distribution.oncotree {Oncotree} | R Documentation |
distribution.oncotree
calculates the joint distribution
of the events defined by the tree, while marginal.distr
calculates the marginal probability of occurrence of each event.
distribution.oncotree(otree, with.probs = TRUE, with.errors=FALSE, edge.weights=if (with.errors) "estimated" else "observed") marginal.distr(otree, with.errors = TRUE, edge.weights=if (with.errors) "estimated" else "observed")
otree |
An object of class oncotree . |
with.probs |
A logical value specifying if only the set of possible outcomes should be returned (if TRUE), or the associated probabilities of occurrence as well. |
with.errors |
A logical value specifying whether false positive and negative error rates should be incorporated into the distribution. |
edge.weights |
A choice of whether the observed or estimated
edge transition probabilities should be used in the calculation
of probabilities. See oncotree.fit for explanation
of the difference. By default, estimated edge transition probabilies
if with.errors=TRUE and the observed ones if
with.errors=FALSE . |
For distribution.oncotree
: a data frame each row of which
gives a possible outcome.
For marginal.distr
: a named numeric vector - the names
are the event names (+ ‘Root’) and the values are the
corresponding marginal probability of occurrence.
Aniko Szabo
data(ov.cgh) ov.tree <- oncotree.fit(ov.cgh) #joint distribution jj <- distribution.oncotree(ov.tree, edge.weights="obs") head(jj) jj.eps <- distribution.oncotree(ov.tree, with.errors=TRUE) head(jj.eps) #marginal distribution marginal.distr(ov.tree, with.error=FALSE) #marginal distribution calculated from the joint apply(jj[1:ov.tree$nmut], 2, function(x){sum(x*jj$Prob)}) ##Same with errors incorporated #marginal distribution marginal.distr(ov.tree, with.error=TRUE) #marginal distribution calculated from the joint apply(jj.eps[1:ov.tree$nmut], 2, function(x){sum(x*jj.eps$Prob)})