Generate Balancing Weights with Minimal Input Processing

weightit.fit() dispatches one of the weight estimation methods determined by method. It is an internal function called by weightit() and should probably not be used except in special cases. Unlike weightit(), weightit.fit() does not accept a formula and data frame interface and instead requires the covariates and treatment to be supplied as a numeric matrix and atomic vector, respectively. In this way, weightit.fit() is to weightit() what lm.fit() is to lm() - a thinner, slightly faster interface that performs minimal argument checking.

Usage

weightit.fit(
  covs,
  treat,
  method = "glm",
  s.weights = NULL,
  by.factor = NULL,
  estimand = "ATE",
  focal = NULL,
  stabilize = FALSE,
  ps = NULL,
  moments = NULL,
  int = FALSE,
  subclass = NULL,
  is.MSM.method = FALSE,
  missing = NULL,
  verbose = FALSE,
  include.obj = FALSE,
  ...
)

Arguments

covs: a numeric matrix of covariates.
treat: a vector of treatment statuses.
method: a string of length 1 containing the name of the method that will be used to estimate weights. See weightit() for allowable options. The default is "glm" for propensity score weighting using a generalized linear model to estimate the propensity score.
s.weights: a numeric vector of sampling weights. See the individual pages for each method for information on whether sampling weights can be supplied.
by.factor: a factor variable for which weighting is to be done within levels. Corresponds to the by argument in weightit().
estimand: the desired estimand. For binary and multi-category treatments, can be "ATE", "ATT", "ATC", and, for some methods, "ATO", "ATM", or "ATOS". The default for both is "ATE". This argument is ignored for continuous treatments. See the individual pages for each method for more information on which estimands are allowed with each method and what literature to read to interpret these estimands.
focal: when multi-category treatments are used and ATT weights are requested, which group to consider the "treated" or focal group. This group will not be weighted, and the other groups will be weighted to be more like the focal group. Must be non-NULL if estimand = "ATT" or "ATC".
stabilize: logical; whether or not to stabilize the weights. For the methods that involve estimating propensity scores, this involves multiplying each unit's weight by the proportion of units in their treatment group. Default is FALSE.
ps: a vector of propensity scores. If specified, method will be ignored and set to "glm".
moments, int, subclass: arguments to customize the weight estimation. See weightit() for details.
is.MSM.method: see weightitMSM(). Typically can be ignored.
missing: character; how missing data should be handled. The options depend on the method used. If NULL, covs covs will be checked for NA values, and if present, missing will be set to "ind". If "", covs covs will not be checked for NA values; this can be faster when it is known there are none.
verbose: whether to print additional information output by the fitting function.
include.obj: whether to include in the output any fit objects created in the process of estimating the weights. For example, with method = "glm", the glm objects containing the propensity score model will be included. See the individual pages for each method for information on what object will be included if TRUE.
...: other arguments for functions called by weightit.fit() that control aspects of fitting that are not covered by the above arguments.

Value

A weightit.fit object with the following elements:

weights: The estimated weights, one for each unit.
treat: The values of the treatment variable.
estimand: The estimand requested. ATC is recoded as ATT.
method: The weight estimation method specified.
ps: The estimated or provided propensity scores. Estimated propensity scores are returned for binary treatments and only when method is "glm", "gbm", "cbps", "super", or "bart".
s.weights: The provided sampling weights.
focal: The focal treatment level if the ATT or ATC was requested.
fit.obj: When include.obj = TRUE, the fit object.
info: Additional information about the fitting. See the individual methods pages for what is included.

The weightit.fit object does not have specialized print(), summary(), or plot() methods. It is simply a list containing the above components. Use as.weightit() to convert it to a weightit object, which does have these methods. See Examples.

Details

weightit.fit() is called by weightit() after the arguments to weightit() have been checked and processed. weightit.fit() dispatches the function used to actually estimate the weights, passing on the supplied arguments directly. weightit.fit() is not meant to be used by anyone other than experienced users who have a specific use case in mind. The returned object contains limited information about the supplied arguments or details of the estimation method; all that is processed by weightit().

Less argument checking or processing occurs in weightit.fit() than does in weightit(), which means supplying incorrect arguments can result in errors, crashes, and invalid weights, and error and warning messages may not be helpful in diagnosing the problem. weightit.fit() does check to make sure weights were actually estimated, though.

weightit.fit() may be most useful in speeding up simulation simulation studies that use weightit() because the covariates can be supplied as a numeric matrix, which is often how they are generated in simulations, without having to go through the potentially slow process of extracting the covariates and treatment from a formula and data frame. If the user is certain the arguments are valid (e.g., by ensuring the estimated weights are consistent with those estimated from weightit() with the same arguments), less time needs to be spent on processing the arguments. Also, the returned object is much smaller than a weightit object because the covariates are not returned alongside the weights.

Examples