VCA (version 1.3.4)

lmerSummary: Derive VCA-Summary Table from an Object Fitted via Function lmer.

Description

This function builds a variance components analysis (VCA) table from an object representing a model fitted by function lmer of the lme4 R-package.

Usage

lmerSummary(obj, VarVC = TRUE, terms = NULL, Mean = NULL, cov = FALSE,
  X = NULL, tab.only = FALSE)

Arguments

obj

(lmerMod) object as returned by function lmer

VarVC

(logical) TRUE = the variance-covariance matrix of variance components will be approximated following the Giesbrecht & Burns approach, FALSE = it will not be approximated

terms

(character) vector, optionally defining the order of variance terms to be used

Mean

(numeric) mean value used for CV-calculation

cov

(logical) TRUE = in case of non-zero covariances a block diagonal matrix will be constructed, FALSE = a diagonal matrix with all off-diagonal elements being equal to zero will be contructed

X

(matrix) design matrix of fixed effects as constructed to meet VCA-package requirements

tab.only

(logical) TRUE = will return only the VCA-results table as 'data.frame', argument 'VarVC' will automatically set to 'FALSE' (see details)

Value

(list) still a premature 'VCA'-object but close to a "complete" 'VCA'-object

Details

It applies the approximation of the variance-covariance matrix of variance components according to Giesbrecht & Burns (1985) and uses this information to approximate the degrees of freedom according to Satterthwaite (see SAS PROC MIXED documentation option 'CL').

This function can be used to create a VCA-results table from almost any fitted 'lmerMod'-object, i.e. one can apply it to a model fitted via function lmer of the lme4-package. The only additional argument that needs to be used is 'tab.only' (see examples).

References

Searle, S.R, Casella, G., McCulloch, C.E. (1992), Variance Components, Wiley New York

Giesbrecht, F.G. and Burns, J.C. (1985), Two-Stage Analysis Based on a Mixed Model: Large-Sample Asymptotic Theory and Small-Sample Simulation Results, Biometrics 41, p. 477-486

See Also

remlVCA, remlMM

Examples

# NOT RUN {
# fit a model with a VCA-function first
data(VCAdata1)
fit0 <- remlVCA(y~(device+lot)/day/run, subset(VCAdata1, sample==5))
fit0

# fit the same model with function 'lmer' of the 'lme4'-package
library(lme4)
fit1 <- lmer(y~(1|device)+(1|lot)+(1|device:lot:day)+(1|device:lot:day:run),
             subset(VCAdata1, sample==5))
lmerSummary(fit1, tab.only=TRUE)
# }