GAM method without resampling in caret produces stop error - r

Question

I wrote a function within lapply to fit a GAM (with splines) for each element in a vector of response variables within a data frame. I opted to use caret to fit the models instead of directly using mgcv or the gam package because I would like to eventually split my data into a train/test set for validation and use various resampling techniques. For now, I simply have the trainControl method set to 'none' like so:
# Set resampling method
# tc <- trainControl(method = "boot", number = 100)
# tc <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
tc <- trainControl(method = "none")
fm <- lapply(group, function(x) {
printFormula <- paste(x, "~", inf.factors)
inputFormula <- as.formula(printFormula)
# Partition input data for model training and testing
# dpart <- createDataPartition(mdata[,x], times = 1, p = 0.7, list = FALSE)
# train <- mdata[ data.partition, ]
# test <- mdata[ -data.partition, ]
cat("Fitting:", printFormula, "\n")
# gam(inputFormula, family = binomial(link = "logit"), data = mdata)
train(inputFormula, family = binomial(link = "logit"), data = mdata, method = "gam",
trControl = tc)
})
When I execute this code, I receive the following error:
Error in train.default(x, y, weights = w, ...) :
Only one model should be specified in tuneGrid with no resampling
If I re-run the code in debugging mode, I can find where caret stops the training process:
if (trControl$method == "none" && nrow(tuneGrid) != 1)
stop("Only one model should be specified in tuneGrid with no resampling")
Clearly the train function fails because of the second condition, but when I look up the tuning parameters for a GAM (with splines) there is only an option for feature selection (not interested, I want to keep all the predictors in the model) and the method. Consequently, I do not include a tuneGrid data frame when I call train. Is this the reason why the model is failing in this way? What parameter would I provide and what would the tuneGrid look like?
I should add that the model is trained successfully when I use bootstrapping or k-fold CV, however these resampling methods take much longer to calculate and I do not need to use them yet.
Any help on this issue would be appreciated!

Answer 1

For that model, the tuning grid looks over two values of the select parameters:
> getModelInfo("gam", regex = FALSE)[[1]]$grid
function(x, y, len = NULL, search = "grid") {
if(search == "grid") {
out <- expand.grid(select = c(TRUE, FALSE), method = "GCV.Cp")
} else {
out <- data.frame(select = sample(c(TRUE, FALSE), size = len, replace = TRUE),
method = sample(c("GCV.Cp", "ML"), size = len, replace = TRUE))
}
out[!duplicated(out),]
}
You should use something like tuneGrid = data.frame(select = FALSE, method = "GCV.Cp") to only evaluate a single model (as error message says).