Description Usage Arguments Value Note See Also Examples
These methods tidy the estimates from
brmsfitobjects
(fitted model objects from the brms package) into a summary.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## S3 method for class 'brmsfit'
tidy(
x,
parameters = NA,
effects = c("fixed", "ran_pars"),
robust = FALSE,
conf.int = TRUE,
conf.level = 0.95,
conf.method = c("quantile", "HPDinterval"),
fix.intercept = TRUE,
...
)
## S3 method for class 'brmsfit'
glance(x, looic = FALSE, ...)
## S3 method for class 'brmsfit'
augment(x, data = stats::model.frame(x), newdata = NULL, se.fit = TRUE, ...)

x 
Fitted model object from the brms package. See

parameters 
Names of parameters for which a summary should be
returned, as given by a character vector or regular expressions.
If 
effects 
A character vector including one or more of 
robust 
Whether to use median and median absolute deviation of the posterior distribution, rather than mean and standard deviation, to derive point estimates and uncertainty 
conf.int 
If 
conf.level 
Defines the range of the posterior uncertainty conf.int,
such that 
conf.method 
method for computing confidence intervals ("quantile" or "HPDinterval") 
fix.intercept 
rename "Intercept" parameter to "(Intercept)", to match behaviour of other model types? 
... 
Extra arguments, not used 
looic 
Should the LOO Information Criterion (and related info) be
included? See 
data 
data frame 
newdata 
new data frame 
se.fit 
return standard errors of fit? 
All tidying methods return a data.frame
without rownames.
The structure depends on the method chosen.
When parameters = NA
, the effects
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 effects = "fixed"
, only populationlevel
effects are returned.
When effects = "ran_vals"
, only grouplevel 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 effects = "ran_pars"
selects the
standard deviations and correlations of the grouplevel parameters.
If conf.int = TRUE
, columns for the lower
and
upper
bounds of the posterior conf.int computed.
The names ‘fixed’, ‘ran_pars’, and ‘ran_vals’ (corresponding to "nonvarying", "hierarchical", and "varying" respectively in previous versions of the package), while technically inappropriate in a Bayesian setting where "fixed" and "random" effects are not welldefined, are used for compatibility with other (frequentist) mixed model types.
At present, the components of parameter estimates are separated by parsing the column names of posterior_samples
(e.g. r_patient[1,Intercept]
for the random effect on the intercept for patient 1, or b_Trt1
for the fixed effect Trt1
. We try to detect underscores in parameter names and warn, but detection may be imperfect.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  ## original model
## Not run:
brms_crossedRE < brm(mpg ~ wt + (1cyl) + (1+wtgear), data = mtcars,
iter = 500, chains = 2)
## End(Not run)
if (.Platform$OS.type!="windows" && require("brms")) {
## too slow on Windows, skip (>5 seconds on rdevelwindows)
## load stored object
load(system.file("extdata", "brms_example.rda", package="broom.mixed"))
fit < brms_crossedRE
tidy(fit)
tidy(fit, parameters = "^sd_", conf.int = FALSE)
tidy(fit, effects = "fixed", conf.method="HPDinterval")
tidy(fit, effects = "ran_vals")
tidy(fit, effects = "ran_pars", robust = TRUE)
# glance method
glance(fit)
## this example will give a warning that it should be run with
## reloo=TRUE; however, doing this will fail
## because the \code{fit} object has been stripped down to save space
suppressWarnings(glance(fit, looic = TRUE, cores = 1))
head(augment(fit))
}

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