set.seed(333)
## 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))

## Classical Model
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson(link = log))
summary(glm.D93)

## Poisson prior and rglmb (same prior as \code{\link{glmb}} with \code{\link{Prior_Setup}})
ps <- Prior_Setup(counts ~ outcome + treatment, family = poisson(), data = d.AD)
rglmb.D93 <- rglmb(
  n = 1000,
  y = ps$y,
  x = as.matrix(ps$x),
  pfamily = dNormal(mu = ps$mu, Sigma = ps$Sigma),
  family = poisson(),
  weights = rep(1, nrow(ps$x))
)
summary(rglmb.D93)


## Menarche model with informative prior. See \code{\link{glmb}} and \code{\link{Prior_Setup}}
## for default g-priors; for data and fitted curves using \code{predict.glmb}, see the
## menarche block in \code{\link{glmb}} and \code{vignette("Chapter-04", package = "glmbayes")}.
data(menarche, package = "MASS")

summary(menarche)
design_df <- data.frame(
  Age = menarche$Age,
  Age2 = menarche$Age - 13,
  Proportion = menarche$Menarche / menarche$Total,
  Total = menarche$Total
)
x <- model.matrix(~ Age2, data = design_df)
y <- design_df$Proportion
wt <- design_df$Total

# Extract coefficient names from design matrix
coef_names <- colnames(x)

# Set up prior mean with names
mu <- matrix(0, nrow = length(coef_names), ncol = 1)
mu[2, 1] <- (log(0.9 / 0.1) - log(0.5 / 0.5)) / 3
rownames(mu) <- coef_names

# Set up prior covariance matrix with named rows and columns
V1 <- 1 * diag(as.numeric(2.0))

# 2 standard deviations for prior estimate at age 13 between 0.1 and 0.9
## Specifies uncertainty around the point estimates
V1[1, 1] <- ((log(0.9 / 0.1) - log(0.5 / 0.5)) / 2)^2
V1[2, 2] <- (3 * mu[2, 1] / 2)^2 # Allows slope to be up to 3 times as large as point estimate
rownames(V1) <- coef_names
colnames(V1) <- coef_names

out <- rglmb(
  n = 1000, y = y, x = x, pfamily = dNormal(mu = mu, Sigma = V1), weights = wt,
  family = binomial(logit)
)
summary(out)

## rglmb with dGamma prior (dispersion-only; coefficients fixed)
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)
ps_dg <- Prior_Setup(weight ~ group, family = gaussian())
rate_dg <- if (!is.null(ps_dg$rate_gamma)) ps_dg$rate_gamma else ps_dg$rate
out_dGamma <- rglmb(
  n = 100, y = ps_dg$y, x = as.matrix(ps_dg$x),
  pfamily = dGamma(shape = ps_dg$shape, rate = rate_dg, beta = ps_dg$coefficients),
  weights = rep(1, length(ps_dg$y)), family = gaussian()
)
summary(out_dGamma)
