View source: R/jackstraw_pam.R
jackstraw_pam  R Documentation 
Test the cluster membership for Partitioning Around Medoids (PAM)
jackstraw_pam(
dat,
pam.dat,
s = NULL,
B = NULL,
center = TRUE,
covariate = NULL,
verbose = FALSE,
pool = TRUE,
...
)
dat 
a matrix with 
pam.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, 
... 
optional arguments to control the kmeans clustering algorithm (refers to 
PAM assigns 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.
For a large dataset, PAM could be too slow. Consider using cluster::clara
and jackstraw::jackstraw_clara
.
The input data (dat
) must be of a class 'matrix'.
jackstraw_pam
returns a list consisting of
F.obs 

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

Neo Christopher Chung nchchung@gmail.com
Chung (2020) Statistical significance of cluster membership for unsupervised evaluation of cell identities. Bioinformatics, 36(10): 3107–3114 https://academic.oup.com/bioinformatics/article/36/10/3107/5788523
## Not run:
library(cluster)
dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE))
pam.dat < pam(dat, k=2)
jackstraw.out < jackstraw_pam(dat, pam.dat = pam.dat)
## End(Not run)
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