library(car); library(lmtest) #para desvios-padrao robustos
#estimar MPL
linprob <- lm(inlf~ nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6,data=MROZ)
#resultados com desvios-padrao e testes t robustos a heterocedasticidade
coeftest(linprob,vcov=hccm)
#previsoes para duas mulheres (extremo)
xpred<-list(nwifeinc=c(100,0), educ=c(5,17), exper=c(0,30),age=c(20,52),kidslt6=c(2,0),kidsge6=c(0,0))
#estimar modelo logit
logitres<-glm(inlf~ nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6,family=binomial(link=logit),data=MROZ)
summary(logitres)
#valor da log verossimilhanca
logLik(logitres)
#Pseudo R2
1 - logitres$deviance/logitres$null.deviance
#estimar modelo probit
probitres<-glm(inlf~ nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6,family=binomial(link=probit),data=MROZ)
summary(probitres)
#valor da log verossimilhanca
logLik(probitres)
#Pseudo R2
1 - probitres$deviance/logitres$null.deviance
#teste de significancia global
lrtest(probitres)
#teste que experiencia e idade sao irrelevantes
restr<-glm(inlf~nwifeinc+educ+kidslt6+kidsge6,family=binomial(link=probit), data=MROZ)
lrtest(restr,probitres)
# previsões
predict (linprob, xpred, type="response")
predict(logitres, xpred, type= "response")
predict(probitres, xpred, type="response")
#efeitos parciais
library(mfx)
logitmfx(inlf~nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6,data=MROZ, atmean=FALSE)
probitmfx(inlf~ nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6, data=MROZ, atmean=FALSE)