给你看个例子，这个例子很好用——
library(glmnet)
age <- c(4,8,7,12,6,9,10,14,7)
gender <- c(1,0,1,1,1,0,1,0,0) ; gender<-as.factor(gender)
bmi_p <- c(0.86,0.45,0.99,0.84,0.85,0.67,0.91,0.29,0.88)
m_edu <- c(0,1,1,2,2,3,2,0,1); m_edu<-as.factor(m_edu)
p_edu <- c(0,2,2,2,2,3,2,0,0); p_edu<-as.factor(p_edu)
f_color <- c("blue", "blue", "yellow", "red", "red", "yellow", "yellow", "red", "yellow")
asthma <- c(1,1,0,1,0,0,0,1,1)
f_color <- as.factor(f_color)
xfactors <- model.matrix(asthma ~ gender + m_edu + p_edu + f_color)[,-1]
x <- as.matrix(data.frame(age, bmi_p, xfactors))
#note alpha =1 for lasso only and can blend with ridge penalty down to alpha=0 ridge only
glmmod<-glmnet(x,y=as.factor(asthma),alpha=1,family='binomial')
#plot variable coefficients vs. shrinkage parameter lambda.
plot(glmmod,xvar="lambda")
grid()
# some results
#model shown for lambda up to first 3 selected variables. Lambda can have manual
# tuning grid for wider range
> glmmod
cv.glmmod <- cv.glmnet(x,y=asthma,alpha=1)
plot(cv.glmmod)
best_lambda <- cv.glmmod$lambda.min
best_lambda
更详细的解释看这里，看这里～～～
http://stats.stackexchange.com/questions/72251/an-example-lasso-regression-using-glmnet-for-binary-outcome
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