Package 'sglasso' Title Lasso Method for Rcon(v,e) Models
نویسنده
چکیده
February 20, 2015 Type Package Title Lasso method for RCON(V,E) models Version 1.1-0 Date 2014-09-04 Author Luigi Augugliaro Maintainer Luigi Augugliaro Depends Matrix Encoding latin1 Description RCON(V, E) models (Højsgaard, et al.,2008) are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. sglasso package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e., a cyclic coordinate minimization (CCM) algorithm and a cyclic coordinate descent (CCD) algorithm. License GPL (>= 2) LazyLoad yes NeedsCompilation yes Repository CRAN Date/Publication 2014-09-22 17:31:46
منابع مشابه
Toward Probabilistic Diagnosis and Understanding of Depression Based on Functional MRI Data Analysis with Logistic Group LASSO
Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an importa...
متن کاملEBglmnet: a comprehensive R package for sparse generalized linear regression models.
EBglmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic net (EBEN) priors for generalized linear models. In our previous studies, both EBlasso and EBEN outperformed other state-of-the-art methods such as lasso and elastic net in inferring sparse genotype and phenotype associations, in which the number of covariates is typically much larger than the ...
متن کاملModel selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order - som...
متن کاملApplications of the lasso and grouped lasso to the estimation of sparse graphical models
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties. We develop efficient algorithms for fitting these models when the numbers of nodes and potential edges are large. We compare them to competing methods including the graphical lasso and SPACE (Peng, Wang, Zhou & Zhu 2008). Surprisingly, we find that for edge selection...
متن کاملPenalized Lasso Methods in Health Data: application to trauma and influenza data of Kerman
Background: Two main issues that challenge model building are number of Events Per Variable and multicollinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression model. The present study aimed to explain problems of traditional regressions due to small sample size and m...
متن کامل