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