prior_summary method provides a summary of the prior distributions
used for the parameters in a given model. In some cases the user-specified
prior does not correspond exactly to the prior used internally by
rstanarm (see the sections below). Especially in these cases, but also
in general, it can be much more useful to visualize the priors. Visualizing
the priors can be done using the
or alternatively by fitting the model with the
prior_PD argument set
TRUE (to draw from the prior predictive distribution instead of
conditioning on the outcome) and then plotting the parameters.
A fitted model object returned by one of the
rstanarm modeling functions. See
Number of digits to use for rounding.
Currently ignored by the method for stanreg objects.
A list of class "prior_summary.stanreg", which has its own print method.
For rstanarm modeling functions that accept a
argument, the specified prior for the intercept term applies to the
intercept after rstanarm internally centers the predictors so they
each have mean zero. The estimate of the intercept returned to the user
correspond to the intercept with the predictors as specified by the user
(unmodified by rstanarm), but when specifying the prior the
intercept can be thought of as the expected outcome when the predictors are
set to their means. The only exception to this is for models fit with the
sparse argument set to
TRUE (which is only possible with a
subset of the modeling functions and never the default).
For some models you may see "
in the printed output and adjusted scales included in the object returned
prior_summary. These adjusted scale values are the prior scales
actually used by rstanarm and are computed by adjusting the prior
scales specified by the user to account for the scales of the predictors
(as described in the documentation for the
argument). To disable internal prior scale adjustments set the
autoscale argument to
FALSE when setting a prior using one of
the distributions that accepts an
autoscale argument. For example,
normal(0, 5, autoscale=FALSE) instead of just
For the models fit with an rstanarm modeling function that supports
QR argument (see e.g,
TRUE then the prior distributions for the regression
coefficients specified using the
prior argument are not relative to
the original predictor variables X but rather to the variables in the
matrix Q obtained from the QR decomposition of X.
In particular, if
prior = normal(location,scale), then this prior on
the coefficients in Q-space can be easily translated into a joint
multivariate normal (MVN) prior on the coefficients on the original
predictors in X. Letting θ denote the coefficients on
Q and β the coefficients on X then if θ ~ N(μ, σ) the corresponding prior on
β is β ~ MVN(Rμ,
R'Rσ), where μ and σ are vectors of the
appropriate length. Technically, rstanarm uses a scaled QR
decomposition to ensure that the columns of the predictor matrix used to
fit the model all have unit scale, when the
to the function passed to the
prior argument is
default), in which case the matrices actually used are
Q* = Q (n-1)^0.5 and R* = (n-1)^(-0.5) R. If
autoscale = FALSE
we instead scale such that the lower-right element of R* is
1, which is useful if you want to specify a prior on the coefficient
of the last predictor in its original units (see the documentation for the
If you are interested in the prior on β implied by the prior on θ, we strongly recommend visualizing it as described above in the Description section, which is simpler than working it out analytically.
The priors help page and the Prior Distributions vignette.
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if (!exists("example_model")) example(example_model) prior_summary(example_model) priors <- prior_summary(example_model) names(priors) priors$prior$scale priors$prior$adjusted_scale # for a glm with adjusted scales (see Details, above), compare # the default (rstanarm adjusting the scales) to setting # autoscale=FALSE for prior on coefficients fit <- stan_glm(mpg ~ wt + am, data = mtcars, prior = normal(0, c(2.5, 4)), prior_intercept = normal(0, 5), iter = 10, chains = 1) # only for demonstration prior_summary(fit) fit2 <- update(fit, prior = normal(0, c(2.5, 4), autoscale=FALSE), prior_intercept = normal(0, 5, autoscale=FALSE)) prior_summary(fit2)
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