Extract effective dimensions

Extract effective dimensions from an object of class fitMod. The table below gives an overview of the effective dimensions and an explanation of their meaning.

Effective Dimension	Explanation
colId	Linear trend along columns
rowId	Linear trend along rows
fCol	Smooth trend along columns
fRow	Smooth trend along rows
fColRow	Linear trend in rows changing smoothly along cols
colfRow	Linear trend in cols changing smoothly along rows
fColfRow	Smooth-by-smooth interaction trend over rows and cols
surface	Sum of smooth trends

Usage

getEffDims(
  fitMod,
  timePoints = names(fitMod),
  EDType = c("dimension", "ratio"),
  outFile = NULL
)

Arguments

fitMod

An object of class fitMod.

timePoints

A character or numeric vector indicating the time point(s) for which the effective dimension should be extracted. 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.

EDType

A character string specifying if the effective dimension ("dimension") or the ratio of effective dimensions ("ratio") should be returned.

outFile

A character string indicating the .csv file to which the results should be written. If NULL no file is written.

Value

A data.frame with effective dimensions per time point.

See also

Other functions for spatial modeling: fitModels(), getCorrected(), getGenoPred(), getHerit(), getVar(), plot.fitMod(), summary.fitMod()

Examples

# \donttest{
## Using the first example dataset (PhenovatorDat1):
data("PhenovatorDat1")
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 SpATS model on few time points:
modPhenoSp <- fitModels(TP = phenoTP,
                        trait = "EffpsII",
                        timePoints = c(1, 6, 20))
#> 2018-05-31 16:37:00
#> 2018-06-02 09:07:00
#> 2018-06-05 14:37:00

## Extract the effective dimensions for all available time points in the
## model object:
effDimSp <- getEffDims(modPhenoSp)
# }