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| setwd("~/Downloads/") | |
| library("foreign") | |
| library("lme4") | |
| library("lattice") | |
| library("sandwich") | |
| library("arm") | |
| library("car") | |
| library("lmtest") | |
| # Load data | |
| base<-read.spss("Inglehart_cut.sav", use.value.labels=TRUE, to.data.frame=TRUE) | |
| # Recode regions | |
| base$RegionsTXTUSA2<-recode(base$Regions, "1='GermanyWest';2='GermanySouth';3='GermanyNorth'; 4='GermanyEast';5='GermanyCenter';6='RussiaPovolzhie';7='RussiaCenter';8='RussiaUral';9='RussiaFarEast';10='RussiaMoskow';11='USANorth';12='USA';13='SpainNonspanish';14='Spainspanish';15='IndiaWest';16='IndiaSouth';17='IndiaCenter';18='IndiaEast';19='IndiaNorth';20='ItalyNorthEast';21='ItalyNorthWest';22='ItalyCenter';23='ItalySouth';24='UkraineEast';25='UkraineSouth';26='UkraineCenter';27='UkraineWest';28='GBEngland';29='GBWales';30='GBScotland'; 31='BrazilNorthwest';32='BrazilNotrheast'; 34='BrazilCenter';35='BrazilSoutheast';36='BrazilSouth'") | |
| # NA for two variables | |
| base$divorced <- ifelse(base$divorced == -99, NA ,base$divorced) | |
| base$children <- ifelse(base$children == -99, NA,base$children) | |
| # Create wave identifier | |
| base$wave<-ifelse(base$wave1==1,1,ifelse(base$wave2==1,2,ifelse(base$wave3==1,3,ifelse(base$wave4==1,4,ifelse(base$wave5==1,5,0))))) | |
| # Subset data, leaving only relevant variables to free memory | |
| basesubset <- base[c("RegionsTXTUSA2","wave","natpride","V235","V238","age","divorced","children")] | |
| base <- NULL | |
| # Fixed effects model (original) | |
| fixed <- lm(natpride ~ age + as.factor(V235) + as.factor(V238) + as.factor(RegionsTXTUSA2) -1, data = basesubset) | |
| # Random effects model (original) | |
| random <- lmer(natpride ~ age + as.factor(V235) + as.factor(V238) + (1|RegionsTXTUSA2), data = basesubset) | |
| # Fixed effects model (with children and divorced) | |
| fixed_family <- lm(natpride ~ age + as.factor(V235) + as.factor(V238) + as.factor(divorced) + as.factor(children) + as.factor(RegionsTXTUSA2) -1, data = basesubset) | |
| # Random effects model (with children and divorced) | |
| random_family <- lmer(natpride ~ age + as.factor(V235) + as.factor(V238) + as.factor(divorced) + as.factor(children) + (1|RegionsTXTUSA2), data = basesubset) | |
| # Huber-Eicker-White Errors | |
| fixed.se <- coeftest(fixed, vcovHC(fixed,type="HC1"))[,2] | |
| fixed_family.se <- coeftest(fixed_family, vcovHC(fixed_family,type="HC1"))[,2] | |
| # Hausman test function (inspired by p. 8, http://www.kirchkamp.de/oekonometrie/pdf/me-p.pdf) | |
| hausman <- function(fixed,random) { | |
| rNames <- names(fixef(random)) | |
| fNames <- names(coef(fixed)) | |
| timevarNames <- intersect(rNames,fNames) | |
| k <- length(timevarNames) | |
| rV <- vcov(random) | |
| rownames(rV)=rNames | |
| colnames(rV)=rNames | |
| bDiff <- as.matrix((fixef(random))[timevarNames] - coef(fixed)[timevarNames]) | |
| vDiff <- as.matrix(vcov(fixed)[timevarNames,timevarNames] - rV[timevarNames, timevarNames]) | |
| H <- as.numeric(abs(t(bDiff) %*% solve(vDiff) %*% bDiff)) | |
| c(H=H,p.value=pchisq(H,k,lower.tail=FALSE)) | |
| } | |
| # Perform Hausman test | |
| hausman(fixed,random) | |
| # Ok in the original specification, can use random effects | |
| hausman(fixed_family,random_family) | |
| # Hausman test fails when children and divorce information is added in the model | |
| # Dot plots | |
| ## Random effects | |
| dotplot(ranef(random, condVar=TRUE), binpositions="all", stackdir = "centerwhole") | |
| dotplot(ranef(random_family, condVar=TRUE), binpositions="all", stackdir = "centerwhole") | |
| ## Fixed effects (with Huber-Eicker-White errors) | |
| end1 <- length(fixed$coefficients) | |
| png("fixed_effects_nat_pride.png",width = 7.5, height = 7.5, units= "in", res = 200) | |
| coefplot(fixed$coefficients[10:end1],fixed.se[10:end1],varnames=fixed$xlevels[[3]],cex.var=0.7,main="Fixed effects estimates on provided data (lines are Huber-Eicker-White 68% and 95% CI)",cex.main=0.9) | |
| dev.off() |
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