plot.mfdr: Plot marginal false discovery rate curves

In ncvreg: Regularization Paths for SCAD and MCP Penalized Regression Models

Plot marginal false discovery rate curves

Description

Plot marginal false discovery rate curves from an mfdr or perm.ncvreg object.

Usage

## S3 method for class 'mfdr'
plot(
  x,
  type = c("mFDR", "EF"),
  log.l = FALSE,
  selected = TRUE,
  legend = TRUE,
  ...
)

Arguments

x	A perm.ncvreg or mfdr object.
type	What to plot on the vertical axis. mFDR plots the marginal false discovery rate; EF plots the expected number of false discoveries along with the actual number of variables included in the model.
log.l	Should horizontal axis be on the log scale? Default is FALSE.
selected	If TRUE (the default), places an axis on top of the plot denoting the number of variables in the model (i.e., that have a nonzero regression coefficient) at that value of lambda.
legend	For type="EF" plots, draw a legend to indicate which line is for the actual selections and which line is for the expected number of false discoveries? Default is TRUE.
...	Other graphical parameters to pass to plot()

Author(s)

Patrick Breheny

References

Breheny P (2019). Marginal false discovery rates for penalized regression models. Biostatistics, 20: 299-314.

See Also

mfdr(), perm.ncvreg()

Examples

data(Prostate)
fit <- ncvreg(Prostate$X, Prostate$y)

obj <- mfdr(fit)
obj[1:10,]

# Some plotting options
plot(obj)
plot(obj, type="EF")
plot(obj, log=TRUE)


# Comparison with perm.ncvreg
op <- par(mfrow=c(2,2))
plot(obj)
plot(obj, type="EF")
pmfit <- perm.ncvreg(Prostate$X, Prostate$y)
plot(pmfit)
plot(pmfit, type="EF")
par(op)

ncvreg documentation built on Sept. 11, 2024, 8:31 p.m.