| rstanarm_tidiers {broom} | R Documentation |
These methods tidy the estimates from stanreg-objects
(fitted model objects from the rstanarm package) into a summary.
## S3 method for class 'stanreg'
tidy(x, parameters = c("non-varying", "varying",
"hierarchical"), intervals = FALSE, prob = 0.9, ...)
## S3 method for class 'stanreg'
glance(x, looic = FALSE, ...)
x |
Fitted model object from the rstanarm package. See
|
parameters |
One of |
intervals |
If |
prob |
See |
... |
For |
looic |
Should the LOO Information Criterion be included? See
|
All tidying methods return a data.frame without rownames.
The structure depends on the method chosen.
When parameters="non-varying" (the default), tidy.stanreg returns
one row for each coefficient, with three columns:
term |
The name of the corresponding term in the model. |
estimate |
A point estimate of the coefficient (posterior median). |
std.error |
A standard error for the point estimate based on
|
For models with group-specific parameters (e.g., models fit with
stan_glmer), setting parameters="varying"
selects the group-level parameters instead of the non-varying regression
coefficients. Addtional columns are added indicating the level and
group. Specifying parameters="hierarchical" selects the
standard deviations and (for certain models) correlations of the group-level
parameters.
If intervals=TRUE, columns for the lower and
upper values of the posterior intervals computed with
posterior_interval are also included.
glance returns one row with the columns
algorithm |
The algorithm used to fit the model. |
pss |
The posterior sample size (except for models fit using optimization). |
nobs |
The number of observations used to fit the model. |
sigma |
The square root of the estimated residual variance. |
looic |
If |
if (require(rstanarm)) {
fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
iter = 500, chains = 2)
tidy(fit, intervals = TRUE, prob = 0.5)
tidy(fit, parameters = "hierarchical")
tidy(fit, parameters = "varying")
glance(fit, looic = TRUE, cores = 1)
}