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

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% confidence intervals are plotted 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.

References

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

Examples

Run this code

# NOT RUN {
# 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)
# }