View source: R/plotcvncvreg.R
plot.cv.ncvreg  R Documentation 
Plots the crossvalidation curve from a cv.ncvreg
or cv.ncvsurv
object,
along with standard error bars.
## 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",
...
)
x 
A 
log.l 
Should horizontal axis be on the log scale? Default is TRUE. 
type 
What to plot on the vertical axis:

selected 
If 
vertical.line 
If 
col 
Controls the color of the dots (CV estimates). 
... 
Other graphical parameters to 
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 CoxSnell Rsquared is used.
Patrick Breheny
Breheny P and Huang J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232253. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/10AOAS388")}
ncvreg()
, cv.ncvreg()
# 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)
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