# Help function to build the sequence of gn candidates in ctmleGeneral This function helps building gn candidates for ctmleGeneral. It returns gn_candidates_cv, gn_candidates, and folds, which could be directly applied to ctmleGeneral. ```r build_gn_seq(A, W, SL.library, folds, verbose = TRUE) ``` ## Arguments - `A`: binary treatment indicator, 1 - treatment, 0 - control - `W`: vector, matrix, or dataframe containing baseline covariates for Q bar - `SL.library`: a vector of the names of the estimators for ctmle (need to be prepared in the format for SL, see more details in SuperLearner package), The theory of ctmle requires the estimators are ordered by the model complexity, with the last one be a consistent estimator. - `folds`: The list of indices for the ctmle cross-validation step - `verbose`: A boolean. If print out the training log for Super Learne ## Returns gn_candidates_cv matrix or dataframe, each column stand for a estimate of propensity score. Estimate in the column with larger index should have smaller empirical loss gn_candidates matrix or dataframe, each column stand for a the cross-validated estimate. For example, the (i,j)-th element is the predicted propensity score by j-th estimator, for i-th observation, when it is in the validation set folds The list of indices for the ctmle cross-validation step details The SuperLearner object used to generate gn_candidates_cv ## Examples ```r N <- 1000 p = 100 V = 5 Wmat <- matrix(rnorm(N * p), ncol = p) gcoef <- matrix(c(-1,-1,rep(-(3/((p)-2)),(p)-2)),ncol=1) W <- as.data.frame(Wmat) g <- 1/(1+exp(Wmat%*%gcoef / 3)) A <- rbinom(N, 1, prob = g) folds <-by(sample(1:N,N), rep(1:V, length=N), list) lasso_fit <- cv.glmnet(x = as.matrix(W), y = A, alpha = 1, nlambda = 100, nfolds = 10) lasso_lambdas <- lasso_fit$lambda[lasso_fit$lambda <= lasso_fit$lambda.min][1:5] # Build template for glmnet SL.glmnet_new <- function (Y, X, newX, family, obsWeights, id, alpha = 1, nlambda = 100, lambda = 0,...) { # browser() if (!is.matrix(X)) { X <- model.matrix(~-1 + ., X) newX <- model.matrix(~-1 + ., newX) } fit <- glmnet::glmnet(x = X, y = Y, lambda = lambda, family = family$family, alpha = alpha) pred <- predict(fit, newx = newX, type = "response") fit <- list(object = fit) class(fit) <- "SL.glmnet" out <- list(pred = pred, fit = fit) return(out) } # Use a sequence of estimator to build gn sequence: SL.cv1lasso <- function (... , alpha = 1, lambda = lasso_lambdas[1]){ SL.glmnet_new(... , alpha = alpha, lambda = lambda) } SL.cv2lasso <- function (... , alpha = 1, lambda = lasso_lambdas[2]){ SL.glmnet_new(... , alpha = alpha, lambda = lambda) } SL.cv3lasso <- function (... , alpha = 1, lambda = lasso_lambdas[3]){ SL.glmnet_new(... , alpha = alpha, lambda = lambda) } SL.cv4lasso <- function (... , alpha = 1, lambda = lasso_lambdas[4]){ SL.glmnet_new(... , alpha = alpha, lambda = lambda) } SL.library = c('SL.cv1lasso', 'SL.cv2lasso', 'SL.cv3lasso', 'SL.cv4lasso', 'SL.glm') gn_seq <- build_gn_seq(A = A, W = W, SL.library = SL.library, folds = folds) gn_seq$gn_candidates_cv gn_seq$gn_candidates ```

build_gn_seq function