BVS (version 4.12.1)

fitBVS: Function to calculate fitness for each model for Bayesian Variant Selection Methods

Description

This function takes one of the models and calculates the fitness/cost value of the model.

Usage

fitBVS(Z,data,forced=NULL,cov=NULL,a1=NULL,rare=FALSE,mult.regions=FALSE, regions=NULL,hap=FALSE,inform=FALSE,which=NULL,which.char=NULL)

Arguments

Z

a p dimensional vector specifying a model of interest. In particular if the jth value of the vector is 0 the jth variant is not included in the model and if the jth value of the vector is 1 the jth variant is included in the model.

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 (need when 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 (or sampled) 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.

which

optional current which matrix of sampled models from sampleBVS that is used to see if a model has already been sampled so that that fitness does not have to be recalculated.

which.char

optional vector that identifies that current models that have been sampled from sampleBVS that is also used to determine if a model has already been sampled.

Value

This function outputs a vector of the following values:

coef

If rare=FALSE we report a vector where each value corresponds to the estimated coefficients for all variables within the model of interest. If rare=TRUE we report a value corresponding to the estimated coefficient of the risk index (or risk indices if multi.regions=TRUE) corresponding to each model of interest.

fitness

The value of the fitness function (log(Model likelihood) - log(Model Prior)) of the model of interest.

logPrM

The value of the log prior on the model of interest.

Details

Uses the glm function to calculate the marginal likelihood and fitness function of the model of interest. If rare = TRUE the marginal likelihood is based on the risk index produced from the subset of variants within the model of interest and if hap = TRUE the marginal likelihood is based on the estimated haplotypes produced from the subset of variants within the model of interest.

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)
p = dim(RareData)[2] -1

## Fit the Null model
fit.null = fitBVS(rep(0,p),data=RareData,rare=TRUE)