Robustheid error-component

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Sun, 26 Oct 2008 10:33:38 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/26/t12250388743x4xq7d8tzh65ly.htm/, Retrieved Sun, 26 Oct 2008 16:34:36 +0000

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/2008/Oct/26/t12250388743x4xq7d8tzh65ly.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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-0,171742608 -0,166771574 -0,161414962 -0,156036741 -0,150896429 -0,146148477 -0,141861106 -0,138041489 -0,134659209 -0,131664423 -0,129000169 -0,126609769 -0,124440755 -0,122446621 -0,120587352 -0,118829328 -0,117144949 -0,115512116 -0,113913662 -0,112336736 -0,11077218 -0,109213906 -0,107658285 -0,106103586 -0,104549458 -0,102996482 -0,101445791 -0,099898764 -0,098356785 -0,096821068 -0,095292541 -0,093771779 -0,092258981 -0,090753982 -0,089256295 -0,087765174 -0,086279694 -0,084798837 -0,083321583 -0,081847005 -0,080374348 -0,078903103 -0,077433068 -0,075964391 -0,074497596 -0,073033588 -0,071573645 -0,070119386 -0,068672724 -0,067235811 -0,06581096 -0,064400572 -0,063007045 -0,061632691 -0,060279647 -0,058949796 -0,057644694 -0,056365505 -0,055112952 -0,053887281 -0,052688237 -0,051515057 -0,050366484 -0,049240785 -0,048135791 -0,047048946 -0,045977366 -0,044917907 -0,043867232 -0,042821894 -0,041778406 -0,040733318 -0,039683286 -0,038625135 -0,037555919 -0,036472968 -0,035373926 -0,034256782 -0,033119887 -0,031961962 -0,030782094 -0,029579729 -0,028354652 -0,027106954 -0,025837007 -0,024545422 -0,023233011 -0,021900741 -0,020549693 -0,019181022 -0,017795916 -0,016395557 -0,014981093 -0,013553607 -0,012114096 -0,010663457 -0,009202472 -0,007731803 -0,006251996 -0,004763485 -0,0032666 -0,001761583 -0,000248607 0,001272204 0,00280075 0,004336931 0,005880618 0,007431626 0,008989688 0,010554426 0,012125323 0,013701706 0,015282717 0,016867302 0,018454193 0,020041903 0,021628719 0,023212709 0,024791729 0,02636344 0,027925328 0,029474738 0,031008908 0,032525005 0,03402018 0,035491611 0,03693656 0,038352428 0,039736813 0,041087566 0,042402847 0,043681187 0,044921534 0,046123318 0,047286493 0,048411595 0,04949979 0,050552922 0,051573562 0,052565052 0,053531549 0,054478062 0,055410479 0,05633559 0,057261084 0,058195538 0,05914837 0,060129767 0,061150572 0,062222132 0,063356098 0,064564184 0,065857871 0,067248073 0,068744771 0,070356616 0,072090529 0,073951308 0,075941275 0,078059967 0,080303931 0,082666614 0,085138392 0,087706761 0,090356687 0,09307113 0,095831738 0,098619689 0,101416639 0,104205746 0,106972694 0,109706651 0,112401075 0,11505428 0,117669692 0,120255712 0,122825156 0,125394269 0,127981369 0,13060524 0,133283479 0,136031066 0,138859509 0,141776921 0,144789371 0,147903689 0,151131651 0,154494962 0,158029827 0,161789098 0,165839243 0,170249032 0,175067431 0,180290421 0,185820847 0,191432001 0,196753033

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.52840761421322e-05 0.00620615207615762 0.00246272987747908 Geometric Mean NaN Harmonic Mean -0.0500561437281806 Quadratic Mean 0.0868861304105121 Winsorized Mean ( 1 / 65 ) 1.35074365482234e-05 0.00619832618881945 0.00217920711765511 Winsorized Mean ( 2 / 65 ) 1.0923253807107e-05 0.00618211483616252 0.00176691214844651 Winsorized Mean ( 3 / 65 ) 8.60541116751227e-06 0.00615861806332445 0.00139729580224480 Winsorized Mean ( 4 / 65 ) 6.92667005076151e-06 0.00612967020573812 0.00113002328319023 Winsorized Mean ( 5 / 65 ) 5.13867512690347e-06 0.0060971111773341 0.000842804891930855 Winsorized Mean ( 6 / 65 ) 1.41020812182862e-06 0.00606239084593813 0.000232615837161584 Winsorized Mean ( 7 / 65 ) -6.78114213197988e-06 0.00602645819613327 -0.00112522843621994 Winsorized Mean ( 8 / 65 ) -2.20904213197969e-05 0.00598982419536826 -0.00368799160030083 Winsorized Mean ( 9 / 65 ) -4.67640812182733e-05 0.00595269502580441 -0.0078559511306316 Winsorized Mean ( 10 / 65 ) -8.22492081218273e-05 0.00591510585868512 -0.0139049427156170 Winsorized Mean ( 11 / 65 ) -0.000129016629441626 0.00587702018099917 -0.0219527286734093 Winsorized Mean ( 12 / 65 ) -0.000186598598984772 0.00583838586359167 -0.0319606486012522 Winsorized Mean ( 13 / 65 ) -0.00025379711675127 0.00579915454862621 -0.0437645030190467 Winsorized Mean ( 14 / 65 ) -0.000328995096446701 0.00575927726273786 -0.0571243719373049 Winsorized Mean ( 15 / 65 ) -0.000410499081218274 0.00571868931125963 -0.0717820218716962 Winsorized Mean ( 16 / 65 ) -0.000496850999999998 0.00567729499485179 -0.0875154453750502 Winsorized Mean ( 17 / 65 ) -0.000587063700507613 0.00563495812865168 -0.104182442372129 Winsorized Mean ( 18 / 65 ) -0.000680756624365483 0.00559150142382542 -0.121748448719833 Winsorized Mean ( 19 / 65 ) -0.00077818457360406 0.00554671445252042 -0.140296490880372 Winsorized Mean ( 20 / 65 ) -0.000880170055837561 0.00550036883756763 -0.160020188069204 Winsorized Mean ( 21 / 65 ) -0.000987959751269035 0.00545223789169256 -0.181202612742625 Winsorized Mean ( 22 / 65 ) -0.00110302969035533 0.00540211787916223 -0.204184676274852 Winsorized Mean ( 23 / 65 ) -0.00122686927918782 0.00534984837184222 -0.229327860139959 Winsorized Mean ( 24 / 65 ) -0.00136076698477157 0.0052953292647104 -0.256974952216875 Winsorized Mean ( 25 / 65 ) -0.00150562079187817 0.00523853297739528 -0.287412678010247 Winsorized Mean ( 26 / 65 ) -0.00166178787817259 0.00517951084000211 -0.320838768274865 Winsorized Mean ( 27 / 65 ) -0.00182898517258883 0.00511839330007088 -0.357335801561693 Winsorized Mean ( 28 / 65 ) -0.00200624194416244 0.0050553844290014 -0.396852499021273 Winsorized Mean ( 29 / 65 ) -0.00219190568527919 0.00499075149006275 -0.439193514171876 Winsorized Mean ( 30 / 65 ) -0.00238369614213198 0.00492481079180982 -0.484017811627641 Winsorized Mean ( 31 / 65 ) -0.00257879881218274 0.00485791123308963 -0.530845190133832 Winsorized Mean ( 32 / 65 ) -0.00277398987817259 0.0047904168783349 -0.579070663081164 Winsorized Mean ( 33 / 65 ) -0.00296577968020304 0.00472269008973429 -0.627985242277438 Winsorized Mean ( 34 / 65 ) -0.00315056743654822 0.00465507608337935 -0.676802565654538 Winsorized Mean ( 35 / 65 ) -0.00332479583756345 0.00458788985107103 -0.72468955129498 Winsorized Mean ( 36 / 65 ) -0.00348509689340102 0.00452140606978096 -0.770799357459582 Winsorized Mean ( 37 / 65 ) -0.00362842155837564 0.00445585211082243 -0.814304754316886 Winsorized Mean ( 38 / 65 ) -0.00375215071573604 0.00439140400486424 -0.854430772386208 Winsorized Mean ( 39 / 65 ) -0.00385418204060914 0.00432818547736313 -0.890484490733337 Winsorized Mean ( 40 / 65 ) -0.00393298853807106 0.00426626933737936 -0.92188003781472 Winsorized Mean ( 41 / 65 ) -0.0039876554822335 0.00420568063388609 -0.948159365716953 Winsorized Mean ( 42 / 65 ) -0.00401788908629442 0.00414640163188024 -0.969006247586404 Winsorized Mean ( 43 / 65 ) -0.00402400534517766 0.00408837744335003 -0.984254854385554 Winsorized Mean ( 44 / 65 ) -0.00400689827918782 0.00403152230095329 -0.993892128102666 Winsorized Mean ( 45 / 65 ) -0.00396799246700508 0.00397572582508806 -0.99805485628959 Winsorized Mean ( 46 / 65 ) -0.00390918321827411 0.00392085930854267 -0.997022058342386 Winsorized Mean ( 47 / 65 ) -0.00383276813705584 0.00386678128631789 -0.991203756627662 Winsorized Mean ( 48 / 65 ) -0.00374137272588833 0.00381334256800757 -0.981126835358817 Winsorized Mean ( 49 / 65 ) -0.00363787378172589 0.00376039076511131 -0.967419081941663 Winsorized Mean ( 50 / 65 ) -0.00352532200507615 0.00370777360599075 -0.950792140971117 Winsorized Mean ( 51 / 65 ) -0.0034068684213198 0.00365534187419306 -0.93202456530058 Winsorized Mean ( 52 / 65 ) -0.00328569179187817 0.00360295125457691 -0.911944558701506 Winsorized Mean ( 53 / 65 ) -0.00316493250253807 0.00355046376456216 -0.891413829970005 Winsorized Mean ( 54 / 65 ) -0.00304763107106599 0.00349774832696818 -0.871312280408595 Winsorized Mean ( 55 / 65 ) -0.00293667234010152 0.00344468111870454 -0.852523713778748 Winsorized Mean ( 56 / 65 ) -0.00283473841116752 0.0033911452699893 -0.835923614436152 Winsorized Mean ( 57 / 65 ) -0.00274426407614214 0.00333703054637142 -0.822367082952285 Winsorized Mean ( 58 / 65 ) -0.00266740288832487 0.00328223272002924 -0.81267939108873 Winsorized Mean ( 59 / 65 ) -0.00260599766497462 0.00322665304973319 -0.8076473128061 Winsorized Mean ( 60 / 65 ) -0.00256155746192893 0.00317019767580039 -0.808011904583272 Winsorized Mean ( 61 / 65 ) -0.00253524230964467 0.0031127771080612 -0.814463169585486 Winsorized Mean ( 62 / 65 ) -0.00252785549746193 0.00305430608819885 -0.827636597140344 Winsorized Mean ( 63 / 65 ) -0.00253984020812183 0.00299470328643851 -0.848110802704053 Winsorized Mean ( 64 / 65 ) -0.00257128467512690 0.00293389145199683 -0.87640756899062 Winsorized Mean ( 65 / 65 ) -0.00262193254314721 0.00287179769402331 -0.912993470467605 Trimmed Mean ( 1 / 65 ) 1.52840761421322e-05 0.00612449606539504 0.00249556469282283 Trimmed Mean ( 2 / 65 ) -0.000112817753846154 0.00604592296263672 -0.0186601375080956 Trimmed Mean ( 3 / 65 ) -0.000372072115183246 0.00597117044030162 -0.062311421002488 Trimmed Mean ( 4 / 65 ) -0.000372072115183246 0.00590041215568007 -0.0630586652874864 Trimmed Mean ( 5 / 65 ) -0.000638986395721925 0.00583343424564919 -0.109538630044301 Trimmed Mean ( 6 / 65 ) -0.000776167627027027 0.00576981057778723 -0.134522202516515 Trimmed Mean ( 7 / 65 ) -0.000915678404371585 0.00570903907033956 -0.160390985784079 Trimmed Mean ( 8 / 65 ) -0.000915678404371585 0.00565062930591488 -0.162048924960107 Trimmed Mean ( 9 / 65 ) -0.00119937081005587 0.0055941475741501 -0.214397420546790 Trimmed Mean ( 10 / 65 ) -0.00134190911864407 0.00553923153823159 -0.242255466192784 Trimmed Mean ( 11 / 65 ) -0.00148371083428571 0.00548558690500423 -0.270474401368466 Trimmed Mean ( 12 / 65 ) -0.00162394980346821 0.00543297596465765 -0.298906126961034 Trimmed Mean ( 13 / 65 ) -0.00176194112280702 0.00538120461998484 -0.327425037186559 Trimmed Mean ( 14 / 65 ) -0.0018971729704142 0.00533011157934008 -0.3559349447332 Trimmed Mean ( 15 / 65 ) -0.00202930771856287 0.00527956115837112 -0.384370529612156 Trimmed Mean ( 16 / 65 ) -0.00202930771856287 0.00522943969957872 -0.388054521161484 Trimmed Mean ( 17 / 65 ) -0.00228364820245399 0.00517965480829755 -0.440888106828219 Trimmed Mean ( 18 / 65 ) -0.00240576261490683 0.00513013634989838 -0.468947109944648 Trimmed Mean ( 19 / 65 ) -0.0025244999245283 0.00508083813665299 -0.496866827210386 Trimmed Mean ( 20 / 65 ) -0.00263982815923567 0.00503173944212628 -0.524635305464097 Trimmed Mean ( 21 / 65 ) -0.00275165159354839 0.00498284569344519 -0.5522249258427 Trimmed Mean ( 22 / 65 ) -0.00285978956209150 0.00493418796022961 -0.579586668595095 Trimmed Mean ( 23 / 65 ) -0.00296396827814570 0.00488582108388765 -0.606646913027619 Trimmed Mean ( 24 / 65 ) -0.0030638248590604 0.00483782047012579 -0.633306853360918 Trimmed Mean ( 25 / 65 ) -0.00315892191156463 0.0047902777356673 -0.659444417605272 Trimmed Mean ( 26 / 65 ) -0.00324877027586207 0.00474329549758997 -0.684918381642624 Trimmed Mean ( 27 / 65 ) -0.00333285729370629 0.0046969816661722 -0.70957426078744 Trimmed Mean ( 28 / 65 ) -0.00341067783687943 0.0046514436367511 -0.733251459811667 Trimmed Mean ( 29 / 65 ) -0.00348176567625899 0.00460678275333569 -0.755791158968351 Trimmed Mean ( 30 / 65 ) -0.00354572299270073 0.00456308937798132 -0.777044387911888 Trimmed Mean ( 31 / 65 ) -0.00360224627407407 0.00452043882625966 -0.796879774845815 Trimmed Mean ( 32 / 65 ) -0.00360224627407407 0.00447888833964008 -0.804272399959751 Trimmed Mean ( 33 / 65 ) -0.00369236873282443 0.00443847517807332 -0.831900277614537 Trimmed Mean ( 34 / 65 ) -0.00372599289147287 0.00439921581030241 -0.846967516971334 Trimmed Mean ( 35 / 65 ) -0.00375224551181102 0.00436110612042029 -0.860388490489062 Trimmed Mean ( 36 / 65 ) -0.00377149296 0.00432412248229849 -0.872198457707716 Trimmed Mean ( 37 / 65 ) -0.0037842346097561 0.00428822350912082 -0.882471401434006 Trimmed Mean ( 38 / 65 ) -0.00379109080165289 0.00425335227783164 -0.891318318826296 Trimmed Mean ( 39 / 65 ) -0.00379278721848739 0.00421943883981446 -0.898884274064788 Trimmed Mean ( 40 / 65 ) -0.0037901365982906 0.00418640282077151 -0.905344459325611 Trimmed Mean ( 41 / 65 ) -0.00378401880869565 0.00415415596435317 -0.910899552440094 Trimmed Mean ( 42 / 65 ) -0.00377535996460177 0.00412260451623686 -0.915770588649126 Trimmed Mean ( 43 / 65 ) -0.00376511152252252 0.00409165133865139 -0.920193635991356 Trimmed Mean ( 44 / 65 ) -0.00375422991743119 0.00406119772642462 -0.924414463497773 Trimmed Mean ( 45 / 65 ) -0.00374365734579439 0.00403114489220842 -0.928683400348697 Trimmed Mean ( 46 / 65 ) -0.00373430411428571 0.00400139513059349 -0.933250526981033 Trimmed Mean ( 47 / 65 ) -0.00372703286407767 0.00397185266245219 -0.93836130914701 Trimmed Mean ( 48 / 65 ) -0.00372264486138614 0.00394242420953322 -0.944252739820431 Trimmed Mean ( 49 / 65 ) -0.00372186847474747 0.00391301933146443 -0.95115003517107 Trimmed Mean ( 50 / 65 ) -0.00372534984536082 0.00388355054867544 -0.959263900049253 Trimmed Mean ( 51 / 65 ) -0.00373364573684211 0.00385393332680673 -0.968788357305523 Trimmed Mean ( 52 / 65 ) -0.00374721839784946 0.00382408593166559 -0.979899109175444 Trimmed Mean ( 53 / 65 ) -0.00374721839784946 0.00379392921928869 -0.987688009253903 Trimmed Mean ( 54 / 65 ) -0.00379155335955056 0.00376338637320595 -1.00748447901740 Trimmed Mean ( 55 / 65 ) -0.00382274805747126 0.00373238263921064 -1.02421118813256 Trimmed Mean ( 56 / 65 ) -0.00386008643529412 0.00370084505882232 -1.04302838242098 Trimmed Mean ( 57 / 65 ) -0.00386008643529412 0.00366870224262467 -1.05216672818139 Trimmed Mean ( 58 / 65 ) -0.00395300924691358 0.00363588417433952 -1.08722089521228 Trimmed Mean ( 59 / 65 ) -0.00400828302531646 0.00360232206631257 -1.11269424319393 Trimmed Mean ( 60 / 65 ) -0.00406909090909091 0.00356794825263733 -1.14045681746733 Trimmed Mean ( 61 / 65 ) -0.00413508737333333 0.00353269612647008 -1.17051884036943 Trimmed Mean ( 62 / 65 ) -0.00420586426027397 0.00349650011554441 -1.20287834156674 Trimmed Mean ( 63 / 65 ) -0.00428095915492958 0.00345929566154254 -1.23752334977365 Trimmed Mean ( 64 / 65 ) -0.00428095915492958 0.00342101922305961 -1.251369511774 Trimmed Mean ( 65 / 65 ) -0.00444203546268657 0.00338160826047617 -1.31358664887490 Median -0.006251996 Midrange 0.0125052125 Midmean - Weighted Average at Xnp -0.0043734157755102 Midmean - Weighted Average at X(n+1)p -0.00372186847474747 Midmean - Empirical Distribution Function -0.00372186847474747 Midmean - Empirical Distribution Function - Averaging -0.00372186847474747 Midmean - Empirical Distribution Function - Interpolation -0.00372186847474747 Midmean - Closest Observation -0.00437137703 Midmean - True Basic - Statistics Graphics Toolkit -0.00372186847474747 Midmean - MS Excel (old versions) -0.00372186847474747 Number of observations 197

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250388743x4xq7d8tzh65ly/1pvyu1225038815.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250388743x4xq7d8tzh65ly/1pvyu1225038815.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250388743x4xq7d8tzh65ly/2q5ij1225038815.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250388743x4xq7d8tzh65ly/2q5ij1225038815.ps (open in new window)

Parameters (Session):

Parameters (R input):

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

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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Software written by Ed van Stee & Patrick Wessa

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