HugeData

This dataset was added to have a very large dataset available for the 
	unit-test suite. It is an unbalanced dataset with three variables and 8070
	observations.

datasets

ANOVA-type estimation (prediction) of random effects in linear mixed models is implemented, following
             Searle et al. 1991 (ANOVA for unbalanced data). For better performance the SWEEP-Operator is now implemented
             for generating the ANOVA Type-1 error sum of squares. The primary objective of this package is to perform
             Variance Component Analyses (VCA). This is a special type of analysis frequently used in verifying the
             precision performance of diagnostics. 
             The Satterthwaite approximation of the total degrees of freedom and for individual variance components is
             implemented. These are used in the Chi-Squared tests against a claimed value, and in the respective confidence intervals. 
             Satterthwaite's approximation of denominator degrees of freedom in t-/F-tests of fixed effects are also
             available. 
             There are several functions for extracting, random effects, fixed effects, variance-covariance
             matrices of random and fixed effects. Residuals (marginal, conditional) can be extracted as raw, standardized
             and studentized residuals.
             Additionally, plotting methods for residuals and random effects and a variability chart are available. The latter
             is useful for visualizing the variability in sub-classes emerging from the experimental design.

Andre Schuetzenmeister

Variance Component Analysis

HugeData function

A data frame with 8070 observations on the following 3 variables.
<ul><li>yThe response variable.</li><li>VC1Variable with 8 levels corresponding to top-level variance component.</li><li>VC2Variable with 3920 levels corresponding to 2nd-level variance component.</li></ul>

HugeData: Huge Dataset with Three Variables

Description

Usage

Arguments

format