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In this article, you’ll learn how to specify prior distributions in
psborrow2
.
Because psborrow2
creates fully-parametrized Bayesian
models, proper prior distributions on all parameters must be specified.
Prior distributions are needed for several parameters, depending on the analysis:
add_covariates()
outcome_surv_weibull_ph()
treatment_details()
borrowing_hierarchical_commensurate()
See the documentation of these functions for more information.
The currently supported prior distributions are created with the
prior_
constructors below:
prior_bernoulli()
prior_beta()
prior_cauchy()
prior_exponential()
prior_gamma()
prior_half_cauchy()
prior_half_normal()
prior_normal()
prior_poisson()
For example, we can create an uninformative normal distribution by specifying a normal prior centered around 0 with a very large standard deviation:
uninformative_normal <- prior_normal(0, 10000)
uninformative_normal
# Normal Distribution
# Parameters:
# Stan R Value
# mu mean 0
# sigma sd 10000
See the documentation for the respective functions above for additional information.
You may sometimes find it useful to visualize prior distributions. In
these scenarios, you can call plot()
on the prior object to
visualize the distribution:
plot()
chooses the default axes for you, but you can
change these to make differences more obvious. Let’s compare a
conservative gamma(0.001, 0.001)
hyperprior distribution on
the commensurability parameter tau
to an more aggressive
gamma(1, 0.001)
distribution with greater density at higher
values of tau
(which will lead to more borrowing in a BDB
analysis):
conservative_tau <- prior_gamma(0.001, 0.001)
aggressive_tau <- prior_gamma(1, 0.001)
plot(aggressive_tau, xlim = c(0, 2000), col = "blue", ylim = c(0, 1e-03))
plot(conservative_tau, xlim = c(0, 2000), col = "red", add = TRUE)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.