--- /dev/null
+Package: valse
+Title: Variable selection with mixture of models
+Date: 2016-12-01
+Version: 0.1-0
+Description: TODO
+Authors@R: c( person("Benjamin Auder", "Developer", role=c("ctb","cre"), email="Benjamin.Auder@math.u-psud.fr"),
+ person("Benjamin Goehry", "User", role="aut", email = "Benjamin.Goehry@math.u-psud.fr"),
+ person("Emilie Devijver", "User", role="ctb", email = "Emilie.Devijver@kuleuven.be"))
+Depends:
+ R (>= 2.15)
+LazyData: yes
+URL: http://git.auder.net/?p=valse.git
+License: MIT
-# model SELECTion
+# VAriable seLection with mixtureS of modEls
-This code is the applied part of the PhD thesis of [Emilie Devijver](http://www.math.u-psud.fr/~devijver/).
+This code is the applied part of the PhD thesis of [Benjamin Gohehry](http://www.math.u-psud.fr/~goehry/).
## Description
-The function selmix delivers a multivariate Gaussian mixture in regression model collection.
-According to the parameter estimation, we can compute classical model selection criterion, as BIC or AIC, or slope heuristic, using the CAPUSHE package.
-The methodology used is described in 'Model-Based Clustering for High-Dimensional Data. Application to Functional Data.',
-available at [this location](https://hal.archives-ouvertes.fr/hal-01060063)
-
-## Arguments
-
-Regressors, denoted by X (of size n x p) and responses, denoted by Y (of size n x q) are must-have arguments.
-
-Optionally, we could add
-
-* gamma: weight power in the Lasso penalty (according to Stadler et al., $\gamma \in \{0,1/2,1\}$;
-* mini: the minimum number of iterations;
-* maxi: the maximum number of iterations;
-* tau: the threshold for stopping EM algorithm;
-* kmin and kmax: the bounds of interesting number of components,
-* rangmin and rangmax: the bounds of interesting rank values.
-
-## Usage
-
- objet = selmix(X,Y)
- objet.run(index)
-
-For index=1, it computes the Lasso-MLE procedure.
-For index=2, it computes the Lasso-Rank procedure.
-
-/!\ Be careful to the current path /!\
-
-## Values
-
-* phiInit, rhoInit, piInit, gamInit: the initialization of the matrices phi, rho, pi and gamma,
-* gridLambda: grid of regularization parameters used to select relevant variables (if kmax-kmin=0, it is, if not, it is the last grid of regularization parameters)
-* A1,A2: indices of variables selected or not selected (matrices of size (p+1) x q x size(gridLambda))
-* Phi,Rho,Pi: estimations of each parameter thanks to the procedure LassoMLE if compute index=1, and thanks to the procedure LassoRank if computed index=2.
-
-
-## Example
-
- n=10;
- p=10;
- q=5;
- X=randn(n,p);
- Y=randn(n,q);
-
- objet=selmix(X,Y);
- objet.run(1);
- objet.run(2);
+TODO : see R package
projects / valse.git / commitdiff
