amlpoisson: Poisson Regression by Asymmetric Maximum Likelihood Estimation

Description

Poisson quantile regression estimated by maximizing an asymmetric likelihood function.

Usage

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

Arguments

w.aml

Numeric, a vector of positive constants controlling the percentiles. The larger the value the larger the fitted percentile value (the proportion of points below the ``w-regression plane''). The default value of unity results in the ordinary maximum

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

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 poissonff.

Value

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

Warning

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).

Details

This method was proposed by Efron (1992) and full details can be obtained there. The model is essentially a Poisson regression model (see poissonff) 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.

References

Efron, B. (1991) Regression percentiles using asymmetric squared error loss. Statistica Sinica, 1, 93--125.

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

Koenker, R. and Bassett, G. (1978) Regression quantiles. Econometrica, 46, 33--50.

Newey, W. K. and Powell, J. L. (1987) Asymmetric least squares estimation and testing. Econometrica, 55, 819--847.

Examples

Run this code

set.seed(1234)
mydat = data.frame(x = sort(runif(nn <- 200)))
mydat = transform(mydat, y = rpois(nn, exp(0-sin(8*x))))
(fit = vgam(y ~ s(x), fam=amlpoisson(w.aml=c(0.02, 0.2, 1, 5, 50)),
            mydat, trace=TRUE))
fit@extra

# Quantile plot
with(mydat, plot(x, jitter(y), col="blue", las=1, main=
     paste(paste(round(fit@extra$percentile, dig=1), collapse=", "),
           "percentile-expectile curves")))
with(mydat, matlines(x, fitted(fit), lwd=2))

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