% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Model-class.R
\docType{class}
\name{Model-class}
\alias{.Model}
\alias{Model-class}
\title{Class for the model input}
\description{
This is the model class for single agent dose escalation,
from which all other specific models inherit. It inherits all slots
from \code{\linkS4class{GeneralModel}}.
}
\details{
The \code{datamodel} must obey the convention that the data input is
called exactly as in the \code{\linkS4class{Data}} class.
All prior distributions for parameters should be contained in the
model function \code{priormodel}. The background is that this can
be used to simulate from the prior distribution, before obtaining any
data.

The \code{dose} function has as first argument \code{prob}, a scalar
toxicity probability which is targeted. Additional arguments are model
parameters. Then it computes, using model parameter(s) (samples), the
resulting dose. Note that the model parameters are called exactly as in the
\code{model} and must be included in the \code{sample} vector. The vectors
of all samples for these parameters will then be supplied to the function.
So your function must be able to process vectors of the model parameters,
i.e. it must vectorize over them.

The \code{prob} function has as first argument \code{dose}, which is a
scalar dose. Additional arguments are model parameters. Then it computes,
using model parameter(s) (samples), the resulting probability of toxicity at
that dose. Again here, the function must vectorize over the model
parameters.

If you work with multivariate parameters, then please assume that your
the two functions receive either one parameter value as a row vector,
or a samples matrix where the rows correspond to the sampling index, i.e.
the layout is then nSamples x dimParameter.

Note that \code{dose} and \code{prob} are the inverse functions of each
other.
}
\section{Slots}{

\describe{
\item{\code{dose}}{a function computing the dose reaching a specific target
probability, based on the model parameters and additional prior settings
(see the details above)}

\item{\code{prob}}{a function computing the probability of toxicity for a specific
dose, based on the model parameters and additional prior settings (see the
details above)}
}}
\seealso{
\code{\linkS4class{LogisticNormal}},
\code{\linkS4class{LogisticLogNormal}},
\code{\linkS4class{LogisticLogNormalSub}},
\code{\linkS4class{LogisticKadane}},
\code{\linkS4class{DualEndpoint}}
}
\keyword{classes}

