R: Least absolute deviations regression

lad {L1pack} | R Documentation

## Least absolute deviations regression

### Description

This function is used to fit linear models considering Laplace errors.

### Usage

    lad(formula, data, method = c("BR", "EM"), 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 `lad` is called.
`method` | character string specifying the algorithm to use. The default algorithm is the Barrodale and Roberts algorithm `method = "BR"`. Other possible value is `method = "EM"` for an EM algorithm using IRLS.
`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.
`control` | a list of control values for the estimation algorithm to replace the default values returned by the function `l1pack.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`.

### Value

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`.

### Author(s)

The design was inspired by the R function `lm`.

### References

Barrodale, I., and 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.

### Examples

    fm <- lad(stack.loss ~ ., data = stackloss, method = "BR")
    summary(fm)

[Package L1pack version 0.40 Index]