Variance Component Analysis
ANOVA-Type Estimation of Mixed Models
ANOVA-Type Estimation of Variance Components for Random Models
Standard 'as.matrix' Method for 'VCA' S3-Objects
Standard 'as.matrix' Method for 'VCAinference' S3-Objects
Build a Nested List.
Check for Availability of Intel's Math Kernel Library
Check Random Model for Given Dataset.
Check Tow Formula Terms for Potential Problems.
Extract Fixed Effects from 'VCA' Object
Variance-Covariance Matrix of Fixed Effects as Function of Covariance ...
Convert Objects to Detailed Error Message.
Fit Linear Mixed Model by ANOVA or REML
Fit Variance Component Model by ANOVA or REML
Generic Method for Extracting Fixed Effects from a Fitted Model
Extract Fixed Effects from 'VCA' Object
Calling F90-implementation of the SWEEP-Operator
Extract Confidence Intervals from VCA-Objects.
Degrees of Freedom for Testing Linear Contrasts of Fixed Effects and L...
Extract Degrees of Freedom from Linear Hypotheses of Fixed Effects or ...
Giesbrecht & Burns Approximation of the Variance-Covariance Matrix of ...
Intermediate Precision for remlVCA-fitted objects of class 'VCA'
Construct Linear Contrast Matrix for Hypothesis Tests
Extract a Specific Matrix from a 'VCA' Object
Overparameterized Design Matrices
ANOVA Sum of Squares via Sweeping
Determine V-Matrix for a 'VCA' Object
Check Whether Design Is Balanced Or Not
Add Legend to Margin.
Construct Variance-Covariance Matrix of Random Effects for Models Fitt...
Derive and Compute Matrices for Objects Fitted by Function 'lmer'
Derive VCA-Summary Table from an Object Fitted via Function lmer
Load 'RevoUtilsMath'-package if available
Least Squares Means of Fixed Effects
Contrast Matrix for LS Means
Extract the Model Frame from a 'VCA' Object
Model Matrix of a Fitted VCA-Object
Moore-Penrose Generalized Inverse of a Matrix
Re-Order Data.Frame
Standard 'plot' Method for 'VCA' S3-Objects.
Plot Random Variates of a Mixed Model ('VCA' Object).
Predictions from a Model Fitted by fitLMM
Standard Printing Method for Objects of Class 'VCA'
Standard Print Method for Objects of Class 'VCAinference'
Wrap Function-Calls to Execute Additional Checks.
Generic Method for Extracting Random Effects from a Fitted Model
Extract Random Effects from 'VCA' Object
Fit Linear Mixed Models via REML
Perform (V)ariance (C)omponent (A)nalysis via REML-Estimation
Re-Scale results of 'VCA' or 'VCAinference'
Extract Residuals of a 'VCA' Object
Satterthwaite Approximation for Total Degrees of Freedom and for Singl...
Automatically Scale Data Calling these Functions: 'anovaVCA', 'anovaMM...
Scale Response Variable to Ensure Robust Numerical Calculations
Solve System of Linear Equations using Inverse of Cholesky-Root
Solve Mixed Model Equations
Bottom-Up Step-Wise VCA-Analysis of the Complete Dataset
Summarize Outcome of a Variance Component Analysis.
Perform t-Tests for Linear Contrasts on Fixed Effects
Perform t-Tests for Linear Contrasts on LS Means
Compute the Trace of a Matrix
Variability Chart for Hierarchical Models.
(V)ariance (C)omponent (A)nalysis.
Inferential Statistics for VCA-Results
Calculate Variance-Covariance Matrix of Fixed Effects for an 'VCA' Obj...
Calculate Variance-Covariance Matrix and Standard Errors of Fixed Effe...
Calculate Variance-Covariance Matrix of Variance Components of 'VCA' o...
ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features.