 + ,137.9 + ,128.8 + ,113.1 + ,0 + ,122.2 + ,135.9 + ,128.4 + ,137.9 + ,136.2 + ,1 + ,113.1 + ,122.2 + ,135.9 + ,128.4 + ,138.0 + ,1 + ,136.2 + ,113.1 + ,122.2 + ,135.9 + ,115.2 + ,1 + ,138.0 + ,136.2 + ,113.1 + ,122.2 + ,111.0 + ,1 + ,115.2 + ,138.0 + ,136.2 + ,113.1 + ,99.2 + ,1 + ,111.0 + ,115.2 + ,138.0 + ,136.2 + ,102.4 + ,1 + ,99.2 + ,111.0 + ,115.2 + ,138.0 + ,112.7 + ,1 + ,102.4 + ,99.2 + ,111.0 + ,115.2 + ,105.5 + ,1 + ,112.7 + ,102.4 + ,99.2 + ,111.0 + ,98.3 + ,1 + ,105.5 + ,112.7 + ,102.4 + ,99.2 + ,116.4 + ,1 + ,98.3 + ,105.5 + ,112.7 + ,102.4 + ,97.4 + ,1 + ,116.4 + ,98.3 + ,105.5 + ,112.7 + ,93.3 + ,1 + ,97.4 + ,116.4 + ,98.3 + ,105.5 + ,117.4 + ,1 + ,93.3 + ,97.4 + ,116.4 + ,98.3) + ,dim=c(6 + ,92) + ,dimnames=list(c('y' + ,'dummy' + ,'y1' + ,'y2' + ,'y3' + ,'y4') + ,1:92)) > y <- array(NA,dim=c(6,92),dimnames=list(c('y','dummy','y1','y2','y3','y4'),1:92)) > 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 dummy y1 y2 y3 y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 97.7 0 98.3 91.6 104.6 111.6 1 0 0 0 0 0 0 0 0 0 0 1 2 106.3 0 97.7 98.3 91.6 104.6 0 1 0 0 0 0 0 0 0 0 0 2 3 102.3 0 106.3 97.7 98.3 91.6 0 0 1 0 0 0 0 0 0 0 0 3 4 106.6 0 102.3 106.3 97.7 98.3 0 0 0 1 0 0 0 0 0 0 0 4 5 108.1 0 106.6 102.3 106.3 97.7 0 0 0 0 1 0 0 0 0 0 0 5 6 93.8 0 108.1 106.6 102.3 106.3 0 0 0 0 0 1 0 0 0 0 0 6 7 88.2 0 93.8 108.1 106.6 102.3 0 0 0 0 0 0 1 0 0 0 0 7 8 108.9 0 88.2 93.8 108.1 106.6 0 0 0 0 0 0 0 1 0 0 0 8 9 114.2 0 108.9 88.2 93.8 108.1 0 0 0 0 0 0 0 0 1 0 0 9 10 102.5 0 114.2 108.9 88.2 93.8 0 0 0 0 0 0 0 0 0 1 0 10 11 94.2 0 102.5 114.2 108.9 88.2 0 0 0 0 0 0 0 0 0 0 1 11 12 97.4 0 94.2 102.5 114.2 108.9 0 0 0 0 0 0 0 0 0 0 0 12 13 98.5 0 97.4 94.2 102.5 114.2 1 0 0 0 0 0 0 0 0 0 0 13 14 106.5 0 98.5 97.4 94.2 102.5 0 1 0 0 0 0 0 0 0 0 0 14 15 102.9 0 106.5 98.5 97.4 94.2 0 0 1 0 0 0 0 0 0 0 0 15 16 97.1 0 102.9 106.5 98.5 97.4 0 0 0 1 0 0 0 0 0 0 0 16 17 103.7 0 97.1 102.9 106.5 98.5 0 0 0 0 1 0 0 0 0 0 0 17 18 93.4 0 103.7 97.1 102.9 106.5 0 0 0 0 0 1 0 0 0 0 0 18 19 85.8 0 93.4 103.7 97.1 102.9 0 0 0 0 0 0 1 0 0 0 0 19 20 108.6 0 85.8 93.4 103.7 97.1 0 0 0 0 0 0 0 1 0 0 0 20 21 110.2 0 108.6 85.8 93.4 103.7 0 0 0 0 0 0 0 0 1 0 0 21 22 101.2 0 110.2 108.6 85.8 93.4 0 0 0 0 0 0 0 0 0 1 0 22 23 101.2 0 101.2 110.2 108.6 85.8 0 0 0 0 0 0 0 0 0 0 1 23 24 96.9 0 101.2 101.2 110.2 108.6 0 0 0 0 0 0 0 0 0 0 0 24 25 99.4 0 96.9 101.2 101.2 110.2 1 0 0 0 0 0 0 0 0 0 0 25 26 118.7 0 99.4 96.9 101.2 101.2 0 1 0 0 0 0 0 0 0 0 0 26 27 108.0 0 118.7 99.4 96.9 101.2 0 0 1 0 0 0 0 0 0 0 0 27 28 101.2 0 108.0 118.7 99.4 96.9 0 0 0 1 0 0 0 0 0 0 0 28 29 119.9 0 101.2 108.0 118.7 99.4 0 0 0 0 1 0 0 0 0 0 0 29 30 94.8 0 119.9 101.2 108.0 118.7 0 0 0 0 0 1 0 0 0 0 0 30 31 95.3 0 94.8 119.9 101.2 108.0 0 0 0 0 0 0 1 0 0 0 0 31 32 118.0 0 95.3 94.8 119.9 101.2 0 0 0 0 0 0 0 1 0 0 0 32 33 115.9 0 118.0 95.3 94.8 119.9 0 0 0 0 0 0 0 0 1 0 0 33 34 111.4 0 115.9 118.0 95.3 94.8 0 0 0 0 0 0 0 0 0 1 0 34 35 108.2 0 111.4 115.9 118.0 95.3 0 0 0 0 0 0 0 0 0 0 1 35 36 108.8 0 108.2 111.4 115.9 118.0 0 0 0 0 0 0 0 0 0 0 0 36 37 109.5 0 108.8 108.2 111.4 115.9 1 0 0 0 0 0 0 0 0 0 0 37 38 124.8 0 109.5 108.8 108.2 111.4 0 1 0 0 0 0 0 0 0 0 0 38 39 115.3 0 124.8 109.5 108.8 108.2 0 0 1 0 0 0 0 0 0 0 0 39 40 109.5 0 115.3 124.8 109.5 108.8 0 0 0 1 0 0 0 0 0 0 0 40 41 124.2 0 109.5 115.3 124.8 109.5 0 0 0 0 1 0 0 0 0 0 0 41 42 92.9 0 124.2 109.5 115.3 124.8 0 0 0 0 0 1 0 0 0 0 0 42 43 98.4 0 92.9 124.2 109.5 115.3 0 0 0 0 0 0 1 0 0 0 0 43 44 120.9 0 98.4 92.9 124.2 109.5 0 0 0 0 0 0 0 1 0 0 0 44 45 111.7 0 120.9 98.4 92.9 124.2 0 0 0 0 0 0 0 0 1 0 0 45 46 116.1 0 111.7 120.9 98.4 92.9 0 0 0 0 0 0 0 0 0 1 0 46 47 109.4 0 116.1 111.7 120.9 98.4 0 0 0 0 0 0 0 0 0 0 1 47 48 111.7 0 109.4 116.1 111.7 120.9 0 0 0 0 0 0 0 0 0 0 0 48 49 114.3 0 111.7 109.4 116.1 111.7 1 0 0 0 0 0 0 0 0 0 0 49 50 133.7 0 114.3 111.7 109.4 116.1 0 1 0 0 0 0 0 0 0 0 0 50 51 114.3 0 133.7 114.3 111.7 109.4 0 0 1 0 0 0 0 0 0 0 0 51 52 126.5 0 114.3 133.7 114.3 111.7 0 0 0 1 0 0 0 0 0 0 0 52 53 131.0 0 126.5 114.3 133.7 114.3 0 0 0 0 1 0 0 0 0 0 0 53 54 104.0 0 131.0 126.5 114.3 133.7 0 0 0 0 0 1 0 0 0 0 0 54 55 108.9 0 104.0 131.0 126.5 114.3 0 0 0 0 0 0 1 0 0 0 0 55 56 128.5 0 108.9 104.0 131.0 126.5 0 0 0 0 0 0 0 1 0 0 0 56 57 132.4 0 128.5 108.9 104.0 131.0 0 0 0 0 0 0 0 0 1 0 0 57 58 128.0 0 132.4 128.5 108.9 104.0 0 0 0 0 0 0 0 0 0 1 0 58 59 116.4 0 128.0 132.4 128.5 108.9 0 0 0 0 0 0 0 0 0 0 1 59 60 120.9 0 116.4 128.0 132.4 128.5 0 0 0 0 0 0 0 0 0 0 0 60 61 118.6 0 120.9 116.4 128.0 132.4 1 0 0 0 0 0 0 0 0 0 0 61 62 133.1 0 118.6 120.9 116.4 128.0 0 1 0 0 0 0 0 0 0 0 0 62 63 121.1 0 133.1 118.6 120.9 116.4 0 0 1 0 0 0 0 0 0 0 0 63 64 127.6 0 121.1 133.1 118.6 120.9 0 0 0 1 0 0 0 0 0 0 0 64 65 135.4 0 127.6 121.1 133.1 118.6 0 0 0 0 1 0 0 0 0 0 0 65 66 114.9 0 135.4 127.6 121.1 133.1 0 0 0 0 0 1 0 0 0 0 0 66 67 114.3 0 114.9 135.4 127.6 121.1 0 0 0 0 0 0 1 0 0 0 0 67 68 128.9 0 114.3 114.9 135.4 127.6 0 0 0 0 0 0 0 1 0 0 0 68 69 138.9 0 128.9 114.3 114.9 135.4 0 0 0 0 0 0 0 0 1 0 0 69 70 129.4 0 138.9 128.9 114.3 114.9 0 0 0 0 0 0 0 0 0 1 0 70 71 115.0 0 129.4 138.9 128.9 114.3 0 0 0 0 0 0 0 0 0 0 1 71 72 128.0 0 115.0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72 73 127.0 0 128.0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73 74 128.8 0 127.0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74 75 137.9 0 128.8 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75 76 128.4 0 137.9 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76 77 135.9 0 128.4 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77 78 122.2 0 135.9 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78 79 113.1 0 122.2 135.9 128.4 137.9 0 0 0 0 0 0 1 0 0 0 0 79 80 136.2 1 113.1 122.2 135.9 128.4 0 0 0 0 0 0 0 1 0 0 0 80 81 138.0 1 136.2 113.1 122.2 135.9 0 0 0 0 0 0 0 0 1 0 0 81 82 115.2 1 138.0 136.2 113.1 122.2 0 0 0 0 0 0 0 0 0 1 0 82 83 111.0 1 115.2 138.0 136.2 113.1 0 0 0 0 0 0 0 0 0 0 1 83 84 99.2 1 111.0 115.2 138.0 136.2 0 0 0 0 0 0 0 0 0 0 0 84 85 102.4 1 99.2 111.0 115.2 138.0 1 0 0 0 0 0 0 0 0 0 0 85 86 112.7 1 102.4 99.2 111.0 115.2 0 1 0 0 0 0 0 0 0 0 0 86 87 105.5 1 112.7 102.4 99.2 111.0 0 0 1 0 0 0 0 0 0 0 0 87 88 98.3 1 105.5 112.7 102.4 99.2 0 0 0 1 0 0 0 0 0 0 0 88 89 116.4 1 98.3 105.5 112.7 102.4 0 0 0 0 1 0 0 0 0 0 0 89 90 97.4 1 116.4 98.3 105.5 112.7 0 0 0 0 0 1 0 0 0 0 0 90 91 93.3 1 97.4 116.4 98.3 105.5 0 0 0 0 0 0 1 0 0 0 0 91 92 117.4 1 93.3 97.4 116.4 98.3 0 0 0 0 0 0 0 1 0 0 0 92 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) dummy y1 y2 y3 y4 26.0462 -10.2214 0.1254 0.3379 0.4679 -0.2704 M1 M2 M3 M4 M5 M6 7.5710 20.2818 8.2752 3.5202 10.4267 -3.9730 M7 M8 M9 M10 M11 t -8.1946 16.2001 27.6669 7.3593 -9.4455 0.1530 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -10.04819 -1.91824 -0.02218 2.68156 8.55368 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 26.04616 8.70639 2.992 0.003768 ** dummy -10.22138 2.34866 -4.352 4.26e-05 *** y1 0.12538 0.10991 1.141 0.257676 y2 0.33785 0.09306 3.631 0.000518 *** y3 0.46787 0.09099 5.142 2.15e-06 *** y4 -0.27042 0.10260 -2.636 0.010222 * M1 7.57100 2.35162 3.219 0.001908 ** M2 20.28176 2.60727 7.779 3.39e-11 *** M3 8.27516 3.60061 2.298 0.024373 * M4 3.52016 3.26025 1.080 0.283773 M5 10.42666 2.64137 3.947 0.000178 *** M6 -3.97298 2.92313 -1.359 0.178226 M7 -8.19458 2.60355 -3.147 0.002374 ** M8 16.20007 2.39496 6.764 2.70e-09 *** M9 27.66695 3.60722 7.670 5.44e-11 *** M10 7.35927 4.65111 1.582 0.117855 M11 -9.44548 3.65983 -2.581 0.011836 * t 0.15300 0.04192 3.649 0.000487 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.133 on 74 degrees of freedom Multiple R-squared: 0.919, Adjusted R-squared: 0.9004 F-statistic: 49.41 on 17 and 74 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,] 0.47146300 0.94292601 0.5285370 [2,] 0.30364160 0.60728319 0.6963584 [3,] 0.43232685 0.86465369 0.5676732 [4,] 0.30378258 0.60756516 0.6962174 [5,] 0.24411569 0.48823138 0.7558843 [6,] 0.35882726 0.71765453 0.6411727 [7,] 0.26798508 0.53597016 0.7320149 [8,] 0.23854101 0.47708202 0.7614590 [9,] 0.20847295 0.41694589 0.7915271 [10,] 0.23300105 0.46600209 0.7669990 [11,] 0.25687132 0.51374264 0.7431287 [12,] 0.19497424 0.38994849 0.8050258 [13,] 0.14253965 0.28507929 0.8574604 [14,] 0.09970377 0.19940755 0.9002962 [15,] 0.07587348 0.15174697 0.9241265 [16,] 0.07437826 0.14875653 0.9256217 [17,] 0.04876700 0.09753399 0.9512330 [18,] 0.03180300 0.06360599 0.9681970 [19,] 0.02186986 0.04373972 0.9781301 [20,] 0.02313347 0.04626694 0.9768665 [21,] 0.01405926 0.02811852 0.9859407 [22,] 0.07481329 0.14962658 0.9251867 [23,] 0.05070214 0.10140428 0.9492979 [24,] 0.03363067 0.06726134 0.9663693 [25,] 0.06498729 0.12997457 0.9350127 [26,] 0.04806548 0.09613097 0.9519345 [27,] 0.03732002 0.07464005 0.9626800 [28,] 0.05932418 0.11864836 0.9406758 [29,] 0.04414655 0.08829309 0.9558535 [30,] 0.11258334 0.22516669 0.8874167 [31,] 0.13630984 0.27261968 0.8636902 [32,] 0.17048724 0.34097447 0.8295128 [33,] 0.12884021 0.25768042 0.8711598 [34,] 0.11717114 0.23434229 0.8828289 [35,] 0.10836831 0.21673661 0.8916317 [36,] 0.07722946 0.15445892 0.9227705 [37,] 0.07440610 0.14881221 0.9255939 [38,] 0.05677142 0.11354285 0.9432286 [39,] 0.04097887 0.08195773 0.9590211 [40,] 0.02856111 0.05712223 0.9714389 [41,] 0.02772639 0.05545279 0.9722736 [42,] 0.02501816 0.05003631 0.9749818 [43,] 0.04918329 0.09836658 0.9508167 [44,] 0.07169407 0.14338813 0.9283059 [45,] 0.04933384 0.09866768 0.9506662 [46,] 0.03111741 0.06223482 0.9688826 [47,] 0.02739036 0.05478072 0.9726096 [48,] 0.13570402 0.27140804 0.8642960 [49,] 0.10709952 0.21419905 0.8929005 [50,] 0.06125072 0.12250143 0.9387493 [51,] 0.30179620 0.60359239 0.6982038 > postscript(file="/var/www/html/rcomp/tmp/1oa5m1262013698.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2tiea1262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3i6221262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ocl11262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/54swh1262013698.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 = 92 Frequency = 1 1 2 3 4 5 1.898088578 -0.364731359 -0.036836358 8.553682294 -0.379427845 6 7 8 9 10 2.123468617 -1.215339096 0.931436948 1.004382826 0.554047417 11 12 13 14 15 -2.617129463 -0.904082140 1.782176319 -3.581251661 -0.443967078 16 17 18 19 20 -3.542735075 -5.504251506 3.422145495 0.692408875 -1.278852126 21 22 23 24 25 -4.985810701 -0.964327491 3.552673912 -1.888111390 -1.929514032 26 27 28 29 30 3.212270223 3.113309467 -6.596202820 1.158105236 0.482920374 31 32 33 34 35 2.168602716 -1.849590542 -1.784092841 -0.556889661 3.683174164 36 37 38 39 40 3.727319211 -0.753256484 1.672793756 0.725576455 -4.615749523 41 42 43 44 45 -0.007530663 -8.362189475 0.308876109 -0.299617298 -7.179218547 46 47 48 49 50 -0.110221287 3.358432512 5.802272679 -1.892964067 7.864804143 51 52 53 54 55 -5.880224688 6.205242564 0.296983442 -2.819586244 -2.940313366 56 57 58 59 60 1.813475452 3.830052498 2.879928294 -0.679423983 0.638586345 61 62 63 64 65 -2.917248547 1.724428961 -4.705147084 5.295493886 1.869250162 66 67 68 69 70 1.977422174 -0.905206184 -5.743257333 2.709646181 1.915001107 71 72 73 74 75 -5.013818105 2.672201544 4.332350508 -6.829833431 4.259607438 76 77 78 79 80 1.595901272 -0.959567964 -1.914476896 -0.856545798 7.608704492 81 82 83 84 85 6.405040584 -3.717538378 -2.283909037 -10.048186250 -0.519632275 86 87 88 89 90 -3.698480632 2.967681848 -6.895632598 3.526439139 5.090295955 91 92 2.747516745 -1.182299594 > postscript(file="/var/www/html/rcomp/tmp/6066m1262013698.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 = 92 Frequency = 1 lag(myerror, k = 1) myerror 0 1.898088578 NA 1 -0.364731359 1.898088578 2 -0.036836358 -0.364731359 3 8.553682294 -0.036836358 4 -0.379427845 8.553682294 5 2.123468617 -0.379427845 6 -1.215339096 2.123468617 7 0.931436948 -1.215339096 8 1.004382826 0.931436948 9 0.554047417 1.004382826 10 -2.617129463 0.554047417 11 -0.904082140 -2.617129463 12 1.782176319 -0.904082140 13 -3.581251661 1.782176319 14 -0.443967078 -3.581251661 15 -3.542735075 -0.443967078 16 -5.504251506 -3.542735075 17 3.422145495 -5.504251506 18 0.692408875 3.422145495 19 -1.278852126 0.692408875 20 -4.985810701 -1.278852126 21 -0.964327491 -4.985810701 22 3.552673912 -0.964327491 23 -1.888111390 3.552673912 24 -1.929514032 -1.888111390 25 3.212270223 -1.929514032 26 3.113309467 3.212270223 27 -6.596202820 3.113309467 28 1.158105236 -6.596202820 29 0.482920374 1.158105236 30 2.168602716 0.482920374 31 -1.849590542 2.168602716 32 -1.784092841 -1.849590542 33 -0.556889661 -1.784092841 34 3.683174164 -0.556889661 35 3.727319211 3.683174164 36 -0.753256484 3.727319211 37 1.672793756 -0.753256484 38 0.725576455 1.672793756 39 -4.615749523 0.725576455 40 -0.007530663 -4.615749523 41 -8.362189475 -0.007530663 42 0.308876109 -8.362189475 43 -0.299617298 0.308876109 44 -7.179218547 -0.299617298 45 -0.110221287 -7.179218547 46 3.358432512 -0.110221287 47 5.802272679 3.358432512 48 -1.892964067 5.802272679 49 7.864804143 -1.892964067 50 -5.880224688 7.864804143 51 6.205242564 -5.880224688 52 0.296983442 6.205242564 53 -2.819586244 0.296983442 54 -2.940313366 -2.819586244 55 1.813475452 -2.940313366 56 3.830052498 1.813475452 57 2.879928294 3.830052498 58 -0.679423983 2.879928294 59 0.638586345 -0.679423983 60 -2.917248547 0.638586345 61 1.724428961 -2.917248547 62 -4.705147084 1.724428961 63 5.295493886 -4.705147084 64 1.869250162 5.295493886 65 1.977422174 1.869250162 66 -0.905206184 1.977422174 67 -5.743257333 -0.905206184 68 2.709646181 -5.743257333 69 1.915001107 2.709646181 70 -5.013818105 1.915001107 71 2.672201544 -5.013818105 72 4.332350508 2.672201544 73 -6.829833431 4.332350508 74 4.259607438 -6.829833431 75 1.595901272 4.259607438 76 -0.959567964 1.595901272 77 -1.914476896 -0.959567964 78 -0.856545798 -1.914476896 79 7.608704492 -0.856545798 80 6.405040584 7.608704492 81 -3.717538378 6.405040584 82 -2.283909037 -3.717538378 83 -10.048186250 -2.283909037 84 -0.519632275 -10.048186250 85 -3.698480632 -0.519632275 86 2.967681848 -3.698480632 87 -6.895632598 2.967681848 88 3.526439139 -6.895632598 89 5.090295955 3.526439139 90 2.747516745 5.090295955 91 -1.182299594 2.747516745 92 NA -1.182299594 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.364731359 1.898088578 [2,] -0.036836358 -0.364731359 [3,] 8.553682294 -0.036836358 [4,] -0.379427845 8.553682294 [5,] 2.123468617 -0.379427845 [6,] -1.215339096 2.123468617 [7,] 0.931436948 -1.215339096 [8,] 1.004382826 0.931436948 [9,] 0.554047417 1.004382826 [10,] -2.617129463 0.554047417 [11,] -0.904082140 -2.617129463 [12,] 1.782176319 -0.904082140 [13,] -3.581251661 1.782176319 [14,] -0.443967078 -3.581251661 [15,] -3.542735075 -0.443967078 [16,] -5.504251506 -3.542735075 [17,] 3.422145495 -5.504251506 [18,] 0.692408875 3.422145495 [19,] -1.278852126 0.692408875 [20,] -4.985810701 -1.278852126 [21,] -0.964327491 -4.985810701 [22,] 3.552673912 -0.964327491 [23,] -1.888111390 3.552673912 [24,] -1.929514032 -1.888111390 [25,] 3.212270223 -1.929514032 [26,] 3.113309467 3.212270223 [27,] -6.596202820 3.113309467 [28,] 1.158105236 -6.596202820 [29,] 0.482920374 1.158105236 [30,] 2.168602716 0.482920374 [31,] -1.849590542 2.168602716 [32,] -1.784092841 -1.849590542 [33,] -0.556889661 -1.784092841 [34,] 3.683174164 -0.556889661 [35,] 3.727319211 3.683174164 [36,] -0.753256484 3.727319211 [37,] 1.672793756 -0.753256484 [38,] 0.725576455 1.672793756 [39,] -4.615749523 0.725576455 [40,] -0.007530663 -4.615749523 [41,] -8.362189475 -0.007530663 [42,] 0.308876109 -8.362189475 [43,] -0.299617298 0.308876109 [44,] -7.179218547 -0.299617298 [45,] -0.110221287 -7.179218547 [46,] 3.358432512 -0.110221287 [47,] 5.802272679 3.358432512 [48,] -1.892964067 5.802272679 [49,] 7.864804143 -1.892964067 [50,] -5.880224688 7.864804143 [51,] 6.205242564 -5.880224688 [52,] 0.296983442 6.205242564 [53,] -2.819586244 0.296983442 [54,] -2.940313366 -2.819586244 [55,] 1.813475452 -2.940313366 [56,] 3.830052498 1.813475452 [57,] 2.879928294 3.830052498 [58,] -0.679423983 2.879928294 [59,] 0.638586345 -0.679423983 [60,] -2.917248547 0.638586345 [61,] 1.724428961 -2.917248547 [62,] -4.705147084 1.724428961 [63,] 5.295493886 -4.705147084 [64,] 1.869250162 5.295493886 [65,] 1.977422174 1.869250162 [66,] -0.905206184 1.977422174 [67,] -5.743257333 -0.905206184 [68,] 2.709646181 -5.743257333 [69,] 1.915001107 2.709646181 [70,] -5.013818105 1.915001107 [71,] 2.672201544 -5.013818105 [72,] 4.332350508 2.672201544 [73,] -6.829833431 4.332350508 [74,] 4.259607438 -6.829833431 [75,] 1.595901272 4.259607438 [76,] -0.959567964 1.595901272 [77,] -1.914476896 -0.959567964 [78,] -0.856545798 -1.914476896 [79,] 7.608704492 -0.856545798 [80,] 6.405040584 7.608704492 [81,] -3.717538378 6.405040584 [82,] -2.283909037 -3.717538378 [83,] -10.048186250 -2.283909037 [84,] -0.519632275 -10.048186250 [85,] -3.698480632 -0.519632275 [86,] 2.967681848 -3.698480632 [87,] -6.895632598 2.967681848 [88,] 3.526439139 -6.895632598 [89,] 5.090295955 3.526439139 [90,] 2.747516745 5.090295955 [91,] -1.182299594 2.747516745 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.364731359 1.898088578 2 -0.036836358 -0.364731359 3 8.553682294 -0.036836358 4 -0.379427845 8.553682294 5 2.123468617 -0.379427845 6 -1.215339096 2.123468617 7 0.931436948 -1.215339096 8 1.004382826 0.931436948 9 0.554047417 1.004382826 10 -2.617129463 0.554047417 11 -0.904082140 -2.617129463 12 1.782176319 -0.904082140 13 -3.581251661 1.782176319 14 -0.443967078 -3.581251661 15 -3.542735075 -0.443967078 16 -5.504251506 -3.542735075 17 3.422145495 -5.504251506 18 0.692408875 3.422145495 19 -1.278852126 0.692408875 20 -4.985810701 -1.278852126 21 -0.964327491 -4.985810701 22 3.552673912 -0.964327491 23 -1.888111390 3.552673912 24 -1.929514032 -1.888111390 25 3.212270223 -1.929514032 26 3.113309467 3.212270223 27 -6.596202820 3.113309467 28 1.158105236 -6.596202820 29 0.482920374 1.158105236 30 2.168602716 0.482920374 31 -1.849590542 2.168602716 32 -1.784092841 -1.849590542 33 -0.556889661 -1.784092841 34 3.683174164 -0.556889661 35 3.727319211 3.683174164 36 -0.753256484 3.727319211 37 1.672793756 -0.753256484 38 0.725576455 1.672793756 39 -4.615749523 0.725576455 40 -0.007530663 -4.615749523 41 -8.362189475 -0.007530663 42 0.308876109 -8.362189475 43 -0.299617298 0.308876109 44 -7.179218547 -0.299617298 45 -0.110221287 -7.179218547 46 3.358432512 -0.110221287 47 5.802272679 3.358432512 48 -1.892964067 5.802272679 49 7.864804143 -1.892964067 50 -5.880224688 7.864804143 51 6.205242564 -5.880224688 52 0.296983442 6.205242564 53 -2.819586244 0.296983442 54 -2.940313366 -2.819586244 55 1.813475452 -2.940313366 56 3.830052498 1.813475452 57 2.879928294 3.830052498 58 -0.679423983 2.879928294 59 0.638586345 -0.679423983 60 -2.917248547 0.638586345 61 1.724428961 -2.917248547 62 -4.705147084 1.724428961 63 5.295493886 -4.705147084 64 1.869250162 5.295493886 65 1.977422174 1.869250162 66 -0.905206184 1.977422174 67 -5.743257333 -0.905206184 68 2.709646181 -5.743257333 69 1.915001107 2.709646181 70 -5.013818105 1.915001107 71 2.672201544 -5.013818105 72 4.332350508 2.672201544 73 -6.829833431 4.332350508 74 4.259607438 -6.829833431 75 1.595901272 4.259607438 76 -0.959567964 1.595901272 77 -1.914476896 -0.959567964 78 -0.856545798 -1.914476896 79 7.608704492 -0.856545798 80 6.405040584 7.608704492 81 -3.717538378 6.405040584 82 -2.283909037 -3.717538378 83 -10.048186250 -2.283909037 84 -0.519632275 -10.048186250 85 -3.698480632 -0.519632275 86 2.967681848 -3.698480632 87 -6.895632598 2.967681848 88 3.526439139 -6.895632598 89 5.090295955 3.526439139 90 2.747516745 5.090295955 91 -1.182299594 2.747516745 > 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/78fum1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8gsmh1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/95fd41262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10jllv1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11p2er1262013699.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/12yxls1262013699.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/134srf1262013699.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/14sgpk1262013699.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/159wha1262013699.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/16gak41262013699.tab") + } > > try(system("convert tmp/1oa5m1262013698.ps tmp/1oa5m1262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/2tiea1262013698.ps tmp/2tiea1262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/3i6221262013698.ps tmp/3i6221262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/4ocl11262013698.ps tmp/4ocl11262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/54swh1262013698.ps tmp/54swh1262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/6066m1262013698.ps tmp/6066m1262013698.png",intern=TRUE)) character(0) > try(system("convert tmp/78fum1262013699.ps tmp/78fum1262013699.png",intern=TRUE)) character(0) > try(system("convert tmp/8gsmh1262013699.ps tmp/8gsmh1262013699.png",intern=TRUE)) character(0) > try(system("convert tmp/95fd41262013699.ps tmp/95fd41262013699.png",intern=TRUE)) character(0) > try(system("convert tmp/10jllv1262013699.ps tmp/10jllv1262013699.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.868 1.572 4.755