detectSingleOut function

Detect outliers for single observations

Detect outlying observations in a time series by modeling each plotId using a local regression.

detectSingleOut(
  TP,
  trait,
  plotIds = NULL,
  checkEdges = TRUE,
  confIntSize = 5,
  nnLocfit = 0.5
)

Arguments

• TP: An object of class TP.
• trait: A character vector indicating the trait to model in TP.
• plotIds: A character vector of plotIds for which the outliers should be detected. If NULL, all plotIds in TP are used.
• checkEdges: Before fitting the local regression should a check be done if the first and last time point for a plot are outlying observations?
• confIntSize: A numeric value defining the confidence interval (see Details).
• nnLocfit: A numeric value defining the constant component of the smoothing parameter nn (see Details).

Returns

An object of class singleOut, a data.frame with the following columns.

• plotId: plotId
• timePoint: time point
• trait: modeled trait
• yPred: prediction from the local regression
• sd_yPred: standard deviation of the prediction
• lwr: lower bound of the confidence interval
• upr: upper bound of the confidence interval
• outlier: flag for detected outlier (a value of 1 indicates the observation is an outlier)

Details

See locfit() help function from the locfit R library. The user can act on:

• nnLocfit: the constant of the smoothing parameter. Increase nnLocfit to have a very smooth curve
• confIntSize: the level to calculate the confidence interval. Increase confIntSize to exclude less outliers

Examples

## Create a TP object containing the data from the Phenovator.
PhenovatorDat1 <- PhenovatorDat1[!PhenovatorDat1$pos %in%
                                 c("c24r41", "c7r18", "c7r49"), ]
phenoTP <- createTimePoints(dat = PhenovatorDat1,
                            experimentName = "Phenovator",
                            genotype = "Genotype",
                            timePoint = "timepoints",
                            repId = "Replicate",
                            plotId = "pos",
                            rowNum = "y", colNum = "x",
                            addCheck = TRUE,
                            checkGenotypes = c("check1", "check2",
                                               "check3", "check4"))

## First select a subset of plants, for example here 9 plants
plantSel <- phenoTP[[1]]$plotId[1:9]
# Then run on the subset
resuVatorHTP <- detectSingleOut(TP = phenoTP,
                                trait = "EffpsII",
                                plotIds = plantSel,
                                confIntSize = 3,
                                nnLocfit = 0.1)

See Also

Other functions for detecting outliers for single observations: detectSingleOutMaize(), plot.singleOut(), removeSingleOut()