Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
## ----setup--------------------------------------------------------------------
library(makemyprior)
## -----------------------------------------------------------------------------
formula <- y ~ x + mc(a) + mc(b)
## -----------------------------------------------------------------------------
p <- 10
m <- 10
n <- m*p
set.seed(1)
data <- list(a = rep(1:p, each = m),
b = rep(1:m, times = p),
x = runif(n))
data$y <- data$x + rnorm(p, 0, 0.5)[data$a] +
rnorm(m, 0, 0.3)[data$b] + rnorm(n, 0, 1)
## -----------------------------------------------------------------------------
prior <- make_prior(formula, data, family = "gaussian",
intercept_prior = c(0, 1000),
covariate_prior = list(x = c(0, 100)))
## ----fig.width = 5, fig.height = 2--------------------------------------------
summary(prior)
plot_prior(prior) # or plot(prior)
plot_tree_structure(prior)
## ----eval = FALSE-------------------------------------------------------------
# new_prior <- makemyprior_gui(prior)
## ----fig.width = 5, fig.height = 2--------------------------------------------
new_prior <- make_prior(
formula, data,
prior = list(
tree = "s1 = (a, b); s2 = (s1, eps)",
w = list(s1 = list(prior = "pcM", param = c(0.7, 0.5)),
s2 = list(prior = "pc1", param = 0.75)),
V = list(s2 = list(prior = "pc0", param = c(3, 0.05)))
),
covariate_prior = list(x = c(0, 100))
)
summary(new_prior)
plot_prior(new_prior)
plot_tree_structure(new_prior)
## ----eval = FALSE-------------------------------------------------------------
# compile_stan(save = TRUE)
## ----eval = FALSE-------------------------------------------------------------
# posterior1 <- inference_stan(new_prior, iter = 1e4, chains = 1, seed = 1)
## ----fig.width = 5, fig.height = 2, eval = FALSE------------------------------
# plot_posterior_stan(posterior1, param = "prior", prior = TRUE) # on the scale of the prior, together with the prior
# plot_posterior_stan(posterior1, param = "variance") # on variance scale
# plot_fixed_posterior(posterior1) # fixed effects
## ----fig.width = 5, fig.height = 2, eval = FALSE------------------------------
# prior1 <- inference_stan(new_prior, use_likelihood = FALSE, iter = 1e4, chains = 1, seed = 1)
# plot_several_posterior_stan(list(Prior = prior1, Posterior = posterior1))
## ----eval = FALSE-------------------------------------------------------------
# posterior2 <- inference_inla(new_prior)
## ----fig.width = 5, fig.height = 2, eval = FALSE------------------------------
# plot_posterior_variance(posterior2) # on variance scale
# plot_fixed_posterior(posterior1)
## -----------------------------------------------------------------------------
?makemyprior_models
## ----fig.width = 5, fig.height = 2--------------------------------------------
prior2 <- make_prior(formula = formula, data = data,
prior = list(tree = "(a); (b); (eps)",
V = list(
a = list(prior = "pc", param = c(1, 0.05)),
b = list(prior = "pc", param = c(2, 0.05)),
eps = list(prior = "pc", param = c(3, 0.05))
)))
plot_prior(prior2)
plot_tree_structure(prior2)
## ----fig.width = 5, fig.height = 2--------------------------------------------
prior3 <- make_prior(formula = formula, data = data,
prior = list(tree = "s1 = (a, b); (eps)",
V = list(
s1 = list(prior = "pc", param = c(3, 0.05)),
eps = list(prior = "pc", param = c(3, 0.05))),
w = list(
s1 = list(prior = "pcM", param = c(0.5, 0.8))
)
))
plot_prior(prior3)
plot_tree_structure(prior3)
## -----------------------------------------------------------------------------
sessionInfo()
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