corr_lfdr {adaptMT} | R Documentation |
corr_lfdr
computes the oracle local FDR estimate, by using revealing all p-values, and computes the Pearson correlation between it and the estimate within each step of adapt
.
corr_lfdr(obj, x, pvals, model = NULL, niter_oracle = 100)
obj |
an 'adapt' object. Output of |
x |
covariates (i.e. side-information). Should be compatible to |
pvals |
a vector of values in [0, 1]. P-values |
model |
an optional argument. If |
niter_oracle |
an positive integer. Number of iterations in EM algorithm |
corra vector of values in [0, 1]. Pearson correlation of oracle local FDR estimate and the estimates within each step. Each value corresponds to an entry of obj$params
oracle_lfdra vector of values in [0, 1]. Oracle local FDR estimate
lfdra matrix of values in [0, 1]. Local FDR estimates within each step.
alphasa vector of values in [0, 1]. The target FDR levels corresponding to each local FDR estimate
nmasksa vector of integers. The number of masked p-values corresponding to each local FDR estimate
# Load estrogen data data(estrogen) pvals <- as.numeric(estrogen$pvals) x <- data.frame(x = as.numeric(estrogen$ord_high)) dist <- beta_family() # Subsample the data for convenience inds <- (x$x <= 5000) pvals <- pvals[inds] x <- x[inds,,drop = FALSE] # Run adapt_glm library("splines") formulas <- paste0("ns(x, df = ", 6:10, ")") res <- adapt_glm(x = x, pvals = pvals, pi_formulas = formulas, mu_formulas = formulas, dist = dist, nfits = 10) # Run corr_lfdr obj <- corr_lfdr(res, x, pvals) obj$corr