predict.psHDM: Predict the P-Splines Hierarchical Curve Data Model

In statgenHTP: High Throughput Phenotyping (HTP) Data Analysis

Predict the P-Splines Hierarchical Curve Data Model

Description

Function that predicts the P-spline Hierarchical Curve Data Model (see fitSplineHDM) on a dense grid. It provides standard errors for curves at each level of the hierarchy. User has to be aware that standard errors at the plot level demand large memory. We suggest set that option at the FALSE level

Usage

## S3 method for class 'psHDM'
predict(
  object,
  newtimes,
  pred = list(pop = TRUE, geno = TRUE, plot = TRUE),
  se = list(pop = TRUE, geno = TRUE, plot = FALSE),
  trace = TRUE,
  ...
)

Arguments

object	An object of class "psHDM" as obtained after fitting (fitSplineHDM) the P-spline Hierarchical Curve Data Model
newtimes	A numeric vector with timepoints at which predictions are desired
pred	A list that controls the hierarchical levels at which predictions are desired (population/genotypes/plots). The default is TRUE.
se	A list that controls the hierarchical levels at which standard errors are desired (population/genotypes/plots). The default is TRUE except at the plot level.
trace	An optional value that controls the function trace. The default is TRUE.
...	Not used.

Value

An object of class psHDM, a list with the following outputs: predict.psHDM newtimes A numeric vector with the timepoints at which predictions and/or standard errors have been obtained. popLevel A data.frame with the estimated population trajectories and first and second order derivatives, and if required their respective standard errors, at the newtimes. genoLevel A data.frame with the estimated genotype-specific deviations and trajectories and their respective first and second order derivatives, and if required their respective standard errors, at the newtimes. plotLevel A data.frame with the estimated plot-specific deviations and trajectories and their respective first and second order derivatives, and if required their respective standard errors, at the newtimes. plotObs A data.frame with the raw data at the original timepoints.

References

Pérez-Valencia, D.M., Rodríguez-Álvarez, M.X., Boer, M.P. et al. A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data. Sci Rep 12, 3177 (2022). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-022-06935-9")}

See Also

Other functions for fitting hierarchical curve data models: fitSplineHDM(), plot.psHDM()

Examples

## 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)

## Predict the P-Splines Hierarchical Curve Data Model on a dense grid
## with standard errors at the population and genotype levels
pred.psHDM <- predict(object = fit.psHDM,
                     newtimes = seq(min(fit.psHDM$time[["timeNumber"]]),
                                   max(fit.psHDM$time[["timeNumber"]]),
                                   length.out = 100),
                     pred = list(pop = TRUE, geno = TRUE, plot = TRUE),
                     se = list(pop = TRUE, geno = TRUE, plot = FALSE))

## Plot the P-Spline predictions at the three levels of the hierarchy

## Plots at population level.
plot(pred.psHDM,
    plotType = "popTra")

## Plots at genotype level.
plot(pred.psHDM,
    plotType = "popGenoTra")

## Plots of derivatives at genotype level.
plot(pred.psHDM,
    plotType = "popGenoDeriv")

## Plots of deviations at genotype level.
plot(pred.psHDM,
    plotType = "genoDev")

## Plots at plot level.
plot(pred.psHDM,
    plotType = "genoPlotTra")

statgenHTP documentation built on April 14, 2023, 9:12 a.m.