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CLME (version 2.0-9)

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

633

Version

2.0-9

License

GPL-3

Maintainer

Casey M Jelsema

Last Published

February 1st, 2018

Functions in CLME (2.0-9)

plot.summary.clme

S3 method to plot objects of class clme

Printout of fitted object.

Constructor method for objects S3 class clme

Constrained Inference for Linear Mixed Effects Models

model.matrix.clme

Extract the model design matrix.

model_terms_clme

Create model matrices for clme

print.summary.clme

S3 method to print a summary for objects of class clme

print.varcorr_clme

Printout for variance components

Produce summary values for objects of class clme

Variance-covariance matrix

Constrained inference for linear mixed models.

Variance components

Fibroid Growth Study

Extract fixed effects

Experiment on mice

Obtain Residual Bootstrap

Akaike information criterion

Number of observations

Bayesian information criterion

Extract random effects

S3 method to plot objects of class clme

Extract random effects

Williams' Type Test Statistic.

Individual confidence intervals

create.constraints

Generate common order constraints

Likelihood ratio type statistic (global)

MINQUE Algorithm

Various types of residuals

Shiny GUI for CLME

Residual variance components

sigma.summary.clme

Residual variance components

Constrained EM algorithm for linear fixed or mixed effects models.

Computes various types of residuals

Extract formula