R: Error in predict.xgboost: Feature names stored in `object` and `newdata` are different - machine-learning

Question

I wrote a script using xgboost to predict soil class for a certain area using data from field and satellite images. The script as below:
`
rm(list=ls())
library(xgboost)
library(caret)
library(raster)
library(sp)
library(rgeos)
library(ggplot2)
setwd("G:/DATA")
data <- read.csv('96PointsClay02finalone.csv')
head(data)
summary(data)
dim(data)
ras <- stack("Allindices04TIFF.tif")
names(ras) <- c("b1", "b2", "b3", "b4", "b5", "b6", "b7", "b10", "b11","DEM",
"R1011", "SCI", "SAVI", "NDVI", "NDSI", "NDSandI", "MBSI",
"GSI", "GSAVI", "EVI", "DryBSI", "BIL", "BI","SRCI")
set.seed(27) # set seed for generating random data.
# createDataPartition() function from the caret package to split the original dataset into a training and testing set and split data into training (80%) and testing set (20%)
parts = createDataPartition(data$Clay, p = .8, list = F)
train = data[parts, ]
test = data[-parts, ]
#define predictor and response variables in training set
train_x = data.matrix(train[, -1])
train_y = train[,1]
#define predictor and response variables in testing set
test_x = data.matrix(test[, -1])
test_y = test[, 1]
#define final training and testing sets
xgb_train = xgb.DMatrix(data = train_x, label = train_y)
xgb_test = xgb.DMatrix(data = test_x, label = test_y)
#defining a watchlist
watchlist = list(train=xgb_train, test=xgb_test)
#fit XGBoost model and display training and testing data at each iteartion
model = xgb.train(data = xgb_train, max.depth = 3, watchlist=watchlist, nrounds = 100)
#define final model
model_xgboost = xgboost(data = xgb_train, max.depth = 3, nrounds = 86, verbose = 0)
summary(model_xgboost)
#use model to make predictions on test data
pred_y = predict(model_xgboost, xgb_test)
# performance metrics on the test data
mean((test_y - pred_y)^2) #mse - Mean Squared Error
caret::RMSE(test_y, pred_y) #rmse - Root Mean Squared Error
y_test_mean = mean(test_y)
rmseE<- function(error)
{
sqrt(mean(error^2))
}
y = test_y
yhat = pred_y
rmseresult=rmseE(y-yhat)
(r2 = R2(yhat , y, form = "traditional"))
cat('The R-square of the test data is ', round(r2,4), ' and the RMSE is ', round(rmseresult,4), '\n')
#use model to make predictions on satellite image
result <- predict(model_xgboost, ras[1:(nrow(ras)*ncol(ras))])
#create a result raster
res <- raster(ras)
#fill in results and add a "1" to them (to get back to initial class numbering! - see above "Prepare data" for more information)
res <- setValues(res,result+1)
#Save the output .tif file into saved directory
writeRaster(res, "xgbmodel_output", format = "GTiff", overwrite=T)
`
The script works well till it reachs
result <- predict(model_xgboost, ras[1:(nrow(ras)*ncol(ras))])
it takes some time then gives this error:
Error in predict.xgb.Booster(model_xgboost, ras[1:(nrow(ras) * ncol(ras))]) :
Feature names stored in `object` and `newdata` are different!
I realize that I am doing something wrong in that line. However, I do not know how to apply the xgboost model to a raster image that represents my study area.
It would be highly appreciated if someone give a hand, enlightened me, and helped me solve this problem....
My data as csv and raster image can be found here.

Answer 1

Finally, I got the reason for this error.
It was my mistake as the number of columns in the traning data was not the same as in the number of layers in the satellite image.