R: Laplace Regression Family Function

laplace {VGAM} | R Documentation

## Laplace Regression Family Function

### Description

Maximum likelihood estimation of the 2-parameter classical Laplace distribution.

### Usage

    laplace(llocation = "identitylink", lscale = "loglink",
            ilocation = NULL, iscale = NULL, imethod = 1, zero = "scale")

### Arguments

`llocation, lscale` | Character. Parameter link functions for location parameter a and scale parameter b. See `Links` for more choices.
`ilocation, iscale` | Optional initial values. If given, it must be numeric and values are recycled to the appropriate length. The default is to choose the value internally.
`imethod` | Initialization method. Either the value 1 or 2.
`zero` | See `CommonVGAMffArguments` for information.

### Details

The Laplace distribution is often known as the double-exponential distribution and, for modelling, has heavier tail than the normal distribution. The Laplace density function is

f(y) = (1/(2b)) exp( -|y-a|/b )

where -Inf<y<Inf, -Inf<a<Inf and b>0. Its mean is a and its variance is 2b^2. This parameterization is called the classical Laplace distribution by Kotz et al. (2001), and the density is symmetric about a.

For `y ~ 1` (where `y` is the response) the maximum likelihood estimate (MLE) for the location parameter is the sample median, and the MLE for b is `mean(abs(y-location))` (replace location by its MLE if unknown).

### Value

An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm` and `vgam`.

### Warning

This family function has not been fully tested. The MLE regularity conditions do not hold for this distribution, therefore misleading inferences may result, e.g., in the `summary` and `vcov` of the object. Hence this family function might be withdrawn from VGAM in the future.

### Note

This family function uses Fisher scoring. Convergence may be slow for non-intercept-only models; half-stepping is frequently required.

### Author(s)

T. W. Yee

### References

Kotz, S., Kozubowski, T. J. and Podgorski, K. (2001). The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance, Boston: Birkhauser.

### See Also

`rlaplace`, `alaplace2` (which differs slightly from this parameterization), `exponential`, `median`.

### Examples

    ldata <- data.frame(y = rlaplace(nn <- 100, loc = 2, scale = exp(1)))
    fit <- vglm(y  ~ 1, laplace, data = ldata, trace = TRUE, crit = "l")
    coef(fit, matrix = TRUE)
    Coef(fit)
    with(ldata, median(y))

    ldata <- data.frame(x = runif(nn <- 1001))
    ldata <- transform(ldata, y = rlaplace(nn, loc = 2, scale = exp(-1 + 1*x)))
    coef(vglm(y ~ x, laplace(iloc = 0.2, imethod = 2, zero = 1), data = ldata,
              trace = TRUE), matrix = TRUE)

[Package VGAM version 1.1-3 Index]