Detect outliers for series of observations

Function for detecting strange time courses. The function uses the estimates for the spline coefficients per time course (typically per plant). Correlations between those coefficient vectors are calculated to identify outlying time courses, i.e., plants. An outlying time course will have low correlation to the majority of time courses. To support the analysis by correlations, a principal component analysis is done on the plant (time course) by spline coefficient matrix. A PCA plot of the plant scores will show the outlying plants. Finally the pairwise-ratios of the slopes of a linear model fitted through the spline coefficients are computed. Plants are tagged when the average pairwise-ratio is lower the a given threshold (thrSlope).

Usage

detectSerieOut(
  corrDat,
  predDat,
  coefDat,
  trait,
  genotypes = NULL,
  geno.decomp = NULL,
  thrCor = 0.9,
  thrPca = 30,
  thrSlope = 0.7
)

Arguments

corrDat

A data.frame with corrected spatial data.

predDat

A data.frame with predicted data from a fitted spline.

coefDat

A data.frame with coefficients from a fitted spline.

trait

A character string indicating the trait for which to detect outliers.

genotypes

A character vector indicating the genotypes for which to detect outliers. If NULL, outlier detection will be done for all genotypes.

geno.decomp

A character vector indicating the variables to use to group the genotypic variance in the model.

thrCor

A numerical value used as threshold for determining outliers based on correlation between plots.

thrPca

A numerical value used as threshold for determining outliers based on angles (in degrees) between PCA scores.

thrSlope

A numerical value used as threshold for determining outliers based on slopes.

Value

An object of class serieOut, a data.frame with outlying series of observations.

See also

Other functions for detecting outliers for series of observations: plot.serieOut(), removeSerieOut()

Examples

# \donttest{
## 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
subGenoVator <- c("G160", "G151")
fit.spline <- fitSpline(inDat = spatCorrectedVator,
                        trait = "EffpsII_corr",
                        genotypes = subGenoVator,
                        knots = 50)

## Extract the data.frames with predicted values and P-Spline coefficients.
predDat <- fit.spline$predDat
coefDat <- fit.spline$coefDat

## The coefficients are then used to tag suspect time courses.
outVator <- detectSerieOut(corrDat = spatCorrectedVator,
                           predDat = predDat,
                           coefDat = coefDat,
                           trait = "EffpsII_corr",
                           genotypes = subGenoVator,
                           thrCor = 0.9,
                           thrPca = 30,
                           thrSlope = 0.7)

## The `outVator` can be visualized for selected genotypes.
plot(outVator, genotypes = "G151")

# }