amlbinomial: Binomial Logistic Regression by Asymmetric Maximum Likelihood...

View source: R/family.qreg.R

amlbinomialR Documentation

Binomial Logistic Regression by Asymmetric Maximum Likelihood Estimation


Binomial quantile regression estimated by maximizing an asymmetric likelihood function.


amlbinomial(w.aml = 1, parallel = FALSE, digw = 4, link = "logitlink")



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 likelihood (MLE) solution.


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.


Passed into Round as the digits argument for the w.aml values; used cosmetically for labelling.


See binomialff.


The general methodology behind this VGAM family function is given in Efron (1992) and full details can be obtained there. This model is essentially a logistic regression model (see binomialff) 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.


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


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).


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 amlbinomial 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).


Thomas W. Yee


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

amlpoisson, amlexponential, amlnormal, extlogF1, alaplace1, denorm.


# Example: binomial data with lots of trials per observation
sizevec <- rep(100, length = (nn <- 200))
mydat <- data.frame(x = sort(runif(nn)))
mydat <- transform(mydat,
                   prob = logitlink(-0 + 2.5*x + x^2, inverse = TRUE))
mydat <- transform(mydat, y = rbinom(nn, size = sizevec, prob = prob))
(fit <- vgam(cbind(y, sizevec - y) ~ s(x, df = 3),
             amlbinomial(w = c(0.01, 0.2, 1, 5, 60)),
             mydat, trace = TRUE))

## Not run: 
par(mfrow = c(1,2))
# Quantile plot
with(mydat, plot(x, jitter(y), col = "blue", las = 1, main =
     paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
           "percentile-expectile curves")))
with(mydat, matlines(x, 100 * fitted(fit), lwd = 2, col = "blue", lty=1))

# Compare the fitted expectiles with the quantiles
with(mydat, plot(x, jitter(y), col = "blue", las = 1, main =
     paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
           "percentile curves are red")))
with(mydat, matlines(x, 100 * fitted(fit), lwd = 2, col = "blue", lty = 1))

for (ii in fit@extra$percentile)
    with(mydat, matlines(x, 100 *
         qbinom(p = ii/100, size = sizevec, prob = prob) / sizevec,
                  col = "red", lwd = 2, lty = 1))

## End(Not run)

VGAM documentation built on July 6, 2022, 5:05 p.m.