pplot: Point-wise Uncertainty Assessment

In OTclust: Mean Partition, Uncertainty Assessment, Cluster Validation and Visualization Selection for Cluster Analysis

Point-wise Uncertainty Assessment

Description

Output both the numerical and graphical point-wise uncertainty assessment for each individual points. The return of clustCPS, visCPS or CPS can be directly used as the input.

Usage

pplot(result, method = 0)

Arguments

result	– the return from function clustCPS, visCPS or CPS.
method	– method for calculating point-wise uncertainty. Using posterior probability matrix is 0 and using topological information between clusters is 1. Default is using posterior probability matrix.

Value

a list, in which P is the posterior probability matrix that each sample below to the reference clusters, point_stab is the point-wise stability for each sample and v is the visualization of the point-wise stability.

Examples

# CPS analysis on selection of visualization methods
data(vis_pollen)
k1=kmeans(vis_pollen$vis,max(vis_pollen$ref))$cluster
k2=kmeans(vis_pollen$vis,max(vis_pollen$ref))$cluster
k=cbind(as.matrix(k1,ncol=1),as.matrix(k2,ncol=1))
c=CPS(vis_pollen$ref, vis_pollen$vis, pert=k)
# Point-wise Uncertainty Assessment
pplot(c)

OTclust documentation built on Oct. 6, 2023, 5:09 p.m.