size.prop: Sample Size Determination for Testing Proportions

Description

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

Usage

size.prop(pi = 0.5, delta, sample = c("two.sample", "one.sample"),
          alternative = c("two.sided", "less", "greater"),
          alpha = 0.05, beta = 0.1, correct = FALSE,
          write = NULL, append = TRUE, check = TRUE, output = TRUE)

Value

Returns an object of class misty.object, which is a list with following entries:

call: function call
type: type of analysis
data: matrix or data frame specified in x
args: specification of function arguments
result: list with the result, i.e., optimal sample size

Arguments

pi: a number indicating the true value of the probability under the null hypothesis (one-sample test), \(\pi\).0 or a number indicating the true value of the probability in group 1 (two-sample test), \(\pi\).1.
delta: minimum difference to be detected, \(\delta\).
sample: a character string specifying one- or two-sample proportion 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), "less" or "greater".
alpha: type-I-risk, \(\alpha\).
beta: type-II-risk, \(\beta\).
correct: a logical indicating whether continuity correction should be applied.
write: a character string naming a text file with file extension ".txt" (e.g., "Output.txt") for writing the output into a text file.
append: logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.
check: logical: if TRUE (default), argument specification is checked.
output: logical: if TRUE (default), output is shown.

Author

Takuya Yanagida takuya.yanagida@univie.ac.at,

References

Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical methods for rates and proportions (3rd ed.). John Wiley & Sons.

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

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

Examples

Run this code

#-------------------------------------------------------------------------------
# Example 1: Two-sided one-sample test
# H0: pi = 0.5, H1: pi != 0.5
# alpha = 0.05, beta = 0.2, delta = 0.2

size.prop(pi = 0.5, delta = 0.2, sample = "one.sample",
          alternative = "two.sided", alpha = 0.05, beta = 0.2)

#-------------------------------------------------------------------------------
# Example 2: Two-sided one-sample test
# H0: pi = 0.5, H1: pi != 0.5
# alpha = 0.05, beta = 0.2, delta = 0.2
# with continuity correction

size.prop(pi = 0.5, delta = 0.2, sample = "one.sample",
          alternative = "two.sided", alpha = 0.05, beta = 0.2,
          correct = TRUE)

#-------------------------------------------------------------------------------
# Example 3: One-sided one-sample test
# H0: pi <= 0.5, H1: pi > 0.5
# alpha = 0.05, beta = 0.2, delta = 0.2

size.prop(pi = 0.5, delta = 0.2, sample = "one.sample",
          alternative = "less", alpha = 0.05, beta = 0.2)

#-------------------------------------------------------------------------------
# Example 4: Two-sided two-sample test
# H0: pi.1 = pi.2 = 0.5, H1: pi.1 != pi.2
# alpha = 0.01, beta = 0.1, delta = 0.2

size.prop(pi = 0.5, delta = 0.2, sample = "two.sample",
          alternative = "two.sided", alpha = 0.01, beta = 0.1)

#-------------------------------------------------------------------------------
# Example 5: One-sided two-sample test
# H0: pi.1 <=  pi.1 = 0.5, H1: pi.1 > pi.2
# alpha = 0.01, beta = 0.1, delta = 0.2

size.prop(pi = 0.5, delta = 0.2, sample = "two.sample",
          alternative = "greater", alpha = 0.01, beta = 0.1)

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