Clustered resampling for inner layer of Caret recursive feature elimination - r-caret

Question

I have data where IDs are contained within clusters.
I would like to perform recursive feature elimination using Caret's rfe function which performs the following procedure:
Clustered resampling for the outer layer (line 2.1) is straightforward, using the index parameter.
However, within each outer resample, I would like to tune tuning parameters using cluster-based cross-validation (inner resampling) (line 2.9). Model tuning in the inner layer is possible by specifying a tuneGrid in rfe and having an appropriate trControl. It is this trControl that I would like to change to allow clustered resampling.
The outer resampling is specified in the rfeControl parameter of rfe.
The inner resampling is specified by trControl of rfe which is passed to train.
The trouble I am having is that I can't seem to specify any inner indices, because after the outer resampling, those indices are no longer valid or no longer present in the outer-resampled data.
I am looking for a way to tell train to take an outer resample (which will be missing a cluster against which to validate), and to tune the model using inner resampling by based on folds of the remaining clusters.
The MWE is as minimal as possible:
library(caret)
library(tidyverse)
library(parallel)
library(doParallel)
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
### Create some random data, 10 features, with some influence over a binomial outcome
set.seed(42)
id <- 1:1000
cluster <- rep(1:10, each = 100)
dat <- data.frame(id, cluster, replicate(10,rnorm(n = 1000, mean = runif(1, 0,100)+cluster, sd = runif(1, 0,20))))
dat <- dat %>% mutate(temp = rowSums(across(X1:X10)), prob = range01(temp), outcome = rbinom(n = nrow(dat), size = 1, prob = prob))
dat$outcome <- as.factor(dat$outcome)
levels(dat$outcome) <- c("control", "case")
dat$outcome <- factor(dat$outcome, levels=rev(levels(dat$outcome)))
### Manual outer folds-based cluster ###
for(i in 1:10) {
assign(paste0("index", i), which(dat$cluster!=i))
}
unit_indices <- list(index1, index2, index3, index4, index5, index6, index7, index8, index9, index10)
### Inner resampling method (THIS IS WHAT I'D LIKE TO CHANGE) ###
cv5 <- trainControl(classProbs = TRUE, method = "cv", number = 5, allowParallel = F) ## Is there a way to have inner cluster-based resampling WITHIN the outer cluster-based resampling?
caret_rfe_functions <- list(summary = twoClassSummary,
fit = function (x, y, first, last, ...) {
train(x, y, ...)
},
pred = caretFuncs$pred,
rank = function(object, x, y) {
vimp <- varImp(object)$importance
vimp <- vimp[order(vimp$Overall,decreasing = TRUE),,drop = FALSE]
vimp$var <- rownames(vimp)
vimp
},
selectSize = function (x, metric = "ROC", tol = 1, maximize = TRUE)
{
if (!maximize) {
best <- min(x[, metric])
perf <- (x[, metric] - best)/best * 100
flag <- perf <= tol
}
else {
best <- max(x[, metric])
perf <- (best - x[, metric])/best * 100
flag <- perf <= tol
}
min(x[flag, "Variables"])
},
selectVar = caretFuncs$selectVar)
caret_rfe_ctrl <- rfeControl(
functions = caret_rfe_functions,
saveDetails = TRUE,
index = unit_indices,
indexOut = NULL,
returnResamp = "all",
allowParallel = T, ### change this if you don't want to / can't go parallel
verbose = TRUE
)
#### Feature selection ####
set.seed(42)
cl <- makePSOCKcluster(10) ### for parallel processing if available
registerDoParallel(cl)
rfe_profile_nnet <- rfe(
form = outcome ~
X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10,
data = dat,
sizes = seq(2,10,1),
rfeControl = caret_rfe_ctrl,
## pass options to train()
method = "nnet",
preProc = c("center", "scale"),
metric = "ROC",
tuneGrid = expand.grid(size = c(1:5), decay = 5),
trControl = cv5) ### I would like to change this to allow inner cluster-based resampling
stopCluster(cl)
rfe_profile_nnet
plot(rfe_profile_nnet)
Presumably the inner cluster-based resampling would be achieved by specifying a new trainControl containing some dynamic inner index based on the outer resample that is selected at the time:
inner_cluster_tune <- trainControl(classProbs = TRUE,
index = {insert magic here}, ### This is the important bit
returnResamp = "all",
summaryFunction = twoClassSummary,
allowParallel = F) ### especially if the outer resample is parallelised
If you try with the original cluster indices e.g.
inner_cluster_tune <- trainControl(classProbs = TRUE,
index = unit_indices,
returnResamp = "all",
summaryFunction = twoClassSummary,
allowParallel = F)
There are various warnings about missing data in the resamples, and things like 24: In [<-.data.frame(*tmp*, , object$method$center, value = structure(list( ... : provided 81 variables to replace 9 variables.
All help greatly appreciated.
As a postscript question , you can see which parameters were used within your rfe like so:
> rfe_profile_nnet$fit
Neural Network
1000 samples
8 predictor
2 classes: 'case', 'control'
Pre-processing: centered (8), scaled (8)
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 800, 800, 800, 800, 800
Resampling results across tuning parameters:
size Accuracy Kappa
1 0.616 0.1605071
2 0.616 0.1686937
3 0.620 0.1820503
4 0.618 0.1788491
5 0.618 0.1788063
Tuning parameter 'decay' was held constant at a value of 5
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were size = 3 and decay = 5.
But does anyone know if this refers to one, or all of the outer resamples? Presumably the same tuning parameters won't necessarily be chosen across all outer resamples