Description Usage Arguments Details Value Author(s) References Examples
View source: R/jackstraw_kmeans.R
Test the cluster membership for Kmeans clustering
1 2 
dat 
a matrix with 
kmeans.dat 
an output from applying 
s 
a number of “synthetic” null variables. Out of 
B 
a number of resampling iterations. 
center 
a logical specifying to center the rows. By default, 
covariate 
a model matrix of covariates with 
verbose 
a logical specifying to print the computational progress. By default, 
pool 
a logical specifying to pool the null statistics across all clusters. By default, 
seed 
a seed for the random number generator. 
... 
optional arguments to control the kmeans clustering algorithm (refers to 
Kmeans clustering 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 input data (dat
) must be of a class 'matrix'.
jackstraw_kmeans
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
1 2 3 4 5 6 7  ## Not run:
set.seed(1234)
dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE))
kmeans.dat < kmeans(dat, centers=2, nstart = 10, iter.max = 100)
jackstraw.out < jackstraw_kmeans(dat, kmeans.dat)
## End(Not run)

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