Bayesian Variable Selection
1 2 3 4 5 | bvs(y, x, forced = NULL, intercept = TRUE, family = c("gaussian",
"binomial"), method = c("sample", "enumerate"), prior_model = list(alpha =
1, beta = p), prior_coef = list("none"), rare = FALSE, regions = NULL,
prior_cov = NULL, a1 = 0, hap = FALSE, iter = 10000, maxk = 3,
parallel = FALSE, control = list())
|
y |
outcome variable |
x |
predictor design matrix |
forced |
(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. |
intercept |
indicates whether models should include an intercept. Default = TRUE. |
family |
specifies error distribution for outcome variable, options include
|
method |
specifies how to search the model space, options include
|
prior_model |
specifies parameters for beta-binomial prior on model size. To specify, pass a list with the following elements
Example: |
prior_coef |
specifies prior for regression coefficients (only for use when family = "gaussian"), options include
|
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. |
regions |
(optional) p x 1 character or factor vector that identifies a user-defined region for each variant. If rare = TRUE, then multiple region-specific risk indices are computed for each model. |
prior_cov |
(optional) if method = "sample", a p x q matrix of q predictor-level covariates that the user wishes to incorporate into the estimation of the marginal inclusion probabilities using the iBMU algorithm. |
a1 |
(optional) if method = "enumerate", a q x 1 vector of specified effects of each predictor-level covariate. |
hap |
(not yet implemented) if hap = TRUE, esimtate a set of haplotypes from the multiple variants within each moel and the marginal likelihood of each model is calculated based on the set of haplotypes. |
iter |
if method = "sample", the number of iterations to run the algorithm. Default = 1000. |
maxk |
if method = "enumerate", the maximum model size (k) to consider when enumerating all possible models. Default = 3. |
control |
specifies 'bvs' control object. |
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