http://www.ikosmidis.com/tags/infinite-estimates/ Recent content in Infinite Estimates on Ioannis Kosmidis Hugo en Thu, 26 Mar 2020 00:00:00 +0000 http://www.ikosmidis.com/post/news-detectseparation-march-2020/ Thu, 26 Mar 2020 00:00:00 +0000 http://www.ikosmidis.com/post/news-detectseparation-march-2020/ <p><img src="http://www.ikosmidis.com/files/hex_detectseparation.svg" width="300" align="right"></p> <p><a href="https://cran.r-project.org/package=detectseparation"><strong>detectseparation</strong></a> provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses.</p> <div id="pre-fit-methods" class="section level2"> <h2>Pre-fit methods</h2> <p>The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the <code>glm()</code> function. They solve the linear programming problems for the detection of separation developed in <span class="citation">Konis (<a href="#ref-konis:2007">2007</a>)</span>, using <a href="https://cran.r-project.org/package=ROI"><strong>ROI</strong></a> or <a href="https://cran.r-project.org/package=lpSolveAPI"><strong>lpSolveAPI</strong></a>.</p> <p>For example, the code chunk below checks for data separation for the logistic regression model for the endometrial data set that has been considered in <span class="citation">Heinze and Schemper (<a href="#ref-heinze+schemper:2002">2002</a>)</span></p> http://www.ikosmidis.com/post/news-lawald-paper/ Tue, 16 Apr 2019 00:00:00 +0000 http://www.ikosmidis.com/post/news-lawald-paper/ <p><a href="https://doi.org/10.1007/s11222-019-09860-6">Mean and median bias reduction in generalized linear models appears in Statistics and Computing</a> (joint work with <a href="https://homes.stat.unipd.it/eulogecloviskennepagui/en/content/home">Kenne Pagui E C</a>, and <a href="https://homes.stat.unipd.it/nicolasartori/">Sartori N</a>) appeared online in <a href="https://link.springer.com/journal/11222">Statistics and Computing</a>.</p>