bootstrap-methods: Method for fitting a mutagenetic trees mixture model and...
In Rtreemix: Rtreemix: Mutagenetic trees mixture models.

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

This method fits an RtreemixModel to a given dataset and then analyzes its variance with the bootstrap method. The data and the number of trees K have to be specified.

1	bootstrap(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. `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. `B` is an `integer` larger than 0 specifying the number of bootstrap samples. Its default value is 1000. `conf.interval` is a `numeric` specifying the Confidence level for the intervals. Its default value is 0.05.

The function returns an object from the class RtreemixModel. This is the mixture model learned on the given data. Besides the edge weights it also contains their confidence intervals resulting from the bootstrap analysis. Confidence intervals for the mixture parameters are also comupted and available.

The bootstrap examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.

Jasmina Bogojeska

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

RtreemixData-class, RtreemixModel-class, fit-methods

## Create an RtreemixData object from a randomly generated RtreemixModel object.
#rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8))
#data <- sim(model = rand.mod, no.draws = 300)

## Create a RtreemixModel and analyze its variance with the bootstrap method.
#mod.boot <- bootstrap(data = data, K = 2, equal.edgeweights = TRUE, B = 10) ## time consuming computation

## See the confidence intervals for the mixture parameters (the weights).
#WeightsCI(mod.boot)
## See the confidence intervals of the conditional probabilities assigned to the edges.
#edgeData(getTree(mod.boot, 2), attr = "ci")