Description Usage Arguments Details Value Author(s) References
View source: R/jackstraw_cluster.R
Test the cluster membership using a userdefined clustering algorithm
1 2 3 4 
dat 
a data matrix with 
k 
a number of clusters. 
cluster 
a vector of cluster assignments. 
centers 
a matrix of all cluster centers. 
algorithm 
a clustering algorithm to use, where an output must include 'cluster' and 'centers'. For exact specification, see 
s 
a number of “synthetic” null variables. Out of 
B 
a number of resampling iterations. 
noise 
specify a parametric distribution to generate a noise term. If 
covariate 
a model matrix of covariates with 
verbose 
a logical specifying to print the computational progress. By default, 
seed 
a seed for the random number generator. 
... 
optional arguments to control the clustering algorithm. 
The clustering algorithms assign m
rows into K
clusters. This function enable statistical
evaluation if the cluster membership is correctly assigned. Each of m
pvalues refers to
the statistical test of that row with regard to its assigned cluster.
Its resampling strategy accounts for the overfitting characteristics due to direct computation of clusters from the observed data
and protects against an anticonservative bias.
The user is expected to explore the data with a given clustering algorithm and
determine the number of clusters k
.
Furthermore, provide cluster
and centers
as given by applying algorithm
onto dat
.
The rows of centers
correspond to k
clusters, as well as available levels in cluster
.
This function allows you to specify a parametric distribution of a noise term. It is an experimental feature.
jackstraw_cluster
returns a list consisting of
F.obs 

F.null 
F null statistics between null variables and cluster centers, from the jackstraw method. 
p.F 

Neo Christopher Chung [email protected]
Chung (2018) Statistical significance for cluster membership. biorxiv, doi:10.1101/248633 https://www.biorxiv.org/content/early/2018/01/16/248633
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