mimr: mIMR (minimum Interaction max Relevance) filter

In gbonte/D2C: Predicting Causal Direction from Dependency Features

mIMR (minimum Interaction max Relevance) filter

Description

Filter based on information theory which aims to prioritise direct causal relationships in feature selection problems where the ratio between the number of features and the number of samples is high. The approach is based on the notion of interaction which is informative about the relevance of an input subset as well as its causal relationship with the target.

Usage

mimr(X, Y, nmax = 5, init = FALSE, lambda = 0.5, spouse.removal = TRUE,
  caus = 1)

Arguments

X	: input matrix
Y	: output vector
nmax	: number of returned features
init	: if TRUE it makes a search in the space of pairs of features to initialize the ranking, otherwise the first ranked feature is the one with the highest mutual information with the output
lambda	: weight 0 \le \lambda \le 1 of the interaction term
spouse.removal	: TRUE OR FALSE. if TRUE it removes the spouses before ranking
caus	: if caus =1 it prioritizes causes otherwise (caus=-1) it prioritizes effects

Value

ranked vector of nmax indices of features

References

Bontempi G., Meyer P.E. (2010) Causal filter selection in microarray data. ICML10

Examples

 set.seed(0)
N<-500
n<-5
X<-array(rnorm(N*n),c(N,n))
Y<-X[,1]-3*X[,3]+4*X[,2]+rnorm(N,sd=0.5)
Z1<-Y+rnorm(N,sd=0.5)
## effect 1
Z2<-2*Y+rnorm(N,sd=0.5)
## effect 2
most.probable.causes<-mimr(cbind(X,Z1,Z2),Y,nmax=3,init=TRUE,spouse=FALSE,lambda=1)
## causes are in the first three columns of the feature dataset
most.probable.effects<-mimr(cbind(X,Z1,Z2),Y,nmax=3,init=TRUE,spouse=FALSE,lambda=1,caus=-1)
## effects are in the last two columns of the feature dataset

gbonte/D2C documentation built on Sept. 8, 2023, 11:22 p.m.