# Covariate-Matched Study Strap for Multi-Study Learning: Fits accept/reject algorithm based on covariate similarity measure ```r cmss(formula = Y ~ ., data, target.study, sim.fn = NA, converge.lim = 50000, bag.size = length(unique(data$Study)), max.straps = 150, paths = 5, stack = "standard", sim.covs = NA, ssl.method = list("lm"), ssl.tuneGrid = list(c()), sim.mets = TRUE, model = FALSE, meanSampling = FALSE, customFNs = list(), stack.standardize = FALSE) ``` ## Arguments - `formula`: Model formula - `data`: A dataframe with all the studies has the following columns in this order: "Study", "Y", "V1", ...., "Vp" - `target.study`: Dataframe of the design matrix (just covariates) of study one aims to make predictions on - `sim.fn`: Optional function to be used as similarity measure for accept/reject step. Default function is: |cor( barx^(r)|,~ barx_target ) | - `converge.lim`: Integer indicating the number of consecutive rejected study straps to reach convergence criteria. - `bag.size`: Integer indicating the bag size tuning parameter. - `max.straps`: Integer indicating the maximum number of accepted straps that can be fit across all paths before the algorithm stops accepting new study straps. - `paths`: Integer indicating the number of paths (an accept/reject path is all of the models accepted before reaching one convergence criteria). - `stack`: String determining whether stacking matrix made on training studies "standard" or on the accepted study straps "ss." Default: "standard." - `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. - `sim.mets`: Boolean indicating whether to calculate default covariate profile similarity measures. - `model`: Indicates whether to attach training data to model object. - `meanSampling`: = FALSE Boolean determining whether to use mean covariates for similarity measure. This can be much quicker. - `customFNs`: Optional list of functions that can be used to add custom covaraite profile similarity measures. - `stack.standardize`: Boolean determining whether stacking weights are standardized to sum to 1. Default is FALSE ## 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 arMod1 <- cmss(formula = Y ~., data = data, target.study = target, converge.lim = 10, bag.size = length(unique(data$Study)), max.straps = 50, paths = 2, ssl.method = list("pcr"), ssl.tuneGrid = list(data.frame("ncomp" = 2)) ) # Fit model with 2 SSLs: Linear Regression and PCA Regression arMod2 <- cmss(formula = Y ~., data = data, target.study = target, converge.lim = 20, bag.size = length(unique(data$Study)), max.straps = 50, paths = 2, ssl.method = list("lm", "pcr"), ssl.tuneGrid = list(NA, data.frame("ncomp" = 2)) ) # Fit model with custom similarity function for # accept/reject step and 2 custom function for Covariate # Profile Similarity weights # custom function for CPS fn1 <- function(x1,x2){ return( abs( cor( colMeans(x1), colMeans(x2) )) ) } fn2 <- function(x1,x2){ return( sum ( ( colMeans(x1) - colMeans(x2) )^2 ) ) } arMod3 <- cmss(formula = Y ~., data = data, target.study = target, sim.fn = fn1, customFNs = list(fn1, fn2), converge.lim = 50, bag.size = length(unique(data$Study)), max.straps = 50, paths = 2, ssl.method = list("lm", "pcr"), ssl.tuneGrid = list(NA, data.frame("ncomp" = 2)) ) ######################### ##### Predictions ###### ######################### preds <- studyStrap.predict(arMod1, target) ```

cmss function