desired_ep_t: Determines the required sample size in a future study to...

In jbiesanz/fabs: Functions for Applied Behavioural Sciences

Determines the required sample size in a future study to achieve the desired expected power (ep) based on the uncertainty associated with an existing study. Uses the t-test of the previous study as input in the sample size planning process. Converts the t-test to the correlational metric, determines sample size for the prospective study, and provides the median confidence interval width for the correlation in the prospective study based on that sample size.

Description

Determines the required sample size in a future study to achieve the desired expected power (ep) based on the uncertainty associated with an existing study. Uses the t-test of the previous study as input in the sample size planning process. Converts the t-test to the correlational metric, determines sample size for the prospective study, and provides the median confidence interval width for the correlation in the prospective study based on that sample size.

Determines the expected power (ep) based on the uncertainty associated with an existing study. Uses the t-test of the previous study as input in the sample size planning process. Converts the t-test to the correlational metric, determines sample size for the prospective study, and provides the median confidence interval width for the correlation in the prospective study based on that sample size.

Usage

desired_ep_t(
  t,
  df,
  desired_ep = 0.8,
  alpha = 0.05,
  filter = 0,
  upper_null = 0,
  estimate_fixed = FALSE,
  future_fixed = FALSE
)

desired_ep_t(
  t,
  df,
  desired_ep = 0.8,
  alpha = 0.05,
  filter = 0,
  upper_null = 0,
  estimate_fixed = FALSE,
  future_fixed = FALSE
)

Arguments

t	The t-test of the previous study.
df	The degrees of freedom associated with the previous study
desired_ep	The desired expected power for the future study.
alpha	The signficance level. Default is .05.
filter	The filter value reflects the probability of nonsignificant results being filtered. filter = 0 means that there is no filtering and you would have observed nonsignificant results. filter = 1 means that only significant results are observed and you would never have seen nonsigificant results if they had occurred. Filtering is based on alpha = .05 and assumes that are have observed a significant result. Filtering is conducted by weighting (actually filtering) the posterior distribution. For instance, if filter = 1, then the posterior of the null (i.e., the noncentrality parameter is 0) is up to 20 times more likely than when the noncentrality parameter is very large. Setting filter > 0 slows estimation.
upper_null	Specifies the upper value of the composite null hypothesis in units of the correlation coefficient eqnr. The default value of upper_null = 0 keeps the point null hypothesis. A value of, for instance, upper_null = .05 would remove all posterior values between -.05 and .05 from consideration when calculating expected power.
estimate_fixed	Specifies whether the predictor in the regression model is either fixed or random. The default is FALSE for random predictors.
future_fixed	Specifies whether the future study will have fixed predictors.

Value

Returns (1) the sample size required for the future study to achieve the specified level of expected power. This reflects the uncertainty associated with the previous study and (2) the median 95 for the correlation in the prospective study.

Returns (1) the sample size required for the future study to achieve the specified level of expected power. This reflects the uncertainty associated with the previous study and (2) the median 95 for the correlation in the prospective study.

Examples

## Not run: 
desired_ep_t(t = 3.0, df=40, desired_ep = 0.80)

## End(Not run)
## Not run: 
desired_ep_t(t = 3.0, df=40, desired_ep = 0.80)

## End(Not run)

jbiesanz/fabs documentation built on July 15, 2022, 11:02 p.m.