tag:crantastic.org,2005:/authors/5998Latest activity for Byron Jaeger2019-10-28T15:23:43Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/941562019-10-28T15:23:40Z2019-10-28T15:23:40ZtibbleOne was released<a href="/packages/tibbleOne">tibbleOne</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Table one is a tabular description of characteristics, e.g., demographics of patients in a clinical trial, presented overall and also stratified by a categorical variable, e.g. treatment group. There are many excellent packages available to create table one. This package focuses on providing table one objects that seamlessly fit into 'R Markdown' analyses.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/859562019-03-13T22:02:16Z2019-03-13T22:02:16ZobliqueRSF was upgraded to version 0.1.1<a href="/packages/obliqueRSF">obliqueRSF</a> was <span class="action">upgraded</span> to version <a href="/packages/obliqueRSF/versions/81738">0.1.1</a><br /><h3>Package description:</h3><p>Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/813002018-11-02T18:02:04Z2018-11-02T18:02:04ZobliqueRSF was released<a href="/packages/obliqueRSF">obliqueRSF</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/651892017-08-05T11:22:07Z2017-08-05T11:22:07Zr2glmm was upgraded to version 0.1.2<a href="/packages/r2glmm">r2glmm</a> was <span class="action">upgraded</span> to version <a href="/packages/r2glmm/versions/62351">0.1.2</a><br /><h3>Package description:</h3><p>The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429> is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods of computation are provided and described as follows. First, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Second, The method introduced by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x> and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>. The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/571722016-11-28T21:21:34Z2016-11-28T21:21:34Zr2glmm was upgraded to version 0.1.1<a href="/packages/r2glmm">r2glmm</a> was <span class="action">upgraded</span> to version <a href="/packages/r2glmm/versions/54771">0.1.1</a><br /><h3>Package description:</h3><p>The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429> is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods of computation are provided and described as follows. Firstly, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Secondly, The method introduced by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x> and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>. The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/550492016-09-15T14:41:20Z2016-09-15T14:41:20Zr2glmm was released<a href="/packages/r2glmm">r2glmm</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429> is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods of computation are provided and described as follows. First, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Second, The method introduced by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x> and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>. The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file.</p>crantastic.org