net.degree: List the degree of every node of each graph in the input list...
In fasjem: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
Lists the degree of every node of each graph in the input list of multiple graphs.
Usage
1
net.degree(theta)
Arguments
theta
An input list of multiple graphs. Each graph is represented as a pXp matrix. (For example, the result of the fasjem algorithm: a list of pXp matrices in which each matrix represents an estimated sparse inverse covariance matrix.)
Value
Degrees, in the format of a list of length p vectors represents the degree of all p nodes of each graph in the input list of multiple graphs.
Author(s)
Beilun Wang
References
Beilun Wang, Ji Gao, Yanjun Qi (2017). A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>
Examples
1
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3
4
5
6
7
8
9
## Not run:
## load an example multi-task dataset with K=2 tasks, p=100 features, and n=200 samples per task:
data(exampleData)
##run
result = fasjem(X = exampleData, method = "fasjem-g", 0.1, 0.1, 0.1, 0.05, 10)
## get degree list:
net.degree(result)
## End(Not run)
fasjem documentation built on May 2, 2019, 9:19 a.m.
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Description Usage Arguments Value Author(s) References Examples
Description
Lists the degree of every node of each graph in the input list of multiple graphs.
Usage
1 | net.degree(theta)
|
Arguments
theta |
An input list of multiple graphs. Each graph is represented as a pXp matrix. (For example, the result of the fasjem algorithm: a list of pXp matrices in which each matrix represents an estimated sparse inverse covariance matrix.) |
Value
Degrees, in the format of a list of length p vectors represents the degree of all p nodes of each graph in the input list of multiple graphs.
Author(s)
Beilun Wang
References
Beilun Wang, Ji Gao, Yanjun Qi (2017). A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>
Examples
1 2 3 4 5 6 7 8 9 | ## Not run:
## load an example multi-task dataset with K=2 tasks, p=100 features, and n=200 samples per task:
data(exampleData)
##run
result = fasjem(X = exampleData, method = "fasjem-g", 0.1, 0.1, 0.1, 0.05, 10)
## get degree list:
net.degree(result)
## End(Not run)
|
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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