MEfunctions {integrativeME}R Documentation

Internal gating functions for integrative Mixture of Experts methodology

Description

Different internal gating functions (or models) are proposed within mixture of experts to integrate gene expression data and clinical data in a binary classification framework.

Usage

MEindep(jcross, train, test, n, nq, nv, ng, indclass, data.cat, data.cont, prop.kmeans, means.kmeans, var.kmeans)

MElogreg(jcross, train, test, n, nq, nv, ng, indclass, data.cat, data.cont, prop.kmeans)

MEloc(jcross, train, test, n, nq, nv, ng, indclass, data.cat, data.cont, prop.kmeans, means.kmeans, var.kmeans, loc.ind)

Arguments

jcross which cross validation sample
train training samples
test test samples
n number of observations or samples
nq number of clinical variables
nv number of genes, the genes should be selected beforehand, see integrativeME.
ng number of experts, should be set to 2 for a binary classification problem.
indclass number of samples of class 0.
data.cat clinical data (categorical).
data.cont gene expression data.
prop.kmeans proportions, initialized with k-means, see also kmeans.init.
means.kmeans means, initialized with k-means, see also kmeans.init.
var.kmeans variance-covariance matrix, initialized with k-means, see also kmeans.init.
loc.ind index of the location variable in the case of the MEloc model.

Details

Given a training set and a test set, the parameters in integrativeME are learnt via the EM algorithm and then tested. All three geting functions are included in the main program integrativeME

Value

prop estimated proportions.
w weighted variable vector in the expert networks function.
loglik loglikelihood of the model after several iterations.
mat.gum main output that is used in integrativeME to predict the class label of each tested observation.

Author(s)

Kim-Anh Le Cao

References

Le Cao et al. (2009), submitted.

Ng, S.K. and McLachlan, G.J. (2008). Expert Networks with Mixed Continuous and Categorical Feature Variables: a Location Modeling Approach. Machine Learning Research Progress, ed. Hanna Peters and Mia Vogel, 1–14

Hunt, L. and Jorgensen, M. (1999). Mixture model clustering using the MULTIMIX program. Australian & New Zealand Journal of Statistics, 41, 2, 154–171.

See Also

integrativeME, kmeans.init


[Package integrativeME version 1.1 Index]