GrassmannOptim

This function is taken from the GrassmannOptim package
by Kofi Placid Adragni and Seongho Wu
https://cran.r-project.org/web/packages/GrassmannOptim/index.html

internal

Produces an economic evaluation of a sample of suitable variables of
cost and effectiveness / utility for two or more interventions,
e.g. from a Bayesian model in the form of MCMC simulations.
This package computes the most cost-effective alternative and
produces graphical summaries and probabilistic sensitivity analysis,
see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

Gianluca Baio

BCEA

Bayesian Cost Effectiveness Analysis

Andrea Berardi

Anna Heath

Nathan Green

GrassmannOptim function

<dl><dt>objfun</dt>
<dd>objfun</dd>
<dt>W</dt>
<dd>W</dd>
<dt>sim_anneal</dt>
<dd>sim_anneal</dd>
<dt>temp_init</dt>
<dd>temp_init</dd>
<dt>cooling_rate</dt>
<dd>cooling_rate</dd>
<dt>max_iter_sa</dt>
<dd>max_iter_sa</dd>
<dt>eps_conv</dt>
<dd>eps_conv</dd>
<dt>max_iter</dt>
<dd>max_iter</dd>
<dt>eps_grad</dt>
<dd>eps_grad</dd>
<dt>eps_f</dt>
<dd>eps_f</dd>
<dt>verbose</dt>
<dd>verbose</dd></dl>

Arguments

GrassmannOptim — GrassmannOptim

<dl>

<dt>objfun</dt>
<dd>objfun</dd>


<dt>W</dt>
<dd>W</dd>


<dt>sim_anneal</dt>
<dd>sim_anneal</dd>


<dt>temp_init</dt>
<dd>temp_init</dd>


<dt>cooling_rate</dt>
<dd>cooling_rate</dd>


<dt>max_iter_sa</dt>
<dd>max_iter_sa</dd>


<dt>eps_conv</dt>
<dd>eps_conv</dd>


<dt>max_iter</dt>
<dd>max_iter</dd>


<dt>eps_grad</dt>
<dd>eps_grad</dd>


<dt>eps_f</dt>
<dd>eps_f</dd>


<dt>verbose</dt>
<dd>verbose</dd>

</dl>

GrassmannOptim: GrassmannOptim

Description

Usage

Value

Arguments