test.trajectory: Tree Dimension Test

In TreeDimensionTest: Trajectory Presence and Heterogeneity in Multivariate Data

Tree Dimension Test

Description

Computes the statistical significance for the presence of trajectory in multivariate data.

Usage

test.trajectory(
  x,
  perm = 100,
  MST = c("boruvka", "exact"),
  dim.reduction = c("pca", "none")
)

Arguments

x	matrix of input data. Rows as observations and columns as features.
perm	number of simulations to compute null distribution parameters by maximum likelihood estimation.
MST	the MST algorithm to be used in test. There are two options: "exact" MST and "boruvka" which is approximate but faster for large samples.
dim.reduction	string parameter with value "pca" to perform dimensionality reduction or "none" to not perform dimensionality reduction before the test.

Details

If the input data is already after dimension reduction, use dim.reduction="none". The method is described in \insertCiteTenha:2022TreeDimensionTest.

Value

A list with the following components:

• tdt_measure The tree dimension value for the given input data

• statistic The S statistic calculated on the input data. S statistic is derived from tree dimension

• tdt_effect Effect size for tree dimension

• leaves Number of leaf/degree1 vertices in the MST of the data

• diameter The tree diameter of MST, where each edge is of unit length

• p.value The pvalue for the S statistic. Pvalue measures presence of trajectory in input x.

• original_dimension If "pca" is selected, the number of dimensions in the original dataset

• pca_components If "pca" is selected, the number of pca components selected after dimensionality reduction

• mst A vector of edges of the mst computed on x. Length of vector is always even.

References

\insertAllCited

TreeDimensionTest documentation built on March 18, 2022, 7:45 p.m.