protein

This data sets includes several predictors of protein
  activity from an experiment run at Glaxo.


datasets

Package for Bayesian Model Averaging in linear models and
generalized linear models using stochastic or
deterministic sampling without replacement from posterior
distributions.  Prior distributions on coefficients are
from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the
mixture of g-priors from
Liang et al (2008)
<http://dx.doi.org/10.1198/016214507000001337>
for linear models or mixtures of
g-priors in GLMs of Li and Clyde (2015)
<http://arxiv.org/abs/1503.06913>. Other model
selection criteria include AIC, BIC and Empirical Bayes estimates of g.
Sampling probabilities may be updated based on the sampled models
using Sampling w/out Replacement or an efficient MCMC algorithm
samples models using the BAS tree structure as an efficient
hash table. Uniform priors over all models or beta-binomial prior distributions on
model size are allowed, and for large p truncated priors on the model
space may be used.  The user may force variables to always be included.
Details behind the sampling algorithm are provided in
Clyde, Ghosh and Littman (2010) <http://dx.doi.org/10.1198/jcgs.2010.09049>.

Merlise Clyde

Bayesian Model Averaging using Bayesian Adaptive Sampling

protein function

<code>protein</code> is a dataframe  with 96 observations  and 8 predictor
  variablesof protein activity:
  <table><tr><td>
  [,1] </td>
<td> buf </td>
<td> factor </td>
</tr><tr><td>  Buffer </td>
<td>
  [,2] </td>
<td> pH  </td>
</tr><tr><td> numeric </td>
<td>  </td>
<td>
  [,3] </td>
</tr><tr><td> NaCl </td>
<td> numeric </td>
<td> </td>
</tr><tr><td>
  [,4] </td>
<td> con </td>
<td> numeric </td>
</tr><tr><td>  protein concentration</td>
<td>
  [,5] </td>
<td> ra  </td>
</tr><tr><td> factor </td>
<td>  reducing agent</td>
<td>
  [,6] </td>
</tr><tr><td> det </td>
<td> factor </td>
<td>   detergent</td>
</tr><tr><td>
  [,7] </td>
<td> MgCl2 </td>
<td>  numeric</td>
</tr><tr><td> </td>
<td>
  [,8] </td>
<td> temp  </td>
</tr><tr><td> numeric</td>
<td>  (temerature)</td>
<td>
  [,9] </td>
</tr><tr><td> prot.act1 </td>
<td> numeric</td>
<td> </td>
</tr><tr><td>
  [,10] </td>
<td> prot.act2 </td>
<td> numeric</td>
</tr><tr><td> </td>
<td>
  [,11] </td>
<td> prot.act3 </td>
</tr><tr><td> numeric</td>
<td> </td>
<td>
  [,1] </td>
</tr></table>

Format

Clyde, M. A. and Parmigiani, G. (1998), Protein Construct
  Storage: Bayesian Variable Selection and Prediction with Mixtures,
  Journal of Biopharmaceutical Statistics, 8, 431-443

protein: Protein Activity Data

Description

Usage

Arguments

Format

Source

[,1]	buf	factor
Buffer	[,2]	pH
numeric		[,3]
NaCl	numeric
[,4]	con	numeric
protein concentration	[,5]	ra
factor	reducing agent	[,6]
det	factor	detergent
[,7]	MgCl2	numeric
	[,8]	temp
numeric	(temerature)	[,9]
prot.act1	numeric
[,10]	prot.act2	numeric
	[,11]	prot.act3
numeric		[,1]