linear_model_estimators: Estimate a series of linear models using different weighting...

In lmiratrix/blkvar: ATE and Treatment Variation Estimation for Blocked and Multisite RCTs

Estimate a series of linear models using different weighting schemes and standard errors.

Description

Estimate a series of linear models using different weighting schemes and standard errors.

Usage

linear_model_estimators(
  Yobs,
  Z,
  B,
  siteID = NULL,
  data = NULL,
  block.stats = NULL,
  control_formula = NULL,
  weight_LM_method = "survey",
  weight_LM_scale_weights = TRUE
)

Arguments

Yobs	Name of outcome variable (assumed to exist in data)
Z	vector of assignment indicators (1==treated)
B	block ids
siteID	If not null, name of siteID that has randomization blocks
data	Dataframe of the data to analyse.
block.stats	Table of precomputed block-level statistics (optional, for speed concerns; this gets precomputed in compare_methods).
control_formula	The control_formula argument must be of the form ~ X1 + X2 + ... + XN. (nothing on left hand side of ~)
weight_LM_method	Argument passed to weight.method of weighted_linear_estimators
weight_LM_scale_weights	Argument passed to sclae.weights of weighted_linear_estimators

Value

Data frame of the various results.

See Also

Other linear model estimators: fixed_effect_estimators(), interacted_linear_estimators(), weighted_linear_estimators()

lmiratrix/blkvar documentation built on Nov. 18, 2024, 1:27 p.m.