estimateTargets: Calcluates point estimates for target estimands

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

Description

This function calculates the estimated values in a direct plug-in method.

Usage

estimateTargets(alphas, num_alphas, num_clusters, unique_clusters,
  grouping_vector, treatment_vector, ps_model_matrix, outcome_vector, fixefs,
  sigma, integrate_alphas, randomization_probability, weight_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
num_clusters	The number of unique clusters (i.e., i.i.d. sample units)
unique_clusters	The ID values for the unique clusters
grouping_vector	The vector of cluster ID for all individuals
treatment_vector	The vector of treatment identifiers for all individuals
ps_model_matrix	The matrix of pre-treatment variables in the propensity score model for all individuals
outcome_vector	The vector of observed outcome for all individuals
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
integrate_alphas	Optional argument passed from estimateEffects
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.

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