# Returns a MetaForest ModelInfo list for use with caret This function allows users to rely on the powerful `caret` package for cross-validating and tuning a MetaForest analysis. Methods for MetaForest are not included in the caret package, because the interface of caret is not entirely compatible with MetaForest's model call. Specifically, MetaForest is not compatible with the `train` methods for classes 'formula' or 'recipe', because the variance of the effect size must be a column of the training data x. The name of this column is specified using the argument 'vi'. ```r ModelInfo_mf() ``` ## Returns ModelInfo list of length 17. ## Details To train a clustered MetaForest, for nested data structures, simply provide the optional argument 'study' to the train function, to specify the study ID. This should again refer to a column of x. When training a clustered MetaForest, make sure to use 'index = groupKFold(your_study_id_variable, k = 10))' in traincontrol, to sample by study ID when creating cross-validation partitions; otherwise the testing error will be positively biased. ## Examples ```r ## Not run: # Prepare data data <- dat.bangertdrowns2004 data[, c(4:12)] <- apply(data[ , c(4:12)], 2, function(x){ x[is.na(x)] <- median(x, na.rm = TRUE) x}) data$subject <- factor(data$subject) data$yi <- as.numeric(data$yi) # Load caret library(caret) set.seed(999) # Specify the resampling method as 10-fold CV fit_control <- trainControl(method = "cv", number = 10) cv_mf_fit <- train(y = data$yi, x = data[,c(3:13, 16)], method = ModelInfo_mf(), trControl = fit_control) # Cross-validated clustered MetaForest data <- get(data(dat.bourassa1996)) data <- escalc(measure = "OR", ai = lh.le, bi = lh.re, ci = rh.le, di= rh.re, data = data, add = 1/2, to = "all") data$mage[is.na(data$mage)] <- median(data$mage, na.rm = TRUE) data[c(5:8)] <- lapply(data[c(5:8)], factor) data$yi <- as.numeric(data$yi) # Set up 10-fold grouped CV fit_control <- trainControl(method = "cv", index = groupKFold(data$sample, k = 10)) # Set up a custom tuning grid for the three tuning parameters of MetaForest rf_grid <- expand.grid(whichweights = c("random", "fixed", "unif"), mtry = c(2, 4, 6), min.node.size = c(2, 4, 6)) # Train the model cv.mf.cluster <- train(y = data$yi, x = data[, c("selection", "investigator", "hand_assess", "eye_assess", "mage", "sex", "vi", "sample")], study = "sample", method = ModelInfo_mf(), trControl = fit_control, tuneGrid = rf_grid) ## End(Not run) ```

ModelInfo_mf function