View source: R/power_correlations.R
power_z_cor | R Documentation |
Calculates the approximate power for a z-test based on a Pearson product-moment correlation.
power_z_cor(
n = NULL,
rho = NULL,
power = NULL,
null = 0,
alpha = NULL,
alternative = c("two.sided", "less", "greater", "equivalence")
)
powerTOSTr(alpha, statistical_power, N, low_eqbound_r, high_eqbound_r)
n |
number of observations. |
rho |
true correlation value (alternative hypothesis). |
power |
statistical power (1-beta). |
null |
the null hypothesis value. |
alpha |
a priori alpha-level (i.e., significance level). |
alternative |
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater", "less", or "equivalence" (TOST). You can specify just the initial letter. |
statistical_power |
Deprecated. desired power (e.g., 0.8) |
N |
Deprecated. number of pairs (e.g., 96) |
low_eqbound_r |
Deprecated. lower equivalence bounds (e.g., -0.3) expressed in a correlation effect size |
high_eqbound_r |
Deprecated. upper equivalence bounds (e.g., 0.3) expressed in a correlation effect size |
An object of the class power.htest. This will include the sample size (n), power, beta (1-power), alpha (significance level), null value(s), alternative hypothesis, and a text string detailing the method.
powerTOSTr
has been replaced by the power_z_cor
function. The function is only retained for historical purposes.
Other Correlations:
boot_cor_test()
,
corsum_test()
,
plot_cor()
,
z_cor_test()
Other power:
power_eq_f()
,
power_t_TOST()
## Sample size for alpha = 0.05, 90% power, equivalence bounds of
## r = -0.1 and r = 0.1, assuming true effect = 0
#powerTOSTr(alpha=0.05, statistical_power=0.9, low_eqbound_r=-0.1, high_eqbound_r=0.1)
power_z_cor(alternative = "equivalence", alpha = .05, null = .1, power = .9, rho = 0)
## Sample size for alpha = 0.05, N=536, equivalence bounds of
## r = -0.1 and r = 0.1, assuming true effect = 0
#powerTOSTr(alpha=0.05, N=536, low_eqbound_r=-0.1, high_eqbound_r=0.1)
power_z_cor(alternative = "equivalence", alpha = .05, null = .1, n = 536, rho = 0)
## Equivalence bounds for alpha = 0.05, N=536, statistical power of
## 0.9, assuming true effect = 0
#powerTOSTr(alpha=0.05, N=536, statistical_power=0.9)
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