Description Usage Arguments Details Author(s) References See Also Examples
Yield a search path for optimal group penalty G-λ and I-λ using the mean-squared errors from the cross-validations.
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x |
An fitted object in "cv.smog" class. |
... |
Other graphical parameters to ggplot2. |
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.
Chong Ma, chongma8903@gmail.com.
ma2019structuralsmog
smog, cv.smog, cv.cglasso.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # 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)
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