bloodpressure

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) 

bloodpressure function

A data frame with 2438 rows and 13 variables:<dl class="dl-horizontal">
<dt>ID</dt><dd>Subject identification number</dd>
<dt>BIRTH_WT</dt><dd>birth weight (lbs)</dd>
<dt>WEIGHT</dt><dd>current weight (lbs)</dd>
<dt>HEIGHT</dt><dd>current height (cm)</dd>
<dt>BMI</dt><dd>current body mass index</dd>
<dt>age</dt><dd>current age (yrs)</dd>
<dt>dias</dt><dd>diastolic blood pressure</dd>
<dt>sys</dt><dd>systolic blood pressure</dd>
<dt>SexM</dt><dd>indicator of sex male</dd>
<dt>RaceB</dt><dd>indicator of race black</dd>
<dt>RaceW</dt><dd>indicator of race white</dd>
<dt>PHIGHBP</dt><dd>indicator that either parent had high blood pressure</dd>
<dt>PDIABET</dt><dd>indicator that either parent had diabetes</dd>
</dl>

Format

Blood pressure data from a clinical study — bloodpressure

A data frame with 2438 rows and 13 variables:<dl class='dl-horizontal'>
<dt>ID</dt><dd>Subject identification number</dd>
<dt>BIRTH_WT</dt><dd>birth weight (lbs)</dd>
<dt>WEIGHT</dt><dd>current weight (lbs)</dd>
<dt>HEIGHT</dt><dd>current height (cm)</dd>
<dt>BMI</dt><dd>current body mass index</dd>
<dt>age</dt><dd>current age (yrs)</dd>
<dt>dias</dt><dd>diastolic blood pressure</dd>
<dt>sys</dt><dd>systolic blood pressure</dd>
<dt>SexM</dt><dd>indicator of sex male</dd>
<dt>RaceB</dt><dd>indicator of race black</dd>
<dt>RaceW</dt><dd>indicator of race white</dd>
<dt>PHIGHBP</dt><dd>indicator that either parent had high blood pressure</dd>
<dt>PDIABET</dt><dd>indicator that either parent had diabetes</dd>
</dl>

bloodpressure: Blood pressure data from a clinical study

Description

Usage

Arguments

Format

References