MPinv

This function is originally impelemented in package 'MASS' as function <code>ginv</code>. 
It was adapted to be able to deal with matrices from the 'Matrix' package,
e.g. sparse matrices.

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.

Andre Schuetzenmeister

Variance Component Analysis

MPinv function

<dl><dt>X</dt>
<dd>(object) two-dimensional, for which a Moore-Penrose inverse
has to be computed</dd>
<dt>tol</dt>
<dd>(numeric) tolerance value to be used in comparisons</dd></dl>

Arguments

Author

Moore-Penrose Generalized Inverse of a Matrix — MPinv

<dl>

<dt>X</dt>
<dd>(object) two-dimensional, for which a Moore-Penrose inverse
has to be computed</dd>


<dt>tol</dt>
<dd>(numeric) tolerance value to be used in comparisons</dd>

</dl>

MPinv: Moore-Penrose Generalized Inverse of a Matrix

Description

Usage

Value

Arguments

Author