adapt_glm {adaptMT}

Adaptive P-value Thresholding with Generalized Linear Models

Description

adapt_glm is a wrapper of adapt that fits pi(x) and mu(x) by glm.

Usage

adapt_glm(x, pvals, pi_formulas, mu_formulas, dist = beta_family(),
  s0 = rep(0.45, length(pvals)), alphas = seq(0.01, 1, 0.01),
  piargs = list(), muargs = list(), ...)

Arguments

x

covariates (i.e. side-information). Should be compatible to models. See Details

pvals

a vector of values in [0, 1]. P-values

pi_formulas

a vector/list of strings/formulas. Formulas for fitting pi(x) by glm. See Details

mu_formulas

a vector/list of strings/formulas. Formulas for fitting mu(x) by glm. See Details

dist

an object of class "gen_exp_family". beta_family() as default

s0

a vector of values in [0, 0.5). Initial threshold.

alphas

a vector of values in (0, 1). Target FDR levels.

piargs

a list. Other arguments passed to glm for fitting pi(x)

muargs

a list. Other arguments passed to glm for fitting mu(x)

...

other arguments passed to adapt (except models)

Details

pi_formulas and mu_formulas can either be a list or a vector with each element being a string or a formula. For instance, suppose x has a single column with name x1, the following five options are valid for the same inputs (ns forms a spline basis with df knots):

1. c("x1", "ns(x1, df = 8)");

2. c("~ x1", "~ ns(x1, df = 8)");

3. list("x1", "ns(x1, df = 8)");

4. list("~ x1", "~ ns(x1, df = 8)");

5. list(~ x1, ~ ns(x1, df = 8))

There is no need to specify the name of the response variable, as this is handled in the function.

When x has a few variables, it is common to use non-parametric GLM by replacing x by a spline basis of x. In this case, ns from library(splines) package is suggested.

See Also

adapt, adapt_gam, adapt_glmnet, glm, ns

Examples

# 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 adapt by manually setting models for glm
models <- lapply(formulas, function(formula){
    piargs <- muargs <- list(formula = formula)
    gen_adapt_model(name = "glm", piargs = piargs, muargs = muargs)
})
res2 <- adapt(x = x, pvals = pvals, models = models,
              dist = dist, nfits = 10)

# Check equivalence
identical(res, res2)