在R中绘制cv.glmnet

时间:2016-04-15 21:02:03

标签: r regression glmnet

使用R,我试图修改我使用cv.glmnet执行脊回归得到的标准图。

我执行岭回归

lam = 10 ^ seq (-2,3, length =100)    
cvfit = cv.glmnet(xTrain, yTrain, alpha = 0, lambda = lam)

我可以通过执行以下

来绘制系数对log lambda
plot(cvfit $glmnet.fit, "lambda")

enter image description here

如何根据实际的lambda值(而不是log lambda)绘制系数,并在图上标记每个预测值?

1 个答案:

答案 0 :(得分:1)

您可以这样操作,将值存储在$beta下的$lambdaglmnet.fit下:

library(glmnet)

xTrain = as.matrix(mtcars[,-1])
yTrain = mtcars[,1]

lam = 10 ^ seq (-2,3, length =30)    
cvfit = cv.glmnet(xTrain, yTrain, alpha = 0, lambda = lam)

betas = as.matrix(cvfit$glmnet.fit$beta)
lambdas = cvfit$lambda
names(lambdas) = colnames(betas)

使用ggplot解决方案,我们尝试将其长时间旋转并使用log10 x比例尺和ggrepel进行绘制以添加标签:

library(ggplot2)
library(tidyr)
library(dplyr)
library(ggrepel)

as.data.frame(betas) %>% 
tibble::rownames_to_column("variable") %>% 
pivot_longer(-variable) %>% 
mutate(lambda=lambdas[name]) %>% 
ggplot(aes(x=lambda,y=value,col=variable)) + 
geom_line() + 
geom_label_repel(data=~subset(.x,lambda==min(lambda)),
aes(label=variable),nudge_x=-0.5) +
scale_x_log10()

enter image description here

在基数R中,也许像这样,我认为不利之处是您不能很好地看到标签:

pal = RColorBrewer::brewer.pal(nrow(betas),"Set3")
plot(NULL,xlim=range(log10(lambdas))+c(-0.3,0.3),
ylim=range(betas),xlab="lambda",ylab="coef",xaxt="n")
for(i in 1:nrow(betas)){
    lines(log10(lambdas),betas[i,],col=pal[i])
}

axis(side=1,at=(-2):2,10^((-2):2))
text(x=log10(min(lambdas)) - 0.1,y = betas[,ncol(betas)],
labels=rownames(betas),cex=0.5)

legend("topright",fill=pal,rownames(betas))

enter image description here