reef

datasets

The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens. The functions in this package use 'rstan' (Stan Development Team 2020) to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'.
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1).
Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3. <https://mc-stan.org/>
Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418.
Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4.
Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955,
Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034,
Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167.

Samuel Thomas

bayesGAM

Fit Multivariate Response Generalized Additive Models using
Hamiltonian Monte Carlo

Wanzhu Tu

Trustees of Columbia University (R/rstanMethods.R) 

reef function

A data frame with 269 rows and 14 variables:<dl class="dl-horizontal">
<dt>ZONE</dt><dd>Management zone</dd>
<dt>site</dt><dd>Name of the habitat site</dd>
<dt>complexity</dt><dd>habitat benthic complexity</dd>
<dt>rugosity</dt><dd>a measurement related to terrain complexity</dd>
<dt>LC</dt><dd>cover of low complexity</dd>
<dt>HC</dt><dd>cover of high complexity</dd>
<dt>SCORE1</dt><dd>PCA score 1 from Wilson, Graham, Polunin</dd>
<dt>SCORE2</dt><dd>PCA score 2 from Wilson, Graham, Polunin</dd>
<dt>macro</dt><dd>indicator of race white</dd>
<dt>species</dt><dd>fish species</dd>
<dt>abundance</dt><dd>fish abundance</dd>
<dt>biomass</dt><dd>fish biomass</dd>
</dl>

Format

Coral reef data from survey data on 6 sites — reef

A data frame with 269 rows and 14 variables:<dl class='dl-horizontal'>
<dt>ZONE</dt><dd>Management zone</dd>
<dt>site</dt><dd>Name of the habitat site</dd>
<dt>complexity</dt><dd>habitat benthic complexity</dd>
<dt>rugosity</dt><dd>a measurement related to terrain complexity</dd>
<dt>LC</dt><dd>cover of low complexity</dd>
<dt>HC</dt><dd>cover of high complexity</dd>
<dt>SCORE1</dt><dd>PCA score 1 from Wilson, Graham, Polunin</dd>
<dt>SCORE2</dt><dd>PCA score 2 from Wilson, Graham, Polunin</dd>
<dt>macro</dt><dd>indicator of race white</dd>
<dt>species</dt><dd>fish species</dd>
<dt>abundance</dt><dd>fish abundance</dd>
<dt>biomass</dt><dd>fish biomass</dd>
</dl>

reef: Coral reef data from survey data on 6 sites

Description

Usage

Arguments

Format

References