LLdecomp-package {LLdecomp} | R Documentation |
The main function "decompfit" takes as input a matrix consisting of discrete variables. Decomposes these using Random Forests and the Message Passing algorithm into cliques and separators. These can then be used to fit a log-linear model. The whole procedure is described in http://arxiv.org/abs/0904.1510.
Package: | LLdecomp |
Type: | Package |
Version: | 1.0 |
Date: | 2009-05-12 |
License: | GPL |
LazyLoad: | yes |
Please note that this is an *early test release*.
The best entry point for the package are the examples in the help
file of the function decompfit
.
Index:
Index:
logilasso Fits a loglinear model or/and performs cross-validation levelcv Performs cross-validation for the specified number of interactions traceplot Plots the solution path from lambdamax to lambdamin for all components of the solution vector beta graphmod Plots a graphical model plot.logilasso Plot method for a logilasso object predict.logilasso Predict method for a logilasso object
Corinne Dahinden
Maintainer: Corinne Dahinden <dahinden@stat.math.ethz.ch>
http://arxiv.org/abs/0904.1510
## Data generation where the first and second variables are dependent. data1 <- matrix(NA,nrow=200,ncol=5) data1[,-1] <- sample(c(0,1),200*4,replace=TRUE) prob <- data1[,2]*0.6+0.2 data1[,1] <- rbinom(200,1,prob) dfit <- decompfit(data1,3)