Using a principal component constructed from the sample space, we simulate
null distributions with univariate Normal distributions using
Then a subset of these distributions is chosen using
pcn_simulate(data, n.sim = 50) pcn_select(data.sim, cl, type = c("rep", "range"), int = 5)
data matrix with rows as samples, columns as features
The number of simulated datasets to simulate
an object from
vector of cluster memberships
select either the representative dataset ("rep") or a range of datasets ("range")
int data sets from median-ranked
data.sim are taken.
Defaults to 5.
pcn_simulate returns a list of length
n.sim. Each element is a
simulated matrix using this "Principal Component Normal" (pcn) procedure.
pcn_select returns a list with elements
type = "range", ranks of each extracted dataset shown
ind: index of representative simulation
dat: simulation data representation of all in pcNormal