estimateVarianceCombined: Estimates of the asymptotic variance of the estimators

In BarkleyBG/stabilizedinterference: Stabilized IPW Estimators for Causal Effects with Partial Interference

Description

These functions carry out the M-estimation procedures for the "sandwich" variance estimators

Usage

estimateVarianceCombined(alphas, num_alphas, mu_alphas_ests, data, num_fixefs,
  fixefs, sigma, x_levels, trt_model_obj, var_names, target_grids,
  randomization_probability, weight_type, verbose, keep_components,
  compute_roots, integrate_alphas, deriv_control, contrast_type)

Arguments

alphas	the range of allocations or policies from 0 to 1. When model_method == "oracle" then model_options must be a list with named numeric vectors fixefs and var_comp. See prepareOracle. For mixed effect model, note that that random intercept's term in the modeling formula (e.g., ( 1 | cluster_ID ) ) must be omitted from formula. Arguments that can be passed through ... include: integrate_alphas. Not yet supported. verbose. Set to TRUE for more verbose messaging. Default FALSE. contrast_type. Not yet supported. keep_components. Set to TRUE for more verbose output. Default FALSE. target_grids. User can supply target estimands with makeTargetGrids. deriv_control. User can supply the deriv_control argument to m_estimate with setup_deriv_control.
num_alphas	The number of allocation parameters
mu_alphas_ests	Point estimates of population mean target estimands
data	the dataframe. Will be coerced from "tbl_df" to data.frame.
num_fixefs	The number of fixed effect parameters from treatment model
fixefs	The estimated values of the fixed effects parameters from the propensity score model
sigma	The estimated value of the (single) random effect variance component from the propensity score model
x_levels	Default NULL unless there are factos in design matrix. From grab_design_levels.
trt_model_obj	Fitted model object from fitModel When model_method = "oracle" see prepareOracle. @return A list including fixefs sigma ps_model_matrix x_levels
var_names	A list of names for outcome, treatment, clustering, and perhaps participation.
target_grids	output from makeTargetGrids
randomization_probability	Optional argument passed from estimateEffects. Usually 1. For example, 2/3 in Perez-Heydrich et al. (2014) Biometrics.
weight_type	Estimators as presented in Liu, Hudgens, and Becker-Dreps (2016) Biometrika. Select "HT" for unstabilized weights. Select "Hajek1" or "Hajek2" for stabilized weights. Select "HT_TV" for the estimators presented in Tchetgen Tchetgen and VanderWeele (2012) SMMR and Perez-Heydrich et al. (2014) Biometrics, which in general target estimands different from those in Liu, Hudgens, and Becker-Dreps (2016) Biometrika.
verbose	Optional argument from estimateEffects
keep_components	Optional argument from estimateEffects
compute_roots	Optional argument from estimateEffects
integrate_alphas	Optional argument passed from estimateEffects
deriv_control	Optional for m_estimate
contrast_type	e.g. "difference" or "negative risk ratio"

BarkleyBG/stabilizedinterference documentation built on May 23, 2019, 8:37 a.m.