data(realestData)
hist(realestData$price[realestData$year==2018])
boxplot(realestData$price~realestData$year)
boxplot(realestData$price[realestData$year==2018]~realestData$NUTS2[realestData$year==2018])
boxplot(realestData$price[realestData$year==2018]~realestData$NUTS4type[realestData$year==2018])
library(lme4)
attach(realestData)
N <- nrow(realestData[(year == 2015),]) # population size in the first period
n <- 75 # sample size in the first period
set.seed(123456)
sampled_elements_in_2015 <- sample(N,n)
con2015 <- rep(0,N)
con2015[sampled_elements_in_2015] <- 1
sampled_elements_in_2016 <- sample(N,n)
con2016 <- rep(0,N)
con2016[sampled_elements_in_2016] <- 1
sampled_elements_in_2017 <- sample(N,n)
con2017 <- rep(0,N)
con2017[sampled_elements_in_2017] <- 1
sampled_elements_in_2018 <- sample(N,n)
con2018 <- rep(0,N)
con2018[sampled_elements_in_2018] <- 1
con=as.logical(con2015, con2016, con2017, con2018)
model1 <- lmer(price ~ premises + area + (1|NUTS2)+(0+premises|NUTS2) +
(1|NUTS4type)+(0+area|NUTS4type), subset=con)
AIC(model1)
model2 <- lmer(price ~ premises + area + (0+premises|NUTS2) + (0+area|NUTS4type), subset = con)
AIC(model2)
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