project.data: Project high-dimensional data on two-dimensional plane by...
In microbiome/microbiomeold: Tools for microbiome analysis
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
Project high-dimensional data on two-dimensional plane by various methods
Usage
1
project.data(amat, type = "PCA")
Arguments
amat
data matrix (samples x features)
type
projection type
(options: PCA, MDS.classical, MDS.nonmetric, Sammon)
Value
projected data matrix
Author(s)
Contact: Leo Lahti microbiome-admin@googlegroups.com
References
D. Hand and H. Mannila and P. Smyth:
Principles of Data Mining. MIT Press. Cambridge, MA, US (2001).
To cite microbiome R package, see citation('microbiome')
Examples
1
2
data(peerj32)
xy <- project.data(peerj32$microbes[,1:3])
microbiome/microbiomeold documentation built on May 22, 2019, 9:57 p.m.
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Description Usage Arguments Value Author(s) References Examples
Description
Project high-dimensional data on two-dimensional plane by various methods
Usage
1 | project.data(amat, type = "PCA")
|
Arguments
amat |
data matrix (samples x features) |
type |
projection type (options: PCA, MDS.classical, MDS.nonmetric, Sammon) |
Value
projected data matrix
Author(s)
Contact: Leo Lahti microbiome-admin@googlegroups.com
References
D. Hand and H. Mannila and P. Smyth: Principles of Data Mining. MIT Press. Cambridge, MA, US (2001).
To cite microbiome R package, see citation('microbiome')
Examples
1 2 | data(peerj32)
xy <- project.data(peerj32$microbes[,1:3])
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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