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| adaptControl | Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure. |
| 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. |
| BIC.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| coef.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| coef.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| family.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| fitted.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| fitted.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| fit_dat | Fit a Penalized Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) 'fit_dat' is used to fit a penalized generalized mixed model via Monte Carlo Expectation Conditional Minimization (MCECM) for a single tuning parameter combinations and is called within 'glmmPen' or 'glmm' (cannot be called directly by user) |
| fixef.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| fixef.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| formula.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| formula.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| glFormula_edit | Extracting Useful Vectors and Matrices from Formula and Data Information |
| glmm | Fit a Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) |
| glmmPen | Fit Penalized Generalized Mixed Models via Monte Carlo Expectation Conditional Minimization (MCECM) |
| glmmPen_FineSearch | Fit a Penalized Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) using a finer penalty grid search 'glmmPen_FineSearch' finds the best model from the selection results of a 'pglmmObj' object created by 'glmmPen', identifies a more targeted grid search around the optimum lambda penalty values, and performs model selection on this finer grid search. |
| lambdaControl | Control of Penalization Parameters and Selection Criteria |
| LambdaSeq | Calculation of Penalty Parameter Sequence (Lambda Sequence) |
| logLik.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| logLik.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| model.frame.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| model.frame.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| model.matrix.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| model.matrix.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| ngrps.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| nobs.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| optimControl | Control of Penalized Generalized Linear Mixed Model Fitting Constructs the control structure for the optimization of the penalized mixed model fit algorithm. |
| pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| pglmmObj-class | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| pglmmObj-method, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| plot.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| plot.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| plot_mcmc | Plot Diagnostics for MCMC Posterior Draws of the Random Effects |
| predict.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| predict.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| print.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| print.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| ranef.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| ranef.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| residuals.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| residuals.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| selectControl | Control of Penalization Parameters and Selection Criteria |
| select_tune | Fit a Sequence of Penalized Generalized Mixed Model via Monte Carlo Expectation Conditional Minimization (MCECM) 'select_tune' is used to fit a sequence of penalized generalized mixed models via Monte Carlo Expectation Conditional Minimization (MCECM) for multiple tuning parameter combinations and is called within 'glmmPen' (cannot be called directly by user) |
| show, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| sigma.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| sigma.pglmmObj, | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |
| sim.data | Simulates data to use for the 'glmmPen' package Simulates data to use for testing the 'glmmPen' package. Possible parameters to specify includes number of total covariates, number of non-zero fixed and random effects, and the magnitude of the random effect covariance values. |
| summary.pglmmObj | Class 'pglmmObj' of Fitted Penalized Generalized Mixed-Effects Models for package 'glmmPen' |