Package: mcen 1.2.1

Ben Sherwood

mcen: Multivariate Cluster Elastic Net

Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arxiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.

Authors:Ben Sherwood [aut, cre], Brad Price [aut]

mcen_1.2.1.tar.gz
mcen_1.2.1.tar.gz(r-4.7-arm64)mcen_1.2.1.tar.gz(r-4.7-x86_64)mcen_1.2.1.tar.gz(r-4.6-arm64)mcen_1.2.1.tar.gz(r-4.6-x86_64)
mcen_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mcen/json (API)

# Install 'mcen' in R:
install.packages('mcen', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 stars 8 scripts 271 downloads 4 exports 24 dependencies

Last updated from:6f2675fd90. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK121
linux-devel-x86_64OK128
source / vignettesOK171
linux-release-arm64OK130
linux-release-x86_64OK137
wasm-releaseOK107

Exports:cluster.valscv.mcenget_best_cvmmcen

Dependencies:bootclasscodetoolsfarawayflexclustforeachglmnetiteratorslatticelme4MASSMatrixminqamodeltoolsnlmenloptrrbibutilsRcppRcppEigenRdpackreformulasrlangshapesurvival

Readme and manuals

Help Manual

Help pageTopics
Adjusts the value of the coefficients to account for the scaling of x and y.beta_adjust
Adjusts the value of the binomial coefficients to account for the scaling of x.beta_adjust_bin
The workhorse function for the binomial updates in mcen. It uses IRWLS glmnet updates to solve the regression problem.bin_horse
Creates the the working response for all responses for glmnet binomial familyCalcHorseBin
Creates the probabilities and working response for the glmnet update for a given response with a binomial familyCalcHorseEBin
Wrapper function for different clustering methodscluster
Returns the cluster values from a cv.mcen object.cluster.vals
Returns the coefficients from the cv.mcen object with the smallest cross-validation error.coef.cv.mcen
Returns the coefficients from an mcen object.coef.mcen
Cross validation for mcen functioncv.mcen
Gets the index position for the model with the smallest cross-validation error.get_best_cvm
matrix multiplymatrix_multiply
Fits an MCEN modelmcen
Calculates cluster assignment and coefficient estimates for a binomial mcen.mcen_bin_workhorse
Estimates the clusters and provides the coefficients for an mcen objectmcen_workhorse
Provides initial estimates for the mcen functionFmcen.init
Calculates the out of sample likelihood for an mcen objectpred_eval
Evaluates prediction error for multiple binomial responses.pred_eval.mbinom_mcen
Calculates the prediction error for a mgauss_mcen object.pred_eval.mgauss_mcen
Makes predictions from the model with the smallest cross-validation error.predict.cv.mcen
predictions from a mcen modelpredict.mcen
Prints nice output for a cv.mcen object.print.cv.mcen
Prints nice output for an mcen object.print.mcen
randomly assign n samples to k groupsrandomly_assign
SetEq test set equivalence of two clustering setsSetEq
Calculates sum of squared error between two vectors or matricessquared_error
Calculates out of sample error on the binomial likelihoodvl_binom