Package 'ggpower'

Effect-size helper functions

Description

Helpers used by the GUI effect-size drawer and by scripting workflows.

Usage

effect_size_d(mean_h1, mean_h0 = 0, sd)
effect_size_f(eta2)
effect_size_f2(r2)
effect_size_f2_increase(r2_full, r2_reduced)
effect_size_h(p1, p2)
effect_size_q(r1, r2)
effect_size_w(p0, p1)
eta2_from_f(f)
odds_ratio_from_probs(p0, p1)
r2_from_f2(f2)

Arguments

mean_h1, mean_h0	Means used to compute Cohen's d.
sd	Common standard deviation.
eta2	Eta-squared value.
r2, r2_full, r2_reduced	R-squared values; r2 is also the second correlation in effect_size_q().
p0, p1, p2	Probabilities or probability vectors.
r1	First correlation in effect_size_q().
f, f2	Cohen effect-size values.

Value

A numeric effect-size or converted variance-explained value.

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Format a ggpower result as structured HTML for Shiny UI

Description

Renders metric cards, input/output blocks, and notes for the Shiny app.

Usage

format_result_html(x)

Arguments

x	A ggpower_result object.

Value

A shiny.tag list suitable for renderUI.

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Evaluate a ggpower calculator script

Description

Evaluates distribution-function calculator expressions, including helpers such as zcdf(), tinv(), ncfcdf(), and binocdf().

Usage

ggpower_calculator(script)

Arguments

script

Character calculator script with arithmetic, assignments, comments, and supported distribution helper functions.

Value

The value of the final expression.

Examples

ggpower_calculator("x <- 2^3\nx + zinv(.975)")

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Create a ggpower result object

Description

Creates the common result object used by the scriptable API and Shiny GUI.

Usage

ggpower_result(test, analysis, inputs, outputs, notes = character(),
  distribution = list())

Arguments

test	Character label for the selected test.
analysis	Character label for the selected analysis mode.
inputs	Named list of input parameters.
outputs	Named list of computed output parameters.
notes	Character vector with method notes or assumptions.
distribution	Named list describing the H0/H1 distributions.

Value

An object of class ggpower_result.

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Plot Power Curve for a One-Sample t-Test

Description

This function creates a ggplot2 power curve for a one-sample t test.

Usage

ggpower_t_one_sample(d, alpha = 0.05, n_range = seq(20, 100, by = 5),
  tails = "two")

Arguments

d	Numeric. The effect size (d).
alpha	Numeric. The significance level (default 0.05).
n_range	Numeric vector. A vector of total sample sizes (default is seq(20, 100, by = 5)).
tails	Character. "two" or "one".

Value

A ggplot object showing the power curve.

Examples

# Plot power curve for d = 0.5 over sample sizes from 20 to 100
ggpower_t_one_sample(d = 0.5, alpha = 0.05, n_range = seq(20, 100, by = 5))

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List supported statistical power tests

Description

Lists the tests available to power_compute().

Usage

ggpower_tests(domain = NULL, module = NULL)

Arguments

domain	Optional character vector to filter by domain (general, biomarker, pharma).
module	Optional character vector to filter by app module (workspace, biomarker, clinical).

Value

A data frame describing tests available to power_compute().

Examples

ggpower_tests()
ggpower_tests(module = "biomarker")

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Plot Power Curve for a Two-Sample t-Test

Description

This function creates a ggplot2 power curve for a two-sample t test.

Usage

ggpower_ttest(d, alpha = 0.05, n_range = seq(10, 100, by = 5),
  tails = "two")

Arguments

d	Numeric. The effect size (Cohen's d).
alpha	Numeric. The significance level (default 0.05).
n_range	Numeric vector. A vector of sample sizes per group (default is seq(10, 100, by = 5)).
tails	Character. "two" or "one".

Value

A ggplot object showing the power curve.

Examples

# Create a power curve for d = 0.5 over a range of sample sizes per group
ggpower_ttest(d = 0.5, alpha = 0.05, n_range = seq(10, 100, by = 5))

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Plot H0 and H1 distributions

Description

Builds a publication-ready distribution overlay for a computed power-analysis result.

Usage

plot_distribution(result)

Arguments

result

A ggpower_result object.

Value

A ggplot object.

Examples

result <- power_compute("t_one_sample", "post_hoc", d = 0.5, n = 40)
plot_distribution(result)

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Plot a power curve

Description

Builds a publication-ready power curve for a selected ggpower test.

Usage

plot_power_curve(test, n_values, analysis = "post_hoc", ...)

Arguments

test	Character test id.
n_values	Numeric vector of total sample sizes.
analysis	Power analysis mode used for fixed parameters.
...	Test-specific fixed parameters.

Value

A ggplot object.

Examples

plot_power_curve("t_one_sample", n_values = c(20, 30, 40), d = 0.5)

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Compute statistical power analyses

Description

Runs a power analysis using the shared ggpower compute engine. The function supports classical test families and analysis modes.

Usage

power_compute(test, analysis = "post_hoc", ...)

Arguments

test	Character test id. Use ggpower_tests() to list available ids.
analysis	One of "a_priori", "compromise", "criterion", "post_hoc", or "sensitivity".
...	Test-specific input parameters.

Value

A ggpower_result object.

Examples

power_compute("t_one_sample", "a_priori", d = 0.625, alpha = 0.05,
  power = 0.95, tails = "one")

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Compute Power for a One-Sample t-Test

Description

Calculates the power for a one-sample t-test given the effect size (d), total sample size (n), and significance level (alpha).

Usage

power_t_one_sample(d, n, alpha = 0.05, tails = "two")

Arguments

d	Numeric. The effect size (difference from the constant divided by sigma).
n	Integer. Total sample size.
alpha	Numeric. The significance level (default is 0.05).
tails	Character. "two" for a two-tailed test or "one" for a one-tailed test.

Value

Numeric. The computed power (1 - beta).

Examples

# Calculate power for an effect size of 0.5 with n = 40 subjects
power_t_one_sample(d = 0.5, n = 40, alpha = 0.05)

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Compute power for a paired-samples t-test

Description

Computes achieved power for a paired-samples t-test using the noncentral t kernel.

Usage

power_t_paired(d, n, alpha = 0.05, tails = "two")

Arguments

d	Numeric paired-samples effect size dz.
n	Integer number of pairs.
alpha	Numeric significance level.
tails	Character, "two" or "one".

Value

Numeric power.

Examples

power_t_paired(d = 0.5, n = 40)

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Compute Power for a Two-Sample t-Test (Equal Sample Sizes)

Description

This function calculates the power for a two-sample t-test when the two groups have equal sample sizes.

Usage

power_t_two_sample(d, n_per_group, alpha = 0.05, tails = "two", n2 = NULL)

Arguments

d	Numeric. The effect size (Cohen's d).
n_per_group	Integer. The sample size per group.
alpha	Numeric. The significance level (default is 0.05).
tails	Character. "two" for a two-tailed test or "one" for a one-tailed test.
n2	Optional second-group sample size. If omitted, equal group sizes are used.

Value

Numeric. The computed power (1 - beta).

Examples

# Compute power for an effect size d = 0.5 with 30 subjects per group
power_t_two_sample(d = 0.5, n_per_group = 30)

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Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(),
  enableBookmarking = NULL,
  uiPattern = "/",
  ...
)

Arguments

onStart	A function that will be called before the app is actually run. This is only needed for shinyAppObj, since in the shinyAppDir case, a global.R file can be used for this purpose.
options	Named options that should be passed to the runApp call (these can be any of the following: "port", "launch.browser", "host", "quiet", "display.mode" and "test.mode"). You can also specify width and height parameters which provide a hint to the embedding environment about the ideal height/width for the app.
enableBookmarking	Can be one of "url", "server", or "disable". The default value, NULL, will respect the setting from any previous calls to enableBookmarking(). See enableBookmarking() for more information on bookmarking your app.
uiPattern	A regular expression that will be applied to each GET request to determine whether the ui should be used to handle the request. Note that the entire request path must match the regular expression in order for the match to be considered successful.
...	arguments to pass to golem_opts. See '?golem::get_golem_options' for more details.

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Save a ggpower plot

Description

Exports publication-ready ggpower plots through ggplot2::ggsave().

Usage

save_power_plot(plot, filename, width = 7, height = 5, dpi = 320)

save_distribution_plot(plot, filename, width = 7, height = 5, dpi = 320)

Arguments

plot	A ggplot object.
filename	Output filename.
width, height	Plot dimensions.
dpi	Resolution for raster outputs.

Value

The filename invisibly.

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Publication-ready ggpower theme

Description

Provides consistent typography, spacing, and grid styling for ggpower figures.

Usage

theme_ggpower(base_size = 12, base_family = "")

Arguments

base_size	Base font size.
base_family	Base font family.

Value

A ggplot2 theme.

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