RtreemixGPS-class {Rtreemix} | R Documentation

## Class "RtreemixGPS"

### Description

A class for describing the genetic progression scores (GPS) of a given set of patterns resulting from a waiting time simulation along the edges of the tree components of a given mutagenetic trees mixture model. It also contains GPS confidence intervals derived with the bootstrap method.

### Objects from the Class

Objects can be created by calls of the form `new("RtreemixGPS", Data, Model, SamplingMode, SamplingParam, GPS, gpsCI)`. The `RtreemixGPS` class contains the GPS values each assigned to the corresponding pattern from the dataset given by `Data` (the parent class). The GPS values are derived in a waiting time simulation for a specified sampling mode and its corresponding sampling parameter. Moreover, this class specifies the confidence intervals for the GPS values derived with the bootstrap method.

The `Data` is an `RtreemixData` object that specifies the patterns for which the GPS values are calculated.

The `Model` is an `RtreemixModel` object that specifies the mutagenetic trees mixture model used for deriving the GPS values.

The `SamplingMode` is a `character` that specifies the sampling mode ("constant" or "exponential") used in the waiting time simulations.

The `SamplingParam` is a `numeric` that specifies the sampling parameter corresponding to the sampling mode given by `SamplingMode`.

The `GPS` is a numeric `vector` that specifies the GPS value of each pattern in the given dataset `Data`. Its length equals the number of patterns in `Data`.

The `gpsCI` is a numeric `matrix` that specifies the confidence intervals for each GPS value in the vector `GPS`. The number of rows equals the number of patients in `Data` and the number of columns equals 2. The first column gives the lower bound and the second column gives the upper bound of each confidence interval.

### Slots

`Model`:

  Object of class `"RtreemixModel"`.

`SamplingMode`:

  Object of class `"character"`. It can have one of the two possible values: "constant" or "exponential".

`SamplingParam`:

  Object of class `"numeric"`.

`GPS`:

  Object of class `"numeric"`. The length of `GPS` must be equal to the number of patterns in the parent `RtreemixData` object.

`gpsCI`:

  Object of class `"matrix"`. It number of columns has to be 2 and the number of rows has to be equal to the length of `GPS`.

### Extends

Class `"RtreemixData"`, directly.

### Methods

GPS

  `signature(object = "RtreemixGPS")`: A method for obtaining the GPS values corresponding to the patterns in the parent `RtreemixData` object.

Model

  `signature(object = "RtreemixGPS")`: A method for obtaining the model used for deriving the GPS values.

SamplingMode

  `signature(object = "RtreemixGPS")`: A method for obtaining the sampling mode ("constant" or "exponential") used for the waiting time simulations.

SamplingParam

  `signature(object = "RtreemixGPS")`: A method for obtaining the sampling parameter corresponding to the specified `SamplingMode`.

getData

  `signature(object = "RtreemixGPS")`: A method for obtaining the set of patterns for which the GPS values are calculated.

gpsCI

  `signature(object = "RtreemixGPS")`: A method for obtaining the GPS confidence intervals.

### Note

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(s)

Jasmina Bogojeska

### References

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

### See Also

`RtreemixData-class`, `RtreemixModel-class`, `gps-methods`, `fit-methods`, `confIntGPS-methods`

### Examples

    ## Generate a random RtreemixModel object with 3 components and 9 genetic events.
    #mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
    #show(mod)
    ## Generate an artificial dataset from the model mod.
    #data <- sim(model = mod, no.draws = 300)
    #show(data)

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

    ## See the slots from the RtreemixGPS object.
    #Model(modGPS)
    #SamplingMode(modGPS)
    #SamplingParam(modGPS)
    #GPS(modGPS)
    ## See data.
    #getData(modGPS)

    ## 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 = 10)
    #show(modGPS2)

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

[Package Rtreemix version 1.50.0 Index]