LTSevol {RFreak}R Documentation

Least Trimmed Squares Robust Regression

Description

Carries out least trimmed squares (LTS) robust regression with an evolutionary algorithm. The LTS regression method minimizes the sum of the h smallest squared residuals. Deprecated. Use robreg.evol instead.

Usage

## Deprecated:
LTSevol(y, x, h = NULL, adjust = FALSE, runs = 1, generations = 10000)

Arguments

y Vector with the response variables
x Matrix or data frame containing the explanatory variables
h Parameter determining the trimming
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

Value

The function LTSevol returns an object of class "ltsEA". 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 LTS regression method, i.e., the sum of the h smallest squared raw residuals

Author(s)

Robin Nunkesser Robin.Nunkesser@tu-dortmund.de

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

"ltsEA"

Examples

# load example data
data(stackloss)

# compute LTS regression
LTSevol(stackloss[, 4],stackloss[, 1:3],adjust=TRUE,runs=1,generations=1000)

[Package RFreak version 0.2-5 Index]