elrm: Software Implementing Exact-Like Inference for Logistic Regression Models

Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics. Exact inference for logistic regression can be problematic when data sets are large and the support of the conditional distribution cannot be represented in memory. Additionally, these methods are not widely implemented except in commercial software packages such as LogXact and SAS. Therefore, we have developed elrm, software for R implementing (approximate) exact inference for binomial regression models from large data sets. We provide a description of the underlying statistical methods and illustrate the use of elrm with examples. We also evaluate elrm by comparing results with those obtained using other methods.

elrm: Software Implementing Exact-Like Inference for Logistic Regression Models

Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics. Exact inference for logistic regression can be problematic when data sets are large and the support of the conditional distribution cannot be represented in memory. Additionally, these methods are not widely implemented except in commercial software packages such as LogXact and SAS. Therefore, we have developed elrm, software for R implementing (approximate) exact inference for binomial regression models from large data sets. We provide a description of the underlying statistical methods and illustrate the use of elrm with examples. We also evaluate elrm by comparing results with those obtained using other methods

A general method for the statistical evaluation of typological distributions

The distribution of linguistic structures in the world is the joint product of universal principles, inheritance from ancestor languages, language contact, social structures, and random fluctuation. This paper proposes a method for evaluating the relative significance of each factor — and in particular, of universal principles — via regression modeling: statistical evidence for universal principles is found if the odds for families to have skewed responses (e.g. all or most members have postnominal relative clauses) as opposed to having an opposite response skewing or no skewing at all, is significantly higher for some condition (e.g. VO order) than for another condition, independently of other factors

Markov chain Monte Carlo exact inference for binomial and multinomial logistic regression models

We develop Metropolis-Hastings algorithms for exact conditional inference, including goodness-of-fit tests, confidence intervals and residual analysis, for binomial and multinomial logistic regression models. We present examples where the exact results, obtained by enumeration, are available for comparison. We also present examples where Monte Carlo methods provide the only feasible approach for exact inference