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ncvreg

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 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% 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.

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. doi: 10.1214/10-AOAS388

See Also

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

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

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