LTSevol {RFreak} | R Documentation |
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.
LTSevol(y, x, h = NULL, adjust = FALSE, runs = 1, generations = 10000)
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 |
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 |
Robin Nunkesser Robin.Nunkesser@tu-dortmund.de
O. Morell (2006). Vergleich von Algorithmen fuer die Least-Trimmed-Squares-Schaetzung mittels statistischer Versuchsplanung. Diploma thesis, TU Dortmund.
P. J. Rousseeuw (1984), Least Median of Squares Regression. Journal of the American Statistical Association 79, 871–881.
"ltsEA"
# load example data data(stackloss) # compute LTS regression LTSevol(stackloss[, 4],stackloss[, 1:3],adjust=TRUE,runs=1,generations=1000)