Error: predictors in new data do not match that of the training data when using raster attribute table (RAT)

Question

我有一个 RandomForest 模型，该模型使用包含数字和分类预测变量的 caret 包进行训练。我正在尝试使用这个训练有素的模型对一个新数据集进行预测，该数据集是一个包含每个预测变量一层的 rasterStack。我已经使用 raster 包中的 ratify 函数将分类栅格图层转换为因子，并通过添加栅格属性 table ( RAT），但是当我预测我收到以下错误时：

# Error in predict.randomForest(modelFit, newdata) : 
# Type of predictors in new data do not match that of the training data.

我想我可能以某种方式错误地表述了 RAT，或者我误解了 RAT 的功能。下面是一个最小的可重现示例。有什么问题吗？

require(caret)
require(raster)

set.seed(150)
data("iris")

# Training dataset
iris.x<-iris[,1:4]
iris.x$Cat<-"Low"
iris.x$Cat[1:60]<-"High"
iris.x$Cat<-as.factor(as.character(iris.x$Cat))
iris.y<-iris$Species

# Train RF model in Caret
ctrl<-trainControl("cv", num=5, p = 0.9)

mod<- train(iris.x,iris.y, 
              method="rf",
              trControl=trainControl(method = "cv"))

# Create raster stack prediction dataset
r <- raster(ncol=10, nrow=5)
tt <- sapply(1:4, function(x) setValues(r,  round(runif(ncell(r),1,5))))

#Categorical raster layer with RAT
r_cat<-raster(ncol=10, nrow=5)
r_cat[1:25]<-1
r_cat[26:50]<-2
ratr_cat <- ratify(r_cat)
rat <- levels(ratr_cat)[[1]]
rat$PCN <- c(1,2)
rat$PCN_level <- c('Low','High')
levels(ratr_cat) <- rat

#Stack raster layers
t.stack <- stack(c(tt,ratr_cat),RAT = TRUE)

#Make sure names in stack match training dataset
names(t.stack)<-c('Sepal.Length','Sepal.Width', 'Petal.Length', 'Petal.Width','Cat')

#Ensure that categorical layer still has RAT and is a factor
t.stack[['Cat']] #yep
is.factor(t.stack[['Cat']]) #yep

#Predict new data using model
mod_pred <- predict(t.stack, mod)

Answer 1

因子 RasterLayer（属性层）似乎是（或被处理为）有序因子。所以你只需要用一个有序的向量来训练模型。你可以实现这一改变一行：

iris.x$Cat<- ordered(as.character(iris.x$Cat), levels = c("Low", "High"))

Error: predictors in new data do not match that of the training data when using raster attribute table (RAT)

Error: predictors in new data do not match that of the training data when using raster attribute table (RAT)

r

random-forest

r-caret

r-raster

rgdal