Science Score: 33.0%
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Low similarity (12.7%) to scientific vocabulary
Repository
Dimensionality reduction for binary data
Basic Info
Statistics
- Stars: 50
- Watchers: 8
- Forks: 9
- Open Issues: 12
- Releases: 0
Metadata Files
README.md
Logistic PCA
logisticPCA is an R package for dimensionality reduction of binary data, as described in Landgraf and Lee (2020) (preprint here).
Installation
To install R, visit r-project.org/.
The package can be installed by downloading from CRAN.
R
install.packages("logisticPCA")
To install the development version, first install devtools from CRAN. Then run the following commands.
```R
install.packages("devtools")
library("devtools") install_github("andland/logisticPCA") ```
Classes
Three types of dimensionality reduction are given. For all the functions, the user must supply the desired dimension k. The data must be an n x d matrix comprised of binary variables (i.e. all 0's and 1's).
Logistic PCA
logisticPCA() estimates the natural parameters of a Bernoulli distribution in a lower dimensional space. This is done by projecting the natural parameters from the saturated model. A rank-k projection matrix, or equivalently a d x k orthogonal matrix U, is solved for to minimize the Bernoulli deviance. Since the natural parameters from the saturated model are either negative or positive infinity, an additional tuning parameter m is needed to approximate them. You can use cv.lpca() to select m by cross validation. Typical values are in the range of 3 to 10.
mu is a main effects vector of length d and U is the d x k loadings matrix.
Logistic SVD
logisticSVD() estimates the natural parameters by a matrix factorization. mu is a main effects vector of length d, B is the d x k loadings matrix, and A is the n x k principal component score matrix.
Convex Logistic PCA
convexLogisticPCA() relaxes the problem of solving for a projection matrix to solving for a matrix in the k-dimensional Fantope, which is the convex hull of rank-k projection matrices. This has the advantage that the global minimum can be obtained efficiently. The disadvantage is that the k-dimensional Fantope solution may have a rank much larger than k, which reduces interpretability. It is also necessary to specify m in this function.
mu is a main effects vector of length d, H is the d x d Fantope matrix, and U is the d x k loadings matrix, which are the first k eigenvectors of H.
Methods
Each of the classes has associated methods to make data analysis easier.
print(): Prints a summary of the fitted model.fitted(): Fits the low dimensional matrix of either natural parameters or probabilities.predict(): Predicts the PCs on new data. Can also predict the low dimensional matrix of natural parameters or probabilities on new data.plot(): Either plots the deviance trace, the first two PC loadings, or the first two PC scores using the packageggplot2.
In addition, there are functions for performing cross validation.
cv.lpca(),cv.lsvd(),cv.clpca(): Run cross validation over the rows of the matrix to assess the fit ofmand/ork.plot.cv(): Plots the results of thecv()method.
Owner
- Name: Andrew Landgraf
- Login: andland
- Kind: user
- Location: Columbus, OH
- Website: http://andrewlandgraf.com
- Repositories: 31
- Profile: https://github.com/andland
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Andrew Landgraf | a****d@g****m | 95 |
| wrathematics | w****s@g****m | 3 |
| Andrew Landgraf | a****w@A****l | 2 |
| Andrew Landgraf | a****d | 2 |
| Drew Schmidt | s****t@m****u | 1 |
| U-Andrew-Desktop\Andrew | A****w@A****) | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 26
- Total pull requests: 1
- Average time to close issues: 5 months
- Average time to close pull requests: 1 day
- Total issue authors: 10
- Total pull request authors: 1
- Average comments per issue: 0.38
- Average comments per pull request: 1.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- andland (17)
- marsangar (1)
- drdna (1)
- novitch (1)
- jleecurrie (1)
- OliverPStuart (1)
- noahaus (1)
- ayanmandal120 (1)
- fangzhiwen2019 (1)
- mashu (1)
Pull Request Authors
- wrathematics (1)
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Packages
- Total packages: 1
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Total downloads:
- cran 317 last-month
- Total docker downloads: 76
- Total dependent packages: 1
- Total dependent repositories: 4
- Total versions: 2
- Total maintainers: 1
cran.r-project.org: logisticPCA
Binary Dimensionality Reduction
- Homepage: https://github.com/andland/logisticPCA
- Documentation: http://cran.r-project.org/web/packages/logisticPCA/logisticPCA.pdf
- License: MIT + file LICENSE
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Latest release: 0.2
published over 10 years ago
Rankings
Maintainers (1)
Dependencies
- ggplot2 * imports
- RSpectra >= 0.10 suggests
- knitr * suggests
- rmarkdown * suggests
- testthat >= 0.11.0 suggests