Description Usage Arguments Details Value Author(s) References Examples
This function takes one of the models and calculates the fitness/cost value of the model.
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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. |
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.
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. |
Melanie Quintana <maw27.wilson@gmail.com>
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.
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