# Merged Approach for Multi-Study Learning: fits a single model on all studies merged into a single dataframe. ```r merged(formula = Y ~ ., data, sim.covs = NA, ssl.method = list("lm"), ssl.tuneGrid = list(c()), model = FALSE) ``` ## Arguments - `formula`: Model formula - `data`: A dataframe with all the studies has the following columns in this order: "Study", "Y", "V1", ...., "Vp" - `sim.covs`: Is a vector of names of covariates or the column numbers of the covariates to be used for the similarity measure. Default is to use all covariates. - `ssl.method`: A list of strings indicating which modeling methods to use - `ssl.tuneGrid`: A list of the tuning parameters in the format of the caret package. Each element must be a dataframe (as required by caret). If no tuning parameters are required then NA is indicated - `model`: Indicates whether to attach training data to model object ## Returns A model object of studyStrap class "ss" that can be used to make predictions. ## Examples ```r ########################## ##### Simulate Data ###### ########################## set.seed(1) # create half of training dataset from 1 distribution X1 <- matrix(rnorm(2000), ncol = 2) # design matrix - 2 covariates B1 <- c(5, 10, 15) # true beta coefficients y1 <- cbind(1, X1) %*% B1 # create 2nd half of training dataset from another distribution X2 <- matrix(rnorm(2000, 1,2), ncol = 2) # design matrix - 2 covariates B2 <- c(10, 5, 0) # true beta coefficients y2 <- cbind(1, X2) %*% B2 X <- rbind(X1, X2) y <- c(y1, y2) study <- sample.int(10, 2000, replace = TRUE) # 10 studies data <- data.frame( Study = study, Y = y, V1 = X[,1], V2 = X[,2] ) # create target study design matrix for covariate profile similarity weighting and # accept/reject algorithm (covaraite-matched study strap) target <- matrix(rnorm(1000, 3, 5), ncol = 2) # design matrix colnames(target) <- c("V1", "V2") ########################## ##### Model Fitting ##### ########################## # Fit model with 1 Single-Study Learner (SSL): PCA Regression mrgMod1 <- merged(formula = Y ~., data = data, sim.covs = NA, ssl.method = list("pcr"), ssl.tuneGrid = list( data.frame("ncomp" = 2)), model = FALSE ) # 2 SSLs: Linear Regression and PCA Regression mrgMod2 <- merged(formula = Y ~., data = data, sim.covs = NA, ssl.method = list("lm", "pcr"), ssl.tuneGrid = list(NA, data.frame("ncomp" = 2) ), model = FALSE ) ######################### ##### Predictions ###### ######################### preds <- studyStrap.predict(mrgMod2, target) ```

merged function