logbin (version 1.2)

logbin-package: Relative Risk Regression Using the Log-Binomial Model

Description

Methods for fitting log-link GLMs and GAMs to binomial data, using EM-type algorithms with more stable convergence properties than standard methods.

Details

Package:

logbin

Type:

Package

Version:

1.2

Date:

2015-05-12

License:

GPL (>= 2)

This package provides methods to fit generalised linear models (GLMs) and generalised additive models (GAMs) with log link functions to binomial data. It has two primary functions: logbin and logbin.smooth, together with various supporting functions.

It is useful in two main situations. The first is when a standard GLM routine, such as glm, fails to converge with such a model. The second is when a flexible semi-parametric component is desired in these models. One of the main purposes of this package is to provide parametric and semi-parametric adjustment of relative risks.

The computational method is a combinatorial EM algorithm (Marschner, 2014), which accommodates the parameter constraints and is more stable than iteratively reweighted least squares. A collection of restricted parameter spaces is defined which covers the full parameter space, and the EM algorithm is applied within each restricted parameter space in order to find a collection of restricted maxima of the log-likelihood function, from which can be obtained the global maximum over the full parameter space.

The methodology implemented in this package is presented in Marschner and Gillett (2012) and Donoghoe and Marschner (2015).

References

Donoghoe, M. W. and I. C. Marschner (2015). Flexible regression models for rate differences, risk differences and relative risks. International Journal of Biostatistics 11(1): 91--108.

Marschner, I. C. (2014). Combinatorial EM algorithms. Statistics and Computing 24(6): 921--940.

Marschner, I. C. and A. C. Gillett (2012). Relative risk regression: reliable and flexible methods for log-binomial models. Biostatistics 13(1): 179--192.

See Also

glm

Examples

## For examples, see example(logbin) and example(logbin.smooth)