enumerateBVS: Function to Enumerate all models for Bayesian Variant...

In BVS: Bayesian Variant Selection: Bayesian Model Uncertainty Techniques for Genetic Association Studies

Description

This function enumerates and calculates summaries for all models in the model space. Not recommended for problems where p>20.

Usage

enumerateBVS(data,forced=NULL,cov=NULL,a1=0,rare=FALSE,mult.regions=FALSE,
             regions=NULL,hap=FALSE,inform=FALSE)

Arguments

data	a (n x (p+1)) dimensional data frame where the first column corresponds to the response variable that is presented as a factor variable corresponding to an individuals disease status (0|1),and the final p columns are the SNPs of interest each coded as a numeric variable that corresponds to the number of copies of minor alleles (0|1|2)
forced	an optional (n x c) matrix of c confounding variables that one wishes to adjust the analysis for and that will be forced into every model.
inform	if inform=TRUE corresponds to the iBMU algorithm of Quintana and Conti (Submitted) that incorporates user specified external predictor-level covariates into the variant selection algorithm.
cov	an optional (p x q) dimensional matrix of q predictor-level covariates that need to be specified if inform=TRUE that the user wishes to incorporate into the estimation of the marginal inclusion probabilities using the iBMU algorithm
a1	a q dimensional vector of specified effects of each predictor-level covariate to be used when inform=TRUE.
rare	if rare=TRUE corresponds to the Bayesian Risk index (BRI) algorithm of Quintana and Conti (2011) that constructs a risk index based on the multiple rare variants within each model. The marginal likelihood of each model is then calculated based on the corresponding risk index.
mult.regions	when rare=TRUE if mult.regions=TRUE then we include multiple region specific risk indices in each model. If mult.regions=FALSE a single risk index is computed for all variants in the model.
regions	if mult.regions=TRUE regions is a p dimensional character or factor vector identifying the user defined region of each variant.
hap	if hap=TRUE we estimate a set of haplotypes from the multiple variants within each model and the marginal likelihood of each model is calculated based on the set of estimated haplotypes.

Value

This function outputs a list of the following values:

fitness	A vector of the fitness values (log(Model likelihood) - log(Model Prior)) of each enumerated model.
logPrM	A vector of the log Model Priors of each enumerated model.
which	A vector identifying the character representation of each model indicator vector.
coef	If rare=FALSE we report a matrix where each row corresponds to the estimated coefficients for all variables within each enumerated model. If rare=TRUE we report a vector where each entry corresponds to the estimated coefficient of the risk index (or multiple risk indices if mult.regions = TRUE) corresponding to each enumerated model.
alpha	If inform=FALSE that is simply a vector of 0's. If inform=TRUE we report a matrix where each row corresponds to the specified effects (alpha's) of each predictor-level covariate for each enumerated model.

Author(s)

Melanie Quintana <[e-mail]>

References

Quintana M, Conti D (2011). Incorporating Model Uncertainty in Detecting Rare Variants: The Bayesian Risk Index. Genetic Epidemiology 35:638-649.

Quintana M, Conti D (Submitted). Integrative Variable Selection via Bayesian Model Uncertainty.

Examples

## Load the data for Rare variant example
data(RareData)

## Enumerate model space for a subset of 5 variants and save output to BVS.out 
## for rare variant example.
RareBVS.out <- enumerateBVS(data=RareData[,1:6],rare=TRUE)

BVS documentation built on May 1, 2019, 10:16 p.m.