https://github.com/qdata/simule
Machine Learning 2017 / "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", / https://cran.r-project.org/web/packages/simule/
https://github.com/qdata/simule
covariance-matrices data-matrices gaussian-graphical-models graphical-models
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Machine Learning 2017 / "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", / https://cran.r-project.org/web/packages/simule/
- Host: GitHub
- URL: https://github.com/qdata/simule
- Owner: QData
- License: apache-2.0
- Created: 2016-01-31T20:23:25.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2019-08-28T16:27:42.000Z (over 5 years ago)
- Last Synced: 2024-05-14T00:16:17.293Z (12 months ago)
- Topics: covariance-matrices, data-matrices, gaussian-graphical-models, graphical-models
- Language: R
- Homepage: https://cran.r-project.org/web/packages/simule/
- Size: 12.5 MB
- Stars: 4
- Watchers: 7
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SIMULE
This is an R implementation of the [SIMULE](https://arxiv.org/abs/1605.03468) algorithm proposed in the following paper:
"A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models",
accepted by Machine Learning @ [URL](https://link.springer.com/article/10.1007/s10994-017-5635-7)
Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper @ [URL](http://link.springer.com/article/10.1007/s10994-017-5635-7) or read the R-package Manual: @ [URL](https://cran.r-project.org/web/packages/simule/simule.pdf)
+ CRAN R Library page: [URL](https://cran.r-project.org/web/packages/simule/)
+ Presentation @ [URL](https://github.com/QData/SIMULE/blob/master/SIMULE-talk.pdf)
## Dependency
It depends on the following existing packages. To use them, simply
```r
library('pcaPP')
library('lpSolve')
library('parallel')
```
If you don't have these packages, simply use
```r
install.packages('packageNameFromAbove')
```
## Usage
0. install the R "simule" package through R console:
```r
install.packages('simule')
```
1. then load the library simule in R console, by running:
```r
library(simule)
```
2. Then, simply run the function ```simule``` on your favorite datasets
For example,
```r
simule(CovarianceMatrixList, lambda = 0.05, epsilon = 0.5, parallel = TRUE)
```
This function will returns a ```list``` (a data structure in R) of graphs estimated by the SIMULE package.
## Three possible types of inputs for the Argument ``` CovarianceMatrixList ```
1. The argument ``` CovarianceMatrixList ``` can represent a ```list``` of data matrices directly:
The i-th item ``` CovarianceMatrixList[[i]]``` represents the i-th matrix in a ```list``` of data matrices ```CovarianceMatrixList```.
** Please make sure the order of the feature variables are the same among all the data matrices in ```CovarianceMatrixList```.**
2. If the input ``` CovarianceMatrixList ``` is Symmetric, the package automatically assumes that the data inputs belong to the following two types:
- The argument ``` CovarianceMatrixList ``` can a ```list``` of covariance matrices.
Assuming ``` X ``` represents a list of data matrices, whose i-th item ``` X[[i]]``` represents the data matrix of the i-th task.
We can use the following function to calculate the covariance matrices:
```r
CovarianceMatrixList[[i]] = cov(X[[i]])
```
- The argument ``` CovarianceMatrixList ``` can represent a ```list``` of kendall's tau correlation matrices.
The kendall's tau correlation matrices can be calculated by using the following command:
```r
cor.fk(X[[i]])
```
(by the ``` 'pcaPP' ``` package.)
The kendall's tau correlation matrices can also be calculated through the following R functions:
```r
cor(X[[i]], method = 'kendall')
```
However the above way of calculating kendall's tau correlation matrix is very slow in R.
## Other Arguments
- ``` lambda ```
The parameter for the sparsity level of the estimated graphs. The larger ```lambda``` you choose, the ***sparser*** graphs you will estimate from the inputs.
- ``` epsilon ```
The parameter reflects the differences of sparsity level between the shared subgraph versus the context-specific subgraphs. The larger ```epsilon``` you choose, the ***denser*** the shared subgraph is (while the context-specific subgraphs are ***sparser***) and vice versa.
- ``` covType ```
This parameter controls SIMULE estimates the sparse Gaussian Graphical models (sGGM) or the sparse Nonparanormal Graphical Models from the input data. This parameter matters only when the input argument ``` CovarianceMatrixList ``` represents a ```list``` of data matrices directly:
When ``` covType = "cov" ``` the package estimates sGGMs from the input.
When ``` covType == "kendall"```, the package estimates sNGMs from the input.
- ``` parallel ```
Logic parameter for parallel implementation or not. If you have a multi-core machine, let ```parallel = TRUE```.