models from multiple related datasets using the SIMULE algorithm. Please run demo(simule) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) 10.1007/s10994-017-5635-7.
simule(X, lambda, epsilon = 1, covType = "cov", intertwined = FALSE,
parallel = FALSE)A List of input matrices. They can be data matrices or covariance/correlation matrices. If every matrix in the X is a symmetric matrix, the matrices are assumed to be covariance/correlation matrices.
A positive number. The hyperparameter controls the sparsity level of the matrices. The \(\lambda_n\) in the following section: Details.
A positive number. The hyperparameter controls the differences between the shared pattern among graphs and the individual part of each graph. The \(\epsilon\) in the following section: Details. If epsilon becomes larger, the generated graphs will be more similar to each other. The default value is 1, which means that we set the same weights to the shared pattern among graphs and the individual part of each graph.
A parameter to decide which Graphical model we choose to estimate from the input data.
If covType = "cov", it means that we estimate multiple sparse Gaussian Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing covariance matrices) the sample covariance matrices as input to the simule algorithm.
If covType = "kendall", it means that we estimate multiple nonparanormal Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing correlation matrices) the kendall's tau correlation matrices as input to the simule algorithm.
indicate whether to use intertwined covariance matrix
A boolean. This parameter decides if the package will use the multithreading architecture or not.
A list of the estimated inverse covariance/correlation matrices.
The shared graph among multiple tasks.
The SIMULE algorithm is a constrained l1 minimization method that can
detect both the shared and the task-specific parts of multiple graphs
explicitly from data (through jointly estimating multiple sparse Gaussian
graphical models or Nonparanormal graphical models). It solves the
following equation: $$ \hat{\Omega}^{(1)}_I, \hat{\Omega}^{(2)}_I,
\dots, \hat{\Omega}^{(K)}_I, \hat{\Omega}_S =
\min\limits_{\Omega^{(i)}_I,\Omega_S}\sum\limits_i ||\Omega^{(i)}_I||_1+
\epsilon K||\Omega_S||_1 $$ Subject to : $$
||\Sigma^{(i)}(\Omega^{(i)}_I + \Omega_S) - I||_{\infty} \le \lambda_{n}, i
= 1,\dots,K \nonumber $$ Please also see the equation (7) in our paper. The
\(\lambda_n\) is the hyperparameter controlling the sparsity level of the
matrices and it is the lambda in our function. The \(\epsilon\) is
the hyperparameter controlling the differences between the shared pattern
among graphs and the individual part of each graph. It is the
epsilon parameter in our function and the default value is 1. For
further details, please see our paper:
http://link.springer.com/article/10.1007/s10994-017-5635-7.
if labels are provided in the datalist as column names, result will contain labels (to be plotted)
Beilun Wang, Ritambhara Singh, Yanjun Qi (2017). A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models. http://link.springer.com/article/10.1007/s10994-017-5635-7
# NOT RUN {
library(JointNets)
data(exampleData)
result = simule(X = exampleData , lambda = 0.1, epsilon = 0.45, covType = "cov", FALSE)
plot(result)
# }
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