R: Tidying methods for a brms model

brms_tidiers {broom}

R Documentation

Tidying methods for a brms model

Description

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

Usage

## S3 method for class '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 `brmsfit-class`.
`parameters`	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.
`par_type`	One of `"all"`, `"non-varying"`, `"varying"`, or `"hierarchical"` (can be abbreviated). See the Value section for details.
`robust`	Whether to use median and median absolute deviation rather than mean and standard deviation.
`intervals`	If `TRUE` columns for the lower and upper bounds of posterior uncertainty intervals are included.
`prob`	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.

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)

## End(Not run)