ttest.ppow: Compute the power using a one- or two-sample unpaired t-test...

In julianje/mcpa: Intuitive power analyses through monte carlo simulations

Description

ttest.ppow computes (via simulation) the power of an experiment that will be analyzed using a t-test. Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power. For an equivalent function that does not rely on pilot data see ttest.pow.

ttest.ppow computes (via simulation) the power of an experiment that will be analyzed using a t-test. Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power. For an equivalent function that does not rely on pilot data see ttest.pow.

Usage

ttest.ppow(x, y = NULL, n, r = 10000, alternative = c("two.sided", "less",
  "greater"), mu = NULL, alpha = 0.05, conf.level = 0.95)

ttest.ppow(x, y = NULL, n, r = 10000, alternative = c("two.sided", "less",
  "greater"), mu = NULL, alpha = 0.05, conf.level = 0.95)

Arguments

x	a data frame with two columns, or a list with pilot data.
y	a list with pilot data. When x is a list and y is not provided, a one-tailed t-test is used.
n	sample size.
r	number of simulations to compute power.
alternative	type of alternative hypothesis in binomial test. Must be "two.sided" (default), "greater", or "less".
mu	mean value according to null hypothesis (default = 0). Only used in one sample t-tests.
alpha	significance threshhold.
x	a data frame with two columns, or a list with pilot data.
y	a list with pilot data. When x is a list and y is not provided, a one-tailed t-test is used.

Value

The probability of finding p < α with the experiment description.

The probability of finding p < α with the experiment description.

See Also

ttest.pow, ttest.ppow, ttest.explore, and ttest.pexplore.

Examples

ttest.ppow(x=c(0, 5, 10), n=16) # Power for a one-sample t-test with n=16. Pilot data consists of three data points.
ttest.ppow(x=c(0, 5, 10), n=16, mu = -5) # Same as above, changing the avarege under the null to -5.
ttest.ppow(x=c(0, 5, 10), y=c(9, 3, 2, 1), n=30) # Power for a two-sample t-test with n=30 (per condition) using unbalanced pilot data.
ttest.ppow(x=c(0, 5, 10), n=16) # Power for a one-sample t-test with n=16. Pilot data consists of three data points.
ttest.ppow(x=c(0, 5, 10), n=16, mu = -5) # Same as above, changing the avarege under the null to -5.
ttest.ppow(x=c(0, 5, 10), y=c(9, 3, 2, 1), n=30) # Power for a two-sample t-test with n=30 (per condition) using unbalanced pilot data.

julianje/mcpa documentation built on May 13, 2019, 6:14 p.m.