fit-methods: Method for fitting mutagenetic trees mixture model to a given...
In Rtreemix: Rtreemix: Mutagenetic trees mixture models.

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

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.

1	fit(data, K, ...)

`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.

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.

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

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!

Jasmina Bogojeska

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

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

## 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)