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 inweightit()
. estimand
the desired estimand. For binary and multicategory 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 multicategory 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
ifestimand = "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 isFALSE
. 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 themethod
used. IfNULL
,covs
covs will be checked forNA
values, and if present,missing
will be set to"ind"
. If""
,covs
covs will not be checked forNA
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"
, theglm
objects containing the propensity score model will be included. See the individual pages for each method for information on what object will be included ifTRUE
. ...
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.
See also
weightit()
, which you should use for estimating weights unless
you know better.
as.weightit()
for converting a weightit.fit
object to a weightit
object.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
# Balancing covariates between treatment groups (binary)
covs < lalonde[c("age", "educ", "race", "married",
"nodegree", "re74", "re75")]
## Create covs matrix, splitting any factors using
## cobalt::splitfactor()
covs_mat < as.matrix(splitfactor(covs))
WF1 < weightit.fit(covs_mat, treat = lalonde$treat,
method = "glm", estimand = "ATT")
str(WF1)
#> List of 9
#> $ weights : num [1:614] 1 1 1 1 1 1 1 1 1 1 ...
#> $ treat : int [1:614] 1 1 1 1 1 1 1 1 1 1 ...
#> .. attr(*, "treat.type")= chr "binary"
#> $ estimand : chr "ATT"
#> $ method : chr "glm"
#> $ ps : num [1:614] 0.639 0.225 0.678 0.776 0.702 ...
#> $ s.weights: num [1:614] 1 1 1 1 1 1 1 1 1 1 ...
#> $ focal : chr "1"
#> $ fit.obj : NULL
#> $ info : Named list()
#>  attr(*, "Mparts")=List of 5
#> ..$ psi_treat:function (Btreat, A, Xtreat, SW)
#> ..$ wfun :function (Btreat, Xtreat, A)
#> ..$ Xtreat : num [1:614, 1:9] 1 1 1 1 1 1 1 1 1 1 ...
#> .. .. attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:614] "1" "2" "3" "4" ...
#> .. .. ..$ : chr [1:9] "(Intercept)" "age" "educ" "race_hispan" ...
#> ..$ A : int [1:614] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ btreat : Named num [1:9] 4.933 0.156 0.424 2.082 3.065 ...
#> .. .. attr(*, "names")= chr [1:9] "(Intercept)" "age" "educ" "race_hispan" ...
#>  attr(*, "class")= chr "weightit.fit"
# Converting to a weightit object for use with
# summary() and bal.tab()
W1 < as.weightit(WF1, covs = covs)
W1
#> A weightit object
#>  method: "" (propensity score weighting with GLM)
#>  number of obs.: 614
#>  sampling weights: none
#>  treatment: 2category
#>  estimand: ATT (focal: 1)
#>  covariates: age, educ, race, married, nodegree, re74, re75
summary(W1)
#> Summary of weights
#>
#>  Weight ranges:
#>
#> Min Max
#> treated 1.0000  1.0000
#> control 0.0092  3.7432
#>
#>  Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 597 573 381 411 303
#> control 3.0301 3.0592 3.2397 3.5231 3.7432
#>
#>  Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.000 0
#> control 1.818 1.289 1.098 0
#>
#>  Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 99.82 185
bal.tab(W1)
#> Balance Measures
#> Type Diff.Adj
#> prop.score Distance 0.0205
#> age Contin. 0.1188
#> educ Contin. 0.0284
#> race_black Binary 0.0022
#> race_hispan Binary 0.0002
#> race_white Binary 0.0021
#> married Binary 0.0186
#> nodegree Binary 0.0184
#> re74 Contin. 0.0021
#> re75 Contin. 0.0110
#>
#> Effective sample sizes
#> Control Treated
#> Unadjusted 429. 185
#> Adjusted 99.82 185