inst/examples/Ex_pfamily.R

In glmbayes: Bayesian Generalized Linear Models (IID Samples)

## During CRAN checks, run examples sequentially.
use_parallel <- identical(Sys.getenv("NOT_CRAN"), "true")

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts    <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome   <- gl(3, 1, 9)
treatment <- gl(3, 3)
print(d.AD <- data.frame(treatment, outcome, counts))

## Set up Prior for Poisson Model
ps <- Prior_Setup(counts ~ outcome + treatment, family = poisson())
ps

## Normal prior for glmb
glmb.D93 <- glmb(
  counts ~ outcome + treatment,
  family  = poisson(),
  pfamily = dNormal(mu = ps$mu, Sigma = ps$Sigma),
  use_parallel = use_parallel
)

## Extract pfamily and pfamily settings for call to glmb
pfamily(glmb.D93)

## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group  <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)

## Set up prior for gaussian model
ps2 <- Prior_Setup(weight ~ group, family = gaussian())
ps2

## Conjugate Normal Prior (fixed dispersion)
lmb.D9 <- lmb(
  weight ~ group,
  pfamily = dNormal(mu = ps2$mu, ps2$Sigma, dispersion = ps2$dispersion),
  use_parallel = use_parallel
)
pfamily(lmb.D9)

## Conjugate Normal_Gamma Prior
lmb.D9_v2 <- lmb(
  weight ~ group,
  pfamily = dNormal_Gamma(
    ps2$mu,
    Sigma_0 = ps2$Sigma_0,
    shape = ps2$shape,
    rate  = ps2$rate
  ),
  use_parallel = use_parallel
)
pfamily(lmb.D9_v2)

## Independent_Normal_Gamma_Prior
lmb.D9_v3 <- lmb(
  weight ~ group,
  dIndependent_Normal_Gamma(
    ps2$mu,
    ps2$Sigma,
    shape = ps2$shape_ING,
    rate  = ps2$rate
  ),
  use_parallel = use_parallel
)
pfamily(lmb.D9_v3)