版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡介
WhatisMultivariateAnalysisMultivariateanalysisisthebestwaytosummarizeadatatableswithmanyvariablesbycreatingafewnewvariablescontainingmostoftheinformation.Thesenewvariablesarethenusedforproblemsolvinganddisplay,i.e.,classification,relationships,controlcharts,andmore.Thenewvariables,thescores,denotedbyt,arecreatedasweightedlinearcombinationsoftheoriginalvariables.Eachobservationshast-values.PCA,thebasicMVmethod,summarizesonedatatable.Plottingthescores(t’s)givesanoverviewoftheobservations(objects)PLSsummarizessimultaneously2datatables(Xthepredictorvariables)and
(Ytheresponsevariables)inordertodeveloparelationshipbetweenthemPCAandPLSarecalledProjectionmethods1/4/20231SIMCA-PGettingstarted.pptWhatisMultivariateAnalysisMWhatisaProjection?
Reductionofdimensionality,modelinlatentvariablesAlgebraicallySummarizestheinformationintheobservationsasafewnew(latent)variablesGeometricallyTheswarmofpointsinaKdimensionalspace
(K=numberofvariables)isapproximatedbya(hyper)planeandthepointsareprojectedonthatplane.1/4/20232SIMCA-PGettingstarted.pptWhatisaProjection?
ReductioNotation
Eachobshasvaluesoft(andu)–Eachvariablehasvaluesofp(andwandc)t:theXscores;thenewsummarizingvariables(coordinatesinthehyperplaneofX-space)u:theYscoresinPLS;thenewsummarizingvariables(coordinatesinthehyperplaneofY-space,whenYismultidimensional)p:thePCloadings.ThesearetheweightsthatinPCAcombinetheoriginalvariablesinXtoformthenewvariables,scorest.w*:thePLSweights.ThesearetheweightsthatinPLScombinetheoriginalvariablesinXtoformthenewvariables,scorest.c:theweightsusedtocombinetheY'stoformthescoresu.1/4/20233SIMCA-PGettingstarted.pptNotation
EachobshasvaluesoNotation
Eachobshasvaluesoft(andu)–Eachvariablehasvaluesofp(andwandc)OneComponentconsistsofonetandonep(PCA)ort,p,w,u,c(PLS).ThetotalnumberofcomponentsisA.Model:Thedataareapproximatedbyaplaneorhyperplane,(themodel)withasmanydimensionsascomponentsextracted.DModX:alsocalledDistancetothemodel,isthedistanceofagivenobservationtothemodelplane.T2:Hotelling’sT2,isacombinationofallthescores(t)ofallAcomponents.T2measureshowfarawayanobservationisfromthecenterofaPCorPLSmodel.1/4/20234SIMCA-PGettingstarted.pptNotation
EachobshasvaluesoNotationR2X:ThefractionofthevariationoftheXvariablesexplainedbythemodel.R2Y:ThefractionofthevariationoftheYvariablesexplainedbythemodel.Q2X:ThefractionofthevariationoftheXvariablespredictedbythemodel.Q2Y:ThefractionofthevariationoftheYvariablespredictedbythemodel.1/4/20235SIMCA-PGettingstarted.pptNotationR2X:ThefractionofMVA–SIMCARoadMap
MethodsavailablePreprocessing;trimmingandWinsorizing(takeawayextremes)PrincipalComponentsAnalysis(PCA;overviewofdata)ProjectiontoLatentStructures(PLS;relationshipsXY)SimcaclassificationPLS-discriminantanalysis(classification)HierarchicalPCAandPLSPredictionsandclassificationofnewdatausinganymodel1/4/20236SIMCA-PGettingstarted.pptMVA–SIMCARoadMap
MethodsaMVA–SIMCARoadMap
Dataset=alldata;Workset=workingcopyofdataWorkmainmenusfromlefttorightandpop-upmenusfromuptodownPlot/Listallowsyoutoplotorlistanythingnon-standard,notfoundunderAnalysis1/4/20237SIMCA-PGettingstarted.pptMVA–SIMCARoadMap
DatasetStepsinusingSIMCA-PusingthewizardStartanewprojectandimportthedatasetUsetheworksetwizardtoguidethroughbuildingtheworksetandfittingthemodelGeneratethereportwritertowalkthroughthemodelresultsandinterpretationWhendisplayingSimca-PplotsalwaysusetheAnalysisadvisertoguideyou.1/4/20238SIMCA-PGettingstarted.pptStepsinusingSIMCA-PusingWorksetwizardonON1/4/20239SIMCA-PGettingstarted.pptWorksetwizardonON12/18/20229Worksetwizard1/4/202310SIMCA-PGettingstarted.pptWorksetwizard12/18/202210SIMCAutotransformvariables
Totransformallvariablesifanyneeded,markthecheckbox1/4/202311SIMCA-PGettingstarted.pptAutotransformvariables
TotraAutomaticcreationofclassesforclassificationordiscrimination1/4/202312SIMCA-PGettingstarted.pptAutomaticcreationofclassesSelectionandFitofmodel1/4/202313SIMCA-PGettingstarted.pptSelectionandFitofmodel12/1Reportwriter
Walksyouthroughthemodelresultswithinterpretation:File|GenerateReport1/4/202314SIMCA-PGettingstarted.pptReportwriter
WalksyouthrouStepsinUsingSIMCA-P,AdvancedModeStartanewprojectandimportthedatasetExploreandpreprocessthedataMakeworkingcopyofselecteddata(workset)formodelbuildingSpecifymodeltypeandfitittotheworksetReviewfit(plots,diagnostics,coefficients,etc.)PredictionsGenerateReport1/4/202315SIMCA-PGettingstarted.pptStepsinUsingSIMCA-P,Advanc1a.FileNew
StartinganewprojectSelectthedatafilecontainingtherawdataoftheprojectdirectory,filetype(XLS,DIF,TXT,…..),filenameAWizardopens(seenextpage)allowingyoutospecify(optionally)therowcontainingtheVariablenames,and(optionally)thecolumnswiththeObs.NumbersandNamesHere(Commands)youcanalsodoadditionalthingssuchastransposingtheinputdatamatrixUsesimplemodewithworksetwizardAtthelastWizardpage,youcan(optionally)specifyanothernameanddirectoryfortheproject.AmapofthemissingdataisshownTheWizardfinishesandputsyouintheSimca-windowAstartingworkset(M1,alldata,allX-s,UV-scaled)isready1/4/202316SIMCA-PGettingstarted.ppt1a.FileNew
Startinganewpr1b.ThesecondscreenoftheWizard1/4/202317SIMCA-PGettingstarted.ppt1b.ThesecondscreenoftheW2.LookingatthedataWiththedatasettableopen(Datasetedit):QuickInfo(bothvarandobswindowscanbeopen)variablesobservationsMovingthecursorinthedatasettableupanddown,orsidewise,changesthedisplayedvariableandobservationInthequickinfooptionsyoucanspecifywhatyouwanttolookat(histograms,auto-correlations,…),aswellaswhichitemsshouldbethebasisfortheplots1/4/202318SIMCA-PGettingstarted.ppt2.LookingatthedataWiththeViewvariablesorObservations,Trim,etc.
QuickInfo1/4/202319SIMCA-PGettingstarted.pptViewvariablesorObservations3.Prepareaworkcopy:TheWorkset
SimpleModewithguidance,orAdvancedModeInWorkset,youprepareaworkingcopyofthepartofthedatayouwillanalyze,i.e.,useasthebasisofyourmodel.Hereyouspecifytransformation,scaling,androlesofvariables(XorYorexcluded).Also,youselecttheobservations(your“trainingset”).Youcanstartwiththepreviousworkset(Workset/Newasmodelxx)andthenmodifyit,e.g.,excludingobservations.WhateveryoudoinWorksetdoesNOTtouchtherawdataNotethatoutliersarejustspecifiedas“notincluded”inthenextworkset(the“polished”data).OutliersareNEVERremovedfromtherawdataset.1/4/202320SIMCA-PGettingstarted.ppt3.Prepareaworkcopy:TheWoWorkset:twoModes,SimpleandAdvanced1/4/202321SIMCA-PGettingstarted.pptWorkset:twoModes,Simpleand4.Analysis
FittheModeltotheWorksetDataEithermenu“Analysis/Autofit”orFastButtonAmodelwithappropriatenumberofcomponentsisfoundIfnothinghappens,getthetwofirstcomponents
(alsomenuorfastbutton)Atableappearsshowingthemodel,componentbycomponent.Morecomponentscanbeadded(menuorfastbutton)Doubleclickonamodeltospecifyatitle1/4/202322SIMCA-PGettingstarted.ppt4.Analysis
FittheModeltot5.Plotresults
Analysis/menu(orfastbuttons)Summary/X/Y-OverviewshowsR2andQ2forallvar.sScores–scatterplot,t1-t2andt1-u1&t2-u2(PLS)Loadings–scatterplot(p1-p2froPCA,wc1-wc2forPLS)DistancetoModel–lineplotContributionplotstointerpretinterestingobservations,e.g.outliers,jumps,…Forallplots,therightmousebutton,propertiesallowschoiceofplotmarkers,andmoreThegraphicaltoolboxallowsfurthermodifications1/4/202323SIMCA-PGettingstarted.ppt5.Plotresults
Analysis/men6a.Outlierswereseeninthescoreplot
(welloutsidetheHotellingellipse)Startanotherworkset (eitherfromWorkset/Newasmodelxx,orusingthegraphicaltool-boxtoremoveoutliersfromthescoreplot)NotethatoutliersshouldNOTbedeletedfromthedatabyEdit/DatasetWhenthenewworksetisall-right,returnto“4.Analysis”tofitanewmodeltothenewworkset (fastbuttonorAnalysis/Autofit)1/4/202324SIMCA-PGettingstarted.ppt6a.Outlierswereseeninthe6b.Nooutlierswereseeninthescoreplots
(ortheyhavebeenexcluded,andthescoreplotsnowlookall-right)Now,interpretthemodelLookat“patterns”,trends,etc.,inthescoreplotsInspecttheloadingplotstointerprettheabovepatternsLookatDModXWhatdothesepatternssayabouttheobjectiveoftheinvestigation?1/4/202325SIMCA-PGettingstarted.ppt6b.NooutlierswereseenintAnalysisAdvisortounderstandandinterpretmodelresults1/4/202326SIMCA-PGettingstarted.pptAnalysisAdvisortounderstand7.Predictions
NewData,PredictionSetUnderPredictions,specifythesetofobservationsforwhichpredictionswillbemade,thepredictionsetNewdatacanbereadinasasecondarydataset (File/Import)andpredictionscanbemadeforthesePredictionset/ComplementWS,givesapredictionsetwiththoseobservationsthatwerenotinthetrainingsetPredictions/Y-predicted,T-predicted,etc.,calculatesanddisplaysthepredictedvaluesaccordingly1/4/202327SIMCA-PGettingstarted.ppt7.Predictions
NewData,Pred8.Generatethereport,withcustomizabletemplates1/4/202328SIMCA-PGettingstarted.ppt8.Generatethereport,withcUseoftheseslidesYoumayuseanyoralloftheseslidesinyourownpresentations,providedthatyoukeep(anddonotmodify)theUmetricslogoandwebreferenceIfyouhaveanyproblemswiththesoftware,orwithunderstandingofthematerial,pleasee-mailusat
info@1/4/202329SIMCA-PGettingstarted.pptUseoftheseslidesYoumayuseWhatisMultivariateAnalysisMultivariateanalysisisthebestwaytosummarizeadatatableswithmanyvariablesbycreatingafewnewvariablescontainingmostoftheinformation.Thesenewvariablesarethenusedforproblemsolvinganddisplay,i.e.,classification,relationships,controlcharts,andmore.Thenewvariables,thescores,denotedbyt,arecreatedasweightedlinearcombinationsoftheoriginalvariables.Eachobservationshast-values.PCA,thebasicMVmethod,summarizesonedatatable.Plottingthescores(t’s)givesanoverviewoftheobservations(objects)PLSsummarizessimultaneously2datatables(Xthepredictorvariables)and
(Ytheresponsevariables)inordertodeveloparelationshipbetweenthemPCAandPLSarecalledProjectionmethods1/4/202330SIMCA-PGettingstarted.pptWhatisMultivariateAnalysisMWhatisaProjection?
Reductionofdimensionality,modelinlatentvariablesAlgebraicallySummarizestheinformationintheobservationsasafewnew(latent)variablesGeometricallyTheswarmofpointsinaKdimensionalspace
(K=numberofvariables)isapproximatedbya(hyper)planeandthepointsareprojectedonthatplane.1/4/202331SIMCA-PGettingstarted.pptWhatisaProjection?
ReductioNotation
Eachobshasvaluesoft(andu)–Eachvariablehasvaluesofp(andwandc)t:theXscores;thenewsummarizingvariables(coordinatesinthehyperplaneofX-space)u:theYscoresinPLS;thenewsummarizingvariables(coordinatesinthehyperplaneofY-space,whenYismultidimensional)p:thePCloadings.ThesearetheweightsthatinPCAcombinetheoriginalvariablesinXtoformthenewvariables,scorest.w*:thePLSweights.ThesearetheweightsthatinPLScombinetheoriginalvariablesinXtoformthenewvariables,scorest.c:theweightsusedtocombinetheY'stoformthescoresu.1/4/202332SIMCA-PGettingstarted.pptNotation
EachobshasvaluesoNotation
Eachobshasvaluesoft(andu)–Eachvariablehasvaluesofp(andwandc)OneComponentconsistsofonetandonep(PCA)ort,p,w,u,c(PLS).ThetotalnumberofcomponentsisA.Model:Thedataareapproximatedbyaplaneorhyperplane,(themodel)withasmanydimensionsascomponentsextracted.DModX:alsocalledDistancetothemodel,isthedistanceofagivenobservationtothemodelplane.T2:Hotelling’sT2,isacombinationofallthescores(t)ofallAcomponents.T2measureshowfarawayanobservationisfromthecenterofaPCorPLSmodel.1/4/202333SIMCA-PGettingstarted.pptNotation
EachobshasvaluesoNotationR2X:ThefractionofthevariationoftheXvariablesexplainedbythemodel.R2Y:ThefractionofthevariationoftheYvariablesexplainedbythemodel.Q2X:ThefractionofthevariationoftheXvariablespredictedbythemodel.Q2Y:ThefractionofthevariationoftheYvariablespredictedbythemodel.1/4/202334SIMCA-PGettingstarted.pptNotationR2X:ThefractionofMVA–SIMCARoadMap
MethodsavailablePreprocessing;trimmingandWinsorizing(takeawayextremes)PrincipalComponentsAnalysis(PCA;overviewofdata)ProjectiontoLatentStructures(PLS;relationshipsXY)SimcaclassificationPLS-discriminantanalysis(classification)HierarchicalPCAandPLSPredictionsandclassificationofnewdatausinganymodel1/4/202335SIMCA-PGettingstarted.pptMVA–SIMCARoadMap
MethodsaMVA–SIMCARoadMap
Dataset=alldata;Workset=workingcopyofdataWorkmainmenusfromlefttorightandpop-upmenusfromuptodownPlot/Listallowsyoutoplotorlistanythingnon-standard,notfoundunderAnalysis1/4/202336SIMCA-PGettingstarted.pptMVA–SIMCARoadMap
DatasetStepsinusingSIMCA-PusingthewizardStartanewprojectandimportthedatasetUsetheworksetwizardtoguidethroughbuildingtheworksetandfittingthemodelGeneratethereportwritertowalkthroughthemodelresultsandinterpretationWhendisplayingSimca-PplotsalwaysusetheAnalysisadvisertoguideyou.1/4/202337SIMCA-PGettingstarted.pptStepsinusingSIMCA-PusingWorksetwizardonON1/4/202338SIMCA-PGettingstarted.pptWorksetwizardonON12/18/20229Worksetwizard1/4/202339SIMCA-PGettingstarted.pptWorksetwizard12/18/202210SIMCAutotransformvariables
Totransformallvariablesifanyneeded,markthecheckbox1/4/202340SIMCA-PGettingstarted.pptAutotransformvariables
TotraAutomaticcreationofclassesforclassificationordiscrimination1/4/202341SIMCA-PGettingstarted.pptAutomaticcreationofclassesSelectionandFitofmodel1/4/202342SIMCA-PGettingstarted.pptSelectionandFitofmodel12/1Reportwriter
Walksyouthroughthemodelresultswithinterpretation:File|GenerateReport1/4/202343SIMCA-PGettingstarted.pptReportwriter
WalksyouthrouStepsinUsingSIMCA-P,AdvancedModeStartanewprojectandimportthedatasetExploreandpreprocessthedataMakeworkingcopyofselecteddata(workset)formodelbuildingSpecifymodeltypeandfitittotheworksetReviewfit(plots,diagnostics,coefficients,etc.)PredictionsGenerateReport1/4/202344SIMCA-PGettingstarted.pptStepsinUsingSIMCA-P,Advanc1a.FileNew
StartinganewprojectSelectthedatafilecontainingtherawdataoftheprojectdirectory,filetype(XLS,DIF,TXT,…..),filenameAWizardopens(seenextpage)allowingyoutospecify(optionally)therowcontainingtheVariablenames,and(optionally)thecolumnswiththeObs.NumbersandNamesHere(Commands)youcanalsodoadditionalthingssuchastransposingtheinputdatamatrixUsesimplemodewithworksetwizardAtthelastWizardpage,youcan(optionally)specifyanothernameanddirectoryfortheproject.AmapofthemissingdataisshownTheWizardfinishesandputsyouintheSimca-windowAstartingworkset(M1,alldata,allX-s,UV-scaled)isready1/4/202345SIMCA-PGettingstarted.ppt1a.FileNew
Startinganewpr1b.ThesecondscreenoftheWizard1/4/202346SIMCA-PGettingstarted.ppt1b.ThesecondscreenoftheW2.LookingatthedataWiththedatasettableopen(Datasetedit):QuickInfo(bothvarandobswindowscanbeopen)variablesobservationsMovingthecursorinthedatasettableupanddown,orsidewise,changesthedisplayedvariableandobservationInthequickinfooptionsyoucanspecifywhatyouwanttolookat(histograms,auto-correlations,…),aswellaswhichitemsshouldbethebasisfortheplots1/4/202347SIMCA-PGettingstarted.ppt2.LookingatthedataWiththeViewvariablesorObservations,Trim,etc.
QuickInfo1/4/202348SIMCA-PGettingstarted.pptViewvariablesorObservations3.Prepareaworkcopy:TheWorkset
SimpleModewithguidance,orAdvancedModeInWorkset,youprepareaworkingcopyofthepartofthedatayouwillanalyze,i.e.,useasthebasisofyourmodel.Hereyouspecifytransformation,scaling,androlesofvariables(XorYorexcluded).Also,youselecttheobservations(your“trainingset”).Youcanstartwiththepreviousworkset(Workset/Newasmodelxx)andthenmodifyit,e.g.,excludingobservations.WhateveryoudoinWorksetdoesNOTtouchtherawdataNotethatoutliersarejustspecifiedas“notincluded”inthenextworkset(the“polished”data).OutliersareNEVERremovedfromtherawdataset.1/4/202349SIMCA-PGettingstarted.ppt3.Prepareaworkcopy:TheWoWorkset:twoModes,SimpleandAdvanced1/4/202350SIMCA-PGettingstarted.pptWorkset:twoModes,Simpleand4.Analysis
FittheModeltotheWorksetDataEithermenu“Analysis/Autofit”orFastButtonAmodelwithappropriatenumberofcomponentsisfoundIfnothinghappens,getthetwofirstcomponents
(alsomenuorfastbutton)Atableappearsshowingthemodel,componentbycomponent.Morecomponentscanbeadded(menuorfastbutton)Doubleclickonamodeltospecifyatitle1/4/202351SIMCA-PGettingstarted.ppt4.Analysis
FittheModeltot5.Plotresults
Analysis/menu(orfastbuttons)Summary/X/Y-OverviewshowsR2andQ2forallvar.sScores–scatterplot,t1-t2andt1-u1&t2-u2(PLS)Loadings–scatterplot(p1-p2froPCA,wc1-wc2forPLS)DistancetoModel–lineplotContributionplotstointerpretinterestingobservations,e.g.outliers,jumps,…Forallplots,therightmousebutton,propertiesallowschoiceofplotmarkers,andmoreThegraphicaltoolboxallowsfur
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 人人文庫網(wǎng)僅提供信息存儲空間,僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。
最新文檔
- 單位使用個人車輛合同模板
- 天驕學(xué)府購房合同模板
- 培訓(xùn)單位用人合同模板
- 小型鉆井機(jī)租賃合同協(xié)議書
- 不掛牌貨車合同模板
- 吊車租賃框架合同模板
- 儲存技術(shù)合同模板
- 字庫設(shè)計(jì)合同模板
- 咋樣寫房屋維修合同模板
- 半掛車定制銷售合同模板
- 說課教學(xué)課件-傳感器與檢測技術(shù)說課
- 國開大學(xué)2023年01月22412《基礎(chǔ)寫作》期末考試答案
- 天車工理論考試試題(完整版)
- 四年級上冊英語人教PEP版課件專題四 情景交際
- 公關(guān)客戶大方法和技巧
- 兒科支氣管肺炎護(hù)理查房
- 約瑟普布羅茲 鐵托
- 農(nóng)村人居環(huán)境整治積分制行動方案
- 小學(xué)數(shù)學(xué)作業(yè)訂正有效性的設(shè)計(jì)研究課題
- 信息知識競賽(python)考試參考題庫(附答案)
- 高壓線下施工安全監(jiān)理實(shí)施細(xì)則
評論
0/150
提交評論