The actual model fitted depends on the design. For the supported designs, the
following models are used:
- ibd
trait = genotype + subBlock + e
- res.ibd
trait = genotype + repId +
repId:subBlock + e
- rcbd
trait = genotype + repId + e
- rowcol
trait = genotype + rowId + colId + e
- res.rowcol
trait = genotype + repId +
repId:rowId + repId:colId + e
In the above models fixed effects are indicated in bold, random
effects in italics. genotype is fitted as fixed or random effect
depending on the value of what
.
In case useCheckId = TRUE
, an extra fixed effect checkId is
included in the model.
Variables in covariates
are fitted as extra fixed effects.
When SpATS
is used for modeling, an extra spatial term is included
in the model. This term is constructed using the function
PSANOVA
from the SpATS package as
PSANOVA(colCoord, rowCoord, nseg = nSeg, nest.div = 2)
where nSeg = (number of columns / 2, number of rows / 2)
. nseg and
nest.div can be modified using the control
parameter.
When asreml
is used for modeling and spatial
is TRUE
six models are fitted with different random terms and covariance structure.
The best model is determined based on a goodness-of-fit criterion, either
AIC or BIC. This can be set using the control parameter criterion
,
default is AIC.
The fitted random terms depend on the structure of the data. If the design
has a regular structure, i.e. all replicates appear the same amount of times
in the design, the following combinations of random and spatial terms are
fitted
random = NULL, spatial = exp(rowCoord):colCoord
random = NULL, spatial = rowCoord:exp(colCoord)
random = NULL, spatial = iexp(rowCoord,colCoord)
random = repId:rowId, spatial = exp(rowCoord):colCoord
random = repId:colId, spatial = rowCoord:exp(colCoord)
random = repId:rowId + repId:colId, spatial = iexp(rowCoord,colCoord)
If the design is not regular the following combinations of random and spatial
terms are fitted
random = NULL, spatial = ar1(rowId):colId
random = NULL, spatial = rowId:ar1(colId)
random = NULL, spatial = ar1(rowId):ar1(colId)
random = repId:rowId, spatial = ar1(rowId):colId
random = repId:colId, spatial = rowId:ar1(colId)
random = repId:rowId + repId:colId, spatial = ar1(rowId):ar1(colId)
If there are no replicates in the model, repId is left out from the random
parts above.