gps-methods: Methods for predicting the GPS of given dataset by using a...
In Rtreemix: Rtreemix: Mutagenetic trees mixture models.

Description Usage Arguments Value Methods Note Author(s) References See Also Examples

These functions compute the genetic progression score (GPS) of each sample in the given data by performing a waiting time simulation along the branchings of the mixture model 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 data equals the number of genetic events in the model.

1	gps(model, data, ...)

`model`	An object of the class `RtreemixModel` specifying the mutagenetic trees mixture model used for deriving the GPS values. The model should NOT have more than 20 genetic events.
`data`	An `RtreemixData` object or a 0-1 `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 `model`.
`...`	`sampling.mode` is a `character` that specifies the sampling mode ("constant" or "exponential") used in the waiting time simulations. Its default value is "exponential". `sampling.param` is a `numeric` that specifies the sampling parameter corresponding to the sampling mode given by `sampling.mode`. Its default value is 1. `no.sim` is an `integer` larger than 0 giving the number of iterations for the waiting time simulations. Its default value is 10. `seed` is a positive `integer` specifying the random generator seed. Its default value is (-1) and then the time is used as a random generator.

The function returns an object from the RtreemixGPS class that containes the calculated GPS values, the model used for the computation, the data, and so on (see RtreemixGPS-class). The GPS values are represented as a numeric vector with length equal to the number of samples in data.

model = "RtreemixModel", data = "RtreemixData", ...: A method for calculating the GPS values of the data given as RtreemixData object.
model = "RtreemixModel", data = "matrix", ...: A method for calculating the GPS values of the data given as 0-1 matrix.
model = "RtreemixModel", data = "missing", ...: A method for calculating the GPS values of the set of all possible patterns.

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.

Jasmina Bogojeska

Estimating cancer survival and clinical outcome based on genetic tumor progression scores, J. Rahnenf\"urer et al.

RtreemixGPS-class, RtreemixData-class, RtreemixModel-class, fit-methods, confIntGPS-methods

## 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