How to predict test data using a GAM with MRF smooth and neighborhood structure?

I am having a problem using the predict() function for a mgcv::gam (training) model on a new (testing) dataset. The problem arises due to a mrf smooth I have integrated to account for the spatial nature of my data.

I use the following call to create my GAM model

## Run GAM with MRF
m <- gam(crime ~ s(district,k=nrow(traindata),
                 bs ='mrf',xt=list(nb=nbtrain)), #define MRF smooth
     data = traindata,
     method = 'REML', 
     family = scat(), #fit scaled t distribution
     gamma = 1.4
)

where I predict the dependent variable crime using the neighbourhood structure, parsed into the model in the smooth term argument xt. The neighbourhood structure comes as a nb object that I created using the poly2nb() function.

Now, if I want to use predict() on a new testing dataset, I don't know how to pass the according neighbourhood structure into the call. Providing just the new data

pred <- predict.gam(m,newdata=testdata)

throws the following error:

Error in predict.gam(m, newdata = testdata) :
7, 16, 20, 28, 35, 36, 37, 43 not in original fit

Here's a full reproduction of the error using the Columbus dataset called from within R directly:

#ERROR REPRODUCTION

## Load packages
require(mgcv)
require(spdep)
require(dplyr)

## Load Columbus Ohio crime data (see ?columbus for details and credits)
data(columb.polys) #Columbus district shapes list
columb.polys <- lapply(columb.polys,na.omit) #omit NAs (unfortunate problem with the Columbus sample data)
data(columb) #Columbus data frame

df <- data.frame(district=numeric(0),x=numeric(0),y= numeric(0)) #Create empty df to store x, y and IDs for each polygon

## Extract x and y coordinates from each polygon and assign district ID
for (i in 1:length(columb.polys)) {
  district <- i-1
  x <- columb.polys[[i]][,1]
  y <- columb.polys[[i]][,2]
  df <- rbind(df,cbind(district,x,y)) #Save in df data.frame
}

## Convert df into SpatialPolygons
sp <- df %>%
       group_by(district) %>%
       do(poly=select(., x, y) %>%Polygon()) %>%
       rowwise() %>%
       do(polys=Polygons(list(.$poly),.$district)) %>%
       {SpatialPolygons(.$polys)}

## Merge SpatialPolygons with data
spdf <- SpatialPolygonsDataFrame(sp,columb)

## Split into training and test sample (80/20 ratio)
splt <- sample(1:2,size=nrow(spdf),replace=TRUE,prob=c(0.8,0.2))
train <- spdf[splt==1,] 
test <- spdf[splt==2,]

## Prepapre both samples and create NB objects
traindata <- train@data #Extract data from SpatialPolygonsDataFrame
testdata <- test@data
traindata <- droplevels(as(train, 'data.frame')) #Drop levels
testdata <- droplevels(as(test, 'data.frame'))
traindata$district <- as.factor(traindata$district) #Factorize
testdata$district <- as.factor(testdata$district)
nbtrain <- poly2nb(train, row.names=train$Precinct, queen=FALSE) #Create NB objects for training and test sample
nbtest <- poly2nb(test, row.names=test$Precinct, queen=FALSE)
names(nbtrain) <- attr(nbtrain, "region.id") #Set region.id
names(nbtest) <- attr(nbtest, "region.id")

## Run GAM with MRF
m <- gam(crime ~ s(district, k=nrow(traindata), bs = 'mrf',xt = list(nb = nbtrain)), # define MRF smooth
         data = traindata,
         method = 'REML', # fast version of REML smoothness selection; alternatively 'GCV.Cp'
         family = scat(), #fit scaled t distribution
         gamma = 1.4
)

## Run prediction using new testing data
pred <- predict.gam(m,newdata=testdata)

Solution

SOLUTION:

I finally found the time to update this post with the solution. Thanks to everyone for helping me out. Here is the code for implementing k-fold CV with a random training-testing split:

#Apply k-fold cross validation
mses <- data.frame() #Create empty df to store CV squared error values
scores <- data.frame() #Create empty df to store CV R2 values
set.seed(42) #Set seed for reproducibility
k <- 10 #Define number of folds
for (i in 1:k) {
  # Create weighting column
  data$weight <- sample(c(0,1),size=nrow(data),replace=TRUE,prob=c(0.2,0.8)) #0 Indicates testing sample, 1 training sample

  #Run GAM with MRF
  ctrl <- gam.control(nthreads = 6) #Set controls
  m <- gam(crime ~ s(disctrict, k=nrow(data), bs = 'mrf',xt = list(nb = nb)), #define MRF smooth
            data = data,
            weights = data$weight, #Use only weight==1 observations (training)
            method = 'REML', 
            control = ctrl,
            family = scat(), 
            gamma = 1.4
           )
  #Generate test dataset
  testdata <- data[data$weight==0,] #Select test data by weight
  #Predict test data
  pred <- predict(m,newdata=testdata)
  #Extract MSES
  mses[i,1] <- mean((data$R_MeanDiff[data$weight==0] - pred)^2)
  scores[i,1] <- summary(m)$r.sq
}
av.mse.GMRF <- mean(mses$V1)
av.r2.GMRF <- mean(scores$V1)