Sample size determination for testing Pearson's correlation coefficient

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Description

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

Usage

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

Arguments

rho

a number indicating the correlation coefficient under the null hypothesis, ρ.0.

delta

minimum difference to be detected, δ.

alternative

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

alpha

type-I-risk, α.

beta

type-II-risk, β.

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., correlation coefficient)
spec specification of function arguments
res list with the result, i.e., optimal sample size

Author(s)

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

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

Examples

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#--------------------------------------
# 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)