RSDT_power {singcar} | R Documentation |
Power calculator for RSDT
Description
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.
Usage
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
)
Arguments
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. |
Value
Returns a single value approximating the power of the test for the given parameters.
Examples
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)