interval_robust: Robust Statistics for Interval Data

In dataSDA: Datasets and Basic Statistics for Symbolic Data Analysis

Robust Statistics for Interval Data

Description

Functions to compute robust statistics for interval-valued data.

Usage

int_trimmed_mean(x, var_name, trim = 0.1, method = "CM", ...)

int_winsorized_mean(x, var_name, trim = 0.1, method = "CM", ...)

int_trimmed_var(x, var_name, trim = 0.1, method = "CM", ...)

int_winsorized_var(x, var_name, trim = 0.1, method = "CM", ...)

Arguments

x	interval-valued data with symbolic_tbl class.
var_name	the variable name or the column location (multiple variables are allowed).
trim	the fraction (0 to 0.5) of observations to be trimmed from each end.
method	methods to calculate statistics: CM (default), VM, QM, SE, FV, EJD, GQ, SPT.
...	additional parameters

Details

These functions provide robust alternatives to standard statistics:

• int_trimmed_mean: Mean after trimming extreme values

• int_winsorized_mean: Mean after winsorizing extreme values

• int_trimmed_var: Variance after trimming extreme values

• int_winsorized_var: Variance after winsorizing extreme values

Trimming vs Winsorizing:

• Trimming: Remove extreme values

• Winsorizing: Replace extreme values with less extreme values

Value

A numeric matrix

Author(s)

Han-Ming Wu

See Also

int_mean int_var int_trimmed_mean

Examples

data(mushroom.int)

# Trimmed mean (10% from each end)
int_trimmed_mean(mushroom.int, var_name = "Pileus.Cap.Width", trim = 0.1)

# Winsorized mean
int_winsorized_mean(mushroom.int, var_name = 2:3, trim = 0.05, method = "CM")

# Trimmed variance
int_trimmed_var(mushroom.int, var_name = c("Stipe.Length"), trim = 0.1)

dataSDA documentation built on June 12, 2026, 9:06 a.m.