Feature Selection (Including Multiple Solutions) and Bayesian Networks


[Up] [Top]

Documentation for package ‘MXM’ version 1.3.1

Help Pages

A B C D E F G I L M N O P R S T U W Z

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.

-- A --

acc.mxm Cross-Validation for SES and MMPC
acc_multinom.mxm Cross-Validation for SES and MMPC
apply_ideq Internal MXM Functions
apply_ideq.ma Internal MXM Functions
apply_ideq.temporal Internal MXM Functions
auc ROC and area under the curve
auc.mxm Cross-Validation for SES and MMPC

-- B --

beta.bsreg Internal MXM Functions
beta.fsreg Internal MXM Functions
beta.fsreg_2 Internal MXM Functions
beta.mod Beta regression
beta.mxm Cross-Validation for SES and MMPC
beta.regs Many simple beta regressions.
betamle.wei Internal MXM Functions
bic.betafsreg Internal MXM Functions
bic.clogit.fsreg Internal MXM Functions
bic.fsreg Variable selection in regression models with forward selection using BIC
bic.gammafsreg Variable selection in generalised linear models with forward selection based on BIC
bic.glm.fsreg Variable selection in generalised linear models with forward selection based on BIC
bic.normlog.fsreg Variable selection in generalised linear models with forward selection based on BIC
bic.tobit.fsreg Internal MXM Functions
bic.zipfsreg Internal MXM Functions
bn.skel.utils Utilities for the skeleton of a (Bayesian) Network
bn.skel.utils2 Utilities for the skeleton of a (Bayesian) Network
bs.reg Variable selection in regression models with backward selection

-- C --

cat.ci Conditional independence test for continuous class variables with and without permutation based p-value
cat_condis Internal MXM Functions
censIndCR Conditional independence test for survival data
censIndER Conditional independence test for survival data
censIndWR Conditional independence test for survival data
ci.fast Symmetric conditional independence test with mixed data
ci.fast2 Symmetric conditional independence test with mixed data
ci.mm Symmetric conditional independence test with mixed data
ci.mm2 Symmetric conditional independence test with mixed data
ci.mxm Cross-Validation for SES and MMPC
ciwr.mxm Cross-Validation for SES and MMPC
clogit.fsreg Internal MXM Functions
clogit.fsreg_2 Internal MXM Functions
comb_condis Internal MXM Functions
compare_p_values Internal MXM Functions
condi Conditional independence test for continuous class variables with and without permutation based p-value
condi.perm Internal MXM Functions
CondIndTests MXM Conditional independence tests
condis Many conditional independence tests counting the number of times a possible collider d-separates two nodes
conf.edge.lower Lower limit of the confidence of an edge
cor.drop1 Drop all possible single terms from a model using the partial correlation
corfs.network Network construction using the partial correlation based forward regression
coxph.mxm Cross-Validation for SES and MMPC
cv.mmpc Cross-Validation for SES and MMPC
cv.permmmpc Cross-Validation for SES and MMPC
cv.permses Cross-Validation for SES and MMPC
cv.ses Cross-Validation for SES and MMPC
cv.waldmmpc Cross-Validation for SES and MMPC
cv.waldses Cross-Validation for SES and MMPC
cvmmpc.par Internal MXM Functions
cvpermmmpc.par Internal MXM Functions
cvpermses.par Internal MXM Functions
cvses.par Internal MXM Functions
cvwaldmmpc.par Internal MXM Functions
cvwaldses.par Internal MXM Functions

-- D --

dag2eg Transforms a DAG into an essential graph
dag_to_eg Internal MXM Functions
disctor_condis Internal MXM Functions
dist.condi Conditional independence test for continuous class variables with and without permutation based p-value

-- E --

ebic.beta.bsreg Internal MXM Functions
ebic.bsreg Backward selection regression using the eBIC
ebic.cr.bsreg Internal MXM Functions
ebic.fbed.beta Internal MXM Functions
ebic.fbed.cr Internal MXM Functions
ebic.fbed.glm Internal MXM Functions
ebic.fbed.glmm Internal MXM Functions
ebic.fbed.lm Internal MXM Functions
ebic.fbed.lmm Internal MXM Functions
ebic.fbed.mmreg Internal MXM Functions
ebic.fbed.multinom Internal MXM Functions
ebic.fbed.nb Internal MXM Functions
ebic.fbed.ordinal Internal MXM Functions
ebic.fbed.tobit Internal MXM Functions
ebic.fbed.wr Internal MXM Functions
ebic.fbed.zip Internal MXM Functions
ebic.glm.bsreg Internal MXM Functions
ebic.glmm.bsreg Backward selection regression for GLMM using the eBIC
ebic.lm.bsreg Internal MXM Functions
ebic.mm.bsreg Internal MXM Functions
ebic.multinom.bsreg Internal MXM Functions
ebic.ordinal.bsreg Internal MXM Functions
ebic.tobit.bsreg Internal MXM Functions
ebic.wr.bsreg Internal MXM Functions
ebic.zip.bsreg Internal MXM Functions
equivdags Check Markov equivalence of two DAGs
euclid_sens.spec.mxm Cross-Validation for SES and MMPC

-- F --

fbed.beta Internal MXM Functions
fbed.cr Internal MXM Functions
fbed.glm Internal MXM Functions
fbed.glmm Internal MXM Functions
fbed.glmm.reg Forward Backward Early Dropping selection regression for GLMM
fbed.lm Internal MXM Functions
fbed.lmm Internal MXM Functions
fbed.mmreg Internal MXM Functions
fbed.multinom Internal MXM Functions
fbed.nb Internal MXM Functions
fbed.ordinal Internal MXM Functions
fbed.reg Forward Backward Early Dropping selection regression
fbed.tobit Internal MXM Functions
fbed.wr Internal MXM Functions
fbed.zip Internal MXM Functions
findAncestors Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s))
findDescendants Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s))
fs.reg Variable selection in regression models with forward selection
fs.reg_2 Internal MXM Functions
fscore.mxm Cross-Validation for SES and MMPC

-- G --

gammabsreg Variable selection in generalised linear regression models with backward selection
gammafsreg Variable selection in generalised linear regression models with forward selection
gammafsreg_2 Internal MXM Functions
generatefolds Generate random folds for cross-validation
glm.bsreg Variable selection in generalised linear regression models with backward selection
glm.fsreg Variable selection in generalised linear regression models with forward selection
glm.fsreg_2 Internal MXM Functions
glm.mxm Cross-Validation for SES and MMPC
glmm.bsreg Backward selection regression for GLMM
glmm.ci.mm Symmetric conditional independence test with clustered data
glmm.pc.skel The skeleton of a Bayesian network produced by the PC algorithm
gSquare G-square conditional independence test for discrete data

-- I --

iamb IAMB variable selection
iamb.betabs Internal MXM Functions
iamb.bs IAMB backward selection phase
iamb.gammabs Internal MXM Functions
iamb.glmbs Internal MXM Functions
iamb.normlogbs Internal MXM Functions
iamb.tobitbs Internal MXM Functions
iamb.zipbs Internal MXM Functions
IdentifyEquivalence Internal MXM Functions
IdentifyEquivalence.ma Internal MXM Functions
IdentifyEquivalence.temporal Internal MXM Functions
identifyTheEquivalent Internal MXM Functions
identifyTheEquivalent.ma Internal MXM Functions
identifyTheEquivalent.temporal Internal MXM Functions
internaliamb.betabs Internal MXM Functions
internaliamb.binombs Internal MXM Functions
internaliamb.gammabs Internal MXM Functions
internaliamb.lmbs Internal MXM Functions
internaliamb.normlogbs Internal MXM Functions
internaliamb.poisbs Internal MXM Functions
internaliamb.tobitbs Internal MXM Functions
internaliamb.zipbs Internal MXM Functions
Internalmammpc Internal MXM Functions
Internalmases Internal MXM Functions
InternalMMPC Internal MXM Functions
InternalMMPC.temporal Internal MXM Functions
InternalSES Internal MXM Functions
InternalSES.temporal Internal MXM Functions
is.dag Check whether a directed graph is acyclic
is.sepset Internal MXM Functions

-- L --

lm.fsreg Variable selection in linear regression models with forward selection
lm.fsreg_2 Internal MXM Functions
lm.fsreg_2.heavy Internal MXM Functions
lm.fsreg_heavy Variable selection in linear regression models with forward selection
lm.mxm Cross-Validation for SES and MMPC
lmm.bsreg Internal MXM Functions
lmrob.mxm Cross-Validation for SES and MMPC
local.mmhc.skel Skeleton (local) around a node of the MMHC algorithm

-- M --

ma.mmpc 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
ma.ses 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 '"mammpc.output"'
mammpc.output-class Class '"mammpc.output"'
mases.output Class '"mases.output"'
mases.output-class Class '"mases.output"'
max_min_assoc Internal MXM Functions
max_min_assoc.ma Internal MXM Functions
max_min_assoc.temporal Internal MXM Functions
mb Returns and plots, if asked, the Markov blanket of a node (or variable)
min_assoc Internal MXM Functions
min_assoc.ma Internal MXM Functions
min_assoc.temporal Internal MXM Functions
mmhc.skel The skeleton of a Bayesian network as produced by MMHC
mmmb Max-min Markov blanket algorithm
MMPC SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
mmpc.model Regression model(s) obtained from SES or MMPC
mmpc.or Bayesian Network construction using a hybrid of MMPC and PC
mmpc.path MMPC solution paths for many combinations of hyper-parameters
MMPC.temporal 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
mmpc.temporal.model Generalised linear mixed model(s) based obtained from temporal SES or MMPC
MMPC.temporal.output Class '"MMPC.temporal.output"'
MMPC.temporal.output-class Class '"MMPC.temporal.output"'
mmpcbackphase Backward phase of MMPC
MMPCoutput Class '"MMPCoutput"'
MMPCoutput-class Class '"MMPCoutput"'
mse.mxm Cross-Validation for SES and MMPC
multinom.mxm Cross-Validation for SES and MMPC

-- N --

nb.mxm Cross-Validation for SES and MMPC
nbdev.mxm Cross-Validation for SES and MMPC
nchoosek Internal MXM Functions
nei Returns and plots, if asked, the node(s) and their neighbour(s), if there are any.
Ness Effective sample size for G^2 test in BNs with case control data
normlog.bsreg Variable selection in generalised linear regression models with backward selection
normlog.fsreg Variable selection in generalised linear regression models with forward selection

-- O --

ordinal.mxm Cross-Validation for SES and MMPC
ordinal.reg Generalised ordinal regression
ord_mae.mxm Cross-Validation for SES and MMPC

-- P --

partialcor Partial correlation
pc.con The skeleton of a Bayesian network produced by the PC algorithm
pc.or The orientations part of the PC algorithm.
pc.skel The skeleton of a Bayesian network produced by the PC algorithm
pc.skel.boot The skeleton of a Bayesian network produced by the PC algorithm
pearson_condis Internal MXM Functions
pearson_condis.rob Internal MXM Functions
perm.apply_ideq Internal MXM Functions
perm.betaregs Many simple beta regressions.
perm.IdentifyEquivalence Internal MXM Functions
perm.identifyTheEquivalent Internal MXM Functions
perm.Internalmmpc Internal MXM Functions
perm.mmpc SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
perm.ses SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
perm.univariateScore Internal MXM Functions
perm.univregs Univariate regression based tests
perm.zipregs Many simple zero inflated Poisson regressions.
permBeta Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
permBinom Binomial regression conditional independence test for success rates (binomial)
permClogit Conditional independence test based on conditional logistic regression for case control studies
permcor Permutation based p-value for the Pearson correlation coefficient
permcorrels Permutation based p-value for the Pearson correlation coefficient
permCR Conditional independence test for survival data
permDcor Fisher and Spearman conditional independence test for continuous class variables
permER Conditional independence test for survival data
permFisher Fisher and Spearman conditional independence test for continuous class variables
permGamma Regression conditional independence test for positive response variables.
permgSquare G-square conditional independence test for discrete data
permIGreg Regression conditional independence test for positive response variables.
permLogistic Conditional independence test for binary, categorical or ordinal class variables
permMVreg Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permNB Regression conditional independence test for discrete (counts) class dependent variables
permNormLog Regression conditional independence test for positive response variables.
permPois Regression conditional independence test for discrete (counts) class dependent variables
permReg Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permRQ Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permTobit Conditional independence test for survival data
permWR Conditional independence test for survival data
permZIP Regression conditional independence test for discrete (counts) class dependent variables
pi0est Estimation of the percentage of Null p-values
plot-method Class '"MMPC.temporal.output"'
plot-method Class '"MMPCoutput"'
plot-method Class '"SES.temporal.output"'
plot-method Class '"SESoutput"'
plot-method Class '"mammpc.output"'
plot-method Class '"mases.output"'
plotnetwork Interactive plot of an (un)directed graph
pois.mxm Cross-Validation for SES and MMPC
poisdev.mxm Cross-Validation for SES and MMPC
proc_time-class Internal MXM Functions
pval.mixbeta Fit a mixture of beta distributions in p-values

-- R --

R0 Internal MXM Functions
R1 Internal MXM Functions
R2 Internal MXM Functions
R3 Internal MXM Functions
rdag Simulation of data from DAG (directed acyclic graph)
rdag2 Simulation of data from DAG (directed acyclic graph)
reg.fit Regression modelling
regbeta Internal MXM Functions
regbetawei Internal MXM Functions
regzip Internal MXM Functions
regzipwei Internal MXM Functions
ridge.plot Ridge regression
ridge.reg Ridge regression
ridgereg.cv Cross validation for the ridge regression
rint.regs Univariate regression based tests
rmdag Simulation of data from DAG (directed acyclic graph)
rq.mxm Cross-Validation for SES and MMPC

-- S --

score.univregs Univariate regression based tests
SES SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
ses.model Regression model(s) obtained from SES or MMPC
SES.temporal 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
SES.temporal.output Class '"SES.temporal.output"'
SES.temporal.output-class Class '"SES.temporal.output"'
SESoutput Class '"SESoutput"'
SESoutput-class Class '"SESoutput"'
shd Structural Hamming distance between two partially oriented DAGs
summary-method Class '"MMPC.temporal.output"'
summary-method Class '"MMPCoutput"'
summary-method Class '"SES.temporal.output"'
summary-method Class '"SESoutput"'
summary-method Class '"mammpc.output"'
summary-method Class '"mases.output"'

-- T --

tc.plot Plot of longitudinal data
testIndBeta Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
testIndBinom Binomial regression conditional independence test for success rates (binomial)
testIndClogit Conditional independence test based on conditional logistic regression for case control studies
testIndFisher Fisher and Spearman conditional independence test for continuous class variables
testIndGamma Regression conditional independence test for positive response variables.
testIndGLMM Linear mixed models conditional independence test for longitudinal class variables
testIndIGreg Regression conditional independence test for positive response variables.
testIndLogistic Conditional independence test for binary, categorical or ordinal class variables
testIndMVreg Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndNB Regression conditional independence test for discrete (counts) class dependent variables
testIndNormLog Regression conditional independence test for positive response variables.
testIndPois Regression conditional independence test for discrete (counts) class dependent variables
testIndReg Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndRQ Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndSpearman Fisher and Spearman conditional independence test for continuous class variables
testIndSpeedglm Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations
testIndTobit Conditional independence test for survival data
testIndZIP Regression conditional independence test for discrete (counts) class dependent variables
topological_sort Topological sort of a DAG
transitiveClosure Returns the transitive closure of an adjacency matrix

-- U --

undir.path Undirected path(s) between two nodes
univariateScore Internal MXM Functions
univariateScore.ma Internal MXM Functions
univariateScore.temporal Internal MXM Functions
univregs Univariate regression based tests

-- W --

wald.betaregs Many simple beta regressions.
wald.Internalmmpc Internal MXM Functions
wald.Internalses Internal MXM Functions
wald.logisticregs Many Wald based tests for logistic and Poisson regressions with continuous predictors
wald.mmpc SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
wald.poissonregs Many Wald based tests for logistic and Poisson regressions with continuous predictors
wald.ses SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
wald.univariateScore Internal MXM Functions
wald.univregs Univariate regression based tests
wald.zipregs Many simple zero inflated Poisson regressions.
waldBeta Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
waldBinary Conditional independence test for binary, categorical or ordinal class variables
waldBinom Binomial regression conditional independence test for success rates (binomial)
waldCR Conditional independence test for survival data
waldER Conditional independence test for survival data
waldGamma Regression conditional independence test for positive response variables.
waldIGreg Regression conditional independence test for positive response variables.
waldmmpc.model Regression model(s) obtained from SES or MMPC
waldmmpc.path MMPC solution paths for many combinations of hyper-parameters
waldMMreg Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
waldNB Regression conditional independence test for discrete (counts) class dependent variables
waldNormLog Regression conditional independence test for positive response variables.
waldOrdinal Conditional independence test for binary, categorical or ordinal class variables
waldPois Regression conditional independence test for discrete (counts) class dependent variables
waldses.model Regression model(s) obtained from SES or MMPC
waldSpeedglm Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations
waldTobit Conditional independence test for survival data
waldWR Conditional independence test for survival data
waldZIP Regression conditional independence test for discrete (counts) class dependent variables
weibreg.mxm Cross-Validation for SES and MMPC

-- Z --

zip.bsreg Internal MXM Functions
zip.fsreg Internal MXM Functions
zip.fsreg_2 Internal MXM Functions
zip.mod Zero inflated Poisson regression
zip.reg Zero inflated Poisson regression
zip.regs Many simple zero inflated Poisson regressions.
zipmle.wei Internal MXM Functions
zipwei Internal MXM Functions