Rtreemix (version 1.34.0)

fit-methods: 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

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. When you have few data samples always use its default value (TRUE) to ensure nonzero probabilities for all possible patterns (sets of events). 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.

Value

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

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.

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)