plot.cv.ncvreg: Plots the cross-validation curve from a cv.ncvreg object

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

Plots the cross-validation curve from a cv.ncvreg object

Description

Plots the cross-validation curve from a cv.ncvreg or cv.ncvsurv object, along with standard error bars.

Usage

## S3 method for class 'cv.ncvreg'
plot(
  x,
  log.l = TRUE,
  type = c("cve", "rsq", "scale", "snr", "pred", "all"),
  selected = TRUE,
  vertical.line = TRUE,
  col = "red",
  ...
)

Arguments

x	A cv.ncvreg or cv.ncvsurv object.
log.l	Should horizontal axis be on the log scale? Default is TRUE.
type	What to plot on the vertical axis. cve plots the cross-validation error (deviance); rsq plots an estimate of the fraction of the deviance explained by the model (R-squared); snr plots an estimate of the signal-to-noise ratio; scale plots, for family="gaussian", an estimate of the scale parameter (standard deviation); pred plots, for family="binomial", the estimated prediction error; all produces all of the above.
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.
vertical.line	If TRUE (the default), draws a vertical line at the value where cross-validaton error is minimized.
col	Controls the color of the dots (CV estimates).
...	Other graphical parameters to plot

Details

Error bars representing approximate 68\ along with the estimates at value of lambda. For rsq and snr applied to models other than linear regression, the Cox-Snell R-squared is used.

Author(s)

Patrick Breheny

References

Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. c("\Sexpr[results=rd]tools:::Rd_expr_doi(\"#1\")", "10.1214/10-AOAS388")\Sexpr{tools:::Rd_expr_doi("10.1214/10-AOAS388")}

See Also

ncvreg, cv.ncvreg

Examples

# Linear regression --------------------------------------------------
data(Prostate)
cvfit <- cv.ncvreg(Prostate$X, Prostate$y)
plot(cvfit)
op <- par(mfrow=c(2,2))
plot(cvfit, type="all")
par(op)

# Logistic regression ------------------------------------------------
data(Heart)
cvfit <- cv.ncvreg(Heart$X, Heart$y, family="binomial")
plot(cvfit)
op <- par(mfrow=c(2,2))
plot(cvfit, type="all")
par(op)

# Cox regression -----------------------------------------------------
data(Lung)
cvfit <- cv.ncvsurv(Lung$X, Lung$y)
op <- par(mfrow=c(1,2))
plot(cvfit)
plot(cvfit, type="rsq")
par(op)

ncvreg documentation built on April 25, 2023, 5:11 p.m.