小编Mes*_*mer的帖子

我对 ggplot 图例有一些问题，这是我的第一个代码，只有 corrGenes 的图例，这很好。

gene1=c(1.041,0.699,0.602,0.602,2.585,0.602,1.000,0.602,1.230,1.176,0.699,0.477,1.322)
    BIME = c(0.477,0.477,0.301,0.477,2.398,0.301,0.602,0.301,0.602,0.699,0.602,0.477,1.176)
    corrGenes=c(0.922,0.982,0.934,0.917,0.993,0.697,0.000,0.440,0.859,0.788,0.912,0.687,0.894)

DF=data.frame(gene1,BIME,corrGenes)

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

当我添加 abline 和 smooth 时，我得到了正确的图：

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  geom_abline(intercept=0, slope=1)+
  stat_smooth(method = "lm",se=FALSE)+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

但没有办法为他们获得传奇，我尝试了许多其他组合：

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  geom_abline(aes(colour="best"),intercept=0, slope=1)+
  stat_smooth(aes(colour="data"),method = "lm",se=FALSE)+
  scale_colour_manual(name="Fit", values=c("data"="blue", "best"="black"))+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

如果有人有解决这个微小但非常烦人的问题的想法，那将非常有帮助！

我尝试从包中plot.roc的函数重现 ROC 曲线。pRocggplot2

library(mlbench)
library(caret)

data(Sonar)

set.seed(998)
fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 10,
                           ## Estimate class probabilities
                           classProbs = TRUE,
                           ## Evaluate performance using 
                           ## the following function
                           summaryFunction = twoClassSummary)

gbmGrid <-  expand.grid(interaction.depth = c(1, 5, 9),
                        n.trees = (1:30)*50,
                        shrinkage = 0.1,
                        n.minobsinnode = 20)

inTraining <- createDataPartition(Sonar$Class, p = .75, list = FALSE)
training <- Sonar[ inTraining,]
testing  <- Sonar[-inTraining,]


set.seed(825)
gbmFit <- train(Class ~ ., data = …