ss.SLR {powerMediation} | R Documentation |
Calculate sample size for testing slope for simple linear regression.
ss.SLR(power, lambda.a, sigma.x, sigma.y, n.lower = 2.01, n.upper = 1e+30, alpha = 0.05, verbose = TRUE)
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.
|
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})
n |
sample size. |
res.uniroot |
results of optimization to find the optimal sample size. |
The test is a two-sided test. Code for one-sided tests will be added later.
Weiliang Qiu stwxq@channing.harvard.edu
Dupont, W.D. and Plummer, W.D.. Power and Sample Size Calculations for Studies Involving Linear Regression. Controlled Clinical Trials. 1998;19:589-601.
minEffect.SLR
,
power.SLR
,
power.SLR.rho
,
ss.SLR.rho
.
ss.SLR(power=0.8, lambda.a=0.8, sigma.x=0.2, sigma.y=0.5, alpha = 0.05, verbose = TRUE)