RSDT_power | R Documentation |
Calculates approximate power, given sample size, using Monte Carlo simulation, for specified case scores, means and standard deviations for the control sample. The means and standard deviations defaults to 0 and 1 respectively, so if no other values are given the case scores are interpreted as deviations from the mean in standard deviations. Hence, the effect size of the dissociation (Z-DCC) would in that case be the difference between the two case scores.
RSDT_power(
case_a,
case_b,
mean_a = 0,
mean_b = 0,
sd_a = 1,
sd_b = 1,
r_ab = 0.5,
sample_size,
alternative = c("two.sided", "greater", "less"),
alpha = 0.05,
nsim = 10000
)
case_a |
A single value from the expected case observation on task A. |
case_b |
A single value from the expected case observation on task B. |
mean_a |
The expected mean from the control sample on task A. Defaults to 0. |
mean_b |
The expected mean from the control sample on task B. Defaults to 0. |
sd_a |
The expected standard deviation from the control sample on task A. Defaults to 1. |
sd_b |
The expected standard deviation from the control sample on task B. Defaults to 1. |
r_ab |
The expected correlation between the tasks. Defaults to 0.5 |
sample_size |
The size of the control sample, vary this parameter to see how the sample size affects power. |
alternative |
The alternative hypothesis. A string of either "two.sided" (default) or "one.sided". |
alpha |
The specified Type I error rate. This can also be varied, with effects on power. Defaults to 0.05. |
nsim |
The number of simulations to run. Higher number gives better accuracy, but low numbers such as 10000 or even 1000 are usually sufficient for the purposes of this calculator. |
Returns a single value approximating the power of the test for the given parameters.
RSDT_power(case_a = -3, case_b = -1, mean_a = 0, mean_b = 0,
sd_a = 1, sd_b = 1, r_ab = 0.5, sample_size = 20, nsim = 1000)
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