tabulate_eval {simulator} | R Documentation |
Make a table of a metric for each pair of models and methods
Description
Each row of the table corresponds to a different model and each column
to a different method. The metric must be a scalar. The way in which
standard error is shown (or not shown) is controlled by se_format
.
Usage
tabulate_eval(
object,
metric_name,
method_names = NULL,
caption = NULL,
center_aggregator = NULL,
spread_aggregator = NULL,
se_format = c("Paren", "PlusMinus", "None"),
output_type = "latex",
format_args = list(nsmall = 0, digits = NULL, scientific = FALSE),
na_string = "--",
bold = c("None", "Smallest", "Largest")
)
Arguments
object |
an object of class |
metric_name |
the name of a metric to tabulate. Must be scalar valued. |
method_names |
character vector indicating methods to include in table. If NULL, then will include all methods found in object's evals. |
caption |
caption of plot. If NULL, then default caption used; if FALSE then no caption (and returns tabular without table). |
center_aggregator |
When NULL (which is default), the sample mean
aggregator is used. User can write specialized aggregators (see
definition of class |
spread_aggregator |
When NULL (which is default), the standard error
of the sample mean is used. User can write specialized aggregators (see
definition of class |
se_format |
format of the standard error |
output_type |
see |
format_args |
arguments to pass to the function |
na_string |
what to write in table in place of NA |
bold |
puts in bold the value that is smallest/largest for each model |
Details
Uses knitr
's function kable
to put table in various formats,
including latex, html, markdown, etc.
Examples
## Not run:
# suppose previously we had run the following:
sim <- new_simulation(name = "normal-example",
label = "Normal Mean Estimation",
dir = tempdir()) %>%
generate_model(make_my_example_model,
n = list(10, 20, 30),
vary_along = "n") %>%
simulate_from_model(nsim = 50, index = 1:3) %>%
run_method(my_example_method) %>%
evaluate(my_example_loss)
# then we could plot this
tabulate_eval(sim, "myloss")
## End(Not run)