size.mean | R Documentation |
This function performs sample size determination the one-sample and two-sample t-tests, proportions, and Pearson product-moment correlation coefficients based on precision requirements (i.e., type-I-risk, type-II-risk and an effect size).
size.mean(delta, sample = c("two.sample", "one.sample"),
alternative = c("two.sided", "less", "greater"),
alpha = 0.05, beta = 0.1, write = NULL, append = TRUE,
check = TRUE, output = TRUE)
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)
size.cor(rho, delta,
alternative = c("two.sided", "less", "greater"),
alpha = 0.05, beta = 0.1, write = NULL, append = TRUE,
check = TRUE, output = TRUE)
delta |
a numeric value indicating the minimum mean difference to
be detected, |
sample |
a character string specified in the function |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
alpha |
a numeric value indicating the type-I-risk, |
beta |
a numeric value indicating the type-II-risk, |
write |
a character string naming a text file with file extension
|
append |
logical: if |
check |
logical: if |
output |
logical: if |
pi |
a numeric value specified in the function |
rho |
a numeric value specified in the function |
correct |
logical: if |
Takuya Yanagida takuya.yanagida@univie.ac.at,
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.
test.t
, prop.test
, cor.test
,
cor.matrix
#----------------------------------------------------------------------------
# Example 1: One- and two-sample t-test
# Example 1a: One-sample t-test
# H0: mu = mu.0, H1: mu != mu.0
# alpha = 0.05, beta = 0.2, delta = 0.5
size.mean(delta = 0.5, sample = "one.sample",
alternative = "two.sided", alpha = 0.05, beta = 0.2)
# Example 1b: One-sided two-sample test
# H0: mu.1 >= mu.2, H1: mu.1 < mu.2
# alpha = 0.01, beta = 0.1, delta = 1
size.mean(delta = 1, sample = "two.sample",
alternative = "less", alpha = 0.01, beta = 0.1)
#----------------------------------------------------------------------------
# Example 2: One- and two-sample test for proportions
# Example 2a: 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 2b: 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)
#----------------------------------------------------------------------------
# Example 3: Testing the Pearson product-moment correlation coefficient
# 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)
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