glmer.R
In rstanarm: Bayesian Applied Regression Modeling via Stan

## ---- SETTINGS-knitr, include=FALSE-------------------------------------------
stopifnot(require(knitr))
opts_chunk$set(
  comment=NA, 
  message = FALSE, 
  warning = FALSE, 
  eval = identical(Sys.getenv("NOT_CRAN"), "true"),
  dev = "png",
  dpi = 150,
  fig.asp = 0.618,
  fig.width = 5,
  out.width = "60%",
  fig.align = "center"
)

## ---- SETTINGS-gg, include=TRUE-----------------------------------------------
library(ggplot2)
library(bayesplot)
theme_set(bayesplot::theme_default())

## ---- results = "hide"--------------------------------------------------------
library(rstanarm)
data(roaches)
roaches$roach1 <- roaches$roach1 / 100
roaches$log_exposure2 <- log(roaches$exposure2)
post <- stan_gamm4(
  y ~ s(roach1) + treatment + log_exposure2,
  random = ~(1 | senior),
  data = roaches, 
  family = neg_binomial_2, 
  QR = TRUE,
  cores = 2,
  chains = 2, 
  adapt_delta = 0.99,
  seed = 12345
)

## -----------------------------------------------------------------------------
plot_nonlinear(post)

## -----------------------------------------------------------------------------
data("Orange", package = "datasets")
Orange$age <- Orange$age / 100
Orange$circumference <- Orange$circumference / 100

## ---- warning=TRUE------------------------------------------------------------
startvec <- c(Asym = 2, xmid = 7.25, scal = 3.5)
library(lme4)
nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
             data = Orange, start = startvec)
summary(nm1)

## ---- echo = FALSE------------------------------------------------------------
grep("^SS[[:lower:]]+", ls("package:stats"), value = TRUE)

## ---- results = "hide"--------------------------------------------------------
post1 <- stan_nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
                    data = Orange, cores = 2, seed = 12345, init_r = 0.5)

## -----------------------------------------------------------------------------
post1

## -----------------------------------------------------------------------------
plot(post1, regex_pars = "^[b]")

## -----------------------------------------------------------------------------
nd <- data.frame(age = 1:20, Tree = factor("6", levels = 1:6))
PPD <- posterior_predict(post1, newdata = nd)
PPD_df <- data.frame(age = as.factor(rep(1:20, each = nrow(PPD))),
                     circumference = c(PPD))
ggplot(PPD_df, aes(age, circumference)) + geom_boxplot()

## ---- eval = FALSE------------------------------------------------------------
#  post3 <- stan_nlmer(conc ~ SSfol(Dose, Time, lKe, lKa, lCl) ~
#                      (0 + lKe + lKa + lCl | Subject), data = Theoph,
#                      cores = 2, seed = 12345,
#                      QR = TRUE, init_r = 0.25, adapt_delta = 0.999)
#  pairs(post3, regex_pars = "^l")
#  pairs(post3, regex_pars = "igma")