Plot the rejection probability of a hypothesis test.
Source:R/visualizer-lib-inference.R
plot_reject_prob.Rd
Plot the probability of rejecting the null hypothesis across various levels of significance.
Usage
plot_reject_prob(
fit_results,
eval_results = NULL,
evaluator_name = NULL,
vary_params = NULL,
feature_col = NULL,
show_features = NULL,
show_identity_line = FALSE,
show = c("line"),
...
)
Arguments
- fit_results
A tibble, as returned by the
fit
method.- eval_results
A list of result tibbles, as returned by the
evaluate
method.- evaluator_name
Name of
Evaluator
containing results to plot. To compute the evaluation summary results from scratch or if the evaluation summary results have not yet been evaluated, set toNULL
.- vary_params
A vector of parameter names that are varied across in the
Experiment
.- feature_col
A character string identifying the column in
fit_results
with the feature names or IDs.- show_features
Vector of feature names corresponding to features to display in the plot. If
NULL
(default), shows all features in the data.- show_identity_line
Logical indicating whether or not to plot the y = x line.
- show
Character vector with elements being one of "boxplot", "point", "line", "bar", "errorbar", "ribbon" indicating what plot layer(s) to construct.
- ...
Additional arguments to pass to
plot_eval_summary()
. This includes arguments for plotting and for passing intoeval_reject_prob()
.
Value
If interactive = TRUE
, returns a plotly
object if
plot_by
is NULL
and a list of plotly
objects if
plot_by
is not NULL
. If interactive = FALSE
, returns
a ggplot
object if plot_by
is NULL
and a list of
ggplot
objects if plot_by
is not NULL
.
See also
Other inference_funs:
eval_reject_prob()
,
eval_testing_curve_funs
,
eval_testing_err_funs
,
plot_testing_curve()
,
plot_testing_err()
Examples
# generate example fit_results data
fit_results <- tibble::tibble(
.rep = rep(1:2, times = 2),
.dgp_name = c("DGP1", "DGP1", "DGP2", "DGP2"),
.method_name = c("Method"),
feature_info = lapply(
1:4,
FUN = function(i) {
tibble::tibble(
# feature names
feature = c("featureA", "featureB", "featureC"),
# estimated p-values
pval = 10^(sample(-3:0, 3, replace = TRUE))
)
}
)
)
# generate example eval_results data
eval_results <- list(
`Reject Prob.` = eval_reject_prob(
fit_results,
nested_data = "feature_info",
feature_col = "feature",
pval_col = "pval"
)
)
# create bar plot using pre-computed evaluation results
plt <- plot_reject_prob(fit_results = fit_results,
eval_results = eval_results,
evaluator_name = "Reject Prob.",
feature_col = "feature")
# or alternatively, create the same plot without pre-computing evaluation results
plt <- plot_reject_prob(fit_results,
nested_data = "feature_info",
feature_col = "feature",
pval_col = "pval")
# can customize plot (see plot_eval_summary() for possible arguments)
plt <- plot_reject_prob(fit_results = fit_results,
eval_results = eval_results,
evaluator_name = "Reject Prob.",
facet_formula = NULL,
plot_by = "feature")