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pcaMethods | ||
========== | ||
# pcaMethods | ||
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R package for performing PCA with applications to missing value imputation | ||
R package for performing | ||
[principal component analysis PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) | ||
with applications to missing value imputation. Provides a single | ||
interface to performing PCA using | ||
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- **SVD:** a fast method which is also the standard method in R but | ||
which is not applicable for data with missing values. | ||
- **NIPALS:** an iterative fast method which is applicable also to | ||
data with missing values. | ||
- **PPCA:** Probabilistic PCA which is applicable also on data with | ||
missing values. Missing value estimation is typically better than | ||
NIPALS but also slower to compute and uses more memory. A port to R | ||
of the | ||
[implementation by Jakob Verbeek](http://lear.inrialpes.fr/~verbeek/software.php). | ||
- **BPCA:** Bayesian PCA which performs very well in the presence of | ||
missing values but is slower than PPCA. A port of the | ||
[matlab implementation by Shigeyuki Oba](http://ishiilab.jp/member/oba/tools/BPCAFill.html). | ||
- **NLPCA:** Non-linear PCA which can find curves in data and in | ||
presence of such can perform accurate missing value | ||
estimation. [Matlab port of the implementation by Mathias Scholz](http://www.nlpca.org/). | ||
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[pcaMethods is a Bioconductor package](http://www.bioconductor.org/packages/release/bioc/html/pcaMethods.html) | ||
and you can install it by | ||
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```R | ||
source("https://bioconductor.org/biocLite.R") | ||
biocLite("pcaMethods") | ||
``` | ||
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## Documentation | ||
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```R | ||
browseVignettes("pcaMethods") | ||
?<function_name> | ||
``` |
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