\name{gps-methods}
 \docType{methods}
 
 \alias{gps}
 \alias{gps-methods}
 \alias{gps,RtreemixModel,RtreemixData-method}
 \alias{gps,RtreemixModel,matrix-method}
 \alias{gps,RtreemixModel,missing-method}
 

 \title{Methods for predicting the GPS of given dataset by
   using a given mutagenetic trees mixture model}
 \description{
   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}.    
 }
 
 \usage{

 gps(model, data, \dots)                   

 }
 
 \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.}    
 }}
 
 \arguments{
   \item{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{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{\dots}{ 
     \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.
   }

 }
 
 \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{\link{RtreemixGPS-class}}). The GPS values are represented as a
   \code{numeric} vector with length equal to the number of samples in \code{data}.
 }
 
 \references{Estimating cancer survival and clinical outcome based on
   genetic tumor progression scores, J. Rahnenf\"urer et al. }
 
 \note{

     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.
 } 
 
 \author{Jasmina Bogojeska}
  
 \seealso{
   \code{\link{RtreemixGPS-class}}, \code{\link{RtreemixData-class}},
   \code{\link{RtreemixModel-class}},
   \code{\link{fit-methods}}, \code{\link{confIntGPS-methods}}
 }
 
 \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 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

 }
 

 \keyword{methods}
 \keyword{survival}