draws-OncoBayes2 | R Documentation |
blrmfit
or blrm_trial
to draws
objectsTransform a blrmfit
or blrm_trial
object to a format supported by the
posterior package.
## S3 method for class 'blrmfit'
as_draws(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrmfit'
as_draws_matrix(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrmfit'
as_draws_array(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrmfit'
as_draws_df(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrmfit'
as_draws_list(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrmfit'
as_draws_rvars(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws_matrix(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws_array(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws_df(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws_list(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
## S3 method for class 'blrm_trial'
as_draws_rvars(x, variable = NULL, regex = FALSE, inc_warmup = FALSE, ...)
x |
A |
variable |
A character vector providing the variables to extract. By default, all variables are extracted. |
regex |
Logical; Should variable be treated as a (vector of)
regular expressions? Any variable in |
inc_warmup |
Should warmup draws be included? Defaults to
|
... |
Arguments passed to individual methods (if applicable). |
To subset iterations, chains, or draws, use the
subset_draws
method after
transforming the input object to a draws
object.
The function is experimental as the set of exported posterior variables are subject to updates.
draws
subset_draws
## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
OncoBayes2.MC.save_warmup = FALSE
)
# fit an example model. See documentation for "combo2" example
example_model("combo2")
post <- as_draws(blrmfit)
## Recover user set sampling defaults
options(.user_mc_options)
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