jackstraw

Statistical Inference for Unsupervised Learning

Test for association between the observed data and their systematic patterns of variations, that are often extracted by unsupervised learning. Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. This allows one to, for example, obtain principal components (PCs) and conduct rigorous statistical testing for association between observed variables and PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and other algorithms, finds subpopulations among the observed variables. The jackstraw test can estimate statistical significance of cluster membership, so that one can evaluate the strength of membership assignments. This package also includes several related methods to support statistical inference and probabilistic feature selection for unsupervised learning.

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Description file content

Package
jackstraw
Type
Package
Title
Statistical Inference for Unsupervised Learning
Version
1.2
Date
2018-08-08
Author
Neo Christopher Chung , John D. Storey , Wei Hao
Maintainer
Neo Christopher Chung
Description
Test for association between the observed data and their systematic patterns of variations, that are often extracted by unsupervised learning. Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. This allows one to, for example, obtain principal components (PCs) and conduct rigorous statistical testing for association between observed variables and PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and other algorithms, finds subpopulations among the observed variables. The jackstraw test can estimate statistical significance of cluster membership, so that one can evaluate the strength of membership assignments. This package also includes several related methods to support statistical inference and probabilistic feature selection for unsupervised learning.
LazyData
true
Depends
R (>= 3.0.0)
URL
BugReports
https://github.com/ncchung/jackstraw/issues
biocViews
Imports
corpcor, cluster, ClusterR, qvalue, methods, lfa, stats
Suggests
parallel, knitr, rmarkdown
License
GPL-2
RoxygenNote
6.0.1
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2018-08-07 20:48:23 UTC; nc
Repository
CRAN
Date/Publication
2018-08-07 21:30:02 UTC

install.packages('jackstraw')

1.2

2 months ago

https://github.com/ncchung/jackstraw

Neo Christopher Chung

GPL-2

Depends on

R (>= 3.0.0)

Imports

corpcor, cluster, ClusterR, qvalue, methods, lfa, stats

Suggests

parallel, knitr, rmarkdown

Discussions