fit-methods {Rtreemix}R Documentation

Method for fitting mutagenetic trees mixture model to a given dataset

Description

Function for fitting a mutagenetic trees mixture model to a given dataset data. The dataset and the number of trees K have to be specified. The function estimates K-oncogenetic trees mixture model from the specified data by using an EM-like learning algorithm. The first tree component of the model has a star topology and is referred to as the noise component.

Usage

## S4 method for signature 'RtreemixData, numeric':
fit(data, K, ...)

Arguments

data An RtreemixData object giving the dataset used for learning the trees mixture model.
K An integer larger than 0 specifying the number of branchings in the mixture model.
... no.start.sol is an integer larger than 0 specifying the number of starting solutions for the k-means algorithm. The default value is 100. eps is a numeric giving the minimum conditional probability to include edge. The default value is 0.01. weighing is a logical specifying whether to use special weights log(Pr(v)) for the edges (root, v). The default value is FALSE. equal.edgeweights is a logical specifying whether to use equal edge weights in the noise component. The default value is TRUE. seed is a positive integer specifying the random generator seed. The default value is (-1) and then the time is used as a random generator. noise is a logical indicating the presence of a noise (star) component in the fitted mixture model. It is mostly relevant for models with a single tree component, since it is assumed that mixture models with at least two components always have the noise as a first component.

Details

When K = 1 and noise = FALSE a single mutagenetic tree is fit to the data. When K = 1 and noise = TRUE a star mutagenetic tree is fit to the data. If K > 1 the first mutagenetic tree is always the star, i.e. the case K > 1 and noise = FALSE is not possible.

Value

The method returns an RtreemixModel object that represents the K-trees mixture model learned from the given dataset.

Author(s)

Jasmina Bogojeska

References

Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.

See Also

RtreemixData-class, RtreemixModel-class, generate-methods, bootstrap-methods, confIntGPS-methods

Examples

## Create an RtreemixData object from a randomly generated RtreemixModel object.
rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
data <- sim(model = rand.mod, no.draws = 300)
show(data)

## Create an RtreemixModel object by fitting model to the given data.
mod <- fit(data = data, K = 3, equal.edgeweights = TRUE, noise = TRUE)
show(mod)
## See the number of tree components in the mixture model.
numTrees(mod)
## See the weights of the branchings from the fitted mixture model.
Weights(mod)
## See a specific tree component k.
getTree(object = mod, k = 2)

[Package Rtreemix version 1.2.0 Index]