\name{confIntGPS-methods}
\docType{methods}

\alias{confIntGPS}
\alias{confIntGPS-methods}
\alias{confIntGPS,RtreemixData,numeric-method}

\title{Method for calculating GPS values and their 95\% bootstrap
confidence intervals}

\description{
The method first calculates the genetic progression score (GPS) for the
patterns in a given dataset \code{data} based on a fitted mutagenetic trees
mixture model with \code{K} components. The \code{data} and \code{K}
have to be specified. Then, it derives a 95\% confidence intervals for
the GPS values with bootstrap analysis.
}

\usage{
confIntGPS(data, K, \dots)
}

\arguments{
\item{data}{An \code{RtreemixData} object containing the samples
(patterns of genetic events) for which the GPS values and their
bootstrap confidence intervals are to be calculated. The number
of genetic events should NOT be greater than 20.}
\item{K}{An \code{integer} larger than 0 specifying the number of
branchings in the mixture model.}
\item{...}{
\code{sampling.mode} is a \code{character} that specifies the
sampling mode ("constant" or "exponential") used in the waiting time
simulations. Its default value is "exponential".
\code{sampling.param} is a \code{numeric} that specifies the
sampling parameter corresponding to the sampling mode given by
\code{sampling.mode}. Its default value is 1.
\code{no.sim} is an \code{integer} larger than 0 giving the number of
iterations for the waiting time simulation. Its default values is
10000.
\code{B} is an \code{integer} larger than 0 specifying the number of
bootstrap samples used in the bootstrap analysis. Its default value
is 1000.
\code{equal.star} is a \code{logical} specifying whether to use
equal edge weights in the noise component. The default value is
\code{TRUE}. When you have few data samples always use its default value
(\code{TRUE}) to ensure nonzero probabilities for all possible
patterns (sets of events).
}
}

\value{
The function returns an object from the \code{RtreemixGPS} class that
containes the calculated GPS values, their 95\% confidence intervals,
the model used for the computation, the data, and so on (see
\code{\link{RtreemixGPS-class}}). The GPS values are represented as a
\code{numeric} vector with length equal to the number of samples in
\code{data}. Their corresponding confidence intervals are given in a
matrix with two columns.
}

\note{
The data for which the GPS values and their corresponding
confidence intervals are to be calculated should not have more
than 20 genetic events. The reason for this is that the number of all possible patterns
for which the GPS values are calculated during a computationally intensive simulations
is in this case $2^20$. This demands too much memory.
The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package.
For trying out the code please copy it and uncomment it.
}

\author{Jasmina Bogojeska }

\seealso{
}

\examples{
## Create an RtreemixData object from a randomly generated RtreemixModel object.
#rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8))
#data <- sim(model = rand.mod, no.draws = 400)

## Create an RtreemixGPS object by calculating GPS values for a given dataset
## and their 95\% confidence intervals using the bootstrap method.
#modGPS2 <- confIntGPS(data = data, K = 2, B = 100) ## time consuming computation
#show(modGPS2)

## See the GPS values for the object modGPS2 and their confidence intervals.
#GPS(modGPS2)
#gpsCI(modGPS2)

## See data.
#getData(modGPS2)
}

\keyword{methods}