R: Exponential Regression by Asymmetric Maximum Likelihood...

amlexponential {VGAM} | R Documentation

## Exponential Regression by Asymmetric Maximum Likelihood Estimation

### Description

Exponential expectile regression estimated by maximizing an asymmetric likelihood function.

### Usage

    amlexponential(w.aml = 1, parallel = FALSE, imethod = 1, digw = 4,
                   link = "loge")

### Arguments

`w.aml` | Numeric, a vector of positive constants controlling the expectiles. The larger the value the larger the fitted expectile value (the proportion of points below the “w-regression plane”). The default value of unity results in the ordinary maximum likelihood (MLE) solution.
`parallel` | If `w.aml` has more than one value then this argument allows the quantile curves to differ by the same amount as a function of the covariates. Setting this to be `TRUE` should force the quantile curves to not cross (although they may not cross anyway). See `CommonVGAMffArguments` for more information.
`imethod` | Integer, either 1 or 2 or 3. Initialization method. Choose another value if convergence fails.
`digw` | Passed into `Round` as the `digits` argument for the `w.aml` values; used cosmetically for labelling.
`link` | See `exponential` and the warning below.

### Details

The general methodology behind this VGAM family function is given in Efron (1992) and full details can be obtained there. This model is essentially an exponential regression model (see `exponential`) but the usual deviance is replaced by an asymmetric squared error loss function; it is multiplied by w.aml for positive residuals. The solution is the set of regression coefficients that minimize the sum of these deviance-type values over the data set, weighted by the `weights` argument (so that it can contain frequencies). Newton-Raphson estimation is used here.

### Value

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

### Warning

Note that the `link` argument of `exponential` and `amlexponential` are currently different: one is the rate parameter and the other is the mean (expectile) parameter.

If `w.aml` has more than one value then the value returned by `deviance` is the sum of all the (weighted) deviances taken over all the `w.aml` values. See Equation (1.6) of Efron (1992).

### Note

On fitting, the `extra` slot has list components `"w.aml"` and `"percentile"`. The latter is the percent of observations below the “w-regression plane”, which is the fitted values. Also, the individual deviance values corresponding to each element of the argument `w.aml` is stored in the `extra` slot.

For `amlexponential` objects, methods functions for the generic functions `qtplot` and `cdf` have not been written yet.

See `amlpoisson` about comments on the jargon, e.g., expectiles etc.

In this documentation the word quantile can often be interchangeably replaced by expectile (things are informal here).

### Author(s)

Thomas W. Yee

### References

Efron, B. (1992) Poisson overdispersion estimates based on the method of asymmetric maximum likelihood. Journal of the American Statistical Association, 87, 98–107.

### See Also

`exponential`, `amlbinomial`, `amlpoisson`, `amlnormal`, `alaplace1`, `lms.bcg`, `deexp`.

### Examples

    nn <- 2000
    mydat <- data.frame(x = seq(0, 1, length = nn))
    mydat <- transform(mydat, mu = loge(-0 + 1.5*x + 0.2*x^2, inverse = TRUE))
    mydat <- transform(mydat, mu = loge(0 - sin(8*x), inverse = TRUE))
    mydat <- transform(mydat,  y = rexp(nn, rate = 1/mu))
    (fit  <- vgam(y ~ s(x,df = 5), amlexponential(w = c(0.001, 0.1, 0.5, 5, 60)),
                  mydat, trace = TRUE))
    fit@extra

    ## Not run:  # These plots are against the sqrt scale (to increase clarity)
    par(mfrow = c(1,2))
    # Quantile plot
    with(mydat, plot(x, sqrt(y), col = "blue", las = 1, main =
         paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
               "percentile-expectile curves")))
    with(mydat, matlines(x, sqrt(fitted(fit)), lwd = 2, col = "blue", lty = 1))

    # Compare the fitted expectiles with the quantiles
    with(mydat, plot(x, sqrt(y), col = "blue", las = 1, main =
         paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
               "percentile curves are orange")))
    with(mydat, matlines(x, sqrt(fitted(fit)), lwd = 2, col = "blue", lty = 1))

    for (ii in fit@extra$percentile)
      with(mydat, matlines(x, sqrt(qexp(p = ii/100, rate = 1/mu)), col = "orange"))
    ## End(Not run)

[Package VGAM version 1.0-3 Index]