size.mean {seqtest}

Sample size determination for testing the arithmetic mean

Description

This function performs sample size computation for the one-sample and two-sample t-test based on precision requirements (i.e., type-I-risk, type-II-risk and an effect size).

Usage

size.mean(theta, sample = c("two.sample", "one.sample"),
          alternative = c("two.sided", "less", "greater"),
          alpha = 0.05, beta = 0.1, output = TRUE)

Arguments

theta

relative minimum difference to be detected, \theta.

sample

a character string specifying one- or two-sample t-test, must be one of "two.sample" (default) or "one.sample".

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

alpha

type-I-risk, \alpha.

beta

type-II-risk, \beta.

output

logical: if TRUE, output is shown.

Value

Returns an object of class size with following entries:

call	function call
type	type of the test (i.e., arithmetic mean)
spec	specification of function arguments
res	list with the result, i.e., optimal sample size

Author(s)

Takuya Yanagida [e-mail],

References

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. New York: John Wiley & Sons.

Rasch, D., Pilz, J., Verdooren, L. R., & Gebhardt, G. (2011). Optimal experimental design with R. Boca Raton: Chapman & Hall/CRC.

See Also

seqtest.mean, size.prop, size.cor, print.size

Examples

#--------------------------------------
# Two-sided one-sample test
# H0: mu = mu.0, H1: mu != mu.0
# alpha = 0.05, beta = 0.2, theta = 0.5

size.mean(theta = 0.5, sample = "one.sample",
          alternative = "two.sided", alpha = 0.05, beta = 0.2)

#--------------------------------------
# One-sided one-sample test
# H0: mu <= mu.0, H1: mu > mu.0
# alpha = 0.05, beta = 0.2, theta = 0.5

size.mean(theta = 0.5, sample = "one.sample",
          alternative = "greater", alpha = 0.05, beta = 0.2)

#--------------------------------------
# Two-sided two-sample test
# H0: mu.1 = mu.2, H1: mu.1 != mu.2
# alpha = 0.01, beta = 0.1, theta = 1

size.mean(theta = 1, sample = "two.sample",
          alternative = "two.sided", alpha = 0.01, beta = 0.1)

#--------------------------------------
# One-sided two-sample test
# H0: mu.1 <= mu.2, H1: mu.1 > mu.2
# alpha = 0.01, beta = 0.1, theta = 1

size.mean(theta = 1, sample = "two.sample",
          alternative = "greater", alpha = 0.01, beta = 0.1)