workshop8

*The author of this computation has been verified*

R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)

Title produced by software: Decomposition by Loess

Date of computation: Sun, 19 Dec 2010 00:26:46 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq.htm/, Retrieved Sun, 19 Dec 2010 01:27:19 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

40,7819 39,5915 38,8859 39,9068 41,47 41,5613 41,6005 41,4113 41,84 42,2892 43,1521 43,5998 43,116 42,4185 42,3687 42,2975 42,8528 43,535 44,7265 45,7293 45,7585 46,1685 46,5075 46,527 46,601 46,4607 46,7135 46,4113 45,55 44,6081 44,4395 44,9847 45,7558 45,3942 45,697 45,5664 46,0205 45,9195 45,8005 45,535 45,4977 45,5782 45,7697 45,2445 45,0615 45,2865 44,791 44,7625 44,7644 44,9973 44,7265 45,1465 44,7465 45,1795 45,6515 45,492 45,2775 45,2115 45,411 45,4005 44,7692 44,8913 45,032 44,879 44,833 44,8257 44,7815 44,479 44,6317 44,5043 44,3217 44,1005 44,047 43,6835 43,7864 44,1807 43,9595 43,937 43,991 43,865 43,671 43,93 43,863 43,7095 43,9435 43,736 43,6295 43,598 43,8726 43,8935 43,5957 43,7155 43,528 43,3415 43,3374 43,332 43,3869 43,5016 43,4875 43,6023 43,3886 43,3105 43,4455 43,5185 43,5755 43,6217 43,644 43,5789 43,5215 43,5033 43,632 43,263 43,3717 43,2745 43,2647 43,324 43,4455 43,409 etc...

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 15 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 4911 0 492 Trend 9 1 1 Low-pass 5 1 1 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 40.7819 41.8822997458307 -0.0146817392252817 39.6961819933945 1.10039974583075 2 39.5915 39.2683414164004 -0.00300604583711205 39.9176646294367 -0.323158583599586 3 38.8859 37.600651014008 0.00214140787726687 40.1690075781147 -1.28524898599201 4 39.9068 39.4002566593236 0.00140080957424818 40.4119425311021 -0.506543340676373 5 41.47 42.1944698547392 0.0141455668839133 40.7313845783769 0.724469854739226 6 41.5613 41.9472969673138 -0.0146817392252817 41.1899847719115 0.385996967313822 7 41.6005 41.6763333530489 -0.00300604583711205 41.5276726927882 0.075833353048914 8 41.4113 41.0583686370844 0.00214140787726687 41.7620899550383 -0.352931362915555 9 41.84 41.6179194976555 0.00140080957424818 42.0606796927702 -0.222080502344475 10 42.2892 42.1217447134269 0.0141455668839133 42.4425097196892 -0.167455286573123 11 43.1521 43.5501108868164 -0.0146817392252817 42.7687708524089 0.398010886816408 12 43.5998 44.2791680723756 -0.00300604583711205 42.9234379734615 0.679368072375617 13 43.116 43.3292984002375 0.00214140787726687 42.9005601918853 0.213298400237463 14 42.4185 42.0821557317928 0.00140080957424818 42.7534434586329 -0.336344268207178 15 42.3687 42.0811325504173 0.0141455668839133 42.6421218826988 -0.287567449582681 16 42.2975 41.8512028088219 -0.0146817392252817 42.7584789304034 -0.446297191178147 17 42.8528 42.5266279038799 -0.00300604583711205 43.1819781419572 -0.326172096120118 18 43.535 43.2456533035713 0.00214140787726687 43.8222052885515 -0.28934669642873 19 44.7265 44.9271379359674 0.00140080957424818 44.5244612544584 0.200637935967393 20 45.7293 46.2569385052336 0.0141455668839133 45.1875159278825 0.527638505233604 21 45.7585 45.7961652368300 -0.0146817392252817 45.7355165023952 0.037665236830037 22 46.1685 46.239546375084 -0.00300604583711205 46.1004596707531 0.0710463750839736 23 46.5075 46.7003193231109 0.00214140787726687 46.3125392690119 0.192819323110854 24 46.527 46.6004208982241 0.00140080957424818 46.4521782922016 0.0734208982241213 25 46.601 46.6543206667744 0.0141455668839133 46.5335337663417 0.053320666774411 26 46.4607 46.4278305812607 -0.0146817392252817 46.5082511579646 -0.0328694187393097 27 46.7135 47.1104451081635 -0.00300604583711205 46.3195609376736 0.396945108163514 28 46.4113 46.8521241419846 0.00214140787726687 45.9683344501381 0.440824141984642 29 45.55 45.5434855309287 0.00140080957424818 45.555113659497 -0.00651446907127706 30 44.6081 43.9841657313525 0.0141455668839133 45.2178887017636 -0.623934268647503 31 44.4395 43.834177549217 -0.0146817392252817 45.0595041900083 -0.605322450783014 32 44.9847 44.8828442026265 -0.00300604583711205 45.0895618432106 -0.101855797373467 33 45.7558 46.2413900877658 0.00214140787726687 45.2680685043570 0.485590087765772 34 45.3942 45.3043996434551 0.00140080957424818 45.4825995469706 -0.0898003565448704 35 45.697 45.7373779898001 0.0141455668839133 45.642476443316 0.0403779898000636 36 45.5664 45.4167018776085 -0.0146817392252817 45.7307798616168 -0.149698122391541 37 46.0205 46.2623635772070 -0.00300604583711205 45.7816424686301 0.241863577207049 38 45.9195 46.0545933807615 0.00214140787726687 45.7822652113612 0.135093380761539 39 45.8005 45.8606153693654 0.00140080957424818 45.7389838210604 0.0601153693653984 40 45.535 45.3821784269736 0.0141455668839133 45.6736760061425 -0.152821573026422 41 45.4977 45.3977915093151 -0.0146817392252817 45.6122902299102 -0.0999084906848822 42 45.5782 45.6247866742022 -0.00300604583711205 45.5346193716349 0.0465866742022314 43 45.7697 46.0867531353601 0.00214140787726687 45.4505054567626 0.317053135360091 44 45.2445 45.1270598886499 0.00140080957424818 45.3605393017758 -0.117440111350078 45 45.0615 44.8911760408282 0.0141455668839133 45.2176783922878 -0.170323959171753 46 45.2865 45.5327192138846 -0.0146817392252817 45.0549625253407 0.246219213884579 47 44.791 44.6347737786358 -0.00300604583711205 44.9502322672013 -0.156226221364186 48 44.7625 44.6334693603697 0.00214140787726687 44.8893892317531 -0.129030639630336 49 44.7644 44.6797415549476 0.00140080957424818 44.8476576354782 -0.0846584450524404 50 44.9973 45.1184275895647 0.0141455668839133 44.8620268435514 0.121127589564715 51 44.7265 44.5700129749122 -0.0146817392252817 44.8976687643131 -0.156487025087799 52 45.1465 45.3296329738088 -0.00300604583711205 44.9663730720283 0.183132973808782 53 44.7465 44.4034428198817 0.00214140787726687 45.087415772241 -0.343057180118286 54 45.1795 45.1448278490120 0.00140080957424818 45.2127713414138 -0.0346721509880439 55 45.6515 45.9918035851128 0.0141455668839133 45.2970508480033 0.340303585112778 56 45.492 45.6379782249221 -0.0146817392252817 45.3607035143031 0.145978224922132 57 45.2775 45.175194925457 -0.00300604583711205 45.3828111203801 -0.102305074543004 58 45.2115 45.0869622900163 0.00214140787726687 45.3338963021064 -0.124537709983692 59 45.411 45.5835603902034 0.00140080957424818 45.2370388002224 0.172560390203401 60 45.4005 45.6357270839866 0.0141455668839133 45.1511273491295 0.235227083986565 61 44.7692 44.4747918951347 -0.0146817392252817 45.0782898440906 -0.294408104865347 62 44.8913 44.7990689350484 -0.00300604583711205 44.9865371107887 -0.0922310649515765 63 45.032 45.1503424969452 0.00214140787726687 44.9115160951776 0.118342496945175 64 44.879 44.8705057509599 0.00140080957424818 44.8860934394658 -0.00849424904006213 65 44.833 44.8020526570699 0.0141455668839133 44.8498017760462 -0.0309473429300766 66 44.8257 44.8896646317923 -0.0146817392252817 44.7764171074329 0.0639646317923521 67 44.7815 44.8590436879044 -0.00300604583711205 44.7069623579327 0.0775436879044307 68 44.479 44.321212291831 0.00214140787726687 44.6346463002917 -0.157787708169003 69 44.6317 44.7255344495499 0.00140080957424818 44.5364647408758 0.093834449549938 70 44.5043 44.5712177050853 0.0141455668839133 44.4232367280308 0.066917705085288 71 44.3217 44.3590606804022 -0.0146817392252817 44.2990210588231 0.0373606804022018 72 44.1005 44.0615085780368 -0.00300604583711205 44.1424974678003 -0.0389914219632033 73 44.047 44.0820185824164 0.00214140787726687 44.0098400097063 0.0350185824164484 74 43.6835 43.4161739734279 0.00140080957424818 43.9494252169978 -0.267326026572093 75 43.7864 43.6303557516617 0.0141455668839133 43.9282986814544 -0.156044248338311 76 44.1807 44.4424538228796 -0.0146817392252817 43.9336279163457 0.261753822879555 77 43.9595 43.9571913406 -0.00300604583711205 43.9648147052372 -0.00230865940005032 78 43.937 43.9116928246242 0.00214140787726687 43.9601657674985 -0.0253071753757652 79 43.991 44.0724588399863 0.00140080957424818 43.9081403504394 0.0814588399863254 80 43.865 43.8413465834429 0.0141455668839133 43.8745078496731 -0.0236534165570532 81 43.671 43.5055067200771 -0.0146817392252817 43.8511750191481 -0.165493279922863 82 43.93 44.0379422985341 -0.00300604583711205 43.825063747303 0.107942298534098 83 43.863 43.9000267026003 0.00214140787726687 43.8238318895225 0.0370267026002722 84 43.7095 43.5979303101655 0.00140080957424818 43.8196688802603 -0.111569689834504 85 43.9435 44.092390564776 0.0141455668839133 43.78046386834 0.148890564776046 86 43.736 43.7424663848598 -0.0146817392252817 43.7442153543654 0.00646638485984141 87 43.6295 43.5218520393220 -0.00300604583711205 43.7401540065151 -0.107647960677951 88 43.598 43.455903222018 0.00214140787726687 43.7379553701047 -0.142096777981969 89 43.8726 44.0112015479561 0.00140080957424818 43.7325976424696 0.138601547956149 90 43.8935 44.0398135698139 0.0141455668839133 43.7330408633022 0.146313569813934 91 43.5957 43.5044685707329 -0.0146817392252817 43.7016131684924 -0.0912314292671397 92 43.7155 43.8189760013327 -0.00300604583711205 43.6150300445044 0.103476001332673 93 43.528 43.5363840594335 0.00214140787726687 43.5174745326892 0.00838405943349585 94 43.3415 43.2377274213339 0.00140080957424818 43.4438717690918 -0.103772578666067 95 43.3374 43.2663568873209 0.0141455668839133 43.3942975457951 -0.0710431126790567 96 43.332 43.2935057111983 -0.0146817392252817 43.385176028027 -0.0384942888016795 97 43.3869 43.3627868071729 -0.00300604583711205 43.4140192386642 -0.0241131928270519 98 43.5016 43.547521459952 0.00214140787726687 43.4535371321708 0.0459214599519697 99 43.4875 43.5026322246544 0.00140080957424818 43.4709669657714 0.0151322246543657 100 43.6023 43.728192147787 0.0141455668839133 43.4622622853291 0.125892147786992 101 43.3886 43.3425028507089 -0.0146817392252817 43.4493788885164 -0.0460971492911142 102 43.3105 43.1786231364019 -0.00300604583711205 43.4453829094352 -0.131876863598109 103 43.4455 43.4316992801857 0.00214140787726687 43.4571593119371 -0.0138007198143271 104 43.5185 43.5360867737716 0.00140080957424818 43.4995124166542 0.0175867737715478 105 43.5755 43.5835341639018 0.0141455668839133 43.5533202692143 0.00803416390181866 106 43.6217 43.6737176523234 -0.0146817392252817 43.5843640869019 0.0520176523234142 107 43.644 43.7033741244651 -0.00300604583711205 43.587631921372 0.0593741244650658 108 43.5789 43.5778748494694 0.00214140787726687 43.5777837426533 -0.00102515053058738 109 43.5215 43.4853071625991 0.00140080957424818 43.5562920278267 -0.0361928374008968 110 43.5033 43.482505302999 0.0141455668839133 43.5099491301171 -0.0207946970010084 111 43.632 43.8196380268172 -0.0146817392252817 43.4590437124081 0.187638026817204 112 43.263 43.1200915107778 -0.00300604583711205 43.4089145350593 -0.142908489222215 113 43.3717 43.3863119468044 0.00214140787726687 43.3549466453183 0.0146119468044148 114 43.2745 43.2279384820838 0.00140080957424818 43.3196607083419 -0.0465615179161745 115 43.2647 43.1889031611785 0.0141455668839133 43.3263512719376 -0.0757968388214749 116 43.324 43.3148404124801 -0.0146817392252817 43.3478413267451 -0.00915958751985357 117 43.4455 43.4987271640796 -0.00300604583711205 43.3952788817575 0.0532271640796438 118 43.4098 43.3268473954404 0.00214140787726687 43.4906111966823 -0.0829526045596083 119 43.41 43.2371135060046 0.00140080957424818 43.5814856844212 -0.172886493995414 120 43.93 44.2024552760276 0.0141455668839133 43.6433991570885 0.2724552760276 121 43.8104 43.9224836674062 -0.0146817392252817 43.7129980718191 0.112083667406225 122 43.54 43.3133691061952 -0.00300604583711205 43.7696369396419 -0.226630893804753 123 43.858 43.9249114286878 0.00214140787726687 43.7889471634349 0.0669114286878525 124 43.8375 43.8617943316534 0.00140080957424818 43.8118048587723 0.0242943316534152 125 43.881 43.9077451455738 0.0141455668839133 43.8401092875422 0.026745145573841 126 43.887 43.9463136461398 -0.0146817392252817 43.8423680930855 0.0593136461398203 127 43.8009 43.7630921674637 -0.00300604583711205 43.8417138783734 -0.0378078325363163 128 43.7877 43.7060400447885 0.00214140787726687 43.8672185473343 -0.0816599552115491 129 43.811 43.686320868775 0.00140080957424818 43.9342783216508 -0.124679131225029 130 44.0625 44.0161202073695 0.0141455668839133 44.0947342257466 -0.0463797926304821 131 44.125 43.8724929845084 -0.0146817392252817 44.3921887547168 -0.252507015491560 132 44.52 44.2924883990121 -0.00300604583711205 44.750517646825 -0.227511600987931 133 45.4005 45.7703582411080 0.00214140787726687 45.0285003510148 0.369858241107956 134 45.89 46.5880932865362 0.00140080957424818 45.1905059038895 0.698093286536249 135 45.189 45.1396903336874 0.0141455668839133 45.2241640994287 -0.0493096663126309 136 44.9035 44.7426821143942 -0.0146817392252817 45.0789996248311 -0.160817885605830 137 44.9351 45.0678369267863 -0.00300604583711205 44.8053691190508 0.132736926786265 138 44.801 45.0314832481481 0.00214140787726687 44.5683753439746 0.230483248148111 139 43.98 43.549377156697 0.00140080957424818 44.4092220337288 -0.43062284330302 140 44.11 43.9205122947468 0.0141455668839133 44.2853421383693 -0.189487705253214 141 44.2661 44.3539984678174 -0.0146817392252817 44.1928832714079 0.0878984678174248 142 44.361 44.5837026025779 -0.00300604583711205 44.1413034432592 0.222702602577861 143 44.099 44.0960284227747 0.00214140787726687 44.099830169348 -0.00297157722526720 144 43.8435 43.6059534359109 0.00140080957424818 44.0796457545148 -0.237546564089087 145 43.8914 43.6547623623871 0.0141455668839133 44.1138920707289 -0.236637637612851 146 44.217 44.2416512527309 -0.0146817392252817 44.2070304864944 0.0246512527309264 147 44.506 44.6722270238501 -0.00300604583711205 44.342779021987 0.166227023850119 148 44.54 44.5794296609884 0.00214140787726687 44.4984289311343 0.0394296609883824 149 44.4465 44.2599558001808 0.00140080957424818 44.6316433902449 -0.186544199819153 150 44.842 44.9153622014257 0.0141455668839133 44.7544922316904 0.0733622014257165 151 44.8946 44.9037399371024 -0.0146817392252817 44.9001418021229 0.00913993710242522 152 44.951 44.8586930106456 -0.00300604583711205 45.0463130351916 -0.092306989354448 153 45.445 45.6664303721913 0.00214140787726687 45.2214282199315 0.221430372191278 154 45.0035 44.5920385556505 0.00140080957424818 45.4135606347753 -0.411461444349527 155 45.769 46.0061271133666 0.0141455668839133 45.5177273197495 0.23712711336659 156 46.09 46.6597297276038 -0.0146817392252817 45.5349520116215 0.569729727603793 157 45.412 45.3036120686245 -0.00300604583711205 45.5233939772126 -0.108387931375511 158 45.12 44.8212299958686 0.00214140787726687 45.4166285962541 -0.298770004131413 159 45.48 45.6867828907035 0.00140080957424818 45.2718162997222 0.206782890703501 160 45.105 44.9804305396509 0.0141455668839133 45.2154238934652 -0.124569460349129 161 45.056 44.9149981112779 -0.0146817392252817 45.2116836279474 -0.141001888722116 162 45.22 45.2647774811577 -0.00300604583711205 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38.0878491643354 0.0141455668839133 38.1730052687807 -0.049650835664572 376 38.0075 37.8774229160826 -0.0146817392252817 38.1522588231427 -0.130077083917399 377 38.0524 37.9546391643348 -0.00300604583711205 38.1531668815023 -0.0977608356652269 378 38.235 38.2925402151993 0.00214140787726687 38.1753183769234 0.0575402151992961 379 38.31 38.4114237003413 0.00140080957424818 38.2071754900845 0.101423700341257 380 38.2615 38.2608607624967 0.0141455668839133 38.2479936706194 -0.000639237503342827 381 38.13 37.9508946660563 -0.0146817392252817 38.3237870731689 -0.17910533394366 382 38.282 38.1092787040321 -0.00300604583711205 38.4577273418050 -0.172721295967847 383 38.581 38.5283525023635 0.00214140787726687 38.6315060897592 -0.0526474976364639 384 39.0801 39.3392892014364 0.00140080957424818 38.8195099889893 0.259189201436421 385 39.0387 39.0806268570424 0.0141455668839133 38.9826275760737 0.041926857042391 386 39.1015 39.1411649016768 -0.0146817392252817 39.0765168375485 0.0396649016767867 387 39.1503 39.2179538424737 -0.00300604583711205 39.0856522033635 0.0676538424736606 388 39.14 39.2414345946606 0.00214140787726687 39.0364239974622 0.101434594660567 389 39.0275 39.092277584281 0.00140080957424818 38.9613216061448 0.0647775842809679 390 38.7665 38.6158120527228 0.0141455668839133 38.9030423803932 -0.150687947277156 391 38.691 38.4882509843341 -0.0146817392252817 38.9084307548912 -0.202749015665930 392 38.849 38.7034471585051 -0.00300604583711205 38.997558887332 -0.145552841494904 393 39.1644 39.1903867336164 0.00214140787726687 39.1362718585063 0.0259867336164064 394 39.4907 39.7282395406897 0.00140080957424818 39.2517596497361 0.237539540689696 395 39.5095 39.7070896780364 0.0141455668839133 39.2977647550797 0.197589678036408 396 39.2795 39.2959362038692 -0.0146817392252817 39.277745535356 0.0164362038692474 397 39.0437 38.8727141343718 -0.00300604583711205 39.2176919114653 -0.170985865628204 398 39.1355 39.0980215542354 0.00214140787726687 39.1708370378874 -0.0374784457646413 399 39.143 39.1064522409393 0.00140080957424818 39.1781469494865 -0.0365477590607313 400 39.185 39.1304131917277 0.0141455668839133 39.2254412413884 -0.054586808272262 401 39.355 39.4396639290186 -0.0146817392252817 39.2850178102066 0.084663929018646 402 39.297 39.2556067086204 -0.00300604583711205 39.3413993372167 -0.0413932913796131 403 39.4514 39.5189671582416 0.00214140787726687 39.3816914338812 0.0675671582415589 404 39.4173 39.4351292394628 0.00140080957424818 39.398069950963 0.0178292394627704 405 39.4305 39.4518384542188 0.0141455668839133 39.3950159788972 0.0213384542188422 406 39.384 39.4066049664166 -0.0146817392252817 39.3760767728087 0.0226049664165586 407 39.3261 39.2865808020956 -0.00300604583711205 39.3686252437415 -0.0395191979044185 408 39.301 39.2100881931914 0.00214140787726687 39.3897703989313 -0.090911806808613 409 39.35 39.2829379900953 0.00140080957424818 39.4156612003305 -0.0670620099047099 410 39.64 39.8403097488179 0.0141455668839133 39.4255446842982 0.200309748817929 411 39.4723 39.5539216401458 -0.0146817392252817 39.4053600990795 0.0816216401457694 412 39.3685 39.3939571059371 -0.00300604583711205 39.3460489399 0.0254571059371358 413 39.1906 39.1188674149181 0.00214140787726687 39.2601911772047 -0.0717325850819464 414 39.1183 39.047289634891 0.00140080957424818 39.1879095555347 -0.0710103651089895 415 39.1325 39.1044816725865 0.0141455668839133 39.1463727605296 -0.0280183274134771 416 39.1144 39.1232468584956 -0.0146817392252817 39.1202348807297 0.00884685849555211 417 39.1614 39.2395239285245 -0.00300604583711205 39.0862821173126 0.0781239285245334 418 39.0908 39.1351140721225 0.00214140787726687 39.0443445200002 0.0443140721225248 419 38.9199 38.8309375859193 0.00140080957424818 39.0074616045064 -0.0889624140806617 420 38.913 38.8266153939211 0.0141455668839133 38.985239039195 -0.08638460607893 421 38.9655 38.9568581279362 -0.0146817392252817 38.988823611289 -0.00864187206375533 422 39.029 39.0488013211265 -0.00300604583711205 39.0122047247106 0.0198013211264794 423 39.089 39.1399286681075 0.00214140787726687 39.0359299240153 0.050928668107467 424 39.07 39.0719330488346 0.00140080957424818 39.0666661415911 0.00193304883461565 425 39.0046 38.8765514852399 0.0141455668839133 39.1185029478762 -0.128048514760117 426 39.1038 39.0393433660757 -0.0146817392252817 39.1829383731496 -0.0644566339243084 427 39.3572 39.4616115292434 -0.00300604583711205 39.2557945165937 0.104411529243379 428 39.388 39.4486493117918 0.00214140787726687 39.325209280331 0.0606493117917637 429 39.382 39.4078941370998 0.00140080957424818 39.354705053326 0.0258941370997618 430 39.4398 39.5240819656477 0.0141455668839133 39.3413724674684 0.0842819656477118 431 39.2537 39.2061223810925 -0.0146817392252817 39.3159593581328 -0.047577618907475 432 39.2301 39.1699820394242 -0.00300604583711205 39.2932240064129 -0.0601179605757949 433 39.2763 39.2591474773402 0.00214140787726687 39.2913111147826 -0.017152522659849 434 39.282 39.206556627928 0.00140080957424818 39.3560425624977 -0.0754433720719945 435 39.3325 39.1318057743729 0.0141455668839133 39.5190486587432 -0.200694225627139 436 39.557 39.3696959816132 -0.0146817392252817 39.7589857576121 -0.187304018386840 437 40.1 40.1847371534332 -0.00300604583711205 40.0182688924039 0.084737153433231 438 40.5875 40.9048891272017 0.00214140787726687 40.267969464921 0.317389127201707 439 40.485 40.458458772322 0.00140080957424818 40.5101404181037 -0.0265412276779884 440 40.55 40.3476281271917 0.0141455668839133 40.7382263059243 -0.20237187280825 441 40.7955 40.6729962293511 -0.0146817392252817 40.9326855098742 -0.122503770648905 442 41.456 41.8082878936318 -0.00300604583711205 41.1067181522054 0.352287893631761 443 41.3557 41.4714487267923 0.00214140787726687 41.2378098653305 0.115748726792262 444 41.374 41.4524177990548 0.00140080957424818 41.2941813913709 0.0784177990548258 445 41.2235 41.1237681727375 0.0141455668839133 41.3090862603786 -0.0997318272624952 446 41.15 40.9570762752576 -0.0146817392252817 41.3576054639677 -0.192923724742421 447 41.3725 41.2968061452892 -0.00300604583711205 41.4511999005479 -0.075693854710778 448 41.6923 41.81775529142 0.00214140787726687 41.5647033007027 0.125455291420003 449 41.8 41.9505849116405 0.00140080957424818 41.6480142787853 0.150584911640493 450 41.8045 41.9333827014802 0.0141455668839133 41.6614717316359 0.128882701480173 451 41.64 41.6643578798876 -0.0146817392252817 41.6303238593377 0.0243578798876243 452 41.36 41.1356292760355 -0.00300604583711205 41.5873767698016 -0.224370723964533 453 41.5745 41.5892038339103 0.00214140787726687 41.5576547582124 0.0147038339103105 454 41.593 41.601419260655 0.00140080957424818 41.5831799297708 0.00841926065496068 455 41.575 41.4514809219487 0.0141455668839133 41.6843735111674 -0.123519078051281 456 41.68 41.5368039083291 -0.0146817392252817 41.8378778308961 -0.143196091670859 457 42.0055 41.9945566171876 -0.00300604583711205 42.0194494286495 -0.0109433828123997 458 42.3188 42.4494720042991 0.00214140787726687 42.1859865878236 0.130672004299107 459 42.565 42.8094704293135 0.00140080957424818 42.3191287611122 0.244470429313516 460 42.3575 42.2592685479538 0.0141455668839133 42.4415858851623 -0.0982314520462353 461 42.29 42.0565031846122 -0.0146817392252817 42.538178554613 -0.233496815387753 462 42.695 42.8165697958933 -0.00300604583711205 42.5764362499438 0.121569795893315 463 43.0028 43.4144238399892 0.00214140787726687 42.5890347521336 0.411623839989161 464 42.4507 42.3324837169965 0.00140080957424818 42.5675154734293 -0.118216283003513 465 42.4705 42.4307893952276 0.0141455668839133 42.4960650378885 -0.0397106047724023 466 42.2875 42.1474353074448 -0.0146817392252817 42.4422464317804 -0.140064692555164 467 42.3172 42.163164471764 -0.00300604583711205 42.4742415740731 -0.154035528236037 468 42.55 42.5196873179902 0.00214140787726687 42.5781712741325 -0.0303126820097788 469 42.7523 42.7684464728942 0.00140080957424818 42.7347527175316 0.0161464728941709 470 42.8993 42.8736663915363 0.0141455668839133 42.9107880415798 -0.0256336084637212 471 43.1555 43.253320402348 -0.0146817392252817 43.0723613368773 0.0978204023479918 472 43.1885 43.2062607733664 -0.00300604583711205 43.1737452724707 0.0177607733664047 473 43.43 43.6833898807279 0.00214140787726687 43.1744687113948 0.253389880727902 474 43.31 43.5410438997107 0.00140080957424818 43.0775552907151 0.231043899710656 475 42.815 42.7325403262106 0.0141455668839133 42.8833141069055 -0.0824596737894083 476 42.7017 42.7962275506997 -0.0146817392252817 42.6218541885256 0.094527550699695 477 42.28 42.1686650380830 -0.00300604583711205 42.3943410077541 -0.111334961916953 478 41.922 41.5889000794413 0.00214140787726687 42.2529585126815 -0.333099920558737 479 42.17 42.1484975824129 0.00140080957424818 42.1901016080128 -0.0215024175870653 480 42.1962 42.1723299031926 0.0141455668839133 42.2059245299235 -0.0238700968074070 481 42.3215 42.3852345264270 -0.0146817392252817 42.2724472127983 0.0637345264269626 482 42.3173 42.3025531072213 -0.00300604583711205 42.3350529386159 -0.0147468927787529 483 42.391 42.4079299217775 0.00214140787726687 42.3719286703453 0.0169299217774608 484 42.463 42.5642274932509 0.00140080957424818 42.3603716971749 0.101227493250875 485 42.4125 42.5359488002487 0.0141455668839133 42.2749056328674 0.123448800248738 486 42.304 42.4968858605440 -0.0146817392252817 42.1257958786812 0.192885860544045 487 41.813 41.7011240178862 -0.00300604583711205 41.9278820279509 -0.111875982113823 488 41.651 41.6149518000014 0.00214140787726687 41.6849067921213 -0.0360481999985538 489 41.539 41.6353431550013 0.00140080957424818 41.4412560354245 0.0963431550012928 490 41.1575 41.102093993685 0.0141455668839133 41.1987604394311 -0.0554060063150388 491 40.9545 40.9672790489523 -0.0146817392252817 40.956402690273 0.0127790489523179

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/1uhge1292718390.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/1uhge1292718390.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/2z5qs1292718390.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/2z5qs1292718390.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/3z5qs1292718390.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/3z5qs1292718390.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/4sepv1292718390.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t129271843313y9d7kxcbd0ckq/4sepv1292718390.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 5 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #seasonal period if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window par3 <- as.numeric(par3) #s.degree if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window par5 <- as.numeric(par5)#t.degree if (par6 != '') par6 <- as.numeric(par6)#l.window par7 <- as.numeric(par7)#l.degree if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust nx <- length(x) x <- ts(x,frequency=par1) if (par6 != '') { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) } else { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) } m$time.series m$win m$deg m$jump m$inner m$outer bitmap(file='test1.png') plot(m,main=main) dev.off() mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Component',header=TRUE) a<-table.element(a,'Window',header=TRUE) a<-table.element(a,'Degree',header=TRUE) a<-table.element(a,'Jump',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,m$win['s']) a<-table.element(a,m$deg['s']) a<-table.element(a,m$jump['s']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,m$win['t']) a<-table.element(a,m$deg['t']) a<-table.element(a,m$jump['t']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Low-pass',header=TRUE) a<-table.element(a,m$win['l']) a<-table.element(a,m$deg['l']) a<-table.element(a,m$jump['l']) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observed',header=TRUE) a<-table.element(a,'Fitted',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Remainder',header=TRUE) a<-table.row.end(a) for (i in 1:nx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]+m$time.series[i,'remainder']) a<-table.element(a,m$time.series[i,'seasonal']) a<-table.element(a,m$time.series[i,'trend']) a<-table.element(a,m$time.series[i,'remainder']) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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