bss.dt.nbPearson: Bayesian sample size in a decision-theoretic approach for the...

In eliardocosta/ssdet: Sample size determination in frequentist and Bayesian approaches

Description

Bayesian sample size in a decision-theoretic approach for the negative binomial/Pearson Type VI model

Usage

bss.dt.nbPearson(lf, lam0, theta0, phi, w, c, rho = NULL, gam = NULL,
  nmax = 100, nrep = 10, lrep = 50, plot = TRUE, ...)

Arguments

lf	1 or 2, representing the loss function used.
lam0	A positive real number representing the prior expected value for the prior gamma distribution.
theta0	A positive real number representing the shape parameter for the prior gamma distribution.
phi	A positive real number representing a scale parameter of the prior distribution.
w	A positive real number representing the aliquot volume.
c	A positive real number representing the cost of colect one aliquot.
rho	A number in (0, 1). The probability of the credible interval is $1-rho$. Only for lost function 1.
gam	A positive real number connected with the credible interval when using lost function 2.
nmax	A positive integer representing the maximum number for compute the Bayes risk. Default is 100.
nrep	A positive integer representing the number of samples taken for each $n$.
lrep	A positive integer representing the number of samples taken for $S_n$.
plot	Boolean. If TRUE (default) it plot the estimated Bayes risks and the fitted curve.
...	Currently ignored.

Value

An integer representing the sample size.

eliardocosta/ssdet documentation built on Dec. 14, 2021, 6:27 a.m.