我正在使用带有插入符号包的随机森林来设置最佳mtry(预测因子的数量)。当我绘制模型以查看RMSE随mtry的变化时,我想在最佳mtry中添加一个点
ctrl <- trainControl(method = "cv", savePred=T, number = 10)
tunegrid <- expand.grid(.mtry=seq(from=2,to=nlayers(covs_processed),by=2))
# Search for the best mtry parameter
rfmodel <- train(fm, data=dat_1963@data, method = "rf", trControl = ctrl,
importance=TRUE, tuneGrid=tunegrid)
plot(rfmodel,main= "Tuning RF 2018")
位置:
rfmodel[11][[1]]$tuneValue[[1]]
24
min(rfmodel$results$RMSE)
2.972381
我尝试使用此代码添加点,但是我可以
points(rfmodel[11][[1]]$tuneValue[[1]],min(rfmodel$results$RMSE),col="red")
可以在这里找到模型: https://drive.google.com/open?id=1tFFgxuCiJNC4PLMekBG7bgEziKGwMJmu
答案 0 :(得分:2)
plot()
中的caret
方法使用lattice
包,而不使用基本图形,因此lines
不能使用。
通过添加新的绘图图层,您可以使用ggplot
方法轻松获得结果。这有两个选择:
library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
data(BloodBrain)
theme_set(theme_bw())
ctrl <- trainControl(method = "cv", number = 10, returnResamp = "all")
set.seed(214)
rfmodel <-
train(
x = bbbDescr, y = logBBB,
method = "rf",
preProc = "zv",
trControl = ctrl,
tuneGrid = data.frame(mtry = 1:10)
)
# rfmodel$resample contains the individual RMSE values per fold. Use the
# ggplot method and add another layer with those points.
ggplot(rfmodel) +
geom_point(data = rfmodel$resample, alpha = .3)
# or add them as colored lines
ggplot(rfmodel) +
geom_line(
data = rfmodel$resample,
# For each resample, plot a different colored line
aes(group = Resample, col = Resample),
alpha = .3) +
# the legend here gets huge so stop it from being printed
theme(legend.position = "none")
由reprex package(v0.2.1)于2019-04-03创建