Description Usage Arguments Details Value Author(s) References See Also Examples

This function builds a variance components analysis (VCA) table
from an object representing a model fitted by function `lmer`

of the `lme4`

R-package.

1 2 |

`obj` |
(lmerMod) object as returned by function |

`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) |

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).

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

Andre Schuetzenmeister [email protected]

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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## 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)
## End(Not run)
``` |

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