priors {baggr} | R Documentation |
This page provides a list of all available distributions
that can be used to specify priors in baggr()
. These convenience functions
are designed to allow the user to write the priors in the most "natural" way when
implementing them in baggr. Apart from
passing on the arguments, their only other role is to perform a rudimentary check
if the distribution is specified correctly.
multinormal(location, Sigma) lkj(shape, order = NULL) normal(location, scale) cauchy(location, scale) uniform(lower, upper)
location |
Mean for normal and multivariate normal (in which case |
Sigma |
Variance-covariance matrix for multivariate normal. |
shape |
Shape parameter for LKJ |
order |
Order of LKJ matrix (typically it does not need to be specified, as it is inferred directly in the model) |
scale |
SD for Normal, scale for Cauchy |
lower |
Lower bound for Uniform |
upper |
Upper bound for Uniform |
The prior choice in baggr is always done via 3 distinct arguments: prior_hypermean
,
prior_hypersd
, and prior_hypercor
.
These respectively refer to the priors on the average of the effects across the groups (hypermean), the standard deviation of the effects across the groups (hypersd), and the correlation in the distribution of parameters across groups when the model allows multivariate shrinkage (say on control group means and effects).
Notation for priors is "plain-text", in that you can write the distributions as
normal(5,10)
, uniform(0,100)
etc.
As with any other argument one has the option to simply input the prior directly,
e.g. prior_hypermean = normal(0,1)
, or by creating a named list of custom priors
and then inputting the list to the argument priors
.
See the examples below for more.
Different parameters admit different priors:
prior_hypermean
will take "normal"
, "uniform"
and "cauchy"
input for a scalar mean.
For a vector mean, it will take any of these arguments and apply them independently to
each component of the vector, or it can also take a "multinormal"
argument
(see the example below).
prior_hypersd
will take "normal"
and "uniform"
prior_hypercor
allows "lkj"
input
Witold Wiecek, Rachael Meager
Lewandowski, Daniel, Dorota Kurowicka, and Harry Joe. "Generating Random Correlation Matrices Based on Vines and Extended Onion Method." Journal of Multivariate Analysis 100, no. 9 (October 1, 2009): 1989-2001.
# (these are not the recommended priors -- for syntax illustration only) # change the priors for 8 schools: baggr(schools, model = "rubin", pooling = "partial", prior_hypermean = normal(5,5), prior_hypersd = normal(0,20)) # passing priors as a list custom_priors <- list(hypercor = lkj(1), hypersd = normal(0,10), hypermean = multinormal(c(0,0),matrix(c(10,3,3,10),2,2))) microcredit_summary_data <- prepare_ma(microcredit, outcome = "consumption") baggr(microcredit_summary_data, model = "mutau", pooling = "partial", prior = custom_priors)