biomarkerTMLE_exposure {biotmle} | R Documentation |
This function performs influence curve-based estimation of the effect of an exposure on biological expression values associated with a given biomarker, controlling for a user-specified set of baseline covariates
biomarkerTMLE_exposure(Y, W, A, a, subj_ids = NULL, family = "gaussian", g_lib, Q_lib)
Y |
(numeric vector) - a vector of expression values for a single biomarker. |
W |
(numeric matrix) - a matrix of baseline covariates to be controlled in the estimation process. |
A |
(numeric vector) - a discretized exposure vector (e.g., from a design matrix whose effect on expression values is of interest. |
a |
(numeric vector) - the levels of A against which comparisons are to be made. |
subj_ids |
(numeric vector) - subject IDs to be passed directly to the same subject should have the exact same numerical identifier. coerced to numeric if not provided in the appropriate form. |
family |
(character) - specification of error family: "binomial" or "gaussian" |
g_lib |
(char vector) - library of learning algorithms to be used in fitting the "g" step of the standard TMLE procedure. |
Q_lib |
(char vector) - library of learning algorithms to be used in fitting the "Q" step of the standard TMLE procedure. |
TMLE-based estimate of the relationship between biomarker expression
and changes in an exposure variable, computed iteratively and saved
in the tmleOut
slot in a biotmle
object.