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VCA (version 1.2.1)

Variance Component Analysis

Description

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.

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Version

Install

install.packages('VCA')

Monthly Downloads

1,476

Version

1.2.1

License

GPL (>= 3)

Maintainer

Andre Schuetzenmeister

Last Published

November 24th, 2015

Functions in VCA (1.2.1)

DfSattHelper

Variance-Covariance Matrix of Fixed Effects as Function of Covariance Parameter Estimates.
CA19_9

Reproducibility Example Dataset from CLSI EP05-A3
anovaDF

ANOVA Type-I Degrees of Freedom.
chol2invData

Dataset Generating Error in Function 'chol2inv'
buildList

Build a Nested List.
getSSQsweep

ANOVA Sum of Squares via Sweeping.
isBalanced

Check Whether Design Is Balanced Or Not.
lsmMat

Contrast Matrix for LS Means.
SattDF

Satterthwaite Approximation for Total Degrees of Freedom and for Single Variance Components.
HugeData

Huge Dataset with Three Variables
MPinv

Moore-Penrose Inverse of a Matrix.
print.VCA

Standard Printing Method for Objects of Class 'VCA'.
lsmeans

Least Squares Means of Fixed Effects.
solveMME

Solve Mixed Model Equations.
dataRS0003_2

Simulated Repeated Measurements Data.
varPlot

Variability Chart for Hierarchical Models.
getSSQqf

ANOVA Sum of Squares via Quadratic Forms
coef.VCA

Extract Fixed Effects from 'VCA' Object.
dataEP05A2_1

Simulated Data of a CLSI EP05-A2 20/2/2 Experiment
ranef.VCA

Extract Random Effects from 'VCA' Object.
dataRS0003_1

Simulated Repeated Measurements Data.
dataEP05A2_2

Simulated Data of a CLSI EP05-A2 20/2/2 Experiment
test.lsmeans

Perform t-Tests for Linear Contrasts on LS Means.
getCmatrix

Coefficient Matrix for (V)ariance (C)omponent (A)nalysis.
fixef.VCA

Extract Fixed Effects from 'VCA' Object.
Csweep

Calling C-implementation of the SWEEP-Operator
as.matrix.VCA

Standard 'as.matrix' Method for 'VCA' S3-Objects.
realData

Real-World Data
dataEP05A3_MS_2

Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment
getDDFM

Degrees of Freedom for Testing Linear Contrast of Fixed Effects and Least Square Means.
print.VCAinference

Standard Print Method for Objects of Class 'VCAinference'.
VCAinference

Inferential Statistics for VCA-Results.
getMM

Overparameterized Design Matrices.
dataRS0003_3

Simulated Repeated Measurements Data.
dataEP05A3_MS_3

Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment
fixef

Generic Method for Extracting Fixed Effects from a Fitted Model.
Orthodont

Orthodont dataset from R-package 'nlme'
ranef

Generic Method for Extracting Random Effects from a Fitted Model.
getMat

Extract a Specific Matrix from a 'VCA' Object.
vcovFixed

Calculate Variance-Covariance Matrix and Standard Errors of Fixed Effects for an 'VCA' Object.
getDF

Extract Degrees of Freedom from Linear Hypotheses of Fixed Effects or LS Means.
dataRS0005_1

Simulated Data of 5/3 Experiment.
getL

Construct Linear Contrast Matrix for Hypothesis Tests.
stepwiseVCA

Bottom-Up Step-Wise VCA-Analysis of the Complete Dataset.
sleepstudy

sleepstudy dataset from R-package 'lme4'
vcovVC

Calculate Variance-Covariance Matrix of Variance Components of 'VCA' objects.
Trace

Compute the Trace of a Matrix.
test.fixef

Perform t-Tests for Linear Contrasts on Fixed Effects.
Sinv

Calling C-implementation of the SWEEP-Operator for Matrix-Inversion
residuals.VCA

Extract Residuals of a 'VCA' Object.
anovaVCA

ANOVA-Type Estimation of Variance Components for Random Models.
Glucose

Inermediate Precision Data from CLSI EP05-A3
VCA-package

(V)ariance (C)omponent (A)nalysis.
VCAdata1

Simulated Data for Variance Component Analysis.
dataEP05A2_3

Simulated Data of a CLSI EP05-A2 20/2/2 Experiment
dataEP05A3_MS_1

Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment
dataRS0005_2

Simulated Data of 5/3 Experiment.
plotRandVar

Plot Random Variates of a Mixed Model ('VCA' Object).
getVCvar

Covariance-Matrix of Variance Components.
getAmatrix

Determine A-Matrix.
dataRS0005_3

Simulated Data of 5/3 Experiment.
anovaMM

ANOVA-Type Estimation of Mixed Models.
Solve

Solve System of Linear Equations using Inverse of Cholesky-Root.
getV

Determine V-Matrix for a 'VCA' Object.
vcov.VCA

Calculate Variance-Covariance Matrix of Fixed Effects for an 'VCA' Object.