search_threshold: Determine the threshold using Locally estimated or weighted...

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Determine the threshold using Locally estimated or weighted Scatterplot Smoothing.

Description

Determine the threshold using Locally estimated or weighted Scatterplot Smoothing.

Usage

search_threshold(
  data,
  outliers,
  sp = NULL,
  plotsetting = list(plot = FALSE, group = NULL),
  var_col = NULL,
  warn = FALSE,
  verbose = FALSE,
  cutoff,
  tloss = seq(0.1, 1, 0.1)
)

Arguments

data	Dataframe. The reference dataframe were absolute outliers will be removed.
outliers	datacleaner. Datacleaner output with outliers flagged in multidetect function.
sp	string. Species name or index if multiple species are considered during outlier detection.
plotsetting	list. to show plot of loess fitted function with local and global maxima (optimal threshold and clean data). The list had two parameters. 1) plot to indicate the plot and group to provide the plot title.
var_col	string. A column with species names if dataset for species is a dataframe not a list. See pred_extract for extracting environmental data.
warn	logical. If TRUE, warning on whether absolute outliers obtained at a low threshold is indicated. Default TRUE.
verbose	logical. If true, then messages about the outlier flagging will be displayed.
cutoff	numeric. Ranging from 0.5 to 0.8 indicating the cutoff to initiate the LOESS model to optimize the identification of absolute outliers.
tloss	seqences Indicates the sequence for tuning the the span parameter of the LOESS model.

Value

Returns numeric of most suitable threshold at globalmaxima or localmaxima of the loess smoothing.

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.