lme4breeding (version 1.0.90)

lmebreed-class: Relationship-based mixed-effects model fits

Description

A mixed-effects model fit by lmebreed. This class extends class "merMod" class and includes one additional slot, relfac, which is a list of (left) Cholesky factors of the relationship matrices derived from "lmebreed" objects.

Objects from the Class

Objects are created by calls to the lmebreed function.

Slots

relfac:

A list of relationship matrix factors. All other slots are inherited from class "merMod".

udu:

A list of eigen decomposition elements. All other slots are inherited from class "merMod".

Extends

Class "merMod", directly.

Methods

fitted

signature(object = "lmebreed"): actually a non-method in that fitted doesn't apply to such objects because of the pre-whitening.

ranef

signature(object = "lmebreed"): back-transforms BLUPs and their conditional variances when models include the relationship between levels of random effects as returned for the object viewed as a "merMod)" object.

residuals

signature(object = "lmebreed"): also a non-method for the same reason as fitted

predict

signature(object = "lmebreed"): a function to compute linear combinations of fixed and random effects as defined by Welham et al. (2004). It has the following arguments:

predict(object, hyperTable=NULL, classify=NULL, ... )

object: is the model fit with the lmebreed function.

hyperTable: a data frame with columns; variable, group, type, include, average. See the Dtable function to understand the format.

A pure "include" term means that the model matrices for that fixed or random effect is filled with 1's for the positions where column names and row names match.

An "include and average" term means that the model matrices for that fixed or random effect is filled with 1/#1's in that row.

An "average" term alone means that all rows for such fixed or random effect will be filled with 1/#levels in the effect.

If a term is not considered "include" or "average" is then totally ignored in the BLUP and SE calculation.

The default behavior when the user doesn't provide the hyperTable is to include and average any fixed effect that is not part of classify. Include any term making a perfect match with the classify argument and include and average any imperfect match with classify argument (e.g., interactions).

classify: is a character value indicating which term we are computing the predictions for.

See Also

lmebreed

Examples

showClass("lmebreed")

data(DT_example)
DT <- DT_example
A <- A_example

## Compound simmetry (CS) model
ans1 <- lmebreed(Yield~Env + (1|Name) + (1|Env:Name),
                 data=DT)
                 
pp <- predict(ans1, classify="Name")
head(pp$pvals)

image(pp$D)

pp$hyperTable