View source: R/tmle3_Spec_mopttx_vim.R
tmle3_mopttx_vim | R Documentation |
O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)
tmle3_mopttx_vim( V = NULL, type = "blip2", method = "SL", learners = NULL, contrast = "linear", maximize = TRUE, complex = TRUE, realistic = FALSE, resource = 1, reference = NULL )
V |
Covariates the rule depends on. |
type |
One of three psudo-blip versions developed to accommodate categorical treatment. "Blip1" corresponds to chosing a reference category, and defining the blip for all other categories relative to the specified reference. Note that in the case of binary treatment, "blip1" is just the usual blip. "Blip2$ corresponds to defining the blip relative to the average of all categories. Finally, "Blip3" corresponds to defining the blip relative to the weighted average of all categories. |
method |
Specifies which methodology to use for learning the rule. Options are "Q" for Q-learning, and "SL" for the Super-Learner approach using split-specific estimates. |
learners |
Library for Y (outcome), A (treatment), and B (blip) estimation. |
contrast |
Defined either a "linear" or "multiplicative" contrast for the delta method. |
maximize |
Specify whether we want to maximize or minimize the mean of the final outcome. |
complex |
If |
realistic |
If |
resource |
Indicates the percent of initially estimated individuals who should be given treatment that get treatment, based on their blip estimate. If resource = 1 all estimated individuals to benefit from treatment get treatment, if resource = 0 none get treatment. |
reference |
reference category for blip1. Default is the smallest numerical category or factor. |
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