TGGLMix: Fit a tree-guided group lasso mixture model model (TGGLMix).
In tohein/linearMTL: Linear Models for Multi-Task Learning

Description Usage Arguments Value See Also

Fit a tree-guided group lasso mixture model using a generalized EM algorithm. May be trained on shared or task specific feature matrices.

TGGLMix(X = NULL, task.specific.features = list(), Y, M, groups, weights,
  lambda, gam = 1, homoscedastic = FALSE, EM.max.iter = 1000,
  EM.epsilon = 1e-04, EM.verbose = 0, sample.data = FALSE,
  TGGL.mu = 1e-05, TGGL.epsilon = 1e-05, TGGL.iter = 25)

`X`	N by J1 matrix of features common to all tasks.
`task.specific.features`	List of features which are specific to each task. Each entry contains an N by J2 matrix for one particular task (where columns are features). List has to be ordered according to the columns of Y.
`Y`	N by K output matrix for every task.
`M`	Number of Clusters.
`groups`	Binary V by K matrix determining group membership: Task k in group v iff groups[v,k] == 1.
`weights`	V dimensional vector with group weights.
`lambda`	Regularization parameter.
`gam`	(Optional) Regularization parameter for component m will be lambda times the prior for component m to the power of gam.
`homoscedastic`	(Optional) Force variance to be the same for all tasks in a component. Default is FALSE.
`EM.max.iter`	(Optional) Maximum number of iterations for EM algorithm.
`EM.epsilon`	(Optional) Desired accuracy. Algorithm will terminate if change in penalized negative log-likelihood drops below EM.epsilon.
`EM.verbose`	(Optional) Integer in 0,1,2. verbose = 0: No output. verbose = 1: Print summary at the end of the optimization. verbose = 2: Print progress during optimization.
`sample.data`	(Optional) Sample data according to posterior probability or not.
`TGGL.mu`	(Optional) Mu parameter for TGGL.
`TGGL.epsilon`	(Optional) Epsilon parameter for TGGL.
`TGGL.iter`	(Optional) Initial number of iterations for TGGL. Will be increased incrementally to ensure convergence. When the number of samples is much larger than the dimensionalty, it can be beneficial to use a large initial number of iterations for TGGL. This is because every run of TGGL requires precomputation of multiple n-by-n matrix products.

List containing

`models`	List of TGGL models for each component.
`posterior`	N by M Matrix containing posterior probabilities.
`prior`	Vector with prior probabilities for each component.
`sigmas`	M by K Matrix with standard deviations for each component.
`obj`	Penalized negative log-likelihood (final objective value).
`loglik`	Likelihood for training data.
`groups`	groups argument.
`weights`	weights argument.
`lambda`	lambda argument.