Extract corrected phenotypic values from an object of class fitMod. After fitting a spatial model at each time point, the raw phenotypic data is corrected by subtracting the (estimated) sources of variation (environmental, design effect) that are of no interest (nuisances). This allows keeping the data resolution at the plot/plant level.
Usage
getCorrected(fitMod, timePoints = names(fitMod), outFile = NULL)
Arguments
- fitMod
An object of class
fitMod
.- timePoints
A character or numeric vector indicating the time point(s) for which the corrected values 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.
- outFile
A character string indicating the .csv file to which the results should be written. If
NULL
no file is written.
See also
Other functions for spatial modeling:
fitModels()
,
getEffDims()
,
getGenoPred()
,
getHerit()
,
getVar()
,
plot.fitMod()
,
summary.fitMod()
Examples
# \donttest{
## Using the first example dataset (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 corrected values for one time point:
spatCorrSp <- getCorrected(modPhenoSp,
timePoints = 6)
head(spatCorrSp)
#> timeNumber timePoint EffpsII_corr EffpsII wt genotype rowId
#> 1 6 2018-06-02 09:07:00 0.6810552 0.678 1636.287 G001 32
#> 2 6 2018-06-02 09:07:00 0.6625282 0.659 1636.287 G001 58
#> 3 6 2018-06-02 09:07:00 0.6286591 0.630 1636.287 G001 21
#> 4 6 2018-06-02 09:07:00 0.7115547 0.722 1636.287 G001 22
#> 5 6 2018-06-02 09:07:00 0.6764931 0.682 1636.287 G001 33
#> 6 6 2018-06-02 09:07:00 0.6455498 0.641 1636.287 G001 8
#> colId plotId
#> 1 14 c14r32
#> 2 17 c17r58
#> 3 20 c20r21
#> 4 6 c6r22
#> 5 5 c5r33
#> 6 21 c21r8
# }