## ----setup, message=FALSE, warning=FALSE,include=TRUE-------
library(lme4)
library(lmerTest)
library(gee)    # for generalized estimating equations
library(MASS)   # For confidence limits for parameters


## ----data---------------------------------------------------
resp <- read.table(file="D:/Dropbox/edps587/lectures/10 logreg/respitory.txt", header=TRUE)

head(resp)


## ----logreg-------------------------------------------------

# glm = Generalized Linear model
model1 <- glm(resp ~ 1 + age, data=resp, family=binomial)
summary(model1)


## -----------------------------------------------------------
round(confint(model1, level = 0.95),digits=2)


## -----------------------------------------------------------
odds <- exp(coefficients(model1))
round(odds,digits=2)

odds2 <- 1/odds
round(odds2,digits=2) 


## ----quasibinom---------------------------------------------
model1b <- glm(resp ~ 1 + age, data=resp, family=quasibinomial)
summary(model1b)


## ----gee----------------------------------------------------
model.gee <- gee(resp ~1 + age, id, data=resp,family=binomial,corstr="exchangeable")
summary(model.gee)


## ----laplace------------------------------------------------
mod1.laplace <- glmer(resp ~ 1 + age  + (1 | id), data=resp, family=binomial)
summary(mod1.laplace)


## ----mle----------------------------------------------------
mod1.quad <- glmer(resp ~ 1 + age +  (1 | id),data=resp, family=binomial, nAGQ=10 )
summary(mod1.quad)


## -----------------------------------------------------------
round(confint(mod1.quad),digits=4)


## ----mleodds------------------------------------------------
odds <- exp(fixef(mod1.quad))
round(odds,digits=2)


## ----extractR, eval=TRUE------------------------------------
setwd("D:/Dropbox/edps587/lectures/10 logreg")
knitr::purl("Rmarkdown_respitory_example.Rmd","rscript_logreg_respitory.txt")