RFreak (version 0.3-0)

LTSevol: 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

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)