MXM package - RDocumentation

Cross-Validation for SES and MMPC

Backward selection regression

Variable selection in regression models with forward selection

Forward selection regression

Variable selection in regression models with forward selection

ROC and area under the curve

BIC based forward selection

Variable selection in regression models with forward selection using BIC

Check Markov equivalence of two DAGs

Transformation of a DAG into an essential graph

Transforms a DAG into an essential graph

Conditional independence tests for survival data

Conditional independence test for survival data

BIC based forward selection with generalised linear models

Variable selection in generalised linear models with forward selection based on BIC

Correlation based tests with and without permutation p-value

Fisher conditional independence test for continuous class variables with and without permutation based p-value

mases.output-class

Class "mases.output"

MMPC solution paths for many combinations of hyper-parameters

The max-min Markov blanket algorithm

Max-min Markov blanket algorithm

Skeleton of the max-min hill-climbing (MMHC) algorithm

The skeleton of a Bayesian network as produced by MMHC

Generate random folds for cross-validation

Constraint based feature selection algorithms for multiple datasets

ma.ses: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with multiple datasets ma.mmpc: Feature selection algorithm for identifying minimal feature subsets with multiple datasets

mammpc.output-class

Class "mammpc.output"

Forward selection with generalised linear regression models

Variable selection in generalised linear regression models with forward selection

Forward selection with linear regression models

Variable selection in linear regression models with forward selection

Backward selection with generalised linear regression models

Variable selection in generalised linear regression models with backward selection

MXM-internal

Internal MXM Functions

MXM-package

This is an R package that currently implements feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. In addition, two algorithms for constructing the skeleton of a Bayesian network are included.

Partial correlation between two variables

Partial correlation

MMPC.temporal.output-class

Class "MMPC.temporal.output"

Orientation rules for the PC algorithm

The orientations part of the PC algorithm.

Neighbours of nodes in an undirected graph

Returns and plots, if asked, the node(s) and their neighbour(s), if there are any.

Skeleton of the PC algorithm

The skeleton of a Bayesian network produced by the PC algorithm

Permutation based p-value for the Pearson correlation coefficient

MMPCoutput-class

Class "MMPCoutput"

CondInditional independence tests

MXM Conditional independence tests

Plot of an (un)directed graph

SES.temporal.output-class

Class "SES.temporal.output"

Constraint based feature selection algorithms for longitudinal and clustered data

SES.temporal: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC.temporal: Feature selection algorithm for identifying minimal feature subsets

Cross-validation for ridge regression

Cross validation for the ridge regression

Constraint based feature selection algorithms

SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets

Regression models fitting

Regression modelling

Ridge regression coefficients plot

Ridge regression

Regression models based on SES and MMPC outputs

Regression model(s) obtained from SES or MMPC

SESoutput-class

Class "SESoutput"

Plot of longitudinal data

Conditional independence test for longitudinal and clustered data

Linear mixed models conditional independence test for longitudinal class variables

Conditional independence tests for sucess rates

Binomial regression conditional independence test for success rates (binomial)

Conditional independence tests for count data

Regression conditional independence test for discrete (counts) class dependent variables

Conditional independence test for binary, categorical or ordinal data

Conditional independence test for binary, categorical or ordinal class variables

Conditional independence tests for continous univariate and multivariate data

Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables

Conditional independence test for continuous, binary and count data with thousands of samples

Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations

Conditional independence test for case control data

Conditional independence test based on conditional logistic regression for case control studies

Conditional independence test for proportions/percentages

Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors

Undirected path(s) between two nodes

Correlation based conditonal independence tests

Fisher and Spearman conditional independence test for continuous class variables

MXM (version 0.9.5)

Discovering Multiple, Statistically-Equivalent Signatures

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Functions in MXM (0.9.5)