如何在R中绘制Logistic回归(LASSO)的ROC曲线？

Question

我将逻辑回归模型拟合到R中的训练数据集,更具体地说是具有L1惩罚的LASSO回归.我用了这个glmnet包.模型的代码如下所示.

t1 <- Sys.time()
glmnet_classifier <- cv.glmnet(x = dtm_train_tfidf,
                           y = tweets_train[['sentiment']], 
                           family = 'binomial', 
                           # L1 penalty
                           alpha = 1,
                           # interested in the area under ROC curve
                           type.measure = "auc",
                           # 5-fold cross-validation
                           nfolds = 5,
                           # high value is less accurate, but has faster training
                           thresh = 1e-3,
                           # again lower number of iterations for faster training
                           maxit = 1e3)
print(difftime(Sys.time(), t1, units = 'mins'))

preds <- predict(glmnet_classifier, dtm_test_tfidf, type = 'response')[ ,1]

现在我想绘制ROC曲线.但是,我无法弄清楚如何准确地绘制它.

当我plot(glmnet_classifier)这是我收到的:

由于这不是Roc曲线,我想知道是否有人知道如何在R中绘制它？我已经提到了ROCR包,但它给了我一个错误:

roc.perf = performance(preds, measure = "tpr", x.measure = "fpr")

有人可以帮忙吗？非常感谢你!

Answer 1

library(pROC)
data("aSAH")

fit <- glm(outcome ~ gender + age + wfns + s100b , data = aSAH, family = binomial)

 roc(aSAH$outcome, as.vector(fitted.values(fit)), percent=F,   boot.n=1000, ci.alpha=0.9, stratified=FALSE, plot=TRUE, grid=TRUE, show.thres=TRUE, legacy.axes = TRUE, reuse.auc = TRUE,
# print.thres = c(0.30,0.35, 0.40, 0.45,0.48, 0.50,0.55, 0.60),#
print.auc = TRUE, print.thres.col = "blue", ci=TRUE, ci.type="bars", print.thres.cex = 0.7, main = paste("ROC curve using","(N = ",nrow(aSAH),")") )

我希望它有所帮助;)