statgenHTP (version 1.0.8)

fitModels: Fit spatial models per time point

Description

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.

Usage

fitModels(
  TP,
  trait,
  timePoints = names(TP),
  extraFixedFactors = NULL,
  geno.decomp = NULL,
  what = c("random", "fixed"),
  useCheck = FALSE,
  useRepId = FALSE,
  engine = c("SpATS", "asreml"),
  spatial = FALSE,
  quiet = FALSE
)

Value

An object of class fitMod, a list of fitted models.

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 TP object.

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 as
PSANOVA(colNum, rowNum, nseg = nSeg, nest.div = 2) where
nSeg = 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 tools:::Rd_expr_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"))
## Fit a model with SpATS for three time points.
modPhenoSp <- fitModels(TP = phenoTP,
                        trait = "EffpsII",
                        timePoints = c(3, 6, 20))
summary(modPhenoSp)

## 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)


## 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)
# }