MESS (version 0.5.12)

power_t_test: Power calculations for one and two sample t tests with unequal sample size

Description

Compute power of test, or determine parameters to obtain target power for equal and unequal sample sizes.

Usage

power_t_test(
  n = NULL,
  delta = NULL,
  sd = 1,
  sig.level = 0.05,
  power = NULL,
  ratio = 1,
  sd.ratio = 1,
  type = c("two.sample", "one.sample", "paired"),
  alternative = c("two.sided", "one.sided"),
  df.method = c("welch", "classical"),
  strict = TRUE
)

Value

Object of class power.htest, a list of the arguments (including the computed one) augmented with method and note elements.

Arguments

n

Number of observations (in the smallest group if two groups)

delta

True difference in means

sd

Standard deviation

sig.level

Significance level (Type I error probability)

power

Power of test (1 minus Type II error probability)

ratio

The ratio n2/n1 between the larger group and the smaller group. Should be a value equal to or greater than 1 since n2 is the larger group. Defaults to 1 (equal group sizes). If ratio is set to NULL (i.e., find the ratio) then the ratio might be smaller than 1 depending on the desired power and ratio of the sd's.

sd.ratio

The ratio sd2/sd1 between the standard deviations in the larger group and the smaller group. Defaults to 1 (equal standard deviations in the two groups)

type

Type of t test

alternative

One- or two-sided test

df.method

Method for calculating the degrees of default. Possibilities are welch (the default) or classical.

strict

Use strict interpretation in two-sided case. Defaults to TRUE unlike the standard power.t.test function.

Author

Claus Ekstrom

Details

Exactly one of the parameters n, delta, power, sd, sig.level, ratio sd.ratio must be passed as NULL, and that parameter is determined from the others. Notice that the last two have non-NULL defaults so NULL must be explicitly passed if you want to compute them.

The default strict = TRUE ensures that the power will include the probability of rejection in the opposite direction of the true effect, in the two-sided case. Without this the power will be half the significance level if the true difference is zero.

See Also

power.t.test, power_prop_test, power.prop.test

Examples

# Sampling with a ratio of 1:4
power_t_test(delta=300, sd=450, power=.8, ratio=4)

# Equal group sizes but different sd's
# The sd in the second group is twice the sd in the second group
power_t_test(delta=300, sd=450, power=.8, sd.ratio=2)

# Fixed group one size to 50 individuals, but looking for the number of individuals in the
# second group. Different sd's with twice the sd in the larger group
power_t_test(n=50, delta=300, sd=450, power=.8, ratio=NULL, sd.ratio=2)