ncvreg: local_mfdr – R documentation

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local_mfdr

Estimate local mFDR for all features

Description

local_mfdr() is called by summary.ncvreg(), which typically offers a more convenient interface to users. If, however, you are working with local mfdrs programmatically rather than interactively, you probably want to use local_mfdr(), which skips the sorting, filtering, and print formatting of summary.ncvreg().

Usage

local_mfdr(fit, lambda, X = NULL, y = NULL, method = c("ashr", "kernel"), ...)

Arguments

`fit`	A fitted `ncvreg` or `ncvsurv` object.
`lambda`	The value of lambda at which inference should be carried out.
`X, y`	The design matrix and response used to fit the model; in most cases, it is not necessary to provide `X` and `y` as they are returned by `ncvreg`, but see the `returnX` argument in `ncvreg()`.
`method`	What method should be used to calculate the local fdr? Options are `ashr` (which tends to be more accurate) and `kernel` (which requires no additional packages). The default is to use `ashr` if the package is installed.
`...`	Additional arguments to `ash()` if using `method='ashr'`.

Value

If all features are penalized, then the object returns a data frame with one row per feature and four columns:

Estimate: The coefficient estimate from the penalized regression fit
z: A test statistic that approximately follows a standard normal distribution under the null hypothesis that the feature is marginally independent of the outcome
mfdr: The estimated marginal local false discovery rate
Selected: Features with nonzero coefficient estimates are given an asterisk

If some features are penalized and others are not, then a list is returned with two elements: pen.vars, which consists of the data frame described above, and unpen.vars, a data frame with four columns: Estimate, SE, Statistic, and p.value. The standard errors and p-values are based on a classical lm/glm/coxph model using the effect of the penalized features as an offset.

Examples

# Linear regression
data(Prostate)
fit <- ncvreg(Prostate$X, Prostate$y)
local_mfdr(fit, 0.1)

fit <- ncvreg(Prostate$X, Prostate$y, penalty.factor=rep(0:1, each=4))
local_mfdr(fit, 0.1)

# Logistic regression
data(Heart)
X <- Heart$X
y <- Heart$y
fit <- ncvreg(X, y, family='binomial')
local_mfdr(fit, 0.1)

# Cox regression
data(Lung)
X <- Lung$X
y <- Lung$y
fit <- ncvsurv(X, y)
local_mfdr(fit, 0.1)

ncvreg

Regularization Paths for SCAD and MCP Penalized Regression Models

v3.13.0

GPL-3

Authors

Patrick Breheny [aut, cre] (<https://orcid.org/0000-0002-0650-1119>)

Initial release

2021-03-29