compute_outliers: Compute outliers detection in derived parameters from a cubic...

In mcanouil/eggla: Early Growth Genetics Longitudinal Analysis

Compute outliers detection in derived parameters from a cubic splines mixed-effects model by time_model().

Description

Based on computed area under the curves (i.e., compute_aucs()) and slopes (i.e., compute_slopes()) for several intervals using a model fitted by time_model(), compute an outlier detection. For details, see methods iqr and zscore of performance::check_outliers().

Usage

compute_outliers(
  fit,
  method,
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
  knots = list(cubic_slope = NULL, linear_splines = c(0.75, 5.5, 11), cubic_splines =
    c(1, 8, 12))[[method]],
  from = c("predicted", "observed"),
  start = 0.25,
  end = 10,
  step = 0.01,
  filter = NULL,
  outlier_method = "iqr",
  outlier_threshold = list(iqr = 2)
)

Arguments

fit	A model object from a statistical model such as from a call to time_model().
method	The type of model provided in fit, i.e., one of "cubic_slope", "linear_splines" or "cubic_splines".
period	The intervals knots on which AUCs are to be computed.
knots	The knots as defined fit and according to method.
from	A string indicating the type of data to be used for the AP and AR computation, either "predicted" or "observed". Default is "predicted".
start	The start of the time window to compute AP and AR.
end	The end of the time window to compute AP and AR.
step	The step to increment the sequence.
filter	A string following data.table syntax for filtering on "i" (i.e., row elements), e.g., filter = "source == 'A'". Argument pass through compute_apar() (see predict_bmi()). Default is NULL.
outlier_method	The outlier detection method(s). Default is "iqr". Can be "cook", "pareto", "zscore", "zscore_robust", "iqr", "ci", "eti", "hdi", "bci", "mahalanobis", "mahalanobis_robust", "mcd", "ics", "optics" or "lof". See performance::check_outliers() https://easystats.github.io/performance/reference/check_outliers.html for details.
outlier_threshold	A list containing the threshold values for each method (e.g., list('mahalanobis' = 7, 'cook' = 1)), above which an observation is considered as outlier. If NULL, default values will be used (see 'Details'). If a numeric value is given, it will be used as the threshold for any of the method run. See performance::check_outliers() https://easystats.github.io/performance/reference/check_outliers.html for details.

Value

A data.frame listing the individuals which are not outliers based on several criteria.

Examples

data("bmigrowth")
ls_mod <- time_model(
  x = "age",
  y = "log(bmi)",
  cov = NULL,
  data = bmigrowth[bmigrowth[["sex"]] == 0, ],
  method = "cubic_splines"
)
head(compute_outliers(
  fit = ls_mod,
  method = "cubic_splines",
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17)#,
  # knots = list(
  #   "cubic_slope" = NULL,
  #   "linear_splines" = c(0.75, 5.5, 11),
  #   "cubic_splines" = c(1, 8, 12)
  # )[[method]]
)[Outlier != 0])

mcanouil/eggla documentation built on April 5, 2025, 3:06 a.m.