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CLME (version 2.0-4)

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

2.0-4

License

GPL-3

Maintainer

Casey M Jelsema

Last Published

July 13th, 2015

Functions in CLME (2.0-4)

Constructor method for objects S3 class clme

Computes various types of residuals

Likelihood ratio type statistic (global)

Constrained inference for linear mixed models.

Constrained Inference for Linear Mixed Effects Models

Extract random effects

Extract fixed effects

Fibroid Growth Study

Akaike information criterion

Bayesian information criterion

Variance components

Shiny GUI for CLME

Individual confidence intervals

print.varcorr_clme

Printout for variance components

Printout of fitted object.

plot.summary.clme

S3 method to plot objects of class clme

Produce summary values for objects of class clme

Number of observations

model.matrix.clme

Extract the model design matrix.

sigma.summary.clme

Residual variance components

Experiment on mice

model.frame.clme

Extracts the model frame

Obtain Residual Bootstrap

Williams' Type Test Statistic.

Extract random effects

S3 method to plot objects of class clme

Constrained EM algorithm for linear fixed or mixed effects models.

model_terms_clme

Create model matrices for clme

Extract formula

Residual variance components

Various types of residuals

MINQUE Algorithm

print.summary.clme

S3 method to print a summary for objects of class clme

create.constraints

Generate common order constraints

Residual variance components

Variance-covariance matrix