Bounded Influence Robust Regression

DESCRIPTION:

Performs bounded influence robust regression.

USAGE:

lmRobBI(formula, data=<<see below>>, weights=<<see below>>,
  subset=<<see below>>, na.action=na.fail, model=F, x=F, y=F,
  contrasts=NULL, control=lmRobBI.control(), ...) 

REQUIRED ARGUMENTS:

formula
a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.

OPTIONAL ARGUMENTS:

data
a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument. If this is missing, then the variables in the formula should be on the search list. This may also be a single number to handle some special cases - see below for details.
weights
vector of observation weights; if supplied, the algorithm fits to minimize a function of the sum of the weights multiplied into the squared residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the subset argument.
subset
expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
na.action
a function to filter missing data. This is applied to the model.frame after any subset argument has been used. The default (with na.fail) is to create an error if any missing values are found. A possible alternative is na.exclude, which deletes observations that contain one or more missing values.
model
logical flag: if TRUE, the model frame is returned in component model.
x
logical flag: if TRUE, the model matrix is returned in component x.
y
logical flag: if TRUE, the response is returned in component y.
control
a list of control parameters to be used in the numerical algorithms. See lmRobBI.control() for the possible control parameters and their default settings.

VALUE:

a list describing the regression. See lmRobBI.object for a complete description of the object returned.

REFERENCES:

Marazzi, A. (1993). Algorithms, Routines, and S Functions for Robust Statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.

SEE ALSO:

, .