plot.cv.smog: plot method for objects of 'cv.smog' class

In chongma8903/smog: Structural Modeling by using Overlapped Group Penalty

plot method for objects of cv.smog class

Description

Yield a search path for optimal tuning parameters λ_G and λ_I using the mean-squared errors from the cross-validations.

Usage

## S3 method for class 'cv.smog'
plot(x, ...)

Arguments

x	An fitted object in "cv.smog" class.
...	Other graphical parameters to ggplot2.

Details

x-axis represents the group tuning parameter λ_G and y-axis for the interaction tuning parameter λ_I, respectively. The point size demonstrates the maganitude of MSE or negative log-likelihood.

Author(s)

Chong Ma, [e-mail].

References

\insertRef

ma2019structuralsmog

See Also

smog, cv.smog, cv.cglasso.

Examples

# generate design matrix x
set.seed(2018)
n=100;p=20
s=10
x=matrix(0,n,1+2*p)
x[,1]=sample(c(0,1),n,replace = TRUE)
x[,seq(2,1+2*p,2)]=matrix(rnorm(n*p),n,p)
x[,seq(3,1+2*p,2)]=x[,seq(2,1+2*p,2)]*x[,1]

g=c(p+1,rep(1:p,rep(2,p)))  # groups 
v=c(0,rep(1,2*p))           # penalization status
label=c("t",rep(c("prog","pred"),p))  # type of predictor variables

# generate beta
beta=c(rnorm(13,0,2),rep(0,ncol(x)-13))
beta[c(2,4,7,9)]=0

# generate y
data=x%*%beta
noise=rnorm(n)
snr=as.numeric(sqrt(var(data)/(s*var(noise))))
y=data+snr*noise

cvfit=cv.smog(x,y,g,v,label,type = "AIC", family="gaussian")
plot(cvfit)

chongma8903/smog documentation built on Sept. 13, 2022, 7:31 p.m.