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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 + ,1 + ,115.0 + ,129.4 + ,138.9 + ,128.9 + ,127.0 + ,1 + ,128.0 + ,115.0 + ,129.4 + ,138.9 + ,128.8 + ,1 + ,127.0 + ,128.0 + ,115.0 + ,129.4 + ,137.9 + ,1 + ,128.8 + ,127.0 + ,128.0 + ,115.0 + ,128.4 + ,1 + ,137.9 + ,128.8 + ,127.0 + ,128.0 + ,135.9 + ,1 + ,128.4 + ,137.9 + ,128.8 + ,127.0 + ,122.2 + ,1 + ,135.9 + ,128.4 + ,137.9 + ,128.8 + ,113.1 + ,1 + ,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' + ,'x' + ,'y1' + ,'y2' + ,'y3' + ,'y4') + ,1:92)) > y <- array(NA,dim=c(6,92),dimnames=list(c('y','x','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 x 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 1 115.0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72 73 127.0 1 128.0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73 74 128.8 1 127.0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74 75 137.9 1 128.8 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75 76 128.4 1 137.9 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76 77 135.9 1 128.4 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77 78 122.2 1 135.9 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78 79 113.1 1 122.2 135.9 128.4 137.9 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) x y1 y2 y3 y4 6.22105 -2.97335 0.30329 0.43052 0.49388 -0.34294 M1 M2 M3 M4 M5 M6 8.26140 20.38931 5.65106 1.25758 8.94166 -5.91155 M7 M8 M9 M10 M11 t -7.84346 17.23558 26.47015 2.61838 -13.43681 0.04841 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -12.8216 -2.7300 0.4627 2.5680 7.8527 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.22105 7.90507 0.787 0.43381 x -2.97335 1.82373 -1.630 0.10727 y1 0.30329 0.11059 2.742 0.00764 ** y2 0.43052 0.09920 4.340 4.45e-05 *** y3 0.49388 0.10021 4.928 4.92e-06 *** y4 -0.34294 0.11127 -3.082 0.00289 ** M1 8.26140 2.58299 3.198 0.00203 ** M2 20.38931 2.87494 7.092 6.62e-10 *** M3 5.65106 3.90391 1.448 0.15197 M4 1.25758 3.53957 0.355 0.72338 M5 8.94166 2.87966 3.105 0.00270 ** M6 -5.91155 3.17459 -1.862 0.06655 . M7 -7.84346 2.86540 -2.737 0.00775 ** M8 17.23558 2.63143 6.550 6.71e-09 *** M9 26.47015 3.95833 6.687 3.74e-09 *** M10 2.61838 4.94999 0.529 0.59841 M11 -13.43681 3.87583 -3.467 0.00088 *** t 0.04841 0.03783 1.280 0.20471 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.55 on 74 degrees of freedom Multiple R-squared: 0.9018, Adjusted R-squared: 0.8793 F-statistic: 39.99 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,] 0.379275916 0.758551833 0.6207241 [2,] 0.222053440 0.444106880 0.7779466 [3,] 0.317941491 0.635882981 0.6820585 [4,] 0.204646399 0.409292798 0.7953536 [5,] 0.153306251 0.306612503 0.8466937 [6,] 0.230074849 0.460149698 0.7699252 [7,] 0.154142817 0.308285634 0.8458572 [8,] 0.134234864 0.268469727 0.8657651 [9,] 0.110934715 0.221869431 0.8890653 [10,] 0.121348362 0.242696724 0.8786516 [11,] 0.130828850 0.261657699 0.8691712 [12,] 0.090570387 0.181140773 0.9094296 [13,] 0.059685195 0.119370390 0.9403148 [14,] 0.037656232 0.075312463 0.9623438 [15,] 0.026018145 0.052036289 0.9739819 [16,] 0.023350260 0.046700519 0.9766497 [17,] 0.014111910 0.028223820 0.9858881 [18,] 0.008042081 0.016084161 0.9919579 [19,] 0.004832367 0.009664734 0.9951676 [20,] 0.005832303 0.011664605 0.9941677 [21,] 0.003274081 0.006548162 0.9967259 [22,] 0.025507897 0.051015794 0.9744921 [23,] 0.015788172 0.031576344 0.9842118 [24,] 0.009469560 0.018939121 0.9905304 [25,] 0.015797468 0.031594936 0.9842025 [26,] 0.009885945 0.019771890 0.9901141 [27,] 0.007030639 0.014061277 0.9929694 [28,] 0.011044017 0.022088033 0.9889560 [29,] 0.008493926 0.016987851 0.9915061 [30,] 0.018350409 0.036700818 0.9816496 [31,] 0.038698678 0.077397355 0.9613013 [32,] 0.044592215 0.089184430 0.9554078 [33,] 0.037215081 0.074430161 0.9627849 [34,] 0.048181312 0.096362624 0.9518187 [35,] 0.063647889 0.127295778 0.9363521 [36,] 0.043007409 0.086014818 0.9569926 [37,] 0.053580901 0.107161802 0.9464191 [38,] 0.044900338 0.089800677 0.9550997 [39,] 0.069574429 0.139148858 0.9304256 [40,] 0.047823992 0.095647983 0.9521760 [41,] 0.069249771 0.138499541 0.9307502 [42,] 0.049122085 0.098244169 0.9508779 [43,] 0.166153928 0.332307855 0.8338461 [44,] 0.171813505 0.343627009 0.8281865 [45,] 0.167396798 0.334793596 0.8326032 [46,] 0.120081538 0.240163075 0.8799185 [47,] 0.122201699 0.244403397 0.8777983 [48,] 0.561366632 0.877266735 0.4386334 [49,] 0.548245993 0.903508014 0.4517540 [50,] 0.414847251 0.829694501 0.5851527 [51,] 0.283738509 0.567477018 0.7162615 > postscript(file="/var/www/html/rcomp/tmp/1ly2i1262009908.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ynxy1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/32mhi1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4vyls1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5aptv1262009908.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 6 0.53271244 -1.72630314 -1.15369111 7.59609540 -2.67157448 0.45187756 7 8 9 10 11 12 -3.06882430 1.09246271 0.81921991 0.26495944 -2.90533165 -1.15476330 13 14 15 16 17 18 1.83423113 -3.96661579 -0.20350489 -3.45661874 -4.85394785 4.66769092 19 20 21 22 23 24 0.86352851 0.02678542 -3.94882341 0.77452162 4.95524967 -1.92635467 25 26 27 28 29 30 -1.43839766 3.69181901 2.87556340 -7.35261424 1.60933919 0.47351412 31 32 33 34 35 36 2.10768244 -1.23285167 -0.90632862 0.40624580 4.44234007 3.28696817 37 38 39 40 41 42 -1.62486766 1.06536966 -0.08021929 -5.38084566 0.11941084 -8.39821180 43 44 45 46 47 48 1.75609097 1.68681413 -5.48834041 2.36804076 5.07506917 6.28751281 49 50 51 52 53 54 -2.56351322 7.69936469 -7.44759992 6.13382447 -1.13625789 -3.71426422 55 56 57 58 59 60 -3.35774716 3.21426094 4.65524937 2.75804768 -1.17934343 0.04345645 61 62 63 64 65 66 -3.42654154 1.87740625 -5.04084159 5.88029147 1.19267344 1.23265993 67 68 69 70 71 72 -1.95002827 -5.09290995 4.25389504 2.50471579 -4.72888329 6.28475324 73 74 75 76 77 78 7.35300539 -4.46291512 7.85270838 4.11506178 1.61413545 0.65722710 79 80 81 82 83 84 2.17951667 1.84810014 0.61512811 -9.07653108 -5.65910055 -12.82157272 85 86 87 88 89 90 -0.66662888 -4.17812556 3.19758502 -7.53519448 4.12622131 4.62950639 91 92 1.46978114 -1.54266172 > postscript(file="/var/www/html/rcomp/tmp/686h11262009908.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 0.53271244 NA 1 -1.72630314 0.53271244 2 -1.15369111 -1.72630314 3 7.59609540 -1.15369111 4 -2.67157448 7.59609540 5 0.45187756 -2.67157448 6 -3.06882430 0.45187756 7 1.09246271 -3.06882430 8 0.81921991 1.09246271 9 0.26495944 0.81921991 10 -2.90533165 0.26495944 11 -1.15476330 -2.90533165 12 1.83423113 -1.15476330 13 -3.96661579 1.83423113 14 -0.20350489 -3.96661579 15 -3.45661874 -0.20350489 16 -4.85394785 -3.45661874 17 4.66769092 -4.85394785 18 0.86352851 4.66769092 19 0.02678542 0.86352851 20 -3.94882341 0.02678542 21 0.77452162 -3.94882341 22 4.95524967 0.77452162 23 -1.92635467 4.95524967 24 -1.43839766 -1.92635467 25 3.69181901 -1.43839766 26 2.87556340 3.69181901 27 -7.35261424 2.87556340 28 1.60933919 -7.35261424 29 0.47351412 1.60933919 30 2.10768244 0.47351412 31 -1.23285167 2.10768244 32 -0.90632862 -1.23285167 33 0.40624580 -0.90632862 34 4.44234007 0.40624580 35 3.28696817 4.44234007 36 -1.62486766 3.28696817 37 1.06536966 -1.62486766 38 -0.08021929 1.06536966 39 -5.38084566 -0.08021929 40 0.11941084 -5.38084566 41 -8.39821180 0.11941084 42 1.75609097 -8.39821180 43 1.68681413 1.75609097 44 -5.48834041 1.68681413 45 2.36804076 -5.48834041 46 5.07506917 2.36804076 47 6.28751281 5.07506917 48 -2.56351322 6.28751281 49 7.69936469 -2.56351322 50 -7.44759992 7.69936469 51 6.13382447 -7.44759992 52 -1.13625789 6.13382447 53 -3.71426422 -1.13625789 54 -3.35774716 -3.71426422 55 3.21426094 -3.35774716 56 4.65524937 3.21426094 57 2.75804768 4.65524937 58 -1.17934343 2.75804768 59 0.04345645 -1.17934343 60 -3.42654154 0.04345645 61 1.87740625 -3.42654154 62 -5.04084159 1.87740625 63 5.88029147 -5.04084159 64 1.19267344 5.88029147 65 1.23265993 1.19267344 66 -1.95002827 1.23265993 67 -5.09290995 -1.95002827 68 4.25389504 -5.09290995 69 2.50471579 4.25389504 70 -4.72888329 2.50471579 71 6.28475324 -4.72888329 72 7.35300539 6.28475324 73 -4.46291512 7.35300539 74 7.85270838 -4.46291512 75 4.11506178 7.85270838 76 1.61413545 4.11506178 77 0.65722710 1.61413545 78 2.17951667 0.65722710 79 1.84810014 2.17951667 80 0.61512811 1.84810014 81 -9.07653108 0.61512811 82 -5.65910055 -9.07653108 83 -12.82157272 -5.65910055 84 -0.66662888 -12.82157272 85 -4.17812556 -0.66662888 86 3.19758502 -4.17812556 87 -7.53519448 3.19758502 88 4.12622131 -7.53519448 89 4.62950639 4.12622131 90 1.46978114 4.62950639 91 -1.54266172 1.46978114 92 NA -1.54266172 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.72630314 0.53271244 [2,] -1.15369111 -1.72630314 [3,] 7.59609540 -1.15369111 [4,] -2.67157448 7.59609540 [5,] 0.45187756 -2.67157448 [6,] -3.06882430 0.45187756 [7,] 1.09246271 -3.06882430 [8,] 0.81921991 1.09246271 [9,] 0.26495944 0.81921991 [10,] -2.90533165 0.26495944 [11,] -1.15476330 -2.90533165 [12,] 1.83423113 -1.15476330 [13,] -3.96661579 1.83423113 [14,] -0.20350489 -3.96661579 [15,] -3.45661874 -0.20350489 [16,] -4.85394785 -3.45661874 [17,] 4.66769092 -4.85394785 [18,] 0.86352851 4.66769092 [19,] 0.02678542 0.86352851 [20,] -3.94882341 0.02678542 [21,] 0.77452162 -3.94882341 [22,] 4.95524967 0.77452162 [23,] -1.92635467 4.95524967 [24,] -1.43839766 -1.92635467 [25,] 3.69181901 -1.43839766 [26,] 2.87556340 3.69181901 [27,] -7.35261424 2.87556340 [28,] 1.60933919 -7.35261424 [29,] 0.47351412 1.60933919 [30,] 2.10768244 0.47351412 [31,] -1.23285167 2.10768244 [32,] -0.90632862 -1.23285167 [33,] 0.40624580 -0.90632862 [34,] 4.44234007 0.40624580 [35,] 3.28696817 4.44234007 [36,] -1.62486766 3.28696817 [37,] 1.06536966 -1.62486766 [38,] -0.08021929 1.06536966 [39,] -5.38084566 -0.08021929 [40,] 0.11941084 -5.38084566 [41,] -8.39821180 0.11941084 [42,] 1.75609097 -8.39821180 [43,] 1.68681413 1.75609097 [44,] -5.48834041 1.68681413 [45,] 2.36804076 -5.48834041 [46,] 5.07506917 2.36804076 [47,] 6.28751281 5.07506917 [48,] -2.56351322 6.28751281 [49,] 7.69936469 -2.56351322 [50,] -7.44759992 7.69936469 [51,] 6.13382447 -7.44759992 [52,] -1.13625789 6.13382447 [53,] -3.71426422 -1.13625789 [54,] -3.35774716 -3.71426422 [55,] 3.21426094 -3.35774716 [56,] 4.65524937 3.21426094 [57,] 2.75804768 4.65524937 [58,] -1.17934343 2.75804768 [59,] 0.04345645 -1.17934343 [60,] -3.42654154 0.04345645 [61,] 1.87740625 -3.42654154 [62,] -5.04084159 1.87740625 [63,] 5.88029147 -5.04084159 [64,] 1.19267344 5.88029147 [65,] 1.23265993 1.19267344 [66,] -1.95002827 1.23265993 [67,] -5.09290995 -1.95002827 [68,] 4.25389504 -5.09290995 [69,] 2.50471579 4.25389504 [70,] -4.72888329 2.50471579 [71,] 6.28475324 -4.72888329 [72,] 7.35300539 6.28475324 [73,] -4.46291512 7.35300539 [74,] 7.85270838 -4.46291512 [75,] 4.11506178 7.85270838 [76,] 1.61413545 4.11506178 [77,] 0.65722710 1.61413545 [78,] 2.17951667 0.65722710 [79,] 1.84810014 2.17951667 [80,] 0.61512811 1.84810014 [81,] -9.07653108 0.61512811 [82,] -5.65910055 -9.07653108 [83,] -12.82157272 -5.65910055 [84,] -0.66662888 -12.82157272 [85,] -4.17812556 -0.66662888 [86,] 3.19758502 -4.17812556 [87,] -7.53519448 3.19758502 [88,] 4.12622131 -7.53519448 [89,] 4.62950639 4.12622131 [90,] 1.46978114 4.62950639 [91,] -1.54266172 1.46978114 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.72630314 0.53271244 2 -1.15369111 -1.72630314 3 7.59609540 -1.15369111 4 -2.67157448 7.59609540 5 0.45187756 -2.67157448 6 -3.06882430 0.45187756 7 1.09246271 -3.06882430 8 0.81921991 1.09246271 9 0.26495944 0.81921991 10 -2.90533165 0.26495944 11 -1.15476330 -2.90533165 12 1.83423113 -1.15476330 13 -3.96661579 1.83423113 14 -0.20350489 -3.96661579 15 -3.45661874 -0.20350489 16 -4.85394785 -3.45661874 17 4.66769092 -4.85394785 18 0.86352851 4.66769092 19 0.02678542 0.86352851 20 -3.94882341 0.02678542 21 0.77452162 -3.94882341 22 4.95524967 0.77452162 23 -1.92635467 4.95524967 24 -1.43839766 -1.92635467 25 3.69181901 -1.43839766 26 2.87556340 3.69181901 27 -7.35261424 2.87556340 28 1.60933919 -7.35261424 29 0.47351412 1.60933919 30 2.10768244 0.47351412 31 -1.23285167 2.10768244 32 -0.90632862 -1.23285167 33 0.40624580 -0.90632862 34 4.44234007 0.40624580 35 3.28696817 4.44234007 36 -1.62486766 3.28696817 37 1.06536966 -1.62486766 38 -0.08021929 1.06536966 39 -5.38084566 -0.08021929 40 0.11941084 -5.38084566 41 -8.39821180 0.11941084 42 1.75609097 -8.39821180 43 1.68681413 1.75609097 44 -5.48834041 1.68681413 45 2.36804076 -5.48834041 46 5.07506917 2.36804076 47 6.28751281 5.07506917 48 -2.56351322 6.28751281 49 7.69936469 -2.56351322 50 -7.44759992 7.69936469 51 6.13382447 -7.44759992 52 -1.13625789 6.13382447 53 -3.71426422 -1.13625789 54 -3.35774716 -3.71426422 55 3.21426094 -3.35774716 56 4.65524937 3.21426094 57 2.75804768 4.65524937 58 -1.17934343 2.75804768 59 0.04345645 -1.17934343 60 -3.42654154 0.04345645 61 1.87740625 -3.42654154 62 -5.04084159 1.87740625 63 5.88029147 -5.04084159 64 1.19267344 5.88029147 65 1.23265993 1.19267344 66 -1.95002827 1.23265993 67 -5.09290995 -1.95002827 68 4.25389504 -5.09290995 69 2.50471579 4.25389504 70 -4.72888329 2.50471579 71 6.28475324 -4.72888329 72 7.35300539 6.28475324 73 -4.46291512 7.35300539 74 7.85270838 -4.46291512 75 4.11506178 7.85270838 76 1.61413545 4.11506178 77 0.65722710 1.61413545 78 2.17951667 0.65722710 79 1.84810014 2.17951667 80 0.61512811 1.84810014 81 -9.07653108 0.61512811 82 -5.65910055 -9.07653108 83 -12.82157272 -5.65910055 84 -0.66662888 -12.82157272 85 -4.17812556 -0.66662888 86 3.19758502 -4.17812556 87 -7.53519448 3.19758502 88 4.12622131 -7.53519448 89 4.62950639 4.12622131 90 1.46978114 4.62950639 91 -1.54266172 1.46978114 > 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/7esam1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/893lv1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9m0rv1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/105mdy1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11htco1262009908.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/12iegb1262009908.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/137rp81262009908.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/1457up1262009908.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/15myfr1262009908.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/16vs5e1262009908.tab") + } > > try(system("convert tmp/1ly2i1262009908.ps tmp/1ly2i1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/2ynxy1262009908.ps tmp/2ynxy1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/32mhi1262009908.ps tmp/32mhi1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/4vyls1262009908.ps tmp/4vyls1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/5aptv1262009908.ps tmp/5aptv1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/686h11262009908.ps tmp/686h11262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/7esam1262009908.ps tmp/7esam1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/893lv1262009908.ps tmp/893lv1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/9m0rv1262009908.ps tmp/9m0rv1262009908.png",intern=TRUE)) character(0) > try(system("convert tmp/105mdy1262009908.ps tmp/105mdy1262009908.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.916 1.629 4.035