Multivariate iteratively re-weighted least squares, with applications to dose-response data

This paper develops a general multivariate iteratively re-weighted least squares (MIRLS) algorithm to fit multinomial data for a large class of link functions. The algorithm is shown to be very useful for the generalized linear model when applied to ordered data using either the logit or the normit link. Since binomial dose-response experiments are often monitored over time, then these dependent binomial responses can be modelled by using either link function. Examples from acute toxicity and teratology illustrate the algorithm. (C) 1998 John Wiley & Sons, Ltd.