Summary
script bijhorend bij samenvatting STAT B
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Met dit script, samen met samenvatting, behaalde ik 19/20 op het examen
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#PDF-principe#Packages laden
library(car)
library(moments)
#R-bestand inladen
load(file.choose())
#Functies1 inladen
source(file.choose())
#Functies2 inladen
source(file.choose())
#handmatige methode voor pakketten inladen => aanklikken
#handmatige methode R-bestand inladen => file → open file
#csv-bestand inladen
Naam <- read.csv2(file.choose())
#handmatige methode CSV-Data inladen => import Dataset → From tekst (base) → (settings ok?) → import
#handmatige methode functies inladen => file → open file → source
KRUISTABEL
, #Data aanmaken
VariabeleRij <- c(rep(“A”, 99), rep(“B”, 50), rep(“C”, 110))
VariabeleKolom <- c(rep(“cat1”, 50), rep(“cat2”, 40), rep(“cat3”, 20), rep(“cat1”, 70), → 2 kwalitatieve (bivariaat)
rep(“cat2”, 10), rep(“cat3”, 2), rep(“cat1”, 5), rep(“cat2”, 410), → niet causaal
rep(“cat3”, 2))
1. kruistabel
#ordering behouden
2. statistische significantie
VariabeleRij <- ordered(VariabeleRij, levels=c(“A”, “B”, “C”))
3. effectgrootte
VariabeleKolom <- ordered(VariabeleKolom, levels=c(“cat1”, “cat2”, “cat3”))
4. associatieplot (visualisatie)
#variabelen (en frequenties) bekijken
table(Data$Variabele)
#Kruistabel maken
Kruistabel <- table (Data$VariabeleRij, Data$VariabeleKolom)
#Marginalen in output opvragen; zowel kolommarginalen als rijmarginalen
addmargins(Kruistabel)
#Marginalen in output opvragen; alleen kolommarginalen
addmargins(Kruistabel, 1)
#Marginalen in output opvragen; alleen rijmarginalen
addmargins(Kruistabel, 2)
#Kruistabel met de totaalproporties (tussen 0 en 1) of totaalpercentages (tussen 0 en 100)
prop.table(Kruistabel)
prop.table(Kruistabel)*100
#Kruistabel met rijproporties
prop.table(Kruistabel, 1)
#Kruistabel met kolomproporties
prop.table(Kruistabel, 2)
#kruistabel met kolompercentages en percentage rijmarginalen
kruistabel.kolom(Data$VariabeleRij, Data$VariabeleKolom)
kruistabel.kolom(Kruistabel)
#tabel met rijpercentages en percentage kolommarginalen
kruistabel.rij(Data$VariabeleRij, Data$VariabeleKolom)
#Chi-kwadraattoets
chisq.test(Kruistabel)
#Chi-kwadraattoets, Cramer’s V en 95% betrouwbaarheidsinterval rond Cramers’s V → effectgrootte
chi.kwadraat.test(table(Data$VariabeleRij, Data$VariabeleKolom))
of
chi.kwadraat.test(Kruistabel)
#visualisatie
#!!! tabel in juiste volgorde…
assocplot(table(Data$VariabeleKolom, Data$VariabeleRij))
T-TEST
→ 1 kwalitatieve met 2 categorieën
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