estimateTargets: Calcluates point estimates for target estimands

Description Usage Arguments

Description

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

Usage

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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.