minEffect.SLR {powerMediation}R Documentation

Minimum detectable slope

Description

Calculate minimal detectable slope given sample size and power for simple linear regression.

Usage

minEffect.SLR(n, power, sigma.x, sigma.y, alpha = 0.05, verbose = TRUE)

Arguments

n sample size.
power power for testing if \lambda=0 for the simple linear regression y_i=\gamma+\lambda x_i + \epsilon_i, \epsilon_i\sim N(0, \sigma_{e}^2).
sigma.x standard deviation of the predictor.
sigma.y standard deviation of the outcome.
alpha type I error rate.
verbose logical. TRUE means printing minimum absolute detectable effect; FALSE means not printing minimum absolute detectable effect.

Details

The test is for testing the null hypothesis \lambda=0 versus the alternative hypothesis \lambda\neq 0 for the simple linear regressions:

y_i=\gamma+\lambda x_i + \epsilon_i, \epsilon_i\sim N(0, \sigma^2_{e})

Value

lambda.a minimum absolute detectable effect.
res.uniroot results of optimization to find the optimal minimum absolute detectable effect.

Note

The test is a two-sided test. Code for one-sided tests will be added later.

Author(s)

Weiliang Qiu stwxq@channing.harvard.edu

References

Dupont, W.D. and Plummer, W.D.. Power and Sample Size Calculations for Studies Involving Linear Regression. Controlled Clinical Trials. 1998;19:589-601.

See Also

power.SLR, power.SLR.rho, ss.SLR, ss.SLR.rho.

Examples

  minEffect.SLR(n=100, power=0.8, sigma.x=0.2, sigma.y=0.5, 
    alpha = 0.05, verbose = TRUE)

[Package powerMediation version 0.0.6 Index]