/
Package agRee February   Title Various Methods for Mea Package agRee February   Title Various Methods for Mea

Package agRee February Title Various Methods for Mea - PDF document

karlyn-bohler
karlyn-bohler . @karlyn-bohler
Follow
493 views
Uploaded On 2015-04-17

Package agRee February Title Various Methods for Mea - PPT Presentation

Version 031 Author Dai Feng Description Plots for visualization and point and interval estimates for different metrics of agreement Maintainer Dai Feng Depends R 302 miscF 012 lme4 104 Imports R2jags 00311 License GPL NeedsCompilation no Reposit ID: 51267

Version 031 Author Dai

Share:

Link:

Embed:

Download Presentation from below link

Download Pdf The PPT/PDF document "Package agRee February Title Various M..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Package`agRee'April3,2020TitleVariousMethodsforMeasuringAgreementVersion0.5-3AuthorDaiFengDescriptionBland-Altmanplotandscatterplotwithidentitylineforvisualizationandpointandintervalestimatesfordifferentmetricsrelatedtoreproducibility/repeatability/agreementincludingtheconcordancecorrelationcoefcient,intraclasscorrelationcoefcient,within-subjectcoefcientofvariation,smallestdetectabledifference,andmeannormalizedsmallestdetectabledifference.MaintainerDaiFengÚif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;DependsRÚif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;(=3.0.2),miscFÚif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;(=0.1-4),lme4Úif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;(=1.0-4)ImportsR2jagsÚif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;(=0.03-11),codaÚif;ng.;&#xstat;&#x@gma;&#xil.c;&#xom00;(=0.16-1)LicenseGPLNeedsCompilationnoLazyDatatrueRepositoryCRANDate/Publication2020-04-0314:10:34UTCRtopicsdocumented:agree.ccc..........................................2agree.icc1..........................................4agree.plot..........................................5agree.sdd..........................................6agree.sddm.........................................7agree.wscv.........................................8judgeRatings........................................9lesionBurden........................................9petVT............................................101 2agree.cccIndex11 agree.cccConcordanceCorrelationCoefcient DescriptionObtaincondenceintervalandpointestimateoftheconcordancecorrelationcoefcient(CCC)proposedinLin(1989).Usageagree.ccc(ratings,conf.level=0.95,method=c("jackknifeZ","jackknife","bootstrap","bootstrapBC","mvn.jeffreys","mvn.conjugate","mvt","lognormalNormal","mvsn","mvst"),nboot=999,nmcmc=10000,mvt.para=list(prior=list(lower.v=4,upper.v=25,Mu0=rep(0,ncol(ratings)),Sigma0=diag(10000,ncol(ratings)),p=ncol(ratings),V=diag(1,ncol(ratings))),initial=list(v=NULL,Sigma=NULL)),NAaction=c("fail","omit"))Argumentsratingsamatrixofobservationswithonesubjectperrowandoneraterpercolumn.conf.levelcondenceleveloftheinterval.Thedefaultis0.95.methodacharacterstringspecifyingthemethodusedtoobtaintheestimateoftheCCC.Itmustbeoneof"jackknifeZ","jackknife","bootstrap","bootstrapBC","mvn.jeffreys","mvn.conjugate","mvt","lognormalNormal","mvsn",and"mvst".Itcanbeab-breviated.Thedefaultis"jackknifeZ".nbootnumberofbootstrapreplicates.Thedefaultvalueis999.nmcmcnumberofiterationsusedintheBayesianapproach.Thedefaultvalueis10000.mvt.paravaluesofhyper-parametersandinitialvaluesofparametersformultivariatet(MVT)distribution.lower.visthelowerboundofdegreesoffreedom(df)oftheMVT.upper.vistheupperboundofdfoftheMVT.Mu0isthemeanvectorofmultivariatenormalpriorofthelocationoftheMVTandthedefaultvalueis0.Sigma0isthevariancematrixofmultivariatenormalpriorofthelocationoftheMVTandthedefaultvalueisadiagonalmatrixwithdiagonalentriesequalto10000.pisthedfofwishartpriorofinverseofthescalematrixoftheMVTandthedefaultvalueisthenumberofraters.VisthescalematrixofwishartpriorofinverseofthescalematrixoftheMVTandthedefaultvalueisidentitymatrix.vistheinitialvalueofthedfoftheMVT.ItsdefaultisNULLandfor agree.ccc3thedefault,thevaluewillbegeneratedbyusingtheECMEAlgorithm.SigmaistheinitialvalueofthescalematrixoftheMVT.ItsdefaultisNULLandforthedefault,thevaluewillbegeneratedbyusingtheECMEAlgorithm.NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsToobtainpointestimateandcondenceinterval,themethodsavailableincludethejackknifemethodwithandwithoutZ-transformation,thebootstrap,andtheBayesianapproachforthemultivariatenormal,multivariatet,lognormal-normal,multivariateskewnormal,andmultivariateskewtdistri-butions.ValuePointestimateandlowerandupperboundsofthecondenceintervaloftheCCC.ReferencesDaiFeng,RichardBaumgartnerandVladimirSvetnik(2016)Estimatingtheconcordancecorrela-tioncoefcientusingauniedBayesianframeworkunderreviewDaiFeng,RichardBaumgartnerandVladimirSvetnik(2015)ABayesianestimateoftheconcor-dancecorrelationcoefcientwithskeweddata.PharmaceuticalStatistics,DOI:10.1002/pst.1692DaiFeng,RichardBaumgartnerandVladimirSvetnik(2015)ArobustBayesianestimateoftheconcordancecorrelationcoefcient.JournalofBiopharmaceuticalStatistics25(3)490-507,DOI:10.1080/10543406.2014.920342DaiFeng,VladimirSvetnik,AlexandreCoimbraandRichardBaumgartner(2014)Acomparisonofcondenceintervalmethodsfortheconcordancecorrelationcoefcientandintraclasscorrelationcoefcientwithsmallnumberofraters.JournalofBiopharmaceuticalStatistics24(2)272-293,DOI:10.1080/10543406.2013.863780.DaiFeng,RichardBaumgartnerandVladimirSvetnik(2014)Ashortnoteonjackkningthecon-cordancecorrelationcoefcient.StatisticsinMedicine33(3)514-516,DOI:10.1002/sim.5931LawrenceI-KueiLin(1989)Aconcordancecorrelationcoefcienttoevaluatereproducibility.Bio-metrics45255-268SeeAlsoepi.ccc,cccvc,mvt.ecmeExamplesdata(judgeRatings)agree.ccc(judgeRatings[,2:3]) 4agree.icc1 agree.icc1Intraclasscorrelationcoefcientforone-wayrandomanovamodel DescriptionObtaincondenceintervalandpointestimateoftheintraclasscorrelationcoefcientforone-wayrandomanovamodel(ICC1).Usageagree.icc1(ratings,conf.level=0.95,method=c("sf"),NAaction=c("fail","omit"))Argumentsratingsamatrixofobservationswithonesubjectperrowandoneraterpercolumn.conf.levelcondenceleveloftheinterval.Thedefaultis0.95.methodacharacterstringspecifyingthemethodusedtoobtaincondenceintervaloftheICC1.Nowonlythe"sf"methodissupported.NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsThepointestimateandcondenceintervalarebasedonaone-wayrandomanovamodelasproposedinShroutandFleiss(1979).ValuePointestimateoftheICC1andlowerandupperboundsofthecondenceinterval.ReferencesPatrickEShroutandJosephLFleiss(1979).Intraclasscorrelations:usesinassessingraterrelia-bility.PsychologicalBulletin86420-428Examplesdata(lesionBurden)agree.icc1(lesionBurden.M) agree.plot5 agree.plotVisualizetheAgreementofRatingsamongDifferentRaters DescriptionDrawBland-Altmanplot(s)andscatterplot(s)withidentityline.Usageagree.plot(ratings,NAaction=c("fail","omit"))Argumentsratingsamatrixofratingsfromdifferentraters,oneraterpercolumn.NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsThefunctionproducesamatrixofplots.Theupperpanelconsistsofscatterplot(s)withidentityline.ThelowerpanelconsistsoftheBland-Altmanplot(s)withcondenceboundsandbiasusingdottedlineinredcolorandthehorizontallinepassingthroughtheorigininblack,respectively.ValueNULLNoteThecondenceboundsaremeanofthedifferencebetweentworatersplusorminustwiceoftheSDofdifference.ReferencesJ.MartinBlandandDouglasG.Altman(1986)Statisticalmethodsforassessingagreementbetweentwomethodsofclinicalmeasurement.Lancet1307-310Examplesdata(judgeRatings)agree.plot(judgeRatings) 6agree.sdd agree.sddSmallestDetectableDifference DescriptionObtaincondenceintervalandpointestimateofthesmallestdetectabledifference(SDD).Usageagree.sdd(ratings,conf.level=0.95,NAaction=c("fail","omit"))Argumentsratingsamatrixofobservationswithonesubjectperrowandoneraterpercolumn.conf.levelcondenceleveloftheinterval.Thedefaultis0.95.NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsThecalculationisbasedonone-wayrandom-effectsANOVAandthedetailscanbefoundinBaum-gartneretal.(2015).ValuePointestimateoftheSDDandlowerandupperboundsofthecondenceinterval.ReferencesRichardBaumgartner,DaiFengandAniketJoshi(2015)Determinationofsmallestdetectabledif-ferenceforPETtracersusingtest-retestdata:applicationinreceptoroccupancystudies(underreview)Examplesdata(petVT)agree.sdd(petVT$cerebellum) agree.sddm7 agree.sddmMeanNormalizedSmallestDetectableDifference DescriptionObtaincondenceintervalandpointestimateofthemeannormalizedsmallestdetectabledifference(SDDm).Usageagree.sddm(ratings,conf.level=0.95,method=c("vst","delta"),NAaction=c("fail","omit"))Argumentsratingsamatrixofobservationswithonesubjectperrowandoneraterpercolumn.conf.levelcondenceleveloftheinterval.Thedefaultis0.95.methodacharacterstringspecifyingthemethodusedtoobtaincondenceintervaloftheWSCV,basedonwhattheSDDmiscalculated.Itmustbeoneof"vst"and"delta"andmaybeabbreviated.Thedefaultis"vst".NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsThecalculationisbasedontherelationshipwiththeWSCVandthedetailscanbefoundinBaum-gartneretal.(2015).ValuePointestimateoftheSDDmandlowerandupperboundsofthecondenceinterval.ReferencesRichardBaumgartner,DaiFengandAniketJoshi(2015)Determinationofsmallestdetectabledif-ferenceforPETtracersusingtest-retestdata:applicationinreceptoroccupancystudies(underreview)Examplesdata(petVT)agree.sddm(petVT$cerebellum) 8agree.wscv agree.wscvWithin-subjectCoefcientofVariation DescriptionObtaincondenceintervalandpointestimateofthewithin-subjectcoefcientofvariation(WSCV).Usageagree.wscv(ratings,conf.level=0.95,method=c("vst","delta"),NAaction=c("fail","omit"))Argumentsratingsamatrixofobservationswithonesubjectperrowandoneraterpercolumn.conf.levelcondenceleveloftheinterval.Thedefaultis0.95.methodacharacterstringspecifyingthemethodusedtoobtaincondenceintervaloftheWSCV.Itmustbeoneof"vst"and"delta"andmaybeabbreviated.Thedefaultis"vst".NAactionacharacterstringspecifyingwhatshouldhappenwhenthedatacontainNAs.Itmustbeoneof"fail"and"omit"andmaybeabbreviated.Thedefaultis"fail"thatcausesthefunctiontoprintanerrormessageandterminateifthereareanyincompleteobservations.Ifitis"omit",thentheentirerow(s)containingincompleteobservation(s)willbedeleted.DetailsThepointestimateisbasedonwhatproposedinQuanandShih(1996).Toobtaincondenceinterval,themethodsavailableincludethedeltamethodproposedinQuanandShih(1996)andthevariancestabilizingtransformationinShoukrietal.(2006).ValuePointestimateoftheWSCVandlowerandupperboundsofthecondenceinterval.ReferencesHuiQuanandWeichungJ.Shih(1996)Assessingreproducibilitybythewithin-subjectcoefcientofvariationwithrandomeffectsmodels.Biometrics521195-1203MohamedMShoukri,NasserElkumandStephenDWalter(2006)Intervalestimationandoptimaldesignforthewithin-subjectcoefcientofvariationforcontinuousandbinaryvariables.BMCMedicalResearchMethodology624Examplesdata(lesionBurden)agree.wscv(lesionBurden.M) judgeRatings9 judgeRatingsRatingsofDifferentJudges DescriptionTheratingsofjudgesonaspeciccharacteristic.UsagejudgeRatingsFormatAmatrixpresentingtheratingsoffourjudgesonsixpeople.SourceB.J.Winer(1971)Statisticalprinciplesinexperimentaldesign,(2nded.).McGraw-Hill,NewYork lesionBurdenTotalLesionBurden DescriptionThetotallesionvolumesmeasuredmanuallyandbyanautomatedtechniqueknownasGeometri-callyConstrainedRegionGrowth.UsagelesionBurdenFormatlesionBurden.Misamatrixpresentingthemanuallymeasuredvolumesonthreepatientseachwithtenreplicates.lesionBurden.Gisamatrixpresentingtheautomaticallymeasuredvolumesonthreepatientseachwithtenreplicates.SourceMohamedMShoukri,NasserElkumandStephenDWalter(2006)Intervalestimationandoptimaldesignforthewithin-subjectcoefcientofvariationforcontinuousandbinaryvariables.BMCMedicalResearchMethodology624 10petVT petVTPETTotalVolumeofDistribution DescriptionTest/retestdatafortotalvolumeofdistribution(VT)fromthreepublishedPETstudies.UsagepetVTFormatAlistpresentingtheVTfromthreestudies.TherstcomponentisthedatafromTable6ofOgdenetal.(2007).ThesecondcomponentisthedatafromTable3ofHostetleretal.(2013).ThethirdcomponentisthedatafromTableIIofGunnetal.(2011).SourceRToddOgdenetal.(2007)Invivoquanticationofserotonintransportersusing[11C]DASBandpositronemissiontomographyinhumans:modelingconsiderationsJournalofCerbralBloodFlow&Metabolism27205-217EricD.Hostetleretal.(2013)Invivoquanticationofcalcitoningene-relatedpeptidereceptoroc-cupancybytelcagepantinrhesusmonkeyandhumanbrainusingthepositronemissiontomographytracer[11C]MK-4232TheJournalofPharmacologyandExperimentalTherapeutics347478-486RogerN.Gunnetal.(2011)Translationalcharacterizationof[11C]GSK931145,aPETligandfortheGlycinetransportertype1SYNAPSE651319-1332 IndexTopicdatasetsjudgeRatings,9lesionBurden,9petVT,10Topichplotagree.plot,5Topichtestagree.ccc,2agree.icc1,4agree.sdd,6agree.sddm,7agree.wscv,8agree.ccc,2agree.icc1,4agree.plot,5agree.sdd,6agree.sddm,7agree.wscv,8amygdala(petVT),10brainStem(petVT),10ccc.lognormalNormal.mcmc(agree.ccc),2ccc.mm(agree.ccc),2ccc.mvn.mcmc(agree.ccc),2ccc.mvt.mcmc(agree.ccc),2ccc.nonpara.bootstrap(agree.ccc),2ccc.nonpara.jackknife(agree.ccc),2cccvc,3cerebellum(petVT),10epi.ccc,3icc1.sf(agree.icc1),4judgeRatings,9lesionBurden,9mvt.ecme,3petVT,10wscv.delta(agree.wscv),8wscv.vst(agree.wscv),811