使用plot.gbm产生边际图时得到错误

Question

总新手到R - 我想从BRT中做出一些边际图，我用gbm包完成并且一直得到相同的错误。 以下是我的代码; boosted.tree_LRFF是我从完成gbm.fit

> plot.gbm(boosted.tree_LRFF, 
+   i.var= 5, 
+   n.trees = train.model$finalModel$tuneValue$n.trees, 
+   continuous.resolution = 100, 
+   return.grid = FALSE, 
+   type = "link") 
Error in plot.window(...) : need finite 'ylim' values 
In addition: Warning messages: 
1: In min(x) : no non-missing arguments to min; returning Inf 
2: In max(x) : no non-missing arguments to max; returning -Inf 

Answer 1

在第一部分，我刚刚得到的输出重新创建用于从“gbm.pdf”为包适合GBM数据集：

library(gbm) 
N <- 1000 
X1 <- runif(N) 
X2 <- 2 * runif(N) 
X3 <- ordered(sample(letters[1:4], N, replace = TRUE), levels = letters[4:1]) 
X4 <- factor(sample(letters[1:6], N, replace = TRUE)) 
X5 <- factor(sample(letters[1:3], N, replace = TRUE)) 
X6 <- 3 * runif(N) 
mu <- c(-1, 0, 1, 2)[as.numeric(X3)] 
SNR <- 10 # signal-to-noise ratio 
Y <- X1 ** 1.5 + 2 * (X2 ** .5) + mu 
sigma <- sqrt(var(Y)/SNR) 
Y <- Y + rnorm(N, 0, sigma) 
# introduce some missing values 
X1[sample(1:N, size = 500)] <- NA 
X4[sample(1:N, size = 300)] <- NA 
data <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3, X4 = X4, X5 = X5, X6 = X6) 

boosted.tree_LRFF <- 
gbm(Y ~ X1 + X2 + X3 + X4 + X5 + X6, 
data = data, 
var.monotone = c(0, 0, 0, 0, 0, 0), 
distribution = "gaussian", 
n.trees = 1000, 
shrinkage = 0.05, 
interaction.depth = 3, 
bag.fraction = 0.5, 
train.fraction = 0.5, 
n.minobsinnode = 10, 
cv.folds = 3, 
keep.data = TRUE, 
verbose = FALSE, 
n.cores = 1) 

现在我绘制树函数值的变量X5，类似于你的情节：

plot(boosted.tree_LRFF, 
i.var = 5, 
n.trees = boosted.tree_LRFF$n.trees, 
continuous.resolution = 100, 
return.grid = FALSE, 
type = "link") 

，我认为你的错误是由于n.trees说法。您可以将其作为常量输入，也可以来自GBM装配对象。在我的例子中，我使用了“boosted.tree_LRFF”，它似乎是你的例子中原始拟合对象的名称（尽管当然我的数据是不同的）。