| fit-methods {Rtreemix} | R Documentation |
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.
## S4 method for signature 'RtreemixData, numeric': 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)