dentist: Quantifying uncertainty by sampling points around maximum likelihood estimates
James D. Boyko, Brian C. O’Meara
Abstract
Abstract It is standard statistical practice to provide measures of uncertainty around parameter estimates. Unfortunately, this very basic and necessary enterprise is often absent in macroevolutionary studies using maximum likelihood estimates (MLEs). dentist is an R package that allows an approximation of confidence intervals (CI) around parameter estimates without an analytic solution to likelihood equations. This package works by ‘denting’ the likelihood surface by sampling points a specified distance around the MLE following what is essentially a Metropolis‐Hastings walk. We describe the importance of estimating uncertainty around parameter estimates, as well as demonstrate the ability of dentist to accurately approximate CI. We introduce several plotting tools to visualize the results of a dentist analysis. dentist is freely available from https://github.com/bomeara/dentist , written in the R language, and can be used for any given likelihood function.