Plot outliers for series of observations

Plot the fitted spline, correlation matrix and PCA biplot for each of the genotypes. Outlying series of observations are shown as filled dots in the fitted spline plot, other observations are shown as open dots.

Usage

# S3 method for class 'serieOut'
plot(
  x,
  ...,
  reason = c("mean corr", "angle", "slope"),
  genotypes = NULL,
  geno.decomp = NULL,
  useTimeNumber = FALSE,
  timeNumber = NULL,
  title = NULL,
  output = TRUE
)

Arguments

x

An object of class serieOut.

...

Ignored.

reason

A character vector indicating which types of outliers should be plotted.

genotypes

A character vector indicating which genotypes should be plotted. If NULL all genotypes are plotted.

geno.decomp

A character vector indicating which levels of geno.decomp should be plotted. If NULL all levels are plotted. Ignored if geno.decomp was not used when fitting models.

useTimeNumber

Should the timeNumber be used instead of the timePoint in the labels on the x-axis?

timeNumber

If useTimeNumber = TRUE, a character vector indicating the column containing the numerical time to use.

title

A character string used as title for the plot. If NULL a default title is added to the plot depending on plotType.

output

Should the plot be output to the current device? If FALSE only a (list of) ggplot object(s) is invisibly returned. Ignored if outFile is specified.

Value

A list of ggplot objects is invisibly returned.

See also

Other functions for detecting outliers for series of observations: detectSerieOut(), 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")


## Only visualize outliers tagged because of low correlation
## between slopes of the regression.
plot(outVator, genotypes = "G151", reason = "slope")

# }