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Type 'q()' to quit R. > x <- array(list(0.04374 + ,0.426 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,0.06134 + ,0.626 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,0.05233 + ,0.482 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,0.05492 + ,0.517 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,0.06425 + ,0.584 + ,0.0349 + ,0.04825 + ,0.04465 + ,0.1047 + ,0.04701 + ,0.456 + ,0.02328 + ,0.03526 + ,0.03243 + ,0.06985 + ,0.01608 + ,0.14 + ,0.00779 + ,0.00937 + ,0.01351 + ,0.02337 + ,0.01567 + ,0.134 + ,0.00829 + ,0.00946 + ,0.01256 + ,0.02487 + ,0.02093 + ,0.191 + ,0.01073 + ,0.01277 + ,0.01717 + ,0.03218 + ,0.02838 + ,0.255 + ,0.01441 + ,0.01725 + ,0.02444 + ,0.04324 + ,0.02143 + ,0.197 + ,0.01079 + ,0.01342 + ,0.01892 + ,0.03237 + ,0.02752 + ,0.249 + ,0.01424 + ,0.01641 + ,0.02214 + ,0.04272 + ,0.01259 + ,0.112 + ,0.00656 + ,0.00717 + ,0.0114 + ,0.01968 + ,0.01642 + ,0.154 + ,0.00728 + ,0.00932 + ,0.01797 + ,0.02184 + ,0.01828 + ,0.158 + ,0.01064 + ,0.00972 + ,0.01246 + ,0.03191 + ,0.01503 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,0.00557 + ,0.00721 + ,0.01095 + ,0.01672 + ,0.02574 + ,0.255 + ,0.01454 + ,0.01582 + ,0.01758 + ,0.04363 + ,0.04087 + ,0.405 + ,0.02336 + ,0.02498 + ,0.02745 + ,0.07008 + ,0.02751 + ,0.263 + ,0.01604 + ,0.01657 + ,0.01879 + ,0.04812 + ,0.02308 + ,0.256 + ,0.01268 + ,0.01365 + ,0.01667 + ,0.03804 + ,0.02296 + ,0.241 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0.01884 + ,0.19 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078) + ,dim=c(6 + ,195) + ,dimnames=list(c('MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA') + ,1:195)) > y <- array(NA,dim=c(6,195),dimnames=list(c('MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA'),1:195)) > 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' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, 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 MDVP:Shimmer MDVP:Shimmer(dB) Shimmer:APQ3 Shimmer:APQ5 MDVP:APQ 1 0.04374 0.426 0.02182 0.03130 0.02971 2 0.06134 0.626 0.03134 0.04518 0.04368 3 0.05233 0.482 0.02757 0.03858 0.03590 4 0.05492 0.517 0.02924 0.04005 0.03772 5 0.06425 0.584 0.03490 0.04825 0.04465 6 0.04701 0.456 0.02328 0.03526 0.03243 7 0.01608 0.140 0.00779 0.00937 0.01351 8 0.01567 0.134 0.00829 0.00946 0.01256 9 0.02093 0.191 0.01073 0.01277 0.01717 10 0.02838 0.255 0.01441 0.01725 0.02444 11 0.02143 0.197 0.01079 0.01342 0.01892 12 0.02752 0.249 0.01424 0.01641 0.02214 13 0.01259 0.112 0.00656 0.00717 0.01140 14 0.01642 0.154 0.00728 0.00932 0.01797 15 0.01828 0.158 0.01064 0.00972 0.01246 16 0.01503 0.126 0.00772 0.00888 0.01359 17 0.02047 0.192 0.00969 0.01200 0.02074 18 0.03327 0.348 0.01441 0.01893 0.03430 19 0.05517 0.542 0.02471 0.03572 0.05767 20 0.03995 0.348 0.01721 0.02374 0.04310 21 0.03810 0.328 0.01667 0.02383 0.04055 22 0.04137 0.370 0.02021 0.02591 0.04525 23 0.04351 0.377 0.02228 0.02540 0.04246 24 0.04192 0.364 0.02187 0.02470 0.03772 25 0.01659 0.164 0.00738 0.00948 0.01497 26 0.03767 0.381 0.01732 0.02245 0.03780 27 0.01966 0.186 0.00889 0.01169 0.01872 28 0.01919 0.198 0.00883 0.01144 0.01826 29 0.01718 0.161 0.00769 0.01012 0.01661 30 0.01791 0.168 0.00793 0.01057 0.01799 31 0.01098 0.097 0.00563 0.00680 0.00802 32 0.01015 0.089 0.00504 0.00641 0.00762 33 0.01263 0.111 0.00640 0.00825 0.00951 34 0.00954 0.085 0.00469 0.00606 0.00719 35 0.00958 0.085 0.00468 0.00610 0.00726 36 0.01194 0.107 0.00586 0.00760 0.00957 37 0.02126 0.189 0.01154 0.01347 0.01612 38 0.01851 0.168 0.00938 0.01160 0.01491 39 0.01444 0.131 0.00726 0.00885 0.01190 40 0.01663 0.151 0.00829 0.01003 0.01366 41 0.01495 0.135 0.00774 0.00941 0.01233 42 0.01463 0.132 0.00742 0.00901 0.01234 43 0.01752 0.164 0.01035 0.01024 0.01133 44 0.01760 0.154 0.01006 0.01038 0.01251 45 0.01419 0.126 0.00777 0.00898 0.01033 46 0.01494 0.134 0.00847 0.00879 0.01014 47 0.01608 0.141 0.00906 0.00977 0.01149 48 0.01152 0.103 0.00614 0.00730 0.00860 49 0.01613 0.143 0.00855 0.00776 0.01433 50 0.01681 0.154 0.00930 0.00802 0.01400 51 0.02184 0.197 0.01241 0.01024 0.01685 52 0.02033 0.185 0.01143 0.00959 0.01614 53 0.02297 0.210 0.01323 0.01072 0.01677 54 0.02498 0.228 0.01396 0.01219 0.01947 55 0.02719 0.255 0.01483 0.01609 0.02067 56 0.03209 0.307 0.01789 0.01992 0.02454 57 0.03715 0.334 0.02032 0.02302 0.02802 58 0.02293 0.221 0.01189 0.01459 0.01948 59 0.02645 0.265 0.01394 0.01625 0.02137 60 0.03225 0.350 0.01805 0.01974 0.02519 61 0.01861 0.170 0.00975 0.01258 0.01382 62 0.01906 0.165 0.01013 0.01296 0.01340 63 0.01643 0.145 0.00867 0.01108 0.01200 64 0.01644 0.145 0.00882 0.01075 0.01179 65 0.01457 0.129 0.00769 0.00957 0.01016 66 0.01745 0.154 0.00942 0.01160 0.01234 67 0.03198 0.313 0.01830 0.01810 0.02428 68 0.03111 0.308 0.01638 0.01759 0.02603 69 0.05384 0.478 0.03152 0.02422 0.03392 70 0.05428 0.497 0.03357 0.02494 0.03635 71 0.03485 0.365 0.01868 0.01906 0.02949 72 0.04978 0.483 0.02749 0.02466 0.03736 73 0.01706 0.152 0.00974 0.00925 0.01345 74 0.02448 0.226 0.01373 0.01375 0.01956 75 0.02442 0.216 0.01432 0.01325 0.01831 76 0.02215 0.206 0.01284 0.01219 0.01715 77 0.03999 0.350 0.02413 0.02231 0.02704 78 0.02199 0.197 0.01284 0.01199 0.01636 79 0.03202 0.263 0.01803 0.01886 0.02455 80 0.03121 0.361 0.01773 0.01783 0.02139 81 0.04024 0.364 0.02266 0.02451 0.02876 82 0.03156 0.296 0.01792 0.01841 0.02190 83 0.02427 0.216 0.01371 0.01421 0.01751 84 0.02223 0.202 0.01277 0.01343 0.01552 85 0.04795 0.435 0.02679 0.03022 0.03510 86 0.03852 0.331 0.02107 0.02493 0.02877 87 0.03759 0.327 0.02073 0.02415 0.02784 88 0.06511 0.580 0.03671 0.04159 0.04683 89 0.06727 0.650 0.03788 0.04254 0.04802 90 0.04313 0.442 0.02297 0.02768 0.03455 91 0.06640 0.634 0.03650 0.04282 0.05114 92 0.07959 0.772 0.04421 0.04962 0.05690 93 0.04190 0.383 0.02383 0.02521 0.03051 94 0.05925 0.637 0.03341 0.03794 0.04398 95 0.03716 0.307 0.02062 0.02321 0.02764 96 0.03272 0.283 0.01813 0.01909 0.02571 97 0.03381 0.307 0.01806 0.02024 0.02809 98 0.03886 0.342 0.02135 0.02174 0.03088 99 0.04689 0.422 0.02542 0.02630 0.03908 100 0.06734 0.659 0.03611 0.03963 0.05783 101 0.09178 0.891 0.05358 0.04791 0.06196 102 0.06170 0.584 0.03223 0.03672 0.05174 103 0.09419 0.930 0.05551 0.05005 0.06023 104 0.01131 0.107 0.00522 0.00659 0.01009 105 0.01030 0.094 0.00469 0.00582 0.00871 106 0.01346 0.126 0.00660 0.00818 0.01059 107 0.01064 0.097 0.00522 0.00632 0.00928 108 0.01450 0.137 0.00633 0.00788 0.01267 109 0.01024 0.093 0.00455 0.00576 0.00993 110 0.03044 0.275 0.01771 0.01815 0.02084 111 0.02286 0.207 0.01192 0.01439 0.01852 112 0.01761 0.155 0.00952 0.01058 0.01307 113 0.02378 0.210 0.01277 0.01483 0.01767 114 0.01680 0.149 0.00861 0.01017 0.01301 115 0.02105 0.209 0.01107 0.01284 0.01604 116 0.01843 0.235 0.00796 0.00832 0.01271 117 0.01458 0.148 0.00606 0.00747 0.01312 118 0.01725 0.175 0.00757 0.00971 0.01652 119 0.01279 0.129 0.00617 0.00744 0.01151 120 0.01299 0.124 0.00679 0.00631 0.01075 121 0.02008 0.221 0.00849 0.01117 0.01734 122 0.01169 0.117 0.00534 0.00630 0.01104 123 0.04479 0.441 0.02587 0.02567 0.03220 124 0.02503 0.231 0.01372 0.01580 0.01931 125 0.02343 0.224 0.01289 0.01420 0.01720 126 0.02362 0.233 0.01235 0.01495 0.01944 127 0.02791 0.246 0.01484 0.01805 0.02259 128 0.02857 0.257 0.01547 0.01859 0.02301 129 0.01033 0.098 0.00538 0.00570 0.00811 130 0.01022 0.090 0.00476 0.00588 0.00903 131 0.01412 0.125 0.00703 0.00820 0.01194 132 0.01516 0.138 0.00721 0.00815 0.01310 133 0.01201 0.106 0.00633 0.00701 0.00915 134 0.01043 0.099 0.00490 0.00621 0.00903 135 0.04932 0.441 0.02683 0.03112 0.03651 136 0.04128 0.379 0.02229 0.02592 0.03316 137 0.04879 0.431 0.02385 0.02973 0.04370 138 0.05279 0.476 0.02896 0.03347 0.04134 139 0.05643 0.517 0.03070 0.03530 0.04451 140 0.03026 0.267 0.01514 0.01812 0.02770 141 0.03273 0.281 0.01713 0.01964 0.02824 142 0.06725 0.571 0.04016 0.04003 0.04464 143 0.03527 0.297 0.02055 0.02076 0.02530 144 0.01997 0.180 0.01117 0.01177 0.01506 145 0.02662 0.228 0.01475 0.01558 0.02006 146 0.02536 0.225 0.01379 0.01478 0.01909 147 0.08143 0.821 0.03804 0.05426 0.08808 148 0.06050 0.618 0.02865 0.04101 0.06359 149 0.07118 0.722 0.03474 0.04580 0.06824 150 0.07170 0.833 0.03515 0.04265 0.06460 151 0.05830 0.784 0.02699 0.03714 0.06259 152 0.11908 1.302 0.05647 0.07940 0.13778 153 0.08684 1.018 0.04284 0.05556 0.08318 154 0.02534 0.241 0.01340 0.01399 0.02056 155 0.02682 0.236 0.01484 0.01405 0.02018 156 0.03087 0.276 0.01659 0.01804 0.02402 157 0.02293 0.223 0.01205 0.01289 0.01771 158 0.04912 0.438 0.02610 0.02161 0.02916 159 0.02852 0.266 0.01500 0.01581 0.02157 160 0.03235 0.339 0.01360 0.01650 0.03105 161 0.04009 0.406 0.01579 0.01994 0.04114 162 0.03273 0.325 0.01644 0.01722 0.02931 163 0.03658 0.369 0.01864 0.01940 0.03091 164 0.01756 0.155 0.00967 0.01033 0.01363 165 0.02814 0.272 0.01579 0.01553 0.02073 166 0.02448 0.217 0.01410 0.01426 0.01621 167 0.01242 0.116 0.00696 0.00747 0.00882 168 0.02030 0.197 0.01186 0.01230 0.01367 169 0.02177 0.189 0.01279 0.01272 0.01439 170 0.02018 0.212 0.01176 0.01191 0.01344 171 0.01897 0.181 0.01084 0.01121 0.01255 172 0.01358 0.129 0.00664 0.00786 0.01140 173 0.01484 0.133 0.00754 0.00950 0.01285 174 0.01472 0.133 0.00748 0.00905 0.01148 175 0.01657 0.145 0.00881 0.01062 0.01318 176 0.01503 0.137 0.00812 0.00933 0.01133 177 0.01725 0.155 0.00874 0.01021 0.01331 178 0.01469 0.132 0.00728 0.00886 0.01230 179 0.01574 0.142 0.00839 0.00956 0.01309 180 0.01450 0.131 0.00725 0.00876 0.01263 181 0.02551 0.237 0.01321 0.01574 0.02148 182 0.01831 0.163 0.00950 0.01103 0.01559 183 0.02145 0.198 0.01155 0.01341 0.01666 184 0.01909 0.171 0.00864 0.01223 0.01949 185 0.01795 0.163 0.00810 0.01144 0.01756 186 0.01564 0.136 0.00667 0.00990 0.01691 187 0.01660 0.154 0.00820 0.00972 0.01491 188 0.01300 0.117 0.00631 0.00789 0.01144 189 0.01185 0.106 0.00557 0.00721 0.01095 190 0.02574 0.255 0.01454 0.01582 0.01758 191 0.04087 0.405 0.02336 0.02498 0.02745 192 0.02751 0.263 0.01604 0.01657 0.01879 193 0.02308 0.256 0.01268 0.01365 0.01667 194 0.02296 0.241 0.01265 0.01321 0.01588 195 0.01884 0.190 0.01026 0.01161 0.01373 Shimmer:DDA 1 0.06545 2 0.09403 3 0.08270 4 0.08771 5 0.10470 6 0.06985 7 0.02337 8 0.02487 9 0.03218 10 0.04324 11 0.03237 12 0.04272 13 0.01968 14 0.02184 15 0.03191 16 0.02316 17 0.02908 18 0.04322 19 0.07413 20 0.05164 21 0.05000 22 0.06062 23 0.06685 24 0.06562 25 0.02214 26 0.05197 27 0.02666 28 0.02650 29 0.02307 30 0.02380 31 0.01689 32 0.01513 33 0.01919 34 0.01407 35 0.01403 36 0.01758 37 0.03463 38 0.02814 39 0.02177 40 0.02488 41 0.02321 42 0.02226 43 0.03104 44 0.03017 45 0.02330 46 0.02542 47 0.02719 48 0.01841 49 0.02566 50 0.02789 51 0.03724 52 0.03429 53 0.03969 54 0.04188 55 0.04450 56 0.05368 57 0.06097 58 0.03568 59 0.04183 60 0.05414 61 0.02925 62 0.03039 63 0.02602 64 0.02647 65 0.02308 66 0.02827 67 0.05490 68 0.04914 69 0.09455 70 0.10070 71 0.05605 72 0.08247 73 0.02921 74 0.04120 75 0.04295 76 0.03851 77 0.07238 78 0.03852 79 0.05408 80 0.05320 81 0.06799 82 0.05377 83 0.04114 84 0.03831 85 0.08037 86 0.06321 87 0.06219 88 0.11012 89 0.11363 90 0.06892 91 0.10949 92 0.13262 93 0.07150 94 0.10024 95 0.06185 96 0.05439 97 0.05417 98 0.06406 99 0.07625 100 0.10833 101 0.16074 102 0.09669 103 0.16654 104 0.01567 105 0.01406 106 0.01979 107 0.01567 108 0.01898 109 0.01364 110 0.05312 111 0.03576 112 0.02855 113 0.03831 114 0.02583 115 0.03320 116 0.02389 117 0.01818 118 0.02270 119 0.01851 120 0.02038 121 0.02548 122 0.01603 123 0.07761 124 0.04115 125 0.03867 126 0.03706 127 0.04451 128 0.04641 129 0.01614 130 0.01428 131 0.02110 132 0.02164 133 0.01898 134 0.01471 135 0.08050 136 0.06688 137 0.07154 138 0.08689 139 0.09211 140 0.04543 141 0.05139 142 0.12047 143 0.06165 144 0.03350 145 0.04426 146 0.04137 147 0.11411 148 0.08595 149 0.10422 150 0.10546 151 0.08096 152 0.16942 153 0.12851 154 0.04019 155 0.04451 156 0.04977 157 0.03615 158 0.07830 159 0.04499 160 0.04079 161 0.04736 162 0.04933 163 0.05592 164 0.02902 165 0.04736 166 0.04231 167 0.02089 168 0.03557 169 0.03836 170 0.03529 171 0.03253 172 0.01992 173 0.02261 174 0.02245 175 0.02643 176 0.02436 177 0.02623 178 0.02184 179 0.02518 180 0.02175 181 0.03964 182 0.02849 183 0.03464 184 0.02592 185 0.02429 186 0.02001 187 0.02460 188 0.01892 189 0.01672 190 0.04363 191 0.07008 192 0.04812 193 0.03804 194 0.03794 195 0.03078 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Shimmer(dB)` `Shimmer:APQ3` `Shimmer:APQ5` 0.00092 0.01336 15.54880 0.19839 `MDVP:APQ` `Shimmer:DDA` 0.22673 -4.84219 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.0039018 -0.0004260 -0.0001264 0.0002145 0.0047699 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.0009200 0.0001181 7.791 4.31e-13 *** `MDVP:Shimmer(dB)` 0.0133599 0.0021208 6.300 2.05e-09 *** `Shimmer:APQ3` 15.5487960 23.0285397 0.675 0.500 `Shimmer:APQ5` 0.1983926 0.0270536 7.333 6.40e-12 *** `MDVP:APQ` 0.2267304 0.0159957 14.175 < 2e-16 *** `Shimmer:DDA` -4.8421938 7.6767921 -0.631 0.529 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0008758 on 189 degrees of freedom Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978 F-statistic: 1.795e+04 on 5 and 189 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,] 1.246027e-02 2.492053e-02 9.875397e-01 [2,] 5.959698e-02 1.191940e-01 9.404030e-01 [3,] 8.343530e-02 1.668706e-01 9.165647e-01 [4,] 8.218156e-02 1.643631e-01 9.178184e-01 [5,] 7.378464e-02 1.475693e-01 9.262154e-01 [6,] 5.324173e-02 1.064835e-01 9.467583e-01 [7,] 2.803917e-02 5.607834e-02 9.719608e-01 [8,] 1.514784e-02 3.029569e-02 9.848522e-01 [9,] 7.366968e-03 1.473394e-02 9.926330e-01 [10,] 3.723069e-03 7.446138e-03 9.962769e-01 [11,] 1.935561e-03 3.871121e-03 9.980644e-01 [12,] 4.116723e-02 8.233446e-02 9.588328e-01 [13,] 3.346116e-02 6.692231e-02 9.665388e-01 [14,] 9.428952e-02 1.885790e-01 9.057105e-01 [15,] 7.850943e-02 1.570189e-01 9.214906e-01 [16,] 6.772283e-02 1.354457e-01 9.322772e-01 [17,] 4.779035e-02 9.558070e-02 9.522096e-01 [18,] 3.544284e-02 7.088567e-02 9.645572e-01 [19,] 2.391234e-02 4.782468e-02 9.760877e-01 [20,] 1.860217e-02 3.720433e-02 9.813978e-01 [21,] 1.208512e-02 2.417024e-02 9.879149e-01 [22,] 7.759636e-03 1.551927e-02 9.922404e-01 [23,] 4.963352e-03 9.926704e-03 9.950366e-01 [24,] 3.105465e-03 6.210931e-03 9.968945e-01 [25,] 1.922740e-03 3.845480e-03 9.980773e-01 [26,] 1.178392e-03 2.356785e-03 9.988216e-01 [27,] 7.002584e-04 1.400517e-03 9.992997e-01 [28,] 4.080879e-04 8.161758e-04 9.995919e-01 [29,] 2.336815e-04 4.673630e-04 9.997663e-01 [30,] 1.281611e-04 2.563222e-04 9.998718e-01 [31,] 7.075319e-05 1.415064e-04 9.999292e-01 [32,] 3.803942e-05 7.607883e-05 9.999620e-01 [33,] 2.383347e-05 4.766694e-05 9.999762e-01 [34,] 1.337651e-05 2.675302e-05 9.999866e-01 [35,] 8.123598e-06 1.624720e-05 9.999919e-01 [36,] 4.282461e-06 8.564923e-06 9.999957e-01 [37,] 2.406618e-06 4.813236e-06 9.999976e-01 [38,] 1.218895e-06 2.437790e-06 9.999988e-01 [39,] 6.335694e-07 1.267139e-06 9.999994e-01 [40,] 3.571680e-07 7.143360e-07 9.999996e-01 [41,] 1.824997e-07 3.649993e-07 9.999998e-01 [42,] 9.695499e-08 1.939100e-07 9.999999e-01 [43,] 7.239550e-08 1.447910e-07 9.999999e-01 [44,] 4.155335e-08 8.310670e-08 1.000000e+00 [45,] 3.073459e-08 6.146919e-08 1.000000e+00 [46,] 1.893518e-08 3.787036e-08 1.000000e+00 [47,] 8.951429e-09 1.790286e-08 1.000000e+00 [48,] 1.295365e-08 2.590731e-08 1.000000e+00 [49,] 6.819886e-09 1.363977e-08 1.000000e+00 [50,] 6.872639e-09 1.374528e-08 1.000000e+00 [51,] 6.007571e-09 1.201514e-08 1.000000e+00 [52,] 2.479805e-07 4.959610e-07 9.999998e-01 [53,] 1.349572e-07 2.699145e-07 9.999999e-01 [54,] 6.949317e-08 1.389863e-07 9.999999e-01 [55,] 3.698403e-08 7.396806e-08 1.000000e+00 [56,] 1.874625e-08 3.749250e-08 1.000000e+00 [57,] 9.204924e-09 1.840985e-08 1.000000e+00 [58,] 4.597010e-09 9.194019e-09 1.000000e+00 [59,] 3.601590e-09 7.203180e-09 1.000000e+00 [60,] 1.728147e-09 3.456293e-09 1.000000e+00 [61,] 2.542404e-05 5.084809e-05 9.999746e-01 [62,] 4.363544e-05 8.727087e-05 9.999564e-01 [63,] 5.866330e-05 1.173266e-04 9.999413e-01 [64,] 5.136231e-05 1.027246e-04 9.999486e-01 [65,] 4.728850e-05 9.457700e-05 9.999527e-01 [66,] 3.997255e-05 7.994511e-05 9.999600e-01 [67,] 4.270928e-05 8.541857e-05 9.999573e-01 [68,] 6.061729e-05 1.212346e-04 9.999394e-01 [69,] 6.634243e-05 1.326849e-04 9.999337e-01 [70,] 6.451614e-05 1.290323e-04 9.999355e-01 [71,] 4.424285e-05 8.848569e-05 9.999558e-01 [72,] 1.010344e-04 2.020688e-04 9.998990e-01 [73,] 6.542486e-05 1.308497e-04 9.999346e-01 [74,] 4.180724e-05 8.361449e-05 9.999582e-01 [75,] 2.692382e-05 5.384764e-05 9.999731e-01 [76,] 1.982936e-05 3.965872e-05 9.999802e-01 [77,] 1.342137e-05 2.684273e-05 9.999866e-01 [78,] 9.441472e-06 1.888294e-05 9.999906e-01 [79,] 6.113717e-06 1.222743e-05 9.999939e-01 [80,] 4.801541e-06 9.603083e-06 9.999952e-01 [81,] 5.886121e-06 1.177224e-05 9.999941e-01 [82,] 7.711175e-06 1.542235e-05 9.999923e-01 [83,] 1.131016e-05 2.262033e-05 9.999887e-01 [84,] 1.641752e-05 3.283503e-05 9.999836e-01 [85,] 1.063293e-05 2.126586e-05 9.999894e-01 [86,] 1.139863e-04 2.279725e-04 9.998860e-01 [87,] 8.268883e-05 1.653777e-04 9.999173e-01 [88,] 5.549086e-05 1.109817e-04 9.999445e-01 [89,] 3.682746e-05 7.365491e-05 9.999632e-01 [90,] 2.592506e-05 5.185012e-05 9.999741e-01 [91,] 1.797832e-05 3.595665e-05 9.999820e-01 [92,] 1.958530e-05 3.917060e-05 9.999804e-01 [93,] 5.060744e-05 1.012149e-04 9.999494e-01 [94,] 4.733674e-05 9.467347e-05 9.999527e-01 [95,] 7.550758e-05 1.510152e-04 9.999245e-01 [96,] 5.073000e-05 1.014600e-04 9.999493e-01 [97,] 3.578052e-05 7.156104e-05 9.999642e-01 [98,] 2.397266e-05 4.794531e-05 9.999760e-01 [99,] 1.553674e-05 3.107347e-05 9.999845e-01 [100,] 1.697179e-05 3.394359e-05 9.999830e-01 [101,] 1.090231e-05 2.180461e-05 9.999891e-01 [102,] 8.403109e-06 1.680622e-05 9.999916e-01 [103,] 5.301379e-06 1.060276e-05 9.999947e-01 [104,] 3.283666e-06 6.567332e-06 9.999967e-01 [105,] 2.165559e-06 4.331117e-06 9.999978e-01 [106,] 1.407186e-06 2.814371e-06 9.999986e-01 [107,] 8.422598e-07 1.684520e-06 9.999992e-01 [108,] 5.091853e-06 1.018371e-05 9.999949e-01 [109,] 6.077474e-06 1.215495e-05 9.999939e-01 [110,] 4.091363e-06 8.182726e-06 9.999959e-01 [111,] 2.645501e-06 5.291003e-06 9.999974e-01 [112,] 2.065192e-06 4.130385e-06 9.999979e-01 [113,] 6.408514e-06 1.281703e-05 9.999936e-01 [114,] 3.950666e-06 7.901333e-06 9.999960e-01 [115,] 8.301054e-06 1.660211e-05 9.999917e-01 [116,] 5.841725e-06 1.168345e-05 9.999942e-01 [117,] 3.740750e-06 7.481500e-06 9.999963e-01 [118,] 2.550460e-06 5.100921e-06 9.999974e-01 [119,] 1.596784e-06 3.193568e-06 9.999984e-01 [120,] 1.057011e-06 2.114021e-06 9.999989e-01 [121,] 7.032543e-07 1.406509e-06 9.999993e-01 [122,] 4.131587e-07 8.263174e-07 9.999996e-01 [123,] 2.413010e-07 4.826020e-07 9.999998e-01 [124,] 1.639033e-07 3.278065e-07 9.999998e-01 [125,] 9.862809e-08 1.972562e-07 9.999999e-01 [126,] 5.525959e-08 1.105192e-07 9.999999e-01 [127,] 1.664162e-07 3.328323e-07 9.999998e-01 [128,] 1.099062e-07 2.198124e-07 9.999999e-01 [129,] 1.206772e-06 2.413544e-06 9.999988e-01 [130,] 1.198618e-06 2.397236e-06 9.999988e-01 [131,] 1.620709e-06 3.241419e-06 9.999984e-01 [132,] 1.069695e-06 2.139389e-06 9.999989e-01 [133,] 6.306100e-07 1.261220e-06 9.999994e-01 [134,] 4.135365e-07 8.270731e-07 9.999996e-01 [135,] 3.402546e-07 6.805093e-07 9.999997e-01 [136,] 3.075015e-07 6.150031e-07 9.999997e-01 [137,] 1.753512e-07 3.507024e-07 9.999998e-01 [138,] 9.916268e-08 1.983254e-07 9.999999e-01 [139,] 4.351119e-06 8.702237e-06 9.999956e-01 [140,] 5.416017e-05 1.083203e-04 9.999458e-01 [141,] 4.255462e-03 8.510925e-03 9.957445e-01 [142,] 6.676848e-03 1.335370e-02 9.933232e-01 [143,] 1.906047e-01 3.812095e-01 8.093953e-01 [144,] 7.930227e-01 4.139545e-01 2.069773e-01 [145,] 8.829013e-01 2.341975e-01 1.170987e-01 [146,] 8.805919e-01 2.388162e-01 1.194081e-01 [147,] 8.995872e-01 2.008256e-01 1.004128e-01 [148,] 8.730684e-01 2.538633e-01 1.269316e-01 [149,] 8.426419e-01 3.147162e-01 1.573581e-01 [150,] 9.999999e-01 2.488284e-07 1.244142e-07 [151,] 9.999999e-01 1.383965e-07 6.919826e-08 [152,] 1.000000e+00 8.338372e-08 4.169186e-08 [153,] 1.000000e+00 8.638555e-13 4.319278e-13 [154,] 1.000000e+00 2.730107e-12 1.365054e-12 [155,] 1.000000e+00 4.268836e-12 2.134418e-12 [156,] 1.000000e+00 1.387310e-11 6.936551e-12 [157,] 1.000000e+00 5.300839e-11 2.650419e-11 [158,] 1.000000e+00 6.302768e-11 3.151384e-11 [159,] 1.000000e+00 2.005701e-10 1.002851e-10 [160,] 1.000000e+00 3.879150e-10 1.939575e-10 [161,] 1.000000e+00 1.099407e-09 5.497033e-10 [162,] 1.000000e+00 9.450157e-10 4.725078e-10 [163,] 1.000000e+00 4.271171e-09 2.135586e-09 [164,] 1.000000e+00 1.263036e-08 6.315178e-09 [165,] 1.000000e+00 3.588638e-08 1.794319e-08 [166,] 9.999999e-01 1.091854e-07 5.459272e-08 [167,] 9.999998e-01 3.706645e-07 1.853322e-07 [168,] 9.999993e-01 1.342808e-06 6.714041e-07 [169,] 1.000000e+00 7.685727e-08 3.842864e-08 [170,] 9.999999e-01 1.222231e-07 6.111155e-08 [171,] 9.999997e-01 5.435805e-07 2.717903e-07 [172,] 9.999983e-01 3.419105e-06 1.709553e-06 [173,] 9.999898e-01 2.043068e-05 1.021534e-05 [174,] 9.999433e-01 1.133231e-04 5.666155e-05 [175,] 9.997778e-01 4.443546e-04 2.221773e-04 [176,] 9.990649e-01 1.870295e-03 9.351477e-04 [177,] 9.951917e-01 9.616620e-03 4.808310e-03 [178,] 9.824899e-01 3.502024e-02 1.751012e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1g5m31386165795.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2tvt41386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/36xjv1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/40fqj1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/50k3c1386165795.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 = 195 Frequency = 1 1 2 3 4 5 1.829718e-03 1.201996e-03 9.460738e-04 6.570942e-04 1.566107e-04 6 7 8 9 10 1.900992e-03 4.045102e-04 -2.388994e-04 1.505696e-05 4.079918e-04 11 12 13 14 15 -1.037465e-04 4.416352e-04 -5.392251e-04 7.750368e-05 -4.290710e-04 16 17 18 19 20 -3.078229e-04 4.497879e-05 1.389820e-03 1.587920e-03 2.354967e-03 21 22 23 24 25 1.787624e-03 -6.004192e-04 1.606797e-04 3.770427e-04 6.601321e-04 26 27 28 29 30 9.792463e-04 5.555628e-04 2.373155e-04 4.745161e-04 5.119229e-04 31 32 33 34 35 -1.584137e-04 -6.194113e-05 -1.564764e-04 -1.422171e-04 -1.642237e-04 36 37 38 39 40 -7.726783e-05 -2.601811e-04 7.527521e-05 -1.536992e-04 1.799179e-04 41 42 43 44 45 -3.963955e-04 -2.236956e-04 -8.197466e-04 -6.050227e-04 -5.280536e-04 46 47 48 49 50 -4.228654e-04 -4.800017e-04 -4.990242e-04 -1.805373e-04 -4.877615e-04 51 52 53 54 55 -2.010918e-04 -3.074915e-04 -2.084977e-04 -8.900093e-05 -1.264292e-04 56 57 58 59 60 -6.864083e-04 1.248555e-04 -3.594854e-04 -2.807109e-04 -1.472944e-03 61 62 63 64 65 -1.769524e-04 -2.875711e-05 -1.603042e-04 -1.905535e-04 -8.801959e-05 66 67 68 69 70 -2.074563e-04 -9.240730e-04 -6.021979e-05 1.769609e-03 -8.335653e-04 71 72 73 74 75 -4.605273e-04 9.435112e-04 -7.801374e-04 -6.086481e-04 -8.523907e-04 76 77 78 79 80 -1.002611e-03 -8.773290e-04 -7.751551e-04 -2.004999e-04 -9.954447e-04 81 82 83 84 85 -4.130762e-05 -2.019753e-04 -2.910685e-04 -6.256311e-04 -1.203176e-04 86 87 88 89 90 1.708745e-04 7.472385e-06 -1.557917e-06 -4.310208e-04 -4.519362e-04 91 92 93 94 95 -4.395559e-04 3.704736e-04 -3.667889e-04 -1.782603e-03 1.405262e-04 96 97 98 99 100 -1.301341e-04 -1.054073e-04 2.805891e-04 2.206842e-04 -2.704208e-04 101 102 103 104 105 6.329181e-04 1.015871e-03 5.651152e-04 7.784810e-05 1.521063e-04 106 107 108 109 110 3.770154e-05 -2.213357e-04 7.946596e-04 -1.613139e-05 -6.316809e-04 111 112 113 114 115 -6.419609e-05 -2.230343e-04 5.227011e-05 1.207068e-04 -2.106554e-04 116 117 118 119 120 1.749666e-03 1.031433e-03 5.334686e-04 -2.461853e-04 -1.682384e-04 121 122 123 124 125 1.429750e-03 4.372377e-05 -8.598349e-04 -5.620912e-04 -3.758830e-04 126 127 128 129 130 -3.623744e-04 -2.174284e-04 -5.023188e-04 -3.683939e-04 1.795532e-05 131 132 133 134 135 5.829910e-05 4.875347e-04 -3.104924e-04 -5.244100e-05 6.788134e-04 136 137 138 139 140 -1.008334e-04 1.877341e-03 -5.742891e-05 1.743700e-04 4.697143e-04 141 142 143 144 145 2.460563e-04 -4.619274e-04 -4.792873e-04 -5.709653e-04 -1.445025e-05 146 147 148 149 150 7.717591e-05 -1.270915e-04 -5.166953e-04 5.439966e-04 6.606023e-04 151 152 153 154 155 -2.291467e-03 -3.901842e-03 -1.402544e-03 1.709641e-05 1.661607e-04 156 157 158 159 160 2.790819e-04 1.403989e-04 4.769870e-03 6.374846e-04 2.637020e-03 161 162 163 164 165 4.273080e-03 6.494813e-04 8.190893e-04 -4.068935e-04 -3.842205e-04 166 167 168 169 170 -2.082621e-04 -5.976817e-04 -9.634036e-04 -5.837551e-04 -9.552150e-04 171 172 173 174 175 -4.699573e-04 4.989324e-06 -4.109880e-04 2.708604e-05 -3.881175e-04 176 177 178 179 180 -4.405330e-04 3.301269e-04 1.824250e-05 -4.695841e-04 -1.827130e-04 181 182 183 184 185 -2.417852e-05 -2.701061e-04 -4.080167e-04 2.082218e-04 2.729944e-04 186 187 188 189 190 2.867985e-04 -6.849565e-05 -1.408046e-04 -4.455870e-05 -5.258240e-04 191 192 193 194 195 -5.192614e-04 -8.675848e-04 -7.094501e-04 -3.803981e-04 -5.226324e-04 > postscript(file="/var/wessaorg/rcomp/tmp/6tx8d1386165795.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 = 195 Frequency = 1 lag(myerror, k = 1) myerror 0 1.829718e-03 NA 1 1.201996e-03 1.829718e-03 2 9.460738e-04 1.201996e-03 3 6.570942e-04 9.460738e-04 4 1.566107e-04 6.570942e-04 5 1.900992e-03 1.566107e-04 6 4.045102e-04 1.900992e-03 7 -2.388994e-04 4.045102e-04 8 1.505696e-05 -2.388994e-04 9 4.079918e-04 1.505696e-05 10 -1.037465e-04 4.079918e-04 11 4.416352e-04 -1.037465e-04 12 -5.392251e-04 4.416352e-04 13 7.750368e-05 -5.392251e-04 14 -4.290710e-04 7.750368e-05 15 -3.078229e-04 -4.290710e-04 16 4.497879e-05 -3.078229e-04 17 1.389820e-03 4.497879e-05 18 1.587920e-03 1.389820e-03 19 2.354967e-03 1.587920e-03 20 1.787624e-03 2.354967e-03 21 -6.004192e-04 1.787624e-03 22 1.606797e-04 -6.004192e-04 23 3.770427e-04 1.606797e-04 24 6.601321e-04 3.770427e-04 25 9.792463e-04 6.601321e-04 26 5.555628e-04 9.792463e-04 27 2.373155e-04 5.555628e-04 28 4.745161e-04 2.373155e-04 29 5.119229e-04 4.745161e-04 30 -1.584137e-04 5.119229e-04 31 -6.194113e-05 -1.584137e-04 32 -1.564764e-04 -6.194113e-05 33 -1.422171e-04 -1.564764e-04 34 -1.642237e-04 -1.422171e-04 35 -7.726783e-05 -1.642237e-04 36 -2.601811e-04 -7.726783e-05 37 7.527521e-05 -2.601811e-04 38 -1.536992e-04 7.527521e-05 39 1.799179e-04 -1.536992e-04 40 -3.963955e-04 1.799179e-04 41 -2.236956e-04 -3.963955e-04 42 -8.197466e-04 -2.236956e-04 43 -6.050227e-04 -8.197466e-04 44 -5.280536e-04 -6.050227e-04 45 -4.228654e-04 -5.280536e-04 46 -4.800017e-04 -4.228654e-04 47 -4.990242e-04 -4.800017e-04 48 -1.805373e-04 -4.990242e-04 49 -4.877615e-04 -1.805373e-04 50 -2.010918e-04 -4.877615e-04 51 -3.074915e-04 -2.010918e-04 52 -2.084977e-04 -3.074915e-04 53 -8.900093e-05 -2.084977e-04 54 -1.264292e-04 -8.900093e-05 55 -6.864083e-04 -1.264292e-04 56 1.248555e-04 -6.864083e-04 57 -3.594854e-04 1.248555e-04 58 -2.807109e-04 -3.594854e-04 59 -1.472944e-03 -2.807109e-04 60 -1.769524e-04 -1.472944e-03 61 -2.875711e-05 -1.769524e-04 62 -1.603042e-04 -2.875711e-05 63 -1.905535e-04 -1.603042e-04 64 -8.801959e-05 -1.905535e-04 65 -2.074563e-04 -8.801959e-05 66 -9.240730e-04 -2.074563e-04 67 -6.021979e-05 -9.240730e-04 68 1.769609e-03 -6.021979e-05 69 -8.335653e-04 1.769609e-03 70 -4.605273e-04 -8.335653e-04 71 9.435112e-04 -4.605273e-04 72 -7.801374e-04 9.435112e-04 73 -6.086481e-04 -7.801374e-04 74 -8.523907e-04 -6.086481e-04 75 -1.002611e-03 -8.523907e-04 76 -8.773290e-04 -1.002611e-03 77 -7.751551e-04 -8.773290e-04 78 -2.004999e-04 -7.751551e-04 79 -9.954447e-04 -2.004999e-04 80 -4.130762e-05 -9.954447e-04 81 -2.019753e-04 -4.130762e-05 82 -2.910685e-04 -2.019753e-04 83 -6.256311e-04 -2.910685e-04 84 -1.203176e-04 -6.256311e-04 85 1.708745e-04 -1.203176e-04 86 7.472385e-06 1.708745e-04 87 -1.557917e-06 7.472385e-06 88 -4.310208e-04 -1.557917e-06 89 -4.519362e-04 -4.310208e-04 90 -4.395559e-04 -4.519362e-04 91 3.704736e-04 -4.395559e-04 92 -3.667889e-04 3.704736e-04 93 -1.782603e-03 -3.667889e-04 94 1.405262e-04 -1.782603e-03 95 -1.301341e-04 1.405262e-04 96 -1.054073e-04 -1.301341e-04 97 2.805891e-04 -1.054073e-04 98 2.206842e-04 2.805891e-04 99 -2.704208e-04 2.206842e-04 100 6.329181e-04 -2.704208e-04 101 1.015871e-03 6.329181e-04 102 5.651152e-04 1.015871e-03 103 7.784810e-05 5.651152e-04 104 1.521063e-04 7.784810e-05 105 3.770154e-05 1.521063e-04 106 -2.213357e-04 3.770154e-05 107 7.946596e-04 -2.213357e-04 108 -1.613139e-05 7.946596e-04 109 -6.316809e-04 -1.613139e-05 110 -6.419609e-05 -6.316809e-04 111 -2.230343e-04 -6.419609e-05 112 5.227011e-05 -2.230343e-04 113 1.207068e-04 5.227011e-05 114 -2.106554e-04 1.207068e-04 115 1.749666e-03 -2.106554e-04 116 1.031433e-03 1.749666e-03 117 5.334686e-04 1.031433e-03 118 -2.461853e-04 5.334686e-04 119 -1.682384e-04 -2.461853e-04 120 1.429750e-03 -1.682384e-04 121 4.372377e-05 1.429750e-03 122 -8.598349e-04 4.372377e-05 123 -5.620912e-04 -8.598349e-04 124 -3.758830e-04 -5.620912e-04 125 -3.623744e-04 -3.758830e-04 126 -2.174284e-04 -3.623744e-04 127 -5.023188e-04 -2.174284e-04 128 -3.683939e-04 -5.023188e-04 129 1.795532e-05 -3.683939e-04 130 5.829910e-05 1.795532e-05 131 4.875347e-04 5.829910e-05 132 -3.104924e-04 4.875347e-04 133 -5.244100e-05 -3.104924e-04 134 6.788134e-04 -5.244100e-05 135 -1.008334e-04 6.788134e-04 136 1.877341e-03 -1.008334e-04 137 -5.742891e-05 1.877341e-03 138 1.743700e-04 -5.742891e-05 139 4.697143e-04 1.743700e-04 140 2.460563e-04 4.697143e-04 141 -4.619274e-04 2.460563e-04 142 -4.792873e-04 -4.619274e-04 143 -5.709653e-04 -4.792873e-04 144 -1.445025e-05 -5.709653e-04 145 7.717591e-05 -1.445025e-05 146 -1.270915e-04 7.717591e-05 147 -5.166953e-04 -1.270915e-04 148 5.439966e-04 -5.166953e-04 149 6.606023e-04 5.439966e-04 150 -2.291467e-03 6.606023e-04 151 -3.901842e-03 -2.291467e-03 152 -1.402544e-03 -3.901842e-03 153 1.709641e-05 -1.402544e-03 154 1.661607e-04 1.709641e-05 155 2.790819e-04 1.661607e-04 156 1.403989e-04 2.790819e-04 157 4.769870e-03 1.403989e-04 158 6.374846e-04 4.769870e-03 159 2.637020e-03 6.374846e-04 160 4.273080e-03 2.637020e-03 161 6.494813e-04 4.273080e-03 162 8.190893e-04 6.494813e-04 163 -4.068935e-04 8.190893e-04 164 -3.842205e-04 -4.068935e-04 165 -2.082621e-04 -3.842205e-04 166 -5.976817e-04 -2.082621e-04 167 -9.634036e-04 -5.976817e-04 168 -5.837551e-04 -9.634036e-04 169 -9.552150e-04 -5.837551e-04 170 -4.699573e-04 -9.552150e-04 171 4.989324e-06 -4.699573e-04 172 -4.109880e-04 4.989324e-06 173 2.708604e-05 -4.109880e-04 174 -3.881175e-04 2.708604e-05 175 -4.405330e-04 -3.881175e-04 176 3.301269e-04 -4.405330e-04 177 1.824250e-05 3.301269e-04 178 -4.695841e-04 1.824250e-05 179 -1.827130e-04 -4.695841e-04 180 -2.417852e-05 -1.827130e-04 181 -2.701061e-04 -2.417852e-05 182 -4.080167e-04 -2.701061e-04 183 2.082218e-04 -4.080167e-04 184 2.729944e-04 2.082218e-04 185 2.867985e-04 2.729944e-04 186 -6.849565e-05 2.867985e-04 187 -1.408046e-04 -6.849565e-05 188 -4.455870e-05 -1.408046e-04 189 -5.258240e-04 -4.455870e-05 190 -5.192614e-04 -5.258240e-04 191 -8.675848e-04 -5.192614e-04 192 -7.094501e-04 -8.675848e-04 193 -3.803981e-04 -7.094501e-04 194 -5.226324e-04 -3.803981e-04 195 NA -5.226324e-04 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.201996e-03 1.829718e-03 [2,] 9.460738e-04 1.201996e-03 [3,] 6.570942e-04 9.460738e-04 [4,] 1.566107e-04 6.570942e-04 [5,] 1.900992e-03 1.566107e-04 [6,] 4.045102e-04 1.900992e-03 [7,] -2.388994e-04 4.045102e-04 [8,] 1.505696e-05 -2.388994e-04 [9,] 4.079918e-04 1.505696e-05 [10,] -1.037465e-04 4.079918e-04 [11,] 4.416352e-04 -1.037465e-04 [12,] -5.392251e-04 4.416352e-04 [13,] 7.750368e-05 -5.392251e-04 [14,] -4.290710e-04 7.750368e-05 [15,] -3.078229e-04 -4.290710e-04 [16,] 4.497879e-05 -3.078229e-04 [17,] 1.389820e-03 4.497879e-05 [18,] 1.587920e-03 1.389820e-03 [19,] 2.354967e-03 1.587920e-03 [20,] 1.787624e-03 2.354967e-03 [21,] -6.004192e-04 1.787624e-03 [22,] 1.606797e-04 -6.004192e-04 [23,] 3.770427e-04 1.606797e-04 [24,] 6.601321e-04 3.770427e-04 [25,] 9.792463e-04 6.601321e-04 [26,] 5.555628e-04 9.792463e-04 [27,] 2.373155e-04 5.555628e-04 [28,] 4.745161e-04 2.373155e-04 [29,] 5.119229e-04 4.745161e-04 [30,] -1.584137e-04 5.119229e-04 [31,] -6.194113e-05 -1.584137e-04 [32,] -1.564764e-04 -6.194113e-05 [33,] -1.422171e-04 -1.564764e-04 [34,] -1.642237e-04 -1.422171e-04 [35,] -7.726783e-05 -1.642237e-04 [36,] -2.601811e-04 -7.726783e-05 [37,] 7.527521e-05 -2.601811e-04 [38,] -1.536992e-04 7.527521e-05 [39,] 1.799179e-04 -1.536992e-04 [40,] -3.963955e-04 1.799179e-04 [41,] -2.236956e-04 -3.963955e-04 [42,] -8.197466e-04 -2.236956e-04 [43,] -6.050227e-04 -8.197466e-04 [44,] -5.280536e-04 -6.050227e-04 [45,] -4.228654e-04 -5.280536e-04 [46,] -4.800017e-04 -4.228654e-04 [47,] -4.990242e-04 -4.800017e-04 [48,] -1.805373e-04 -4.990242e-04 [49,] -4.877615e-04 -1.805373e-04 [50,] -2.010918e-04 -4.877615e-04 [51,] -3.074915e-04 -2.010918e-04 [52,] -2.084977e-04 -3.074915e-04 [53,] -8.900093e-05 -2.084977e-04 [54,] -1.264292e-04 -8.900093e-05 [55,] -6.864083e-04 -1.264292e-04 [56,] 1.248555e-04 -6.864083e-04 [57,] -3.594854e-04 1.248555e-04 [58,] -2.807109e-04 -3.594854e-04 [59,] -1.472944e-03 -2.807109e-04 [60,] -1.769524e-04 -1.472944e-03 [61,] -2.875711e-05 -1.769524e-04 [62,] -1.603042e-04 -2.875711e-05 [63,] -1.905535e-04 -1.603042e-04 [64,] -8.801959e-05 -1.905535e-04 [65,] -2.074563e-04 -8.801959e-05 [66,] -9.240730e-04 -2.074563e-04 [67,] -6.021979e-05 -9.240730e-04 [68,] 1.769609e-03 -6.021979e-05 [69,] -8.335653e-04 1.769609e-03 [70,] -4.605273e-04 -8.335653e-04 [71,] 9.435112e-04 -4.605273e-04 [72,] -7.801374e-04 9.435112e-04 [73,] -6.086481e-04 -7.801374e-04 [74,] -8.523907e-04 -6.086481e-04 [75,] -1.002611e-03 -8.523907e-04 [76,] -8.773290e-04 -1.002611e-03 [77,] -7.751551e-04 -8.773290e-04 [78,] -2.004999e-04 -7.751551e-04 [79,] -9.954447e-04 -2.004999e-04 [80,] -4.130762e-05 -9.954447e-04 [81,] -2.019753e-04 -4.130762e-05 [82,] -2.910685e-04 -2.019753e-04 [83,] -6.256311e-04 -2.910685e-04 [84,] -1.203176e-04 -6.256311e-04 [85,] 1.708745e-04 -1.203176e-04 [86,] 7.472385e-06 1.708745e-04 [87,] -1.557917e-06 7.472385e-06 [88,] -4.310208e-04 -1.557917e-06 [89,] -4.519362e-04 -4.310208e-04 [90,] -4.395559e-04 -4.519362e-04 [91,] 3.704736e-04 -4.395559e-04 [92,] -3.667889e-04 3.704736e-04 [93,] -1.782603e-03 -3.667889e-04 [94,] 1.405262e-04 -1.782603e-03 [95,] -1.301341e-04 1.405262e-04 [96,] -1.054073e-04 -1.301341e-04 [97,] 2.805891e-04 -1.054073e-04 [98,] 2.206842e-04 2.805891e-04 [99,] -2.704208e-04 2.206842e-04 [100,] 6.329181e-04 -2.704208e-04 [101,] 1.015871e-03 6.329181e-04 [102,] 5.651152e-04 1.015871e-03 [103,] 7.784810e-05 5.651152e-04 [104,] 1.521063e-04 7.784810e-05 [105,] 3.770154e-05 1.521063e-04 [106,] -2.213357e-04 3.770154e-05 [107,] 7.946596e-04 -2.213357e-04 [108,] -1.613139e-05 7.946596e-04 [109,] -6.316809e-04 -1.613139e-05 [110,] -6.419609e-05 -6.316809e-04 [111,] -2.230343e-04 -6.419609e-05 [112,] 5.227011e-05 -2.230343e-04 [113,] 1.207068e-04 5.227011e-05 [114,] -2.106554e-04 1.207068e-04 [115,] 1.749666e-03 -2.106554e-04 [116,] 1.031433e-03 1.749666e-03 [117,] 5.334686e-04 1.031433e-03 [118,] -2.461853e-04 5.334686e-04 [119,] -1.682384e-04 -2.461853e-04 [120,] 1.429750e-03 -1.682384e-04 [121,] 4.372377e-05 1.429750e-03 [122,] -8.598349e-04 4.372377e-05 [123,] -5.620912e-04 -8.598349e-04 [124,] -3.758830e-04 -5.620912e-04 [125,] -3.623744e-04 -3.758830e-04 [126,] -2.174284e-04 -3.623744e-04 [127,] -5.023188e-04 -2.174284e-04 [128,] -3.683939e-04 -5.023188e-04 [129,] 1.795532e-05 -3.683939e-04 [130,] 5.829910e-05 1.795532e-05 [131,] 4.875347e-04 5.829910e-05 [132,] -3.104924e-04 4.875347e-04 [133,] -5.244100e-05 -3.104924e-04 [134,] 6.788134e-04 -5.244100e-05 [135,] -1.008334e-04 6.788134e-04 [136,] 1.877341e-03 -1.008334e-04 [137,] -5.742891e-05 1.877341e-03 [138,] 1.743700e-04 -5.742891e-05 [139,] 4.697143e-04 1.743700e-04 [140,] 2.460563e-04 4.697143e-04 [141,] -4.619274e-04 2.460563e-04 [142,] -4.792873e-04 -4.619274e-04 [143,] -5.709653e-04 -4.792873e-04 [144,] -1.445025e-05 -5.709653e-04 [145,] 7.717591e-05 -1.445025e-05 [146,] -1.270915e-04 7.717591e-05 [147,] -5.166953e-04 -1.270915e-04 [148,] 5.439966e-04 -5.166953e-04 [149,] 6.606023e-04 5.439966e-04 [150,] -2.291467e-03 6.606023e-04 [151,] -3.901842e-03 -2.291467e-03 [152,] -1.402544e-03 -3.901842e-03 [153,] 1.709641e-05 -1.402544e-03 [154,] 1.661607e-04 1.709641e-05 [155,] 2.790819e-04 1.661607e-04 [156,] 1.403989e-04 2.790819e-04 [157,] 4.769870e-03 1.403989e-04 [158,] 6.374846e-04 4.769870e-03 [159,] 2.637020e-03 6.374846e-04 [160,] 4.273080e-03 2.637020e-03 [161,] 6.494813e-04 4.273080e-03 [162,] 8.190893e-04 6.494813e-04 [163,] -4.068935e-04 8.190893e-04 [164,] -3.842205e-04 -4.068935e-04 [165,] -2.082621e-04 -3.842205e-04 [166,] -5.976817e-04 -2.082621e-04 [167,] -9.634036e-04 -5.976817e-04 [168,] -5.837551e-04 -9.634036e-04 [169,] -9.552150e-04 -5.837551e-04 [170,] -4.699573e-04 -9.552150e-04 [171,] 4.989324e-06 -4.699573e-04 [172,] -4.109880e-04 4.989324e-06 [173,] 2.708604e-05 -4.109880e-04 [174,] -3.881175e-04 2.708604e-05 [175,] -4.405330e-04 -3.881175e-04 [176,] 3.301269e-04 -4.405330e-04 [177,] 1.824250e-05 3.301269e-04 [178,] -4.695841e-04 1.824250e-05 [179,] -1.827130e-04 -4.695841e-04 [180,] -2.417852e-05 -1.827130e-04 [181,] -2.701061e-04 -2.417852e-05 [182,] -4.080167e-04 -2.701061e-04 [183,] 2.082218e-04 -4.080167e-04 [184,] 2.729944e-04 2.082218e-04 [185,] 2.867985e-04 2.729944e-04 [186,] -6.849565e-05 2.867985e-04 [187,] -1.408046e-04 -6.849565e-05 [188,] -4.455870e-05 -1.408046e-04 [189,] -5.258240e-04 -4.455870e-05 [190,] -5.192614e-04 -5.258240e-04 [191,] -8.675848e-04 -5.192614e-04 [192,] -7.094501e-04 -8.675848e-04 [193,] -3.803981e-04 -7.094501e-04 [194,] -5.226324e-04 -3.803981e-04 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.201996e-03 1.829718e-03 2 9.460738e-04 1.201996e-03 3 6.570942e-04 9.460738e-04 4 1.566107e-04 6.570942e-04 5 1.900992e-03 1.566107e-04 6 4.045102e-04 1.900992e-03 7 -2.388994e-04 4.045102e-04 8 1.505696e-05 -2.388994e-04 9 4.079918e-04 1.505696e-05 10 -1.037465e-04 4.079918e-04 11 4.416352e-04 -1.037465e-04 12 -5.392251e-04 4.416352e-04 13 7.750368e-05 -5.392251e-04 14 -4.290710e-04 7.750368e-05 15 -3.078229e-04 -4.290710e-04 16 4.497879e-05 -3.078229e-04 17 1.389820e-03 4.497879e-05 18 1.587920e-03 1.389820e-03 19 2.354967e-03 1.587920e-03 20 1.787624e-03 2.354967e-03 21 -6.004192e-04 1.787624e-03 22 1.606797e-04 -6.004192e-04 23 3.770427e-04 1.606797e-04 24 6.601321e-04 3.770427e-04 25 9.792463e-04 6.601321e-04 26 5.555628e-04 9.792463e-04 27 2.373155e-04 5.555628e-04 28 4.745161e-04 2.373155e-04 29 5.119229e-04 4.745161e-04 30 -1.584137e-04 5.119229e-04 31 -6.194113e-05 -1.584137e-04 32 -1.564764e-04 -6.194113e-05 33 -1.422171e-04 -1.564764e-04 34 -1.642237e-04 -1.422171e-04 35 -7.726783e-05 -1.642237e-04 36 -2.601811e-04 -7.726783e-05 37 7.527521e-05 -2.601811e-04 38 -1.536992e-04 7.527521e-05 39 1.799179e-04 -1.536992e-04 40 -3.963955e-04 1.799179e-04 41 -2.236956e-04 -3.963955e-04 42 -8.197466e-04 -2.236956e-04 43 -6.050227e-04 -8.197466e-04 44 -5.280536e-04 -6.050227e-04 45 -4.228654e-04 -5.280536e-04 46 -4.800017e-04 -4.228654e-04 47 -4.990242e-04 -4.800017e-04 48 -1.805373e-04 -4.990242e-04 49 -4.877615e-04 -1.805373e-04 50 -2.010918e-04 -4.877615e-04 51 -3.074915e-04 -2.010918e-04 52 -2.084977e-04 -3.074915e-04 53 -8.900093e-05 -2.084977e-04 54 -1.264292e-04 -8.900093e-05 55 -6.864083e-04 -1.264292e-04 56 1.248555e-04 -6.864083e-04 57 -3.594854e-04 1.248555e-04 58 -2.807109e-04 -3.594854e-04 59 -1.472944e-03 -2.807109e-04 60 -1.769524e-04 -1.472944e-03 61 -2.875711e-05 -1.769524e-04 62 -1.603042e-04 -2.875711e-05 63 -1.905535e-04 -1.603042e-04 64 -8.801959e-05 -1.905535e-04 65 -2.074563e-04 -8.801959e-05 66 -9.240730e-04 -2.074563e-04 67 -6.021979e-05 -9.240730e-04 68 1.769609e-03 -6.021979e-05 69 -8.335653e-04 1.769609e-03 70 -4.605273e-04 -8.335653e-04 71 9.435112e-04 -4.605273e-04 72 -7.801374e-04 9.435112e-04 73 -6.086481e-04 -7.801374e-04 74 -8.523907e-04 -6.086481e-04 75 -1.002611e-03 -8.523907e-04 76 -8.773290e-04 -1.002611e-03 77 -7.751551e-04 -8.773290e-04 78 -2.004999e-04 -7.751551e-04 79 -9.954447e-04 -2.004999e-04 80 -4.130762e-05 -9.954447e-04 81 -2.019753e-04 -4.130762e-05 82 -2.910685e-04 -2.019753e-04 83 -6.256311e-04 -2.910685e-04 84 -1.203176e-04 -6.256311e-04 85 1.708745e-04 -1.203176e-04 86 7.472385e-06 1.708745e-04 87 -1.557917e-06 7.472385e-06 88 -4.310208e-04 -1.557917e-06 89 -4.519362e-04 -4.310208e-04 90 -4.395559e-04 -4.519362e-04 91 3.704736e-04 -4.395559e-04 92 -3.667889e-04 3.704736e-04 93 -1.782603e-03 -3.667889e-04 94 1.405262e-04 -1.782603e-03 95 -1.301341e-04 1.405262e-04 96 -1.054073e-04 -1.301341e-04 97 2.805891e-04 -1.054073e-04 98 2.206842e-04 2.805891e-04 99 -2.704208e-04 2.206842e-04 100 6.329181e-04 -2.704208e-04 101 1.015871e-03 6.329181e-04 102 5.651152e-04 1.015871e-03 103 7.784810e-05 5.651152e-04 104 1.521063e-04 7.784810e-05 105 3.770154e-05 1.521063e-04 106 -2.213357e-04 3.770154e-05 107 7.946596e-04 -2.213357e-04 108 -1.613139e-05 7.946596e-04 109 -6.316809e-04 -1.613139e-05 110 -6.419609e-05 -6.316809e-04 111 -2.230343e-04 -6.419609e-05 112 5.227011e-05 -2.230343e-04 113 1.207068e-04 5.227011e-05 114 -2.106554e-04 1.207068e-04 115 1.749666e-03 -2.106554e-04 116 1.031433e-03 1.749666e-03 117 5.334686e-04 1.031433e-03 118 -2.461853e-04 5.334686e-04 119 -1.682384e-04 -2.461853e-04 120 1.429750e-03 -1.682384e-04 121 4.372377e-05 1.429750e-03 122 -8.598349e-04 4.372377e-05 123 -5.620912e-04 -8.598349e-04 124 -3.758830e-04 -5.620912e-04 125 -3.623744e-04 -3.758830e-04 126 -2.174284e-04 -3.623744e-04 127 -5.023188e-04 -2.174284e-04 128 -3.683939e-04 -5.023188e-04 129 1.795532e-05 -3.683939e-04 130 5.829910e-05 1.795532e-05 131 4.875347e-04 5.829910e-05 132 -3.104924e-04 4.875347e-04 133 -5.244100e-05 -3.104924e-04 134 6.788134e-04 -5.244100e-05 135 -1.008334e-04 6.788134e-04 136 1.877341e-03 -1.008334e-04 137 -5.742891e-05 1.877341e-03 138 1.743700e-04 -5.742891e-05 139 4.697143e-04 1.743700e-04 140 2.460563e-04 4.697143e-04 141 -4.619274e-04 2.460563e-04 142 -4.792873e-04 -4.619274e-04 143 -5.709653e-04 -4.792873e-04 144 -1.445025e-05 -5.709653e-04 145 7.717591e-05 -1.445025e-05 146 -1.270915e-04 7.717591e-05 147 -5.166953e-04 -1.270915e-04 148 5.439966e-04 -5.166953e-04 149 6.606023e-04 5.439966e-04 150 -2.291467e-03 6.606023e-04 151 -3.901842e-03 -2.291467e-03 152 -1.402544e-03 -3.901842e-03 153 1.709641e-05 -1.402544e-03 154 1.661607e-04 1.709641e-05 155 2.790819e-04 1.661607e-04 156 1.403989e-04 2.790819e-04 157 4.769870e-03 1.403989e-04 158 6.374846e-04 4.769870e-03 159 2.637020e-03 6.374846e-04 160 4.273080e-03 2.637020e-03 161 6.494813e-04 4.273080e-03 162 8.190893e-04 6.494813e-04 163 -4.068935e-04 8.190893e-04 164 -3.842205e-04 -4.068935e-04 165 -2.082621e-04 -3.842205e-04 166 -5.976817e-04 -2.082621e-04 167 -9.634036e-04 -5.976817e-04 168 -5.837551e-04 -9.634036e-04 169 -9.552150e-04 -5.837551e-04 170 -4.699573e-04 -9.552150e-04 171 4.989324e-06 -4.699573e-04 172 -4.109880e-04 4.989324e-06 173 2.708604e-05 -4.109880e-04 174 -3.881175e-04 2.708604e-05 175 -4.405330e-04 -3.881175e-04 176 3.301269e-04 -4.405330e-04 177 1.824250e-05 3.301269e-04 178 -4.695841e-04 1.824250e-05 179 -1.827130e-04 -4.695841e-04 180 -2.417852e-05 -1.827130e-04 181 -2.701061e-04 -2.417852e-05 182 -4.080167e-04 -2.701061e-04 183 2.082218e-04 -4.080167e-04 184 2.729944e-04 2.082218e-04 185 2.867985e-04 2.729944e-04 186 -6.849565e-05 2.867985e-04 187 -1.408046e-04 -6.849565e-05 188 -4.455870e-05 -1.408046e-04 189 -5.258240e-04 -4.455870e-05 190 -5.192614e-04 -5.258240e-04 191 -8.675848e-04 -5.192614e-04 192 -7.094501e-04 -8.675848e-04 193 -3.803981e-04 -7.094501e-04 194 -5.226324e-04 -3.803981e-04 > 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/wessaorg/rcomp/tmp/7f5bn1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8nimg1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9au781386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/101gpc1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$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/wessaorg/rcomp/tmp/11a6ra1386165795.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$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12rsqa1386165795.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, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13cx5v1386165795.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,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1465xo1386165795.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,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15510n1386165796.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,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + 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,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + 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,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + 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/wessaorg/rcomp/tmp/1647401386165796.tab") + } > > try(system("convert tmp/1g5m31386165795.ps tmp/1g5m31386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/2tvt41386165795.ps tmp/2tvt41386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/36xjv1386165795.ps tmp/36xjv1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/40fqj1386165795.ps tmp/40fqj1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/50k3c1386165795.ps tmp/50k3c1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/6tx8d1386165795.ps tmp/6tx8d1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/7f5bn1386165795.ps tmp/7f5bn1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/8nimg1386165795.ps tmp/8nimg1386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/9au781386165795.ps tmp/9au781386165795.png",intern=TRUE)) character(0) > try(system("convert tmp/101gpc1386165795.ps tmp/101gpc1386165795.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.438 2.326 14.783