statgenHTP (version 1.0.8)

estimateSplineParameters: Extract estimates from fitted splines.

Description

Function for extracting parameter estimates from fitted splines on a specified interval.

Usage

estimateSplineParameters(
  x,
  estimate = c("predictions", "derivatives", "derivatives2"),
  what = c("min", "max", "mean", "AUC", "p"),
  AUCScale = c("min", "hour", "day"),
  timeMin = NULL,
  timeMax = NULL,
  genotypes = NULL,
  plotIds = NULL,
  fitLevel = c("geno", "plot", "genoDev", "plotDev")
)

Value

An object of class splineEst, a data.frame containing the estimated parameters.

Arguments

x

An object of class HTPSpline, the output of the fitSpline function, or class splineHDm, the output of the fitSplineHDM function

estimate

The P-Spline component for which the estimate should be extracted, the predictions, the first derivatives or the second derivatives ("derivatives2")

what

The types of estimate that should be extracted. Either minimum ("min"), maximum ("max"), mean, area under the curve ("AUC") or a percentile. Percentiles should be given as p + percentile. E.g. for the 10th percentile specify what = "p10". Multiple types of estimate can be extracted at once.

AUCScale

The area under the curve is dependent on the scale used on the x-axis. By default the area is computed assuming a scale in minutes. This can be changed to either hours or days.

timeMin

The lower bound of the time interval from which the estimates should be extracted. If NULL the smallest time value for which the splines were fitted is used. timeMin should be given as a numerical value that corresponds to the time scale used for fitting the splines. See the examples.

timeMax

The upper bound of the time interval from which the estimates should be extracted. If NULL the largest time value for which the splines were fitted is used. timeMin should be given as a numerical value that corresponds to the time scale used for fitting the splines. See the examples.

genotypes

A character vector indicating the genotypes for which estimates should be extracted. If NULL, estimates will be extracted for all genotypes for which splines where fitted.

plotIds

A character vector indicating the plotIds for which estimates should be extracted. If NULL, estimates will be extracted for all plotIds for which splines where fitted.

fitLevel

A character string indicating at which level of the data the parameter estimates should be made. Only used for splines fitted using fitSplineHDM.

See Also

Other functions for spline parameter estimation: plot.splineEst()

Examples

### Estimate parameters for fitted P-splines.

## Run the function to fit P-splines on a subset of genotypes.
subGeno <- c("G160", "G151")
fit.spline <- fitSpline(inDat = spatCorrectedVator,
                        trait = "EffpsII_corr",
                        genotypes = subGeno,
                        knots = 50)

## Estimate the maximum value of the predictions at the beginning of the time course.
## The spline was fitted at a timePoints scale, i.e. date-time so
## timeMin and timeMax should be given at this scale as well.
paramVator <- estimateSplineParameters(x = fit.spline,
                                       estimate = "predictions",
                                       what = "max",
                                       timeMin = 1527784620,
                                       timeMax = 1528500000,
                                       genotypes = subGeno)
head(paramVator)

## Create a boxplot of the estimates.
plot(paramVator, plotType = "box")

## Estimate the minimum and maximum value of the predictions.
paramVator2 <- estimateSplineParameters(x = fit.spline,
                                        estimate = "predictions",
                                        what = c("min", "max"),
                                        genotypes = subGeno)
head(paramVator2)


### Estimate parameters for fitted HDM-splines.

## The data from the Phenovator platform have been corrected for spatial
## trends and outliers for single observations have been removed.

## We need to specify the genotype-by-treatment interaction.
## Treatment: water regime (WW, WD).
spatCorrectedArch[["treat"]] <- substr(spatCorrectedArch[["geno.decomp"]],
                                      start = 1, stop = 2)
spatCorrectedArch[["genoTreat"]] <-
  interaction(spatCorrectedArch[["genotype"]],
             spatCorrectedArch[["treat"]], sep = "_")

## Fit P-Splines Hierarchical Curve Data Model for selection of genotypes.
fit.psHDM  <- fitSplineHDM(inDat = spatCorrectedArch,
                          trait = "LeafArea_corr",
                          genotypes = c("GenoA14_WD", "GenoA51_WD",
                                       "GenoB11_WW", "GenoB02_WD",
                                       "GenoB02_WW"),
                          time = "timeNumber",
                          pop = "geno.decomp",
                          genotype = "genoTreat",
                          plotId = "plotId",
                          difVar = list(geno = FALSE, plot = FALSE),
                          smoothPop = list(nseg = 4, bdeg = 3, pord = 2),
                          smoothGeno = list(nseg = 4, bdeg = 3, pord = 2),
                          smoothPlot = list(nseg = 4, bdeg = 3, pord = 2),
                          weights = "wt",
                          trace = FALSE)

## Estimate minimum, maximum, and mean for predictions at the genotype level.
## The spline was fitted at the timeNumber scale, so timeMax
## should be given at that scale as well.
paramArch <- estimateSplineParameters(x = fit.psHDM,
                                     what = c("min", "max", "mean"),
                                     fitLevel = "geno",
                                     estimate = "predictions",
                                     timeMax = 28)
head(paramArch)

## Create a boxplot of the estimates.
plot(paramArch, plotType = "box")

## Estimate area under the curve for predictions at the plot level.
paramArch2 <- estimateSplineParameters(x = fit.psHDM,
                                      what = "AUC",
                                      fitLevel = "plot",
                                      estimate = "predictions")
head(paramArch2)