misty (version 0.4.5)

size.cor: Sample Size Determination for Testing Pearson's Correlation Coefficient

Description

This function performs sample size computation for testing Pearson's product-moment correlation coefficient based on precision requirements (i.e., type-I-risk, type-II-risk and an effect size).

Usage

size.cor(rho, delta, alternative = c("two.sided", "less", "greater"),
         alpha = 0.05, beta = 0.1, check = TRUE, output = TRUE)

Arguments

rho

a number indicating the correlation coefficient under the null hypothesis, \(\rho\).0.

delta

a numeric value indicating the minimum difference to be detected, \(\delta\).

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, \(\beta\).

check

logical: if TRUE, argument specification is checked.

output

logical: if TRUE, output is shown.

Value

Returns an object of class misty.object with following entries:

call	function call
type	type of the test (i.e., correlation coefficient)
args	specification of function arguments
result	list with the result, i.e., optimal sample size

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

size.mean, size.prop

Examples

# NOT RUN {
#--------------------------------------
# H0: rho = 0.3, H1: rho != 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2

size.cor(rho = 0.3, delta = 0.2, alpha = 0.05, beta = 0.2)

#--------------------------------------
# H0: rho <= 0.3, H1: rho > 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2

size.cor(rho = 0.3, delta = 0.2, alternative = "greater", alpha = 0.05, beta = 0.2)
# }