Least absolute deviations regression
This function is used to fit linear models considering Laplace errors.
lad(formula, data, subset, na.action, method = "BR", tol = 1e-7, maxiter = 200, x = FALSE, y = FALSE, contrasts = NULL)
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 lad is called.subset: an optional expression indicating the subset of the rows of data that should be used in the fit.na.action: a function that indicates what should happen when the data contain NAs.method: character string specifying the fitting method to be used; the options are "BR" Barrodale and Roberts' method (the default) and "EM" for an EM algorithm using IRLS.tol: the relative tolerance for the iterative algorithm. Default is tol = 1e-7.maxiter: The maximum number of iterations for the EM method. Default to 200.x, y: logicals. If TRUE the corresponding components of the fit (the model matrix, the response) are returned.contrasts: an optional list. See the contrasts.arg of model.matrix.default.An object of class lad representing the linear model fit. Generic function print, show the results of the fit.
The functions print and summary are used to obtain and print a summary of the results. The generic accessor functions coefficients, fitted.values
and residuals extract various useful features of the value returned by lad.
The design was inspired by the R function lm.
Barrodale, I., Roberts, F.D.K. (1974). Solution of an overdetermined system of equations in the L1 norm. Communications of the ACM 17 , 319-320.
Phillips, R.F. (2002). Least absolute deviations estimation via the EM algorithm. Statistics and Computing 12 , 281-285.
fm <- lad(stack.loss ~ ., data = stackloss, method = "BR") summary(fm) data(ereturns) fm <- lad(m.marietta ~ CRSP, data = ereturns, method = "EM") summary(fm) # basic observations fm$basic