sommer (version 4.1.7)

predict.mmec: Predict form of a LMM fitted with mmec

Description

predict method for class "mmec".

Usage

# S3 method for mmec
predict(object, Dtable=NULL, 
        D=NULL,
        ...)

Value

pvals

the table of predictions according to the specified arguments.

hypertable

the summary table specifying the ignored, include and average sets.

model

the mixed model used within predict.

C11

the inverse of the coefficient matrix corresponding to the fixed effects.

C12

the inverse of the coefficient matrix corresponding to the covariance between fixed and random effects.

C22

the inverse of the coefficient matrix corresponding to the random effects.

Xextended

the model matrix for fixed effects used to compute BLUPs and SEs.

Zextended

the model matrix for random effects used to compute BLUPs and SEs.

Arguments

object

a mixed model of class "mmec"

Dtable

a table specifying the terms to be included or averaged.

An ignored term means that the model matrix part for that fixed or random effect are not used in the BLUP and SE calculation.

An averaged term means that the model matrices for that fixed or random effect is filled purely with the value 1/#levels for that term.

D

an optional D matrix specifying the matrix to be used for the predictions.

...

Further arguments to be passed to the model fit (i.e. iters, etc.).

Author

Giovanny Covarrubias

Details

This function allows to produce predictions specifying those variables that define the margins of the hypertable to be predicted (argument classify). Predictions are obtained for each combination of values of the specified variables that is present in the data set used to fit the model. See vignettes for more details.

For predicted values the pertinent design matrices X and Z together with BLUEs (b) and BLUPs (u) are multiplied and added together.

predicted.value equal Xb + Zu.1 + ... + Zu.n

For computing standard errors for predictions the parts of the coefficient matrix:

C11 equal (X'ViX)-

C12 equal 0 - [(X'V-X)-X' V- GZ]

C22 equal PEV equal G - [Z'G[V- - (VX*tXVXVX)]GZ']

and are summarized as:

standard.errors equal sqrt( rowSums(XC11*X') + rowSums(2*(XC12*Z')) + rowSums(ZC22*Z') )

when both fixed and random effects are present in the inclusion set. If only fixed and random effects are included, only the respective terms from the SE for fixed or random effects are calculated.

References

Welham, S., Cullis, B., Gogel, B., Gilmour, A., and Thompson, R. (2004). Prediction in linear mixed models. Australian and New Zealand Journal of Statistics, 46, 325 - 347.

See Also

predict, mmec

Examples

data(DT_yatesoats)
DT <- DT_yatesoats
m3 <- mmec(fixed=Y ~ V + N + V:N ,
           random = ~ B + B:MP,
           rcov=~units,
           data = DT)

#############################
## predict means for nitrogen
#############################
Dt <- m3$Dtable; Dt
# prediction matrix D based on (equivalent to classify in asreml)
Dt[2,"D"] = TRUE
# first fixed effect just average
Dt[1,"average"] = TRUE
# second fixed effect include
Dt[2,"include"] = TRUE
# third fixed effect include and average
Dt[3,"include"] = TRUE
Dt[3,"average"] = TRUE
Dt

pp=predict(object=m3, Dtable=Dt)
pp$pvals

#############################
## predict means for variety
#############################

Dt <- m3$Dtable; Dt
# prediction matrix D based on (equivalent to classify in asreml)
Dt[1,"D"] = TRUE
# first fixed effect include
Dt[1,"include"] = TRUE
# second fixed effect just average
Dt[2,"average"] = TRUE
# third fixed effect include and average
Dt[3,"include"] = TRUE
Dt[3,"average"] = TRUE
Dt

pp=predict(object=m3, Dtable=Dt)
pp$pvals

#############################
## predict means for nitrogen:variety
#############################
# prediction matrix D based on (equivalent to classify in asreml)
Dt <- m3$Dtable; Dt
Dt[3,"D"] = TRUE
# first fixed effect include and average
Dt[1,"include"] = TRUE
Dt[1,"average"] = TRUE
# second fixed effect include and average
Dt[2,"include"] = TRUE
Dt[2,"average"] = TRUE
# third fixed effect include and average
Dt[3,"include"] = TRUE
Dt[3,"average"] = TRUE
Dt

pp=predict(object=m3, Dtable=Dt)
pp$pvals