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

    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.

### 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.

### Note

When you have too few data samples always use the default value `TRUE` for the `equal.edgeweights`. Like this you make sure that all possible patterns (sets of events) have non-zero probabilities. If they don't the fitting procedure will not be completed and you will get an error!

### 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 1.48.0 Index]