optsamplesize_iHpm | R Documentation |
Compute the optimal sample size for the improved trimmed weighted Hochberg procedure with allowance for different data maturities
optsamplesize_iHpm(
alpha,
k,
betavec,
deltavec,
rho,
maturity,
ninterval = c(2, 2000),
alphalist = seq(from = 0, to = alpha, by = 0.005)
)
alpha |
the significance level |
k |
a pre-specified constant in the improved trimmed weighted Hochberg procedure |
betavec |
a numeric vector of two values, including one minus the desired power for rejecting H1 and one minus the desired power for rejecting H2 |
deltavec |
a numeric vector of two values representing the effect sizes for the two hypotheses |
rho |
the correlation coefficient between two test statistics |
maturity |
a numeric vector of two values representing the data maturities for the two hypotheses |
ninterval |
a vector containing the end-points of the interval to be searched for optimal sample size |
alphalist |
a vector of discrete alpha values |
the overall optimal sample size for the improved trimmed weighted Hochberg procedure with allowance for different data maturities
Jiangtao Gou
Fengqing Zhang
Gou, J., Chang, Y., Li, T., and Zhang, F. (2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.
rrr <- 2
alpha <- 0.025
k <- 0.6761
ninterval <- c(2, 1000)
betavec <- c(0.10, 0.10)
rho <- 0.4
maturity <- c(0.65, 0.70)
psivec <- c(0.67, 0.73)
thetavec <- log(psivec)
deltavec <- (-thetavec)*sqrt(rrr)/(1+rrr)
result <- optsamplesize_iHpm(alpha = alpha, k = k,
betavec = betavec, deltavec = deltavec,
rho = rho, maturity = maturity,
ninterval = ninterval)
result$nopt
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