build_gn_seq {ctmle}

Help function to build the sequence of gn candidates in ctmleGeneral

Description

This function helps building gn candidates for ctmleGeneral. It returns gn_candidates_cv, gn_candidates, and folds, which could be directly applied to ctmleGeneral.

Usage

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

Value

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

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