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Samenvatting MVA module 4: Multivariate technieken

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Dit is een samenvatting van de 4de module van multivariate analyse gegeven door prof. van Rossem. Deze samenvatting is gebaseerd op de slides en aangevuld met lesnotities en het boek.

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May 4, 2025
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Module 4: Multivariate technieken


MULTIVARIATE
ANALYSE

,Inhoudsopgave
Module 4: Multivariate technieken..................................................................3
Hoofdcomponenten en factoranalyse.............................................................................3
Principes van hoofdcomponenten- en factoranalyse...................................................3
Probleem................................................................................................................. 3
Een wiskundig interludium.......................................................................................3
Hoofdcomponenten & factoren................................................................................5
Assumpties bij hoofdcomponenten- en factoranalyse..............................................6
Covariantie of correlatiematrix................................................................................6
Is de correlatiematrix geschikt....................................................................................7
Bartlett toets voor sfeericiteit..................................................................................7
KMO toets voor toereikendheid van de steekproef..................................................8
Hoofdcomponenten-analyse: principes....................................................................8
Factoranalyse........................................................................................................ 10
Selectie van factoren................................................................................................. 10
Aantal variabelen per factor?.................................................................................11
Hoeveel factoren te weerhouden?.........................................................................11
Rotatie....................................................................................................................... 12
Ongedetermineerdheid van facotroplossing..........................................................12
Rotaties................................................................................................................. 12
Factorscores.............................................................................................................. 15
Schalen.................................................................................................................. 15
Factorscores.......................................................................................................... 16
Clusteranalyse.............................................................................................................. 18
Inleiding.................................................................................................................... 18
Afstanden.................................................................................................................. 19
Afstanden: eigenschappen....................................................................................19
Afstands- en proximiteitsmaten.............................................................................19
Verschillende afstandsmaten voor continue variabelen.........................................19
Enkele proximiteitsmaten voor metrische variabelen............................................21
Enkele similariteitsmaten voor dichotome variabelen...........................................22
Hiërarchische clusteranalyse.....................................................................................22
Hoe?...................................................................................................................... 22
Keuzes................................................................................................................... 22
Principes................................................................................................................ 22
Dendogram............................................................................................................ 23
Clusteringmethoden.............................................................................................. 23
Het aantal clusters bepalen...................................................................................24
Geschatte afstanden en de kwaliteit van clusteroplossingen................................25
Hoe verschillende clusters interpreteren...............................................................25
K-Means clusteranalyse............................................................................................. 27
K-means clustering: principes................................................................................27
k-Means vs K-medians clustering...........................................................................27
Kwaliteit van clusteroplossing...............................................................................27
Discriminantanalyse..................................................................................................... 28
Wat?.......................................................................................................................... 28
Basisprincipe............................................................................................................. 28
Voorwaarden voor discriminantanalyse.................................................................29
Sommen der kruisproducten (SSCP)......................................................................29
Berekenen van discriminatiefuncties.........................................................................30
Berekenen van LDFS.............................................................................................. 30
Ruwe & gestandaardiseerde canonieke discrimantfunctiecoëfficiënten....................30
Ruwe coëfficiënten................................................................................................ 30
Discriminantfunctiescores.....................................................................................31
Zwaartepunt of centroid scores.............................................................................31
Gestandaardiseerde coëfficiënten.........................................................................31


1

, Structuurmatrix..................................................................................................... 31
Canonische correlatie . .......................................................................................... 31
Toetsen voor aantal LDFS en WILKS . .........................................................................33
Wilk’s Lambda . ..................................................................................................... 33
Toets voor aantal functies......................................................................................33
Classificatie............................................................................................................... 34
Posteriori waarschijnlijkheden...............................................................................34




2

, MULTIVARIATE ANALYSE

Module 4: Multivariate technieken
Hoofdcomponenten en factoranalyse
Principes van hoofdcomponenten- en factoranalyse
Technieken voor datareductie: gaan trachten om een reeks geobserveerde
variabelen (op het interval of ratio niveau) te vervangen door een reeks
niet geobserveerde of latente variabelen op het interval niveau
 aantal variabelen in de analyse reduceren

Multipele indicatoren
- Onderliggende dimensies bv schaalitems
- Reductie aantal variabelen
- Vermijden multicollineariteit
Ontdekken van onderliggende datastructuren

Probleem
Kunnen we de K waargenomen variabelen X vervangen door P latente
variabelen Y waarbij
- P≤K
- Y j=a1 j X 1+ a2 j X 2 +…+ aKj X K (elk van de latente variabelen Y kan
geschreven worden als een lineaire combinatie van de
waargenomen variabelen X)
- Waarbij we P zo klein mogelijk willen houden
- Waar Y 1 … Y P zoveel mogelijk van de variantie in X verklaren
- Var ( Y 1 ) ≥Var ( Y 2 ) ≥ …≥ var ( Y K )
- Verschillende Y onafhankelijk van elkaar

Een wiskundig interludium
Vectoren: grafische voorstelling




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