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CLME (version 2.0-12)

Constrained Inference for Linear Mixed Effects Models

Description

Estimation and inference for linear models where some or all of the fixed-effects coefficients are subject to order restrictions. This package uses the robust residual bootstrap methodology for inference, and can handle some structure in the residual variance matrix.

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Version

Install

install.packages('CLME')

Monthly Downloads

206

Version

2.0-12

License

GPL-3

Maintainer

Casey M Jelsema

Last Published

June 7th, 2020

Functions in CLME (2.0-12)

Akaike information criterion

model_terms_clme

Create model matrices for clme

model.matrix.clme

Extract the model design matrix.

Bayesian information criterion

Extract formula

Constructor method for objects S3 class clme

Variance-covariance matrix

Likelihood ratio type statistic (global)

Produce summary values for objects of class clme

MINQUE Algorithm

plot.summary.clme

S3 method to plot objects of class clme

Extract random effects

Constrained Inference for Linear Mixed Effects Models

Printout of fitted object.

Extract random effects

Individual confidence intervals

create.constraints

Generate common order constraints

print.varcorr_clme

Printout for variance components

Extract fixed effects

print.summary.clme

S3 method to print a summary for objects of class clme

Fibroid Growth Study

Number of observations

S3 method to plot objects of class clme

Various types of residuals

Experiment on mice

Obtain Residual Bootstrap

Residual variance components

Shiny GUI for CLME

sigma.summary.clme

Residual variance components

Williams' Type Test Statistic.

Constrained inference for linear mixed models.

Variance components

Computes various types of residuals

Constrained EM algorithm for linear fixed or mixed effects models.