medmad: Function for detecting regression outliers

In galts: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation

Function for detecting regression outliers

Description

A method for detecting regression outliers.

Usage

medmad(formula, h=NULL, csteps=20)

Arguments

formula	Dependent ~ Independents style formula as same in lm() and glm().
h	User defined variable to define the majority of the data. Default is floor(n/2)+floor((p+1)/2) where n is the number of observations and p is the number of parameters to estimate
csteps	Number of C-steps to be performed for each candidate solution. Default is 2.

Value

coefficients	A vector of estimated parameters
crit	LTS criterion value for the reported coefficients
residuals	Calculated residuals from the final estimate of model

Author(s)

Mehmet Hakan Satman

Examples

	n <- 100
	x1 <- rnorm(n,0,10)
	x2 <- rnorm(n,0,10)
	x3 <- rnorm(n,0,10)
	x4 <- rnorm(n,0,10)
	e <- rnorm(n)
	x <- cbind(1, x1, x2, x3, x4)
	p <- 5
	betas <- rep(5,p)
	c <- 0.20
	h <- n - n*c
	y <- 5 + 5*x1 + 5*x2 + 5*x3 + 5*x4 + e
	x1[(h + 1):n] <- rnorm(n-h, 100, 10)
	x2[(h + 1):n] <- rnorm(n-h, 100, 10)
	x3[(h + 1):n] <- rnorm(n-h, 100, 10)
	x4[(h + 1):n] <- rnorm(n-h, 100, 10)

	mm <- medmad(formula = y ~ x1 + x2 + x3 + x4, csteps = 10)

galts documentation built on Aug. 20, 2023, 9:08 a.m.