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heavyLm {heavy}

Linear models under heavy-tailed distributions
Package: 
heavy
Version: 
0.2-3

Description

This function is used to fit linear models considering heavy-tailed errors.

Usage

heavyLm(formula, data, family = Student(df = 4), subset, na.action, 
  control, model = TRUE, x = FALSE, y = FALSE, contrasts = NULL)

Arguments

formula
an object of class "formula": a symbolic description of the model to be fitted.
data
an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which heavyLm is called.
family
a description of the error distribution to be used in the model. By default the Student-t distribution with 4 degrees of freedom is considered.
subset
an optional expression indicating the subset of the rows of data that should be used in the fitting process.
na.action
a function that indicates what should happen when the data contain NAs.
control
a list of control values for the estimation algorithm to replace the default values returned by the function heavy.control.
model, x, y
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response) are returned.
contrasts
an optional list. See the contrasts.arg of model.matrix.default.

Values

an object of class heavyLm representing the linear model fit. Generic functions print and summary, show the results of the fit.

References

Dempster, A.P., Laird, N.M., and Rubin, D.B. (1980). Iteratively reweighted least squares for linear regression when errors are Normal/Independent distributed. In P.R. Krishnaiah (Ed.), Multivariate Analysis V, p. 35-57. North-Holland.

Examples

data(ereturns)
fit <- heavyLm(m.marietta ~ CRSP, data = ereturns, family = Student(df = 5))
summary(fit)

Documentation reproduced from package heavy, version 0.2-3. License: GPL (>= 2)