brms tidiers are deprecated.

# S3 method for brmsfit
tidy(x, parameters = NA, par_type = c("all",
"non-varying", "varying", "hierarchical"), robust = FALSE,
intervals = TRUE, prob = 0.9, ...)

## Arguments

x Fitted model object from the brms package. See brms::brmsfit-class(). Names of parameters for which a summary should be returned, as given by a character vector or regular expressions. If NA (the default) summarized parameters are specified by the par_type argument. One of "all", "non-varying", "varying", or "hierarchical" (can be abbreviated). See the Value section for details. Whether to use median and median absolute deviation rather than mean and standard deviation. If TRUE columns for the lower and upper bounds of posterior uncertainty intervals are included. Defines the range of the posterior uncertainty intervals, such that 100 * prob% of the parameter's posterior distribution lies within the corresponding interval. Only used if intervals = TRUE. Extra arguments, not used

## Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

When parameters = NA, the par_type argument is used to determine which parameters to summarize.

Generally, tidy.brmsfit returns one row for each coefficient, with at least three columns:

term

The name of the model parameter.

estimate

A point estimate of the coefficient (mean or median).

std.error

A standard error for the point estimate (sd or mad).

When par_type = "non-varying", only population-level effects are returned. When par_type = "varying", only group-level effects are returned. In this case, two additional columns are added:
group

The name of the grouping factor.

level

The name of the level of the grouping factor.

Specifying par_type = "hierarchical" selects the standard deviations and correlations of the group-level parameters. If intervals = TRUE, columns for the lower and upper bounds of the posterior intervals computed.

## Details

These methods tidy the estimates from brms::brmsfit() (fitted model objects from the brms package) into a summary.

brms::brms(), brms::brmsfit()

## Examples

# NOT RUN {
library(brms)
fit <- brm(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
iter = 500, chains = 2)
tidy(fit)
tidy(fit, parameters = "^sd_", intervals = FALSE)
tidy(fit, par_type = "non-varying")
tidy(fit, par_type = "varying")
tidy(fit, par_type = "hierarchical", robust = TRUE)
# }