generic.t.test: Statistical Power for the Generic T-Test

In pwrss: Statistical Power and Sample Size Calculation Tools

Statistical Power for the Generic T-Test

Description

Calculates power for the generic T-Test with (optional) Type 1 and Type 2 error plots.

Usage

power.t.test(ncp, null.ncp = 0, df, alpha = 0.05,
             alternative = c("two.sided", "one.sided", "two.one.sided"),
             plot = TRUE, verbose = TRUE, pretty = FALSE)

Arguments

ncp	non-centrality parameter for the alternative.
null.ncp	non-centrality parameter for the null. When alternative = "two.one.sided", the function expects two values in the form c(lower, upper). If a single value is provided, it is interpreted as the absolute bound and automatically expanded to c(-value, +value).
df	degrees of freedom.
alpha	type 1 error rate, defined as the probability of incorrectly rejecting a true null hypothesis, denoted as \alpha.
alternative	character; direction or type of the hypothesis test: "one.sided", "two.sided", or "two.one.sided". "two.one.sided" is used for equivalence and minimal effect testing.
plot	logical; FALSE switches off Type 1 and Type 2 error plot. TRUE by default.
verbose	logical; whether the output should be printed on the console. TRUE by default.
pretty	logical; whether the output should show Unicode characters (if encoding allows for it). FALSE by default.

Value

df	degrees of freedom.
ncp	non-centrality parameter under alternative.
ncp.null	non-centrality parameter under null.
t.alpha	critical value(s).
power	statistical power (1-\beta).

Examples

# two-sided
# power defined as the probability of observing a test statistic
# greater than the positive critical value OR
# less than the negative critical value
power.t.test(ncp = 1.96, df = 100, alpha = 0.05,
             alternative = "two.sided")

# one-sided
# power is defined as the probability of observing a test statistic
# greater than the critical value
power.t.test(ncp = 1.96, df = 100, alpha = 0.05,
             alternative = "one.sided")

# equivalence
# power is defined as the probability of observing a test statistic
# greater than the upper critical value (for the lower bound) AND
# less than the lower critical value (for the upper bound)
power.t.test(ncp = 0, df = 100,
             null.ncp = c(-2, 2), alpha = 0.05,
             alternative = "two.one.sided")

# minimal effect testing
# power is defined as the probability of observing a test statistic
# greater than the upper critical value (for the upper bound) OR
# less than the lower critical value (for the lower bound).
power.t.test(ncp = 2, df = 100,
             null.ncp = c(-1, 1), alpha = 0.05,
             alternative = "two.one.sided")

pwrss documentation built on Sept. 16, 2025, 9:11 a.m.