Using standard formula notation from glmer ({lme4}), defines a Stan model ({rstan}) and optionally samples from the posterior. Can optionally compute WAIC. Supports model families: "gaussian", "binomial", "poisson", "negative binomial", "beta", "gamma", "lognormal", "beta-binomial", "ordered",and "zero-inflated poisson, negative binomial and gamma".
If you have not install RStan, please read RStan document and install it.
And you need devtools to install glmmstan from GitHub.
install.packages("devtools")
After install these packages, excute the following in R:
library(devtools)
install_github("norimune/glmmstan")
data(baseball)
#"gaussian"
fit1 <- glmmstan(salary_log~1,data=baseball,family="gaussian")
output_result(fit1) #output glmm result
output_stan(fit1) #output summarized stan result (including rhat index)
print(fit1) #output stan result (same print() in rstan)
#"lognormal" with random effect
fit2 <- glmmstan(salary~HR+(1+HR|team),data=baseball,family="lognormal")
output_result(fit2)$WAIC #output only WAIC
#"negative binomial" with offset term
fit3 <- glmmstan(HR~1,data=baseball,family="nbinomial",offset="ATbats")
output_result(fit3)$beta #output only coefficients and scale parameters
#"ordered" with centering indipendent variables
fit4 <- glmmstan(Cluster~salary,data=baseball,family="ordered",center=TRUE)
output_result(fit4)
output_code(fit4) #confirm the stan code
#output only stan code, datase, and stan model
code1 <- glmmstan(HR~1+(1|player),data=baseball,family="poisson",codeonly=TRUE)
dataset1 <- glmmstan(HR~1+(1|player),data=baseball,family="poisson",dataonly=TRUE)
model1 <- glmmstan(HR~1+(1|player),data=baseball,family="poisson",modelonly=TRUE)
fit5 <- stan(model_code=code1, data=dataset1)
fit6 <- sampling(model1,data=dataset1)
#intra-class correlations
model <- iccmodel()
y <- subset(baseball,select=c(HR,HIT))
fit7 <- iccstan(y,baseball$team,model=model)
output_icc(fit7)
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