我在使用randomForest和raster包时遇到了问题。首先，我创建了分类器：
library(raster)
library(randomForest)
# Set some user variables
fn = "image.pix"
outraster = "classified.pix"
training_band = 2
validation_band = 1
original_classes = c(125,126,136,137,151,152,159,170)
reclassd_classes = c(122,122,136,137,150,150,150,170)
# Get the training data
myraster = stack(fn)
training_class = subset(myraster, training_band)
# Reclass the training data classes as required
training_class = subs(training_class, data.frame(original_classes,reclassd_classes))
# Find pixels that have training data and prepare the data used to create the classifier
is_training = Which(training_class != 0, cells=TRUE)
training_predictors = extract(myraster, is_training)[,3:nlayers(myraster)]
training_response = as.factor(extract(training_class, is_training))
remove(is_training)
# Create and save the forest, use odd number of trees to avoid breaking ties at random
r_tree = randomForest(training_predictors, y=training_response, ntree = 201, keep.forest=TRUE) # Runs out of memory, does not allow more trees than this...
remove(training_predictors, training_response)
到目前为止，一切都很好。通过查看错误率、混淆矩阵等，我可以看出森林被正确创建。然而，当我尝试对一些数据进行分类时，我遇到了以下问题：predictions中返回全部NA值。
# Classify the whole image
predictor_data = subset(myraster, 3:nlayers(myraster))
layerNames(predictor_data) = layerNames(myraster)[3:nlayers(myraster)]
predictions = predict(predictor_data, r_tree, type='response', progress='text')
并给出了以下警告：
Warning messages:
1: In `[<-.factor`(`*tmp*`, , value = c(1, 1, 1, 1, 1, 1, ... :
invalid factor level, NAs generated
(keeps going like this)...
然而，直接调用predict.randomForest函数可行并返回预期的predictions（这对我来说不是一个好选择，因为图像很大，我无法在内存中存储整个矩阵）：
# Classify the whole image and write it to file
predictor_data = subset(myraster, 3:nlayers(myraster))
layerNames(predictor_data) = layerNames(myraster)[3:nlayers(myraster)]
predictor_data = extract(predictor_data, extent(predictor_data))
predictions = predict(r_tree, newdata=predictor_data)
如何使它直接与“raster”版本配合使用？ 我知道这是可能的，如 predict{raster} 的示例所示。