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Given samples from a posterior or a list of candidate groups, BLiP performs resolution-adaptive signal detection to maximize power while controlling (e.g.) the FDR.

BLiP.Rd

Note: when working with image data or a continuous set of locations, consider using BLiP_cts.

Usage

BLiP(
  samples = NULL,
  cand_groups = NULL,
  weight_fn = "inverse_size",
  error = "fdr",
  q = 0.1,
  max_pep = 1,
  deterministic = T,
  verbose = F,
  perturb = T,
  max_iters = 100,
  search_method = "binary",
  solver = NULL
)

Arguments

samples

(N,p)-shaped matrix of posterior samples where a nonzero value indicates the presence of a signal.

cand_groups

A list of lists, where the inner lists must have a "group" attribute, corresponding to the features in the group and a "pep" attribute, corresponding to a posterior error probability.

weight_fn

How to weight discoveries. Can be one of 'inverse_size' or 'log_inverse_size' or a function which takes a candidate group as an input and returns a weight.

error

Bayesian error rate to control: one of "fwer", "pfer", "fdr", "local_fdr".

q

The level at which to control the Bayesian FWER/PFER/FDR/local FDR.

max_pep

Never select any group with a pep greater than max_pep.

deterministic

Whether or not BLiP should return a deterministic solution.

verbose

If TRUE, gives occasional progress reports.

perturb

If TRUE, adds a tiny (random) perturbation to the weights to ensure the existence of a unique optimal solution.

max_iters

Maximum number of binary-search iterations for FWER when.

search_method

For FWER control, how to find the optimal parameter for the LP. Either 'binary' (defalt) or 'none'.

solver

The solver to use within CVXR. By default, will use Gurobi, CBC, or ECOS (in that order), depending on whether they are installed.

Value

A list of candidate groups, which asserts that each group contains a signal.

Examples

# Example 1: sparse linear regression
set.seed(123); n <- 100; p <- 200
data <- blipr::generate_regression_data(n=n, p=p)
# sample from the posterior, e.g., using NPrior
nprior <- NPrior::NPrior_run(
  X=data$X, y=data$y, N=5000, prior='SpSL-G'
)
#> Error in n - 1: non-numeric argument to binary operator
# run blip on posterior samples
detections <- blipr::BLiP(samples=t(nprior$ThetaSamples), q=0.1, error='fdr')
#> Error in t(nprior$ThetaSamples): object 'nprior' not found

# Example 2: Running BLiP directly on candidate groups
cand_groups <- list(
  list(group=c(1), pep=0.1),
  list(group=c(2), pep=0.5),
  list(group=c(1,2), pep=0.01)
 )
detections <- blipr::BLiP(cand_groups=cand_groups, q=0.1, error='fdr')