ss.SLR {powerMediation}R Documentation

Sample size for testing slope for simple linear regression

Description

Calculate sample size for testing slope for simple linear regression.

Usage

ss.SLR(power, lambda.a, sigma.x, sigma.y, n.lower = 2.01, n.upper = 1e+30, alpha = 0.05, verbose = TRUE)

Arguments

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).
lambda.a regression coefficient in 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.
n.lower lower bound for the sample size.
n.upper upper bound for the sample size.
alpha type I error rate.
verbose logical. TRUE means printing sample size; FALSE means not printing sample size.

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

n sample size.
res.uniroot results of optimization to find the optimal sample size.

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

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

Examples

  ss.SLR(power=0.8, lambda.a=0.8, sigma.x=0.2, sigma.y=0.5, 
    alpha = 0.05, verbose = TRUE)

[Package powerMediation version 0.0.6 Index]