weightit() allows for the easy generation of balancing weights using
a variety of available methods for binary, continuous, and multi-category
treatments. Many of these methods exist in other packages, which
weightit() calls; these packages must be installed to use the desired
method.
weightit(
formula,
data = NULL,
method = "glm",
estimand = "ATE",
stabilize = FALSE,
focal = NULL,
by = NULL,
s.weights = NULL,
ps = NULL,
moments = NULL,
int = FALSE,
subclass = NULL,
missing = NULL,
verbose = FALSE,
include.obj = FALSE,
keep.mparts = TRUE,
...
)A weightit object with the following elements:
The estimated weights, one for each unit.
The values of the treatment variable.
The covariates used in the fitting. Only includes the raw covariates, which may have been altered in the fitting process.
The estimand requested.
The weight estimation method specified.
The estimated or provided propensity scores. Estimated propensity scores are
returned for binary treatments and only when method is "glm", "gbm", "cbps", "ipt", "super", or "bart".
The provided sampling weights.
The focal treatment level if the ATT or ATC was requested.
A data.frame containing the by variable when specified.
When include.obj = TRUE, the fit object.
Additional information about the fitting. See the individual methods pages for what is included.
When keep.mparts is TRUE (the default) and the chosen method is compatible with M-estimation, the components related to M-estimation for use in glm_weightit() are stored in the "Mparts" attribute. When by is specified, keep.mparts is set to FALSE.
a formula with a treatment variable on the left hand side and
the covariates to be balanced on the right hand side. See glm() for more
details. Interactions and functions of covariates are allowed.
an optional data set in the form of a data frame that contains
the variables in formula.
a string of length 1 containing the name of the method that
will be used to estimate weights. See Details below for allowable options.
The default is "glm" for propensity score weighting using a
generalized linear model to estimate the propensity score.
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.
whether or not and how to stabilize the weights. If TRUE, each unit's weight will be multiplied by a standardization factor, which is the inverse of the unconditional probability (or density) of each unit's observed treatment value. If a formula, a generalized linear model will be fit with the included predictors, and the inverse of the corresponding weight will be used as the standardization factor. Can only be used with continuous treatments or when estimand = "ATE". Default is FALSE for no standardization. See also the num.formula argument at weightitMSM()
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. If specified, estimand will automatically be set to
"ATT".
a string containing the name of the variable in data for
which weighting is to be done within categories or a one-sided formula with
the stratifying variable on the right-hand side. For example, if by = "gender" or by = ~gender, a separate propensity score model or optimization will occur within each level of the variable "gender". Only one
by variable is allowed; to stratify by multiply variables
simultaneously, create a new variable that is a full cross of those
variables using interaction().
A vector of sampling weights or the name of a variable in
data that contains sampling weights. These can also be matching
weights if weighting is to be used on matched data. See the individual pages
for each method for information on whether sampling weights can be supplied.
A vector of propensity scores or the name of a variable in
data containing propensity scores. If not NULL, method
is ignored unless it is a user-supplied function, and the propensity scores will be used to create weights.
formula must include the treatment variable in data, but the
listed covariates will play no role in the weight estimation. Using
ps is similar to calling get_w_from_ps() directly, but produces a
full weightit object rather than just producing weights.
numeric; for some methods, the greatest power of each
covariate to be balanced. For example, if moments = 3, for each
non-categorical covariate, the covariate, its square, and its cube will be
balanced. This argument is ignored for other methods; to balance powers of
the covariates, appropriate functions must be entered in formula. See
the individual pages for each method for information on whether they accept
moments.
logical; for some methods, whether first-order
interactions of the covariates are to be balanced. This argument is ignored
for other methods; to balance interactions between the variables,
appropriate functions must be entered in formula. See the individual
pages for each method for information on whether they accept int.
numeric; the number of subclasses to use for
computing weights using marginal mean weighting with subclasses (MMWS). If
NULL, standard inverse probability weights (and their extensions)
will be computed; if a number greater than 1, subclasses will be formed and
weights will be computed based on subclass membership. Attempting to set a
non-NULL value for methods that don't compute a propensity score will
result in an error; see each method's help page for information on whether
MMWS weights are compatible with the method. See get_w_from_ps() for
details and references.
character; how missing data should be handled. The
options and defaults depend on the method used. Ignored if no missing
data is present. It should be noted that multiple imputation outperforms all
available missingness methods available in weightit() and should
probably be used instead. Consider the MatchThem
package for the use of weightit() with multiply imputed data.
logical; whether to print additional information
output by the fitting function.
logical; 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.
logical; whether to include in the output components necessary to estimate standard errors that account for estimation of the weights in glm_weightit(). Default is TRUE if such parts are present. See the individual pages for each method for whether these components are produced. Set to FALSE to keep the output object smaller, e.g., if standard errors will not be computed using glm_weightit().
other arguments for functions called by weightit() that
control aspects of fitting that are not covered by the above arguments. See Details.
The primary purpose of weightit() is as a dispatcher to functions
that perform the estimation of balancing weights using the requested
method. Below are the methods allowed and links to pages containing
more information about them, including additional arguments and outputs
(e.g., when include.obj = TRUE), how missing values are treated,
which estimands are allowed, and whether sampling weights are allowed.
"glm" | Propensity score weighting using generalized linear models |
"gbm" | Propensity score weighting using generalized boosted modeling |
"cbps" | Covariate Balancing Propensity Score weighting |
"npcbps" | Non-parametric Covariate Balancing Propensity Score weighting |
"ebal" | Entropy balancing |
"ipt" | Inverse probability tilting |
"optweight" | Optimization-based weighting |
"super" | Propensity score weighting using SuperLearner |
"bart" | Propensity score weighting using Bayesian additive regression trees (BART) |
"energy" | Energy balancing |
method can also be supplied as a user-defined function; see
method_user for instructions and examples.
When using weightit(), please cite both the WeightIt package
(using citation("WeightIt")) and the paper(s) in the references
section of the method used.
weightitMSM() for estimating weights with sequential (i.e.,
longitudinal) treatments for use in estimating marginal structural models
(MSMs).
weightit.fit(), which is a lower-level dispatcher function that accepts a
matrix of covariates and a vector of treatment statuses rather than a
formula and data frame and performs minimal argument checking and
processing. It may be useful for speeding up simulation studies for which
the correct arguments are known. In general weightit() should be
used.
summary.weightit() for summarizing the weights
library("cobalt")
data("lalonde", package = "cobalt")
#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "glm", estimand = "ATT"))
summary(W1)
bal.tab(W1)
#Balancing covariates with respect to race (multi-category)
(W2 <- weightit(race ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "ebal", estimand = "ATE"))
summary(W2)
bal.tab(W2)
#Balancing covariates with respect to re75 (continuous)
(W3 <- weightit(re75 ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "cbps"))
summary(W3)
bal.tab(W3)
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