Using a principal component constructed from the sample space, we simulate
null distributions with univariate Normal distributions using pcn_simulate.
Then a subset of these distributions is chosen using pcn_select.
Usage
pcn_simulate(data, n.sim = 50)
pcn_select(data.sim, cl, type = c("rep", "range"), int = 5)Arguments
- data
data matrix with rows as samples, columns as features
- n.sim
The number of simulated datasets to simulate
- data.sim
an object from
pcn_simulate- cl
vector of cluster memberships
- type
select either the representative dataset ("rep") or a range of datasets ("range")
- int
every
intdata sets from median-rankeddata.simare taken. Defaults to 5.
Value
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
ranks: Whentype = "range", ranks of each extracted dataset shownind: index of representative simulationdat: simulation data representation of all in pcNormal
