desired_ep_Ftest: 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 F-test of the previous study as input in the sample size planning process.

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 F-test of the previous study as input in the sample size planning process.

Usage

desired_ep_Ftest(
  Ftest,
  df1,
  df2,
  desired_ep = 0.8,
  alpha = 0.05,
  filter = 0,
  upper_null = 0,
  estimate_fixed = TRUE,
  future_fixed = TRUE
)

Arguments

Ftest	The F-test of the previous study.
df1	The numerator degrees of freedom.
df2	The denominator degrees of freedom.
desired_ep	The desired expected power for the future study.
alpha	The significance level. Default is α = .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 Cohen's f. 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

Examples

## Not run: 
desired_ep_Ftest(Ftest = 5.0, df1=2, df2=50, desired_ep = 0.80)

## End(Not run)

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