Perform REML analysis at each time point using either SpATS or asreml. The idea is to is to accurately separate the genetic effects from the spatial effects at each time point. SpATS is used as a default method. See details for the exact models fitted.
Arguments
- TP
An object of class
TP.- trait
A character string indicating the trait used as response variable in the model.
- timePoints
A character or numeric vector indicating the time points to be modeled. When using a character string to reference a time point, the value has to be an exact match to one of the existing time points. When using a number it will be matched by its number ("timeNumber") in the timePoints attribute of the
TPobject.- extraFixedFactors
A character vector indicating the variables to use as extra fixed effects in the model.
- geno.decomp
A character vector indicating the variables to use to group the genotypic variance in the model.
- what
A character vector specifying whether "genotype" should be fitted as "random" or "fixed" effect. Note that when using
geno.decomp, fitting a model with genotype as "fixed" effect is not possible.- useCheck
Should check genotypes be used as an extra factor in the model?
- useRepId
Should repId be used as a fixed effect in the model? When fitting a spatial model rowId and colId are also nested within repId in the random part of the model.
- engine
A character string indicating the engine used to fit the models.
- spatial
Should a spatial model be fitted for asreml?
- quiet
Should printed progress messages be suppressed?
Details
The actual model fitted depends on the function parameters specified. The
basic model is the following:
trait = genotype + e
In case useCheck = TRUE, instead of genotype, genoCheck is used as
genotype and check is used as an extra fixed effect. So then the model
becomes:
trait = check + genoCheck + e
Variables in extraFixedFactors are fitted as extra fixed
effects.
When SpATS is used for modeling, an extra spatial term is always
included in the model. This term is constructed using the function
PSANOVA from the SpATS package asPSANOVA(colNum, rowNum, nseg = nSeg, nest.div = 2)
wherenSeg = c(number of columns, number of rows).
When asreml is used for modeling and spatial = TRUE,
four models are fitted with different covariance structures.
The best model is determined based on a goodness-of-fit criterion, AIC,
on 20% of the time points or at least 10 time points. The best model is then
run on all time points.
The following combinations of random and spatial terms are fitted
random = repId:rowId + repId:colId, spatial = NULL
random = repId:rowId + repId:colId, spatial = ar1(rowId):colId
random = repId:colId + 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.
When geno.decomp is specified, the genotypic variance is decomposed
following the variable(s) chosen. For example, when a treatment is used in
geno.decomp, the initial model becomes:
trait = treatment + treatment:genotype + e
References
Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2017). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics doi:10.1016/j.spasta.2017.10.003 Butler, D. G., et al. (2018). ASReml-R Reference Manual Version 4. VSN International Ltd, http://asreml.org
See also
Other functions for spatial modeling:
getCorrected(),
getEffDims(),
getGenoPred(),
getHerit(),
getVar(),
plot.fitMod(),
summary.fitMod()
Examples
## Using the first example dataset (PhenovatorDat1):
## Fit a model using SpATS on few time points:
# \donttest{
## Create an object of class TP.
phenoTP <- createTimePoints(dat = PhenovatorDat1,
experimentName = "Phenovator",
genotype = "Genotype",
timePoint = "timepoints",
repId = "Replicate",
plotId = "pos",
rowNum = "y", colNum = "x",
addCheck = TRUE,
checkGenotypes = c("check1", "check2",
"check3", "check4"))
#> Warning: The following plotIds have observations for less than 50% of the time points:
#> c24r41, c7r18, c7r49
## Fit a model with SpATS for three time points.
modPhenoSp <- fitModels(TP = phenoTP,
trait = "EffpsII",
timePoints = c(3, 6, 20))
#> 2018-06-01 11:37:00
#> 2018-06-02 09:07:00
#> 2018-06-05 14:37:00
summary(modPhenoSp)
#> Models in modPhenoSp where fitted for experiment Phenovator.
#>
#> It contains 3 time points.
#> The models were fitted using SpATS.
#>
## Fit a model with SpATS for a single time point with extra fixed factors
## and check genotypes:
modPhenoSpCheck <- fitModels(TP = phenoTP,
trait = "EffpsII",
extraFixedFactors = c("repId", "Image_pos"),
useCheck = TRUE,
timePoints = 3)
#> 2018-06-01 11:37:00
## Fit a model with asreml on few time points with a spatial function:
if (requireNamespace("asreml", quietly = TRUE)) {
modPhenoSpAs <- fitModels(TP = phenoTP,
trait = "EffpsII",
timePoints = c(1, 6, 20),
engine = "asreml",
spatial = TRUE)
}
## Using the second example dataset (PhenoarchDat1):
## Fit a model with SpATS on one time points with two variables for
## geno.decomp:
data("PhenoarchDat1")
phenoTParch <- createTimePoints(dat = PhenoarchDat1,
experimentName = "Phenoarch",
genotype = "Genotype",
timePoint = "Date",
plotId = "pos",
rowNum = "Row",
colNum = "Col")
modPhenoSpGD <- fitModels(TP = phenoTParch,
trait = "LeafArea",
geno.decomp = c("Scenario", "population"),
timePoints = 16)
#> 2017-04-28
# }