scalar_wts | R Documentation |
This function calculates the scalar weights
scalar_wts(treat, treat_formula, numerator_formula, data, treat_mod, link_function, ...)
treat |
is the name of the treatment variable contained in
|
treat_formula |
an object of class "formula" (or one that can be
coerced to that class) that regresses |
numerator_formula |
an object of class "formula" (or one that can be
coerced to that class) that regresses |
data |
is a dataframe containing |
treat_mod |
a description of the error distribution to be used in the
model for treatment. Options include: |
link_function |
is either "log", "inverse", or "identity" for the
"Gamma" |
... |
additional arguments to be passed to the treatment regression fitting function. |
scalar_wts
returns an object of class "causaldrf_wts",
a list that contains the following components:
param |
summary of estimated weights. |
t_mod |
the result of the treatment model fit. |
num_mod |
the result of the numerator model fit. |
weights |
estimated weights for each unit. |
call |
the matched call. |
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response models. Manuscript in preparation.
iptw_est
, ismw_est
,
reg_est
, aipwee_est
, wtrg_est
,
etc. for other estimates.
t_mod
, overlap_fun
to prepare the data
for use in the different estimates.
## Example from Schafer (2015). example_data <- sim_data scalar_wts_list <- scalar_wts(treat = T, treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8, numerator_formula = T ~ 1, data = example_data, treat_mod = "Normal") sample_index <- sample(1:1000, 100) plot(example_data$T[sample_index], scalar_wts_list$weights[sample_index], xlab = "T", ylab = "weights", main = "scalar_wts") rm(example_data, scalar_wts_list, sample_index)
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