rank_DE {adaptest} | R Documentation |
Computes ranking of biomarkers based effect sizes, which are computed by
Targeted Minimum Loss-Based Estimation. This function is designed to be
called inside adaptest
; it should not be run by itself outside of that
context.
rank_DE(Y, A, W, absolute = FALSE, negative = FALSE, learning_library = c("SL.glm", "SL.step", "SL.glm.interaction", "SL.gam"))
Y |
(numeric vector) - continuous or binary biomarkers outcome variables |
A |
(numeric vector) - binary treatment indicator: |
W |
(numeric vector, numeric matrix, or numeric data.frame) - matrix of baseline covariates where each column corrspond to one baseline covariate. Each row correspond to one observation |
absolute |
(logical) - whether or not to test for absolute effect size.
If |
negative |
(logical) - whether or not to test for negative effect size.
If |
learning_library |
(character vector) - library of learning algorithms to be used in fitting the "Q" and "g" step of the standard TMLE procedure. |
an integer vector
containing ranks of biomarkers.
set.seed(1234) data(simpleArray) simulated_array <- simulated_array simulated_treatment <- simulated_treatment rank_DE(Y = simulated_array, A = simulated_treatment, W = rep(1, length(simulated_treatment)), absolute = FALSE, negative = FALSE)