fitSpline {statgenHTP} | R Documentation |
Fit Splines
Description
Fit P-Splines on corrected or raw data. The number of
knots is chosen by the user. The function outputs are predicted P-Spline
values and their first and second derivatives on a dense grid. The
outputs can then be used for outlier detection for time series
(see detectSerieOut
) and to estimate relevant parameters from
the curve for further analysis (see estimateSplineParameters
).
Usage
fitSpline(
inDat,
trait,
genotypes = NULL,
plotIds = NULL,
knots = 50,
useTimeNumber = FALSE,
timeNumber = NULL,
minNoTP = NULL
)
Arguments
inDat |
A data.frame with corrected spatial data. |
trait |
A character string indicating the trait for which the spline should be fitted. |
genotypes |
A character vector indicating the genotypes for which
splines should be fitted. If |
plotIds |
A character vector indicating the plotIds for which splines
should be fitted. If |
knots |
The number of knots to use when fitting the spline. |
useTimeNumber |
Should the timeNumber be used instead of the timePoint? |
timeNumber |
If |
minNoTP |
The minimum number of time points for which data should be available for a plant. Defaults to 80% of all time points present in the TP object. No splines are fitted for plants with less than the minimum number of timepoints. |
Value
An object of class HTPSpline
, a list with two
data.frames
, predDat
with predicted values and coefDat
with P-Spline coefficients on a dense grid.
See Also
Other functions for fitting splines:
plot.HTPSpline()
Examples
## The data from the Phenovator platform have been corrected for spatial
## trends and outliers for single observations have been removed.
## Fit P-Splines on a subset of genotypes
subGeno <- c("G070", "G160")
fit.spline <- fitSpline(inDat = spatCorrectedVator,
trait = "EffpsII_corr",
genotypes = subGeno,
knots = 50)
## Extract the data.frames with predicted values and P-Spline coefficients.
predDat <- fit.spline$predDat
head(predDat)
coefDat <- fit.spline$coefDat
head(coefDat)
## Visualize the P-Spline predictions for one genotype.
plot(fit.spline, genotypes = "G160")
## Visualize the P-Spline predictions and first derivatives for one plant.
plot(fit.spline, plotIds = "c10r29", plotType = "predictions")
plot(fit.spline, plotIds = "c10r29", plotType = "derivatives")