ncvreg (version 3.14.3)

plot.cv.ncvreg: 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 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()

Author

Patrick Breheny

Details

Error bars representing approximate 68% confidence intervals are plotted along with the estimates across values of lambda. For rsq and snr applied to models other than linear regression, the Cox-Snell R-squared is used.

References

Breheny P and Huang J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. 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)