robreg.evol: Robust Evolutionary Regression

In RFreak: R/FrEAK interface

Description

Carries out robust regression with an evolutionary algorithm. ltsreg, ltareg, lmsreg, lqsreg, and lqdreg are wrappers.

Usage

robreg.evol(x, y, method = c("lts", "lta", "lms", "lqs", "lqd"), 
	quantile=NULL, adjust=FALSE, runs=1, generations=10000, duration=0)

## Wrappers:
ltsreg.evol(...)
ltareg.evol(...)
lmsreg.evol(...)
lqsreg.evol(...)
lqdreg.evol(...)

Arguments

x	Matrix or data frame containing the explanatory variables
y	Vector with the response variables
method	The method to be used. One of "lts", "lta", "lms", "lqs", and "lqd".
quantile	The quantile to be used: see Details.
adjust	Whether to perform intercept adjustment at each step
runs	Number of independent runs
generations	Number of generations after which the algorithm will be stopped
duration	Duration in seconds after which the algorithm will be stopped
...	Arguments to be passed to the default method

Details

Suppose there are n data points and p regressors, including any intercept.

The first four methods minimize some function of the sorted squared residuals. For methods "lqs" and "lms" it is the quantile squared residual, and for "lts" ("lts") it is the sum of the quantile smallest squared (absolute) residuals. "lqd" minimizes approximately the quartile of the absolute residual differences.

Value

The function robreg.evol returns an object of class "evolreg". This object contains:

summary	Summary of the FrEAK run
best	The best subset found
coefficients	Vector of coefficient estimates
crit	The value of the objective function of the regression method

Author(s)

Robin Nunkesser [e-mail]

References

O. Morell, T. Bernholt, R. Fried, J. Kunert, and R. Nunkesser (2008). An Evolutionary Algorithm for LTS-Regression: A Comparative Study. Proceedings of COMPSTAT 2008. To Appear.

P. J. Rousseeuw (1984), Least Median of Squares Regression. Journal of the American Statistical Association 79, 871–881.

See Also

"evolreg"

Examples

# load example data
data(stackloss)

# compute different regressions
robreg.evol(stackloss[, 1:3], stackloss[, 4], method= "lts", generations=1000)
lqsreg.evol(stackloss[, 1:3], stackloss[, 4], generations=1000)
lqdreg.evol(stackloss[, 1:3], stackloss[, 4], generations=1000)

RFreak documentation built on May 1, 2019, 10:53 p.m.