R 使用nnet软件包进行光栅图像分类

我对R中“nnet”包的光栅分类有问题。我无法使用预测的单元格类别创建光栅。这是我的代码：

library(sp)
library(nnet)
library(caret)
library(rgdal)
library(raster)

images_list=list.files(path="maps", full.names=TRUE, ignore.case = TRUE) 

rasters_stack <- stack(images_list)   

table = read.csv("points.csv", sep = ",")

indeks <- createDataPartition(table$class, p=2/3, list=FALSE)
training <- table[indeks, ] 
test    <- table[-indeks, ]

model = nnet(factor(class) ~ blue + green + red, data = training, size=2)

predict(rasters_stack, model, type="class", filename="output.img",
         progress="text", overwrite=TRUE)

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我想试一试。你能提供输入数据还是假数据输入？如作者通过电子邮件指出的数据。

Error in v * x@data@gain : non-numeric argument to binary operator
In addition: Warning messages:
1: In rgdal::putRasterData(x@file@transient, v, band = 1, offset = off) :
  NAs introduced by coercion
2: In rgdal::putRasterData(x@file@transient, v, band = 1, offset = off) :
  NAs introduced by coercion
3: In rgdal::putRasterData(x@file@transient, v, band = 1, offset = off) :
  NAs introduced by coercion
4: In rgdal::putRasterData(x@file@transient, v, band = 1, offset = off) :
  NAs introduced by coercion
5: In .gd_SetStatistics(object, ...) : NAs introduced by coercion