## ----setup, include=FALSE, cache=FALSE----------------------------------------
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
error = FALSE,
collapse = TRUE,
comment = NA,
R.options = list(width = 220),
dev.args = list(bg = 'transparent'),
dev = 'png',
fig.align = 'center',
out.width = '75%',
fig.asp = .75,
cache.rebuild = FALSE,
cache = FALSE
)
## ----loadbayes, echo=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
brms_model <- mixedup:::brms_model
rstanarm_model <- mixedup:::rstanarm_model
## ----mods-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(dplyr)
library(lme4)
library(glmmTMB)
library(nlme)
library(brms)
library(rstanarm)
library(mgcv)
lmer_model <-
lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
lme_model <-
lme(Reaction ~ Days, random = ~ 1 + Days | Subject, data = sleepstudy)
tmb_model <-
glmmTMB(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
# brms_model <-
# brm(Reaction ~ Days + (1 + Days | Subject),
# data = sleepstudy,
# cores = 4,
# refresh = -1,
# verbose = FALSE
# )
# rstanarm_model <-
# stan_glmer(Reaction ~ Days + (1 + Days | Subject),
# data = sleepstudy,
# cores = 4,
# refresh = -1,
# verbose = FALSE
# )
# this is akin to (1 | Subject) + (0 + Days | Subject) in lme4
mgcv_model <-
gam(
Reaction ~ Days +
s(Subject, bs = 're') +
s(Days, Subject, bs = 're'),
data = lme4::sleepstudy,
method = 'REML'
)
## ----results--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(mixedup)
extract_random_effects(lmer_model)
extract_random_effects(lme_model)
extract_random_effects(tmb_model)
extract_random_effects(brms_model)
extract_random_effects(rstanarm_model)
extract_random_effects(mgcv_model)
## ----options--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
lmer_model <-
lmer(y ~ service + (1 | d) + (1 | s), data = InstEval[1:5000,])
extract_random_effects(
lmer_model,
ci_level = .9,
digits = 2
) %>%
head()
## ----tmb_zip--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tmb_zip <- glmmTMB(
count ~ spp + mined + (1 | site),
zi = ~ spp + mined + (1 | site),
family = truncated_poisson,
data = Salamanders
)
extract_random_effects(
tmb_zip,
component = 'zi'
) %>%
head()
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