#Load paket lme4 dan multilevel
library(lme4)
library(multilevel)
# Model dengan random intercept#
Null.Model<-lme(migrant2005~1,random=~1|villageid,data=dsmigration, control=list(opt=”optim”))
VarCorr(Null.Model)
tmod<-aov(migrant2005~as.factor(villageid),data=dsmigration)
# Menghitung “Intra Class Correlation” / persentase variasi yang bersumber dari perbedaan antar grup#
ICC1(tmod)
GREL.DAT<-GmeanRel(Null.Model)
names(GREL.DAT)
GREL.DAT$ICC
GREL.DAT$MeanRel
mean(GREL.DAT$MeanRel) #Average group-mean reliability
ICC2(tmod)
mod.ml<-lme(migrant2005~1,random=~1|villageid,data=dsmigration,method=”ML”,
control=list(opt=”optim”))
VarCorr(mod.ml)
Null.Model.2<-gls(migrant2005~1,data=dsmigration,
control=list(opt=”optim”))
anova(Null.Model,Null.Model.2)
# Model dengan Fixed Effect dan Random intercept#
Model.1<-lme(migrant2005~ses,random=~1|villageid,data=dsmigration,
control=list(opt=”optim”))
summary(Model.1)
VarCorr(Model.1)
library(lattice)
xyplot(migrant2005~SES|as.factor(villageid),data=dsmigration[1:1582,],
type=c(“p”,”g”,”r”),col=”dark blue”,col.line=”black”,
xlab=”Socio Economic Index”,
ylab=”migration”)
#Model dengan Random Effect(slope) dan Random Intercept#
Model.2<-lme(migrant2005~ses,random=~ses|villageid, data=dsmigration,
control=list(opt=”optim”))
summary(Model.2)
Model.2a<-update(Model.2,random=~1|villageid)
# Significance Test untuk Random Slope#
anova(Model.2,Model.2a)
Final.Model<-lme(migrant2005~HRS+LEAD+G.HRS+LEAD:G.HRS,
random=~LEAD|villageid,data=dsmigration,control=list(opt=”optim”))
round(summary(Final.Model)$tTable,dig=3)