stability.sim(no.trees = 3, no.events = 9, prob = c(0.2, 0.8), no.draws = 300, no.rands = 100, no.sim = 1)integer larger than 2 giving the number of tree components
of the mixture models considered in the stability analysis. The
default value is 3.integer larger than 0 giving the number of genetic events of the mixture models
considered in the stability analysis.numeric vector of length 2 specifying the
boundaries for the edge weights of the randomly generated trees. The
first component of the vector (the lower boundary) must be smaller
than the second component (the upper boundary). The default value is (0.2, 0.8).integer larger than 0 giving the size of the
data sample drawn from the random models used for learning the
mixture models. The default value is 300.integer larger than 0 specifying the number
of random models used for calculating the p-values. The default value is 100.integer larger than 0 specifying the number of
iterations used for the waiting time simulations (a part of the GPS
calculation). The default value is 1.matrix with 4 columns and no.sim rows. The first two columns give the similarity
values and their corresponding p-values when the Euclidian distance
is used as a similarity measure for comparing the respective GPS
vectors. The last two columns depict the same results, but with the
rank correlation distance used as a similarity measure.
matrix with 6
columns and no.sim rows. Each two columns give the values of the comparissons between
the true and the fitted probability distributions and their
corresponding p-values, when using the cosine distance, the L1 distance, and the
Kullback-Leibler divergence as similarity measures.matrix with 2
columns and no.sim rows that give the value of the comparisson of the topologies
between the true and the corresponding fitted model and their
p-values. The similarity measure underlying the number of different
edges was used.comp3. However, the similarity measure for
comparing the tree topologies besides the number of distinct edges
includes the L1 distances of the level vectors of events. See
get.tree.levels.matrix where the columns correspond to the
true GPS vector from each simulation iteration. The matrix has
no.sim columns and no.draws rows.comp5, but the matrix contains the
fitted GPS values from each simulation iteration.list where each component corresponds to the
true models generated in each simulation iteration. the length of
the list is no.sim.comp7, but the list contains the fitted models.RtreemixData-class, RtreemixModel-class, RtreemixGPS-class,
RtreemixStats-class, fit-methods,
gps-methods, distribution-methods,
generate-methods, sim-methods,
L1.dist, Pval.dist,
comp.models, comp.trees,
get.tree.levels, kullback.leibler
## Stability analysis - a toy example
#stability.sim(no.trees = 3, no.rands = 5, no.sim = 4, no.draws = 300)
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