\HeaderA{gps-methods}{Methods for predicting the GPS of given dataset by
using a given mutagenetic trees mixture model}{gps.Rdash.methods}
\aliasA{gps}{gps-methods}{gps}
\aliasA{gps,RtreemixModel,matrix-method}{gps-methods}{gps,RtreemixModel,matrix.Rdash.method}
\aliasA{gps,RtreemixModel,missing-method}{gps-methods}{gps,RtreemixModel,missing.Rdash.method}
\aliasA{gps,RtreemixModel,RtreemixData-method}{gps-methods}{gps,RtreemixModel,RtreemixData.Rdash.method}
\keyword{methods}{gps-methods}
\keyword{survival}{gps-methods}
\begin{Description}\relax
These functions compute the genetic progression score (GPS) of each
sample in the given \code{data} by performing a waiting time
simulation along the branchings of the mixture model \code{model}. The
model has to be specified. If a dataset is missing a GPS for all
possible patterns is calculated. The number of events of the samples
in \code{data} equals the number of genetic events in the \code{model}.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S4 method for signature 'RtreemixModel, RtreemixData':
gps(model, data, ...)
## S4 method for signature 'RtreemixModel, matrix':
gps(model, data, ...)
## S4 method for signature 'RtreemixModel, missing':
gps(model, data, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{model}] An object of the class \code{RtreemixModel} specifying
the mutagenetic trees mixture model used for deriving the GPS values. 
The model should NOT have more than 20 genetic events.
\item[\code{data}] An \code{RtreemixData} object or a 0-1 \code{matrix}
containing the samples (patterns of genetic events) for which the GPS values
are to be calculated. The length of each of them has to be equal
to the number of genetic events in the \code{model}.
\item[\code{...}] \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 simulations. Its default value is 10.
\code{seed} is a positive \code{integer} specifying the random generator
seed. Its default value is (-1) and then the time is used as a
random generator.

\end{ldescription}
\end{Arguments}
\begin{Value}
The function returns an object from the \code{RtreemixGPS} class that
containes the calculated GPS values, the model used for the
computation, the data, and so on (see
\code{\LinkA{RtreemixGPS-class}{RtreemixGPS.Rdash.class}}). The GPS values are represented as a
\code{numeric} vector with length equal to the number of samples in \code{data}.
\end{Value}
\begin{Section}{Methods}
\describe{
\item[model = "RtreemixModel", data = "RtreemixData", ...] A method for calculating
the GPS values of the data given as \code{RtreemixData} object.
\item[model = "RtreemixModel", data = "matrix", ...] A method for calculating
the GPS values of the data given as 0-1 \code{matrix}.
\item[model = "RtreemixModel", data = "missing", ...] A method for calculating
the GPS values of the set of all possible patterns.    
}
\end{Section}
\begin{Note}\relax
The mixture model used for deriving the GPS values 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.
\end{Note}
\begin{Author}\relax
Jasmina Bogojeska
\end{Author}
\begin{References}\relax
Estimating cancer survival and clinical outcome based on
genetic tumor progression scores, J. Rahnenf\"urer et al.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{RtreemixGPS-class}{RtreemixGPS.Rdash.class}}, \code{\LinkA{RtreemixData-class}{RtreemixData.Rdash.class}},
\code{\LinkA{RtreemixModel-class}{RtreemixModel.Rdash.class}},
\code{\LinkA{fit-methods}{fit.Rdash.methods}}, \code{\LinkA{confIntGPS-methods}{confIntGPS.Rdash.methods}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## 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 RtreemixModel object by fitting model to the given data.
#mod <- fit(data = data, K = 2, equal.edgeweights = TRUE, noise = TRUE)
#show(mod)

## Create an RtreemixGPS object by calculating the GPS for all possible patterns.
#modGPS.all <- gps(model = mod, no.sim = 1000) ## time consuming copmutations
#show(modGPS.all)

## See the GPS values for all possible data.
#GPS(modGPS.all) ## time consuming copmutations

## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod.
#modGPS <- gps(model = mod, data = data, no.sim = 1000)
#show(modGPS) ## time consuming copmutations

## See the GPS values for data.
#GPS(modGPS) ## time consuming copmutations
\end{ExampleCode}
\end{Examples}

