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Type 'q()' to quit R. > x <- array(list(-0.03086 + ,-0.01025 + ,0.04860 + ,0.04399 + ,-0.03429 + ,0.00779 + ,0.00149 + ,0.01848 + ,0.00338 + ,0.00099 + ,-0.01826 + ,0.04033 + ,-0.03086 + ,-0.01025 + ,0.04860 + ,0.04399 + ,-0.03429 + ,0.01244 + ,0.00149 + ,0.01848 + ,0.00338 + ,0.00099 + ,-0.02352 + ,0.04033 + ,-0.03086 + ,-0.01025 + ,0.04860 + ,0.04399 + ,0.01150 + ,0.01244 + ,0.00149 + ,0.01848 + ,0.00338 + ,0.00573 + ,-0.02352 + ,0.04033 + ,-0.03086 + ,-0.01025 + ,0.04860 + ,-0.00793 + ,0.01150 + ,0.01244 + ,0.00149 + ,0.01848 + ,0.01805 + ,0.00573 + ,-0.02352 + ,0.04033 + ,-0.03086 + ,-0.01025 + ,-0.01514 + ,-0.00793 + ,0.01150 + ,0.01244 + ,0.00149 + ,-0.01887 + ,0.01805 + ,0.00573 + ,-0.02352 + ,0.04033 + ,-0.03086 + ,0.01778 + ,-0.01514 + ,-0.00793 + ,0.01150 + ,0.01244 + ,0.04363 + ,-0.01887 + ,0.01805 + ,0.00573 + ,-0.02352 + ,0.04033 + ,0.00634 + ,0.01778 + ,-0.01514 + ,-0.00793 + ,0.01150 + ,0.02875 + ,0.04363 + ,-0.01887 + ,0.01805 + ,0.00573 + ,-0.02352 + ,0.00770 + 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,dimnames=list(c('(1-B)lnYt' + ,'(1-B)lnY_[t-1]' + ,'(1-B)lnY_[t-2]' + ,'(1-B)lnY_[t-3]' + ,'(1-B)lnY_[t-4]' + ,'(1-B)lnY_[t-5]' + ,'(1-B)lnX_[t-1]' + ,'(1-B)lnX_[t-2]' + ,'(1-B)lnX_[t-3]' + ,'(1-B)lnX_[t-4]' + ,'(1-B)lnX_[t-5]') + ,1:95)) > y <- array(NA,dim=c(11,95),dimnames=list(c('(1-B)lnYt','(1-B)lnY_[t-1]','(1-B)lnY_[t-2]','(1-B)lnY_[t-3]','(1-B)lnY_[t-4]','(1-B)lnY_[t-5]','(1-B)lnX_[t-1]','(1-B)lnX_[t-2]','(1-B)lnX_[t-3]','(1-B)lnX_[t-4]','(1-B)lnX_[t-5]'),1:95)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '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 (1-B)lnYt (1-B)lnY_[t-1] (1-B)lnY_[t-2] (1-B)lnY_[t-3] (1-B)lnY_[t-4] 1 -0.03086 -0.01025 0.04860 0.04399 -0.03429 2 0.04033 -0.03086 -0.01025 0.04860 0.04399 3 -0.02352 0.04033 -0.03086 -0.01025 0.04860 4 0.00573 -0.02352 0.04033 -0.03086 -0.01025 5 0.01805 0.00573 -0.02352 0.04033 -0.03086 6 -0.01887 0.01805 0.00573 -0.02352 0.04033 7 0.04363 -0.01887 0.01805 0.00573 -0.02352 8 0.02875 0.04363 -0.01887 0.01805 0.00573 9 -0.00393 0.02875 0.04363 -0.01887 0.01805 10 0.05280 -0.00393 0.02875 0.04363 -0.01887 11 -0.00351 0.05280 -0.00393 0.02875 0.04363 12 0.05407 -0.00351 0.05280 -0.00393 0.02875 13 -0.01299 0.05407 -0.00351 0.05280 -0.00393 14 0.00747 -0.01299 0.05407 -0.00351 0.05280 15 -0.03288 0.00747 -0.01299 0.05407 -0.00351 16 -0.05013 -0.03288 0.00747 -0.01299 0.05407 17 0.03715 -0.05013 -0.03288 0.00747 -0.01299 18 0.00205 0.03715 -0.05013 -0.03288 0.00747 19 0.02912 0.00205 0.03715 -0.05013 -0.03288 20 -0.00832 0.02912 0.00205 0.03715 -0.05013 21 0.02908 -0.00832 0.02912 0.00205 0.03715 22 -0.00942 0.02908 -0.00832 0.02912 0.00205 23 0.04381 -0.00942 0.02908 -0.00832 0.02912 24 0.00603 0.04381 -0.00942 0.02908 -0.00832 25 0.02253 0.00603 0.04381 -0.00942 0.02908 26 0.05789 0.02253 0.00603 0.04381 -0.00942 27 -0.03783 0.05789 0.02253 0.00603 0.04381 28 -0.03176 -0.03783 0.05789 0.02253 0.00603 29 -0.00572 -0.03176 -0.03783 0.05789 0.02253 30 0.01040 -0.00572 -0.03176 -0.03783 0.05789 31 0.03662 0.01040 -0.00572 -0.03176 -0.03783 32 0.03771 0.03662 0.01040 -0.00572 -0.03176 33 0.05981 0.03771 0.03662 0.01040 -0.00572 34 -0.03204 0.05981 0.03771 0.03662 0.01040 35 0.02837 -0.03204 0.05981 0.03771 0.03662 36 0.05003 0.02837 -0.03204 0.05981 0.03771 37 0.04980 0.05003 0.02837 -0.03204 0.05981 38 -0.02299 0.04980 0.05003 0.02837 -0.03204 39 0.04030 -0.02299 0.04980 0.05003 0.02837 40 0.03176 0.04030 -0.02299 0.04980 0.05003 41 -0.00135 0.03176 0.04030 -0.02299 0.04980 42 -0.02473 -0.00135 0.03176 0.04030 -0.02299 43 -0.00171 -0.02473 -0.00135 0.03176 0.04030 44 -0.01575 -0.00171 -0.02473 -0.00135 0.03176 45 -0.02624 -0.01575 -0.00171 -0.02473 -0.00135 46 0.06724 -0.02624 -0.01575 -0.00171 -0.02473 47 -0.01362 0.06724 -0.02624 -0.01575 -0.00171 48 -0.00422 -0.01362 0.06724 -0.02624 -0.01575 49 0.00754 -0.00422 -0.01362 0.06724 -0.02624 50 0.00087 0.00754 -0.00422 -0.01362 0.06724 51 0.02715 0.00087 0.00754 -0.00422 -0.01362 52 0.02976 0.02715 0.00087 0.00754 -0.00422 53 0.07946 0.02976 0.02715 0.00087 0.00754 54 0.01909 0.07946 0.02976 0.02715 0.00087 55 -0.02483 0.01909 0.07946 0.02976 0.02715 56 -0.01870 -0.02483 0.01909 0.07946 0.02976 57 0.09682 -0.01870 -0.02483 0.01909 0.07946 58 0.03823 0.09682 -0.01870 -0.02483 0.01909 59 0.09571 0.03823 0.09682 -0.01870 -0.02483 60 -0.04663 0.09571 0.03823 0.09682 -0.01870 61 -0.01359 -0.04663 0.09571 0.03823 0.09682 62 0.05114 -0.01359 -0.04663 0.09571 0.03823 63 -0.04275 0.05114 -0.01359 -0.04663 0.09571 64 0.05739 -0.04275 0.05114 -0.01359 -0.04663 65 0.01186 0.05739 -0.04275 0.05114 -0.01359 66 0.01066 0.01186 0.05739 -0.04275 0.05114 67 -0.07387 0.01066 0.01186 0.05739 -0.04275 68 -0.04131 -0.07387 0.01066 0.01186 0.05739 69 -0.17889 -0.04131 -0.07387 0.01066 0.01186 70 -0.12781 -0.17889 -0.04131 -0.07387 0.01066 71 -0.26933 -0.12781 -0.17889 -0.04131 -0.07387 72 -0.05095 -0.26933 -0.12781 -0.17889 -0.04131 73 -0.01074 -0.05095 -0.26933 -0.12781 -0.17889 74 0.08172 -0.01074 -0.05095 -0.26933 -0.12781 75 0.11870 0.08172 -0.01074 -0.05095 -0.26933 76 0.08475 0.11870 0.08172 -0.01074 -0.05095 77 0.04663 0.08475 0.11870 0.08172 -0.01074 78 -0.04415 0.04663 0.08475 0.11870 0.08172 79 0.00970 -0.04415 0.04663 0.08475 0.11870 80 -0.03341 0.00970 -0.04415 0.04663 0.08475 81 0.04031 -0.03341 0.00970 -0.04415 0.04663 82 0.01938 0.04031 -0.03341 0.00970 -0.04415 83 0.05928 0.01938 0.04031 -0.03341 0.00970 84 0.02343 0.05928 0.01938 0.04031 -0.03341 85 -0.04536 0.02343 0.05928 0.01938 0.04031 86 0.03355 -0.04536 0.02343 0.05928 0.01938 87 0.05659 0.03355 -0.04536 0.02343 0.05928 88 -0.06579 0.05659 0.03355 -0.04536 0.02343 89 -0.04267 -0.06579 0.05659 0.03355 -0.04536 90 -0.02422 -0.04267 -0.06579 0.05659 0.03355 91 0.07584 -0.02422 -0.04267 -0.06579 0.05659 92 -0.00903 0.07584 -0.02422 -0.04267 -0.06579 93 0.06617 -0.00903 0.07584 -0.02422 -0.04267 94 0.04485 0.06617 -0.00903 0.07584 -0.02422 95 -0.00665 0.04485 0.06617 -0.00903 0.07584 (1-B)lnY_[t-5] (1-B)lnX_[t-1] (1-B)lnX_[t-2] (1-B)lnX_[t-3] (1-B)lnX_[t-4] 1 0.00779 0.00149 0.01848 0.00338 0.00099 2 -0.03429 0.01244 0.00149 0.01848 0.00338 3 0.04399 0.01150 0.01244 0.00149 0.01848 4 0.04860 -0.00793 0.01150 0.01244 0.00149 5 -0.01025 -0.01514 -0.00793 0.01150 0.01244 6 -0.03086 0.01778 -0.01514 -0.00793 0.01150 7 0.04033 0.00634 0.01778 -0.01514 -0.00793 8 -0.02352 0.00770 0.00634 0.01778 -0.01514 9 0.00573 0.00692 0.00770 0.00634 0.01778 10 0.01805 0.00029 0.00692 0.00770 0.00634 11 -0.01887 0.02487 0.00029 0.00692 0.00770 12 0.04363 0.01708 0.02487 0.00029 0.00692 13 0.02875 0.02540 0.01708 0.02487 0.00029 14 -0.00393 0.02935 0.02540 0.01708 0.02487 15 0.05280 0.02615 0.02935 0.02540 0.01708 16 -0.00351 0.00424 0.02615 0.02935 0.02540 17 0.05407 -0.00032 0.00424 0.02615 0.02935 18 -0.01299 -0.02353 -0.00032 0.00424 0.02615 19 0.00747 0.01387 -0.02353 -0.00032 0.00424 20 -0.03288 0.01286 0.01387 -0.02353 -0.00032 21 -0.05013 -0.00609 0.01286 0.01387 -0.02353 22 0.03715 0.00635 -0.00609 0.01286 0.01387 23 0.00205 0.02049 0.00635 -0.00609 0.01286 24 0.02912 0.00332 0.02049 0.00635 -0.00609 25 -0.00832 0.00409 0.00332 0.02049 0.00635 26 0.02908 0.02753 0.00409 0.00332 0.02049 27 -0.00942 0.01205 0.02753 0.00409 0.00332 28 0.04381 0.01773 0.01205 0.02753 0.00409 29 0.00603 -0.00897 0.01773 0.01205 0.02753 30 0.02253 -0.01226 -0.00897 0.01773 0.01205 31 0.05789 0.00644 -0.01226 -0.00897 0.01773 32 -0.03783 -0.00059 0.00644 -0.01226 -0.00897 33 -0.03176 0.01707 -0.00059 0.00644 -0.01226 34 -0.00572 -0.00104 0.01707 -0.00059 0.00644 35 0.01040 0.01272 -0.00104 0.01707 -0.00059 36 0.03662 0.01859 0.01272 -0.00104 0.01707 37 0.03771 0.03238 0.01859 0.01272 -0.00104 38 0.05981 0.03132 0.03238 0.01859 0.01272 39 -0.03204 0.01412 0.03132 0.03238 0.01859 40 0.02837 0.00588 0.01412 0.03132 0.03238 41 0.05003 0.05686 0.00588 0.01412 0.03132 42 0.04980 0.05681 0.05686 0.00588 0.01412 43 -0.02299 -0.04078 0.05681 0.05686 0.00588 44 0.04030 0.02507 -0.04078 0.05681 0.05686 45 0.03176 0.00600 0.02507 -0.04078 0.05681 46 -0.00135 0.00249 0.00600 0.02507 -0.04078 47 -0.02473 0.01885 0.00249 0.00600 0.02507 48 -0.00171 0.00125 0.01885 0.00249 0.00600 49 -0.01575 0.00695 0.00125 0.01885 0.00249 50 -0.02624 -0.01563 0.00695 0.00125 0.01885 51 0.06724 0.00814 -0.01563 0.00695 0.00125 52 -0.01362 0.02368 0.00814 -0.01563 0.00695 53 -0.00422 0.04099 0.02368 0.00814 -0.01563 54 0.00754 0.00731 0.04099 0.02368 0.00814 55 0.00087 -0.01730 0.00731 0.04099 0.02368 56 0.02715 -0.00183 -0.01730 0.00731 0.04099 57 0.02976 -0.03830 -0.00183 -0.01730 0.00731 58 0.07946 -0.01249 -0.03830 -0.00183 -0.01730 59 0.01909 0.01229 -0.01249 -0.03830 -0.00183 60 -0.02483 -0.01747 0.01229 -0.01249 -0.03830 61 -0.01870 -0.02645 -0.01747 0.01229 -0.01249 62 0.09682 0.04038 -0.02645 -0.01747 0.01229 63 0.03823 0.02925 0.04038 -0.02645 -0.01747 64 0.09571 0.02270 0.02925 0.04038 -0.02645 65 -0.04663 -0.00460 0.02270 0.02925 0.04038 66 -0.01359 -0.01894 -0.00460 0.02270 0.02925 67 0.05114 -0.00966 -0.01894 -0.00460 0.02270 68 -0.04275 0.00392 -0.00966 -0.01894 -0.00460 69 0.05739 -0.03105 0.00392 -0.00966 -0.01894 70 0.01186 -0.02790 -0.03105 0.00392 -0.00966 71 0.01066 -0.09625 -0.02790 -0.03105 0.00392 72 -0.07387 -0.05388 -0.09625 -0.02790 -0.03105 73 -0.04131 -0.05034 -0.05388 -0.09625 -0.02790 74 -0.17889 -0.02846 -0.05034 -0.05388 -0.09625 75 -0.12781 -0.01454 -0.02846 -0.05034 -0.05388 76 -0.26933 0.01284 -0.01454 -0.02846 -0.05034 77 -0.05095 0.03762 0.01284 -0.01454 -0.02846 78 -0.01074 0.01973 0.03762 0.01284 -0.01454 79 0.08172 0.03178 0.01973 0.03762 0.01284 80 0.11870 0.01329 0.03178 0.01973 0.03762 81 0.08475 0.05094 0.01329 0.03178 0.01973 82 0.04663 -0.00804 0.05094 0.01329 0.03178 83 -0.04415 0.01116 -0.00804 0.05094 0.01329 84 0.00970 0.01128 0.01116 -0.00804 0.05094 85 -0.03341 0.02227 0.01128 0.01116 -0.00804 86 0.04031 0.01494 0.02227 0.01128 0.01116 87 0.01938 -0.02514 0.01494 0.02227 0.01128 88 0.05928 0.02975 -0.02514 0.01494 0.02227 89 0.02343 0.05216 0.02975 -0.02514 0.01494 90 -0.04536 -0.04459 0.05216 0.02975 -0.02514 91 0.03355 -0.02212 -0.04459 0.05216 0.02975 92 0.05659 0.03171 -0.02212 -0.04459 0.05216 93 -0.06579 0.02985 0.03171 -0.02212 -0.04459 94 -0.04267 0.01545 0.02985 0.03171 -0.02212 95 -0.02422 0.01140 0.01545 0.02985 0.03171 (1-B)lnX_[t-5] 1 -0.01826 2 0.00099 3 0.00338 4 0.01848 5 0.00149 6 0.01244 7 0.01150 8 -0.00793 9 -0.01514 10 0.01778 11 0.00634 12 0.00770 13 0.00692 14 0.00029 15 0.02487 16 0.01708 17 0.02540 18 0.02935 19 0.02615 20 0.00424 21 -0.00032 22 -0.02353 23 0.01387 24 0.01286 25 -0.00609 26 0.00635 27 0.02049 28 0.00332 29 0.00409 30 0.02753 31 0.01205 32 0.01773 33 -0.00897 34 -0.01226 35 0.00644 36 -0.00059 37 0.01707 38 -0.00104 39 0.01272 40 0.01859 41 0.03238 42 0.03132 43 0.01412 44 0.00588 45 0.05686 46 0.05681 47 -0.04078 48 0.02507 49 0.00600 50 0.00249 51 0.01885 52 0.00125 53 0.00695 54 -0.01563 55 0.00814 56 0.02368 57 0.04099 58 0.00731 59 -0.01730 60 -0.00183 61 -0.03830 62 -0.01249 63 0.01229 64 -0.01747 65 -0.02645 66 0.04038 67 0.02925 68 0.02270 69 -0.00460 70 -0.01894 71 -0.00966 72 0.00392 73 -0.03105 74 -0.02790 75 -0.09625 76 -0.05388 77 -0.05034 78 -0.02846 79 -0.01454 80 0.01284 81 0.03762 82 0.01973 83 0.03178 84 0.01329 85 0.05094 86 -0.00804 87 0.01116 88 0.01128 89 0.02227 90 0.01494 91 -0.02514 92 0.02975 93 0.05216 94 -0.04459 95 -0.02212 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `(1-B)lnY_[t-1]` `(1-B)lnY_[t-2]` `(1-B)lnY_[t-3]` 0.002987 0.279842 0.092460 -0.099940 `(1-B)lnY_[t-4]` `(1-B)lnY_[t-5]` `(1-B)lnX_[t-1]` `(1-B)lnX_[t-2]` -0.133172 -0.131647 0.630661 -0.338976 `(1-B)lnX_[t-3]` `(1-B)lnX_[t-4]` `(1-B)lnX_[t-5]` 0.510545 -0.405092 0.148230 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.162457 -0.029780 0.006978 0.026954 0.147021 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.002987 0.005339 0.559 0.5774 `(1-B)lnY_[t-1]` 0.279842 0.111326 2.514 0.0139 * `(1-B)lnY_[t-2]` 0.092460 0.127783 0.724 0.4713 `(1-B)lnY_[t-3]` -0.099940 0.125216 -0.798 0.4270 `(1-B)lnY_[t-4]` -0.133172 0.121284 -1.098 0.2753 `(1-B)lnY_[t-5]` -0.131647 0.125205 -1.051 0.2961 `(1-B)lnX_[t-1]` 0.630661 0.274683 2.296 0.0242 * `(1-B)lnX_[t-2]` -0.338976 0.270783 -1.252 0.2141 `(1-B)lnX_[t-3]` 0.510545 0.264283 1.932 0.0568 . `(1-B)lnX_[t-4]` -0.405092 0.268436 -1.509 0.1350 `(1-B)lnX_[t-5]` 0.148230 0.243634 0.608 0.5446 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.04873 on 84 degrees of freedom Multiple R-squared: 0.2921, Adjusted R-squared: 0.2078 F-statistic: 3.466 on 10 and 84 DF, p-value: 0.0007522 > 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,] 3.191502e-01 6.383004e-01 0.6808498 [2,] 1.988411e-01 3.976822e-01 0.8011589 [3,] 2.655635e-01 5.311271e-01 0.7344365 [4,] 2.479599e-01 4.959197e-01 0.7520401 [5,] 2.165562e-01 4.331125e-01 0.7834438 [6,] 1.374572e-01 2.749144e-01 0.8625428 [7,] 8.367198e-02 1.673440e-01 0.9163280 [8,] 4.864329e-02 9.728658e-02 0.9513567 [9,] 2.867067e-02 5.734134e-02 0.9713293 [10,] 1.986286e-02 3.972572e-02 0.9801371 [11,] 1.040060e-02 2.080120e-02 0.9895994 [12,] 5.732891e-03 1.146578e-02 0.9942671 [13,] 8.527400e-03 1.705480e-02 0.9914726 [14,] 6.396508e-03 1.279302e-02 0.9936035 [15,] 1.174148e-02 2.348296e-02 0.9882585 [16,] 7.220313e-03 1.444063e-02 0.9927797 [17,] 4.186189e-03 8.372378e-03 0.9958138 [18,] 2.403455e-03 4.806911e-03 0.9975965 [19,] 1.478109e-03 2.956219e-03 0.9985219 [20,] 1.448566e-03 2.897133e-03 0.9985514 [21,] 9.110173e-04 1.822035e-03 0.9990890 [22,] 4.806981e-04 9.613962e-04 0.9995193 [23,] 5.064331e-04 1.012866e-03 0.9994936 [24,] 4.805524e-04 9.611048e-04 0.9995194 [25,] 2.782701e-04 5.565403e-04 0.9997217 [26,] 4.669169e-04 9.338338e-04 0.9995331 [27,] 4.277752e-04 8.555503e-04 0.9995722 [28,] 2.812696e-04 5.625393e-04 0.9997187 [29,] 1.779231e-04 3.558461e-04 0.9998221 [30,] 1.170411e-04 2.340822e-04 0.9998830 [31,] 7.451147e-05 1.490229e-04 0.9999255 [32,] 4.389806e-05 8.779613e-05 0.9999561 [33,] 2.327526e-05 4.655051e-05 0.9999767 [34,] 1.617989e-05 3.235978e-05 0.9999838 [35,] 7.771604e-06 1.554321e-05 0.9999922 [36,] 3.904325e-06 7.808649e-06 0.9999961 [37,] 1.800643e-06 3.601287e-06 0.9999982 [38,] 8.250665e-07 1.650133e-06 0.9999992 [39,] 3.945517e-07 7.891034e-07 0.9999996 [40,] 5.957792e-07 1.191558e-06 0.9999994 [41,] 4.770119e-07 9.540239e-07 0.9999995 [42,] 2.191375e-07 4.382751e-07 0.9999998 [43,] 1.027396e-07 2.054793e-07 0.9999999 [44,] 1.081922e-05 2.163844e-05 0.9999892 [45,] 6.419243e-06 1.283849e-05 0.9999936 [46,] 6.216102e-05 1.243220e-04 0.9999378 [47,] 1.436707e-04 2.873414e-04 0.9998563 [48,] 3.177439e-04 6.354879e-04 0.9996823 [49,] 4.391837e-04 8.783675e-04 0.9995608 [50,] 7.642347e-04 1.528469e-03 0.9992358 [51,] 4.638446e-04 9.276892e-04 0.9995362 [52,] 4.993563e-04 9.987126e-04 0.9995006 [53,] 4.578574e-04 9.157147e-04 0.9995421 [54,] 8.086458e-04 1.617292e-03 0.9991914 [55,] 1.188907e-03 2.377813e-03 0.9988111 [56,] 2.864620e-02 5.729240e-02 0.9713538 [57,] 4.200764e-02 8.401528e-02 0.9579924 [58,] 4.936763e-01 9.873527e-01 0.5063237 [59,] 4.943617e-01 9.887234e-01 0.5056383 [60,] 4.957595e-01 9.915190e-01 0.5042405 [61,] 4.238387e-01 8.476775e-01 0.5761613 [62,] 3.701807e-01 7.403614e-01 0.6298193 [63,] 2.942704e-01 5.885408e-01 0.7057296 [64,] 2.503789e-01 5.007579e-01 0.7496211 [65,] 1.782534e-01 3.565068e-01 0.8217466 [66,] 1.341278e-01 2.682555e-01 0.8658722 [67,] 7.581091e-02 1.516218e-01 0.9241891 [68,] 1.310764e-01 2.621527e-01 0.8689236 > postscript(file="/var/www/html/rcomp/tmp/13adl1293047476.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/56su91293047476.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 = 95 Frequency = 1 1 2 3 4 5 -0.0279099565 0.0375751724 -0.0205120194 0.0079574506 0.0200131597 6 7 8 9 10 -0.0379622576 0.0518458295 -0.0019730894 -0.0132683120 0.0506427135 11 12 13 14 15 -0.0316495364 0.0555356121 -0.0460796671 0.0006939173 -0.0411471282 16 17 18 19 20 -0.0402027358 0.0537792346 0.0081095683 -0.0049993585 -0.0190908212 21 22 23 24 25 0.0159160413 -0.0152296393 0.0395798438 -0.0054773361 0.0072008993 26 27 28 29 30 0.0446813302 -0.0559511804 -0.0406783852 0.0293193411 0.0152848751 31 32 33 34 35 0.0324190874 0.0162848933 0.0210908314 -0.0397895978 0.0205507056 36 37 38 39 40 0.0580078158 0.0163519325 -0.0510568609 0.0345278354 0.0304446762 41 42 43 44 45 -0.0392174526 -0.0412319872 0.0240977410 -0.0430479940 0.0169876321 46 47 48 49 50 0.0321340027 -0.0359613850 -0.0115129194 -0.0053183716 0.0191440098 51 52 53 54 55 0.0135710775 0.0159286369 0.0368287875 -0.0022537958 -0.0329544595 56 57 58 59 60 0.0036275762 0.1470211475 0.0081360612 0.0798026627 -0.0697016219 61 62 63 64 65 0.0069775144 0.0649872905 -0.0458266991 0.0335280433 0.0098687769 66 67 68 69 70 0.0044562793 -0.0672813363 -0.0227156837 -0.1344344852 -0.0773548452 71 72 73 74 75 -0.1624566962 0.0025845205 0.0393223981 0.0124997709 0.0537166620 76 77 78 79 80 -0.0132269068 -0.0070357871 -0.0546075537 0.0245879230 0.0021982278 81 82 83 84 85 0.0173003887 0.0349324189 0.0041855124 0.0223578106 -0.0842045708 86 87 88 89 90 0.0529830071 0.0736602514 -0.1088863816 -0.0393062530 0.0131838345 91 92 93 94 95 0.0769373609 -0.0245880915 0.0193560574 0.0047518547 -0.0233648458 > postscript(file="/var/www/html/rcomp/tmp/66su91293047476.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 = 95 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.0279099565 NA 1 0.0375751724 -0.0279099565 2 -0.0205120194 0.0375751724 3 0.0079574506 -0.0205120194 4 0.0200131597 0.0079574506 5 -0.0379622576 0.0200131597 6 0.0518458295 -0.0379622576 7 -0.0019730894 0.0518458295 8 -0.0132683120 -0.0019730894 9 0.0506427135 -0.0132683120 10 -0.0316495364 0.0506427135 11 0.0555356121 -0.0316495364 12 -0.0460796671 0.0555356121 13 0.0006939173 -0.0460796671 14 -0.0411471282 0.0006939173 15 -0.0402027358 -0.0411471282 16 0.0537792346 -0.0402027358 17 0.0081095683 0.0537792346 18 -0.0049993585 0.0081095683 19 -0.0190908212 -0.0049993585 20 0.0159160413 -0.0190908212 21 -0.0152296393 0.0159160413 22 0.0395798438 -0.0152296393 23 -0.0054773361 0.0395798438 24 0.0072008993 -0.0054773361 25 0.0446813302 0.0072008993 26 -0.0559511804 0.0446813302 27 -0.0406783852 -0.0559511804 28 0.0293193411 -0.0406783852 29 0.0152848751 0.0293193411 30 0.0324190874 0.0152848751 31 0.0162848933 0.0324190874 32 0.0210908314 0.0162848933 33 -0.0397895978 0.0210908314 34 0.0205507056 -0.0397895978 35 0.0580078158 0.0205507056 36 0.0163519325 0.0580078158 37 -0.0510568609 0.0163519325 38 0.0345278354 -0.0510568609 39 0.0304446762 0.0345278354 40 -0.0392174526 0.0304446762 41 -0.0412319872 -0.0392174526 42 0.0240977410 -0.0412319872 43 -0.0430479940 0.0240977410 44 0.0169876321 -0.0430479940 45 0.0321340027 0.0169876321 46 -0.0359613850 0.0321340027 47 -0.0115129194 -0.0359613850 48 -0.0053183716 -0.0115129194 49 0.0191440098 -0.0053183716 50 0.0135710775 0.0191440098 51 0.0159286369 0.0135710775 52 0.0368287875 0.0159286369 53 -0.0022537958 0.0368287875 54 -0.0329544595 -0.0022537958 55 0.0036275762 -0.0329544595 56 0.1470211475 0.0036275762 57 0.0081360612 0.1470211475 58 0.0798026627 0.0081360612 59 -0.0697016219 0.0798026627 60 0.0069775144 -0.0697016219 61 0.0649872905 0.0069775144 62 -0.0458266991 0.0649872905 63 0.0335280433 -0.0458266991 64 0.0098687769 0.0335280433 65 0.0044562793 0.0098687769 66 -0.0672813363 0.0044562793 67 -0.0227156837 -0.0672813363 68 -0.1344344852 -0.0227156837 69 -0.0773548452 -0.1344344852 70 -0.1624566962 -0.0773548452 71 0.0025845205 -0.1624566962 72 0.0393223981 0.0025845205 73 0.0124997709 0.0393223981 74 0.0537166620 0.0124997709 75 -0.0132269068 0.0537166620 76 -0.0070357871 -0.0132269068 77 -0.0546075537 -0.0070357871 78 0.0245879230 -0.0546075537 79 0.0021982278 0.0245879230 80 0.0173003887 0.0021982278 81 0.0349324189 0.0173003887 82 0.0041855124 0.0349324189 83 0.0223578106 0.0041855124 84 -0.0842045708 0.0223578106 85 0.0529830071 -0.0842045708 86 0.0736602514 0.0529830071 87 -0.1088863816 0.0736602514 88 -0.0393062530 -0.1088863816 89 0.0131838345 -0.0393062530 90 0.0769373609 0.0131838345 91 -0.0245880915 0.0769373609 92 0.0193560574 -0.0245880915 93 0.0047518547 0.0193560574 94 -0.0233648458 0.0047518547 95 NA -0.0233648458 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.0375751724 -0.0279099565 [2,] -0.0205120194 0.0375751724 [3,] 0.0079574506 -0.0205120194 [4,] 0.0200131597 0.0079574506 [5,] -0.0379622576 0.0200131597 [6,] 0.0518458295 -0.0379622576 [7,] -0.0019730894 0.0518458295 [8,] -0.0132683120 -0.0019730894 [9,] 0.0506427135 -0.0132683120 [10,] -0.0316495364 0.0506427135 [11,] 0.0555356121 -0.0316495364 [12,] -0.0460796671 0.0555356121 [13,] 0.0006939173 -0.0460796671 [14,] -0.0411471282 0.0006939173 [15,] -0.0402027358 -0.0411471282 [16,] 0.0537792346 -0.0402027358 [17,] 0.0081095683 0.0537792346 [18,] -0.0049993585 0.0081095683 [19,] -0.0190908212 -0.0049993585 [20,] 0.0159160413 -0.0190908212 [21,] -0.0152296393 0.0159160413 [22,] 0.0395798438 -0.0152296393 [23,] -0.0054773361 0.0395798438 [24,] 0.0072008993 -0.0054773361 [25,] 0.0446813302 0.0072008993 [26,] -0.0559511804 0.0446813302 [27,] -0.0406783852 -0.0559511804 [28,] 0.0293193411 -0.0406783852 [29,] 0.0152848751 0.0293193411 [30,] 0.0324190874 0.0152848751 [31,] 0.0162848933 0.0324190874 [32,] 0.0210908314 0.0162848933 [33,] -0.0397895978 0.0210908314 [34,] 0.0205507056 -0.0397895978 [35,] 0.0580078158 0.0205507056 [36,] 0.0163519325 0.0580078158 [37,] -0.0510568609 0.0163519325 [38,] 0.0345278354 -0.0510568609 [39,] 0.0304446762 0.0345278354 [40,] -0.0392174526 0.0304446762 [41,] -0.0412319872 -0.0392174526 [42,] 0.0240977410 -0.0412319872 [43,] -0.0430479940 0.0240977410 [44,] 0.0169876321 -0.0430479940 [45,] 0.0321340027 0.0169876321 [46,] -0.0359613850 0.0321340027 [47,] -0.0115129194 -0.0359613850 [48,] -0.0053183716 -0.0115129194 [49,] 0.0191440098 -0.0053183716 [50,] 0.0135710775 0.0191440098 [51,] 0.0159286369 0.0135710775 [52,] 0.0368287875 0.0159286369 [53,] -0.0022537958 0.0368287875 [54,] -0.0329544595 -0.0022537958 [55,] 0.0036275762 -0.0329544595 [56,] 0.1470211475 0.0036275762 [57,] 0.0081360612 0.1470211475 [58,] 0.0798026627 0.0081360612 [59,] -0.0697016219 0.0798026627 [60,] 0.0069775144 -0.0697016219 [61,] 0.0649872905 0.0069775144 [62,] -0.0458266991 0.0649872905 [63,] 0.0335280433 -0.0458266991 [64,] 0.0098687769 0.0335280433 [65,] 0.0044562793 0.0098687769 [66,] -0.0672813363 0.0044562793 [67,] -0.0227156837 -0.0672813363 [68,] -0.1344344852 -0.0227156837 [69,] -0.0773548452 -0.1344344852 [70,] -0.1624566962 -0.0773548452 [71,] 0.0025845205 -0.1624566962 [72,] 0.0393223981 0.0025845205 [73,] 0.0124997709 0.0393223981 [74,] 0.0537166620 0.0124997709 [75,] -0.0132269068 0.0537166620 [76,] -0.0070357871 -0.0132269068 [77,] -0.0546075537 -0.0070357871 [78,] 0.0245879230 -0.0546075537 [79,] 0.0021982278 0.0245879230 [80,] 0.0173003887 0.0021982278 [81,] 0.0349324189 0.0173003887 [82,] 0.0041855124 0.0349324189 [83,] 0.0223578106 0.0041855124 [84,] -0.0842045708 0.0223578106 [85,] 0.0529830071 -0.0842045708 [86,] 0.0736602514 0.0529830071 [87,] -0.1088863816 0.0736602514 [88,] -0.0393062530 -0.1088863816 [89,] 0.0131838345 -0.0393062530 [90,] 0.0769373609 0.0131838345 [91,] -0.0245880915 0.0769373609 [92,] 0.0193560574 -0.0245880915 [93,] 0.0047518547 0.0193560574 [94,] -0.0233648458 0.0047518547 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.0375751724 -0.0279099565 2 -0.0205120194 0.0375751724 3 0.0079574506 -0.0205120194 4 0.0200131597 0.0079574506 5 -0.0379622576 0.0200131597 6 0.0518458295 -0.0379622576 7 -0.0019730894 0.0518458295 8 -0.0132683120 -0.0019730894 9 0.0506427135 -0.0132683120 10 -0.0316495364 0.0506427135 11 0.0555356121 -0.0316495364 12 -0.0460796671 0.0555356121 13 0.0006939173 -0.0460796671 14 -0.0411471282 0.0006939173 15 -0.0402027358 -0.0411471282 16 0.0537792346 -0.0402027358 17 0.0081095683 0.0537792346 18 -0.0049993585 0.0081095683 19 -0.0190908212 -0.0049993585 20 0.0159160413 -0.0190908212 21 -0.0152296393 0.0159160413 22 0.0395798438 -0.0152296393 23 -0.0054773361 0.0395798438 24 0.0072008993 -0.0054773361 25 0.0446813302 0.0072008993 26 -0.0559511804 0.0446813302 27 -0.0406783852 -0.0559511804 28 0.0293193411 -0.0406783852 29 0.0152848751 0.0293193411 30 0.0324190874 0.0152848751 31 0.0162848933 0.0324190874 32 0.0210908314 0.0162848933 33 -0.0397895978 0.0210908314 34 0.0205507056 -0.0397895978 35 0.0580078158 0.0205507056 36 0.0163519325 0.0580078158 37 -0.0510568609 0.0163519325 38 0.0345278354 -0.0510568609 39 0.0304446762 0.0345278354 40 -0.0392174526 0.0304446762 41 -0.0412319872 -0.0392174526 42 0.0240977410 -0.0412319872 43 -0.0430479940 0.0240977410 44 0.0169876321 -0.0430479940 45 0.0321340027 0.0169876321 46 -0.0359613850 0.0321340027 47 -0.0115129194 -0.0359613850 48 -0.0053183716 -0.0115129194 49 0.0191440098 -0.0053183716 50 0.0135710775 0.0191440098 51 0.0159286369 0.0135710775 52 0.0368287875 0.0159286369 53 -0.0022537958 0.0368287875 54 -0.0329544595 -0.0022537958 55 0.0036275762 -0.0329544595 56 0.1470211475 0.0036275762 57 0.0081360612 0.1470211475 58 0.0798026627 0.0081360612 59 -0.0697016219 0.0798026627 60 0.0069775144 -0.0697016219 61 0.0649872905 0.0069775144 62 -0.0458266991 0.0649872905 63 0.0335280433 -0.0458266991 64 0.0098687769 0.0335280433 65 0.0044562793 0.0098687769 66 -0.0672813363 0.0044562793 67 -0.0227156837 -0.0672813363 68 -0.1344344852 -0.0227156837 69 -0.0773548452 -0.1344344852 70 -0.1624566962 -0.0773548452 71 0.0025845205 -0.1624566962 72 0.0393223981 0.0025845205 73 0.0124997709 0.0393223981 74 0.0537166620 0.0124997709 75 -0.0132269068 0.0537166620 76 -0.0070357871 -0.0132269068 77 -0.0546075537 -0.0070357871 78 0.0245879230 -0.0546075537 79 0.0021982278 0.0245879230 80 0.0173003887 0.0021982278 81 0.0349324189 0.0173003887 82 0.0041855124 0.0349324189 83 0.0223578106 0.0041855124 84 -0.0842045708 0.0223578106 85 0.0529830071 -0.0842045708 86 0.0736602514 0.0529830071 87 -0.1088863816 0.0736602514 88 -0.0393062530 -0.1088863816 89 0.0131838345 -0.0393062530 90 0.0769373609 0.0131838345 91 -0.0245880915 0.0769373609 92 0.0193560574 -0.0245880915 93 0.0047518547 0.0193560574 94 -0.0233648458 0.0047518547 > 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/7zjbt1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8zjbt1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9rtaf1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10rtaf1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11dtr21293047476.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/123xex1293047476.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/13ndm21293047476.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/14ym351293047476.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/15152t1293047476.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/16fei11293047476.tab") + } > > try(system("convert tmp/13adl1293047476.ps tmp/13adl1293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/2v1u61293047476.ps tmp/2v1u61293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/3v1u61293047476.ps tmp/3v1u61293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/4v1u61293047476.ps tmp/4v1u61293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/56su91293047476.ps tmp/56su91293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/66su91293047476.ps tmp/66su91293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/7zjbt1293047476.ps tmp/7zjbt1293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/8zjbt1293047476.ps tmp/8zjbt1293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/9rtaf1293047476.ps tmp/9rtaf1293047476.png",intern=TRUE)) character(0) > try(system("convert tmp/10rtaf1293047476.ps tmp/10rtaf1293047476.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.249 1.696 8.518