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
        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 Liang et
        al hyper-g priors (JASA 2008). Other model selection criterian
        include AIC and BIC. Sampling probabilities may be updated
        based on the sampled models. Allows uniform or beta-binomial
        prior distributions on models.

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:
  <tabular>llll</tabular>{
  [,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	[,12] 	prot.act4  	numeric	protein activity 
}

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