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

Description Usage Arguments Value Author(s) References Examples

Description

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

Usage

1
2
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 <maw27.wilson@gmail.com>

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

1
2
3
4
5
6
## 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)


Search within the BVS package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.