Package: glmmPen 1.5.4.8

glmmPen: High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties.

Authors:Hillary Heiling [aut, cre], Naim Rashid [aut], Quefeng Li [aut], Joseph Ibrahim [aut]

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

Peer review:

Bug tracker:https://github.com/hheiling/glmmpen/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • basal - Basal dataset: A composition of cancer datasets with top scoring pairs (TSPs) as covariates and binary response indicating if the subject's cancer subtype was basal-like. A dataset composed of four datasets combined from studies that contain gene expression data from subjects with several types of cancer. Two of these datasets contain gene expression data for subjects with Pancreatic Ductal Adenocarcinoma (PDAC), one dataset contains data for subjects with Breast Cancer, and the fourth dataset contains data for subjects with Bladder Cancer. The response of interest is whether or not the subject's cancer subtype was the basal-like subtype. See articles Rashid et al. (2020) "Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction" and Moffitt et al. (2015) "Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma" for further details on these four datasets.

On CRAN:

4.01 score 6 stars 17 scripts 250 downloads 21 exports 68 dependencies

Last updated 3 months agofrom:c7911363cc. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-win-x86_64NOTEOct 29 2024
R-4.5-linux-x86_64NOTEOct 29 2024
R-4.4-win-x86_64NOTEOct 29 2024
R-4.4-mac-x86_64NOTEOct 29 2024
R-4.4-mac-aarch64NOTEOct 29 2024
R-4.3-win-x86_64NOTEOct 29 2024
R-4.3-mac-x86_64NOTEOct 29 2024
R-4.3-mac-aarch64NOTEOct 29 2024

Exports:adaptControlglmmglmm_FAglmmPenglmmPen_FAlambdaControlLambdaSeqoptimControlphmmphmm_FAphmmPenphmmPen_FAplot_mcmcrControlselectControlsim.datasim.data.FAsim.data.piecewise.expsim.data.weibullsurvival_datasurvivalControl

Dependencies:abindbackportsBHbigmemorybigmemory.sribootcallrcheckmateclicolorspacedescdistributionalfansifarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsmgcvminqamunsellmvtnormncvregnlmenloptrnumDerivpillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrsurvivaltensorAtibbleutf8uuidvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure.adaptControl
Basal dataset: A composition of cancer datasets with top scoring pairs (TSPs) as covariates and binary response indicating if the subject's cancer subtype was basal-like. A dataset composed of four datasets combined from studies that contain gene expression data from subjects with several types of cancer. Two of these datasets contain gene expression data for subjects with Pancreatic Ductal Adenocarcinoma (PDAC), one dataset contains data for subjects with Breast Cancer, and the fourth dataset contains data for subjects with Bladder Cancer. The response of interest is whether or not the subject's cancer subtype was the basal-like subtype. See articles Rashid et al. (2020) "Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction" and Moffitt et al. (2015) "Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma" for further details on these four datasets.basal
Fit a Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM)glmm
Fit a Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM)glmm_FA
Fit Penalized Generalized Mixed Models via Monte Carlo Expectation Conditional Minimization (MCECM)glmmPen
Fit Penalized Generalized Mixed Models via Monte Carlo Expectation Conditional Minimization (MCECM)glmmPen_FA
Control of Penalization Parameters and Selection CriterialambdaControl selectControl
Calculation of Penalty Parameter Sequence (Lambda Sequence)LambdaSeq
Control of Penalized Generalized Linear Mixed Model FittingoptimControl
Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen'BIC.pglmmObj coef.pglmmObj coef.pglmmObj, family.pglmmObj fitted.pglmmObj fitted.pglmmObj, fixef.pglmmObj fixef.pglmmObj, formula.pglmmObj formula.pglmmObj, logLik.pglmmObj logLik.pglmmObj, model.frame.pglmmObj model.frame.pglmmObj, model.matrix.pglmmObj model.matrix.pglmmObj, ngrps.pglmmObj nobs.pglmmObj pglmmObj pglmmObj-class pglmmObj-method, plot.pglmmObj plot.pglmmObj, predict.pglmmObj predict.pglmmObj, print.pglmmObj print.pglmmObj, ranef.pglmmObj ranef.pglmmObj, residuals.pglmmObj residuals.pglmmObj, show, sigma.pglmmObj sigma.pglmmObj, summary.pglmmObj
Fit a Proportional Hazards Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) using a Piecewise Constant Hazard Mixed Model Approximationphmm
Fit a Proportional Hazards Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) using a Piecewise Constant Hazard Mixed Model Approximationphmm_FA
Fit Penalized Proportional Hazards Mixed Models via Monte Carlo Expectation Conditional Minimization (MCECM) using a Piecewise Constant Hazard Mixed Model ApproximationphmmPen
Fit a Penalized Proportional Hazards Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) using a Piecewise Constant Hazard Mixed Model ApproximationphmmPen_FA
Plot Diagnostics for MCMC Posterior Draws of the Random Effectsplot_mcmc
Control of Latent Factor Model Number Estimation Constructs the control structure for the estimation of the number of latent factors (r) for use within the 'glmmPen_FA' and 'glmm_FA' estimation procedures.rControl
Simulates data to use for the 'glmmPen' packagesim.data sim.data.FA sim.data.piecewise.exp sim.data.weibull
Convert Input Survival Data Into Long-Form Data Needed for Fitting a Piecewise Exponential Modelsurvival_data
Control for Fitting Piecewise Constant Hazard Mixed Models as an Approximation to Fitting Cox Proportional Hazards Mixed ModelssurvivalControl