bal.tab.df.formula  R Documentation 
Generates balance statistics for unadjusted, matched, weighted, or stratified data using either a data.frame
or formula interface.
## S3 method for class 'data.frame' bal.tab(x, treat, stats, int = FALSE, poly = 1, distance = NULL, addl = NULL, data = NULL, continuous, binary, s.d.denom, thresholds = NULL, weights = NULL, cluster = NULL, imp = NULL, pairwise = TRUE, s.weights = NULL, abs = FALSE, subset = NULL, quick = TRUE, subclass = NULL, match.strata = NULL, method, estimand = NULL, focal = NULL, ...) ## S3 method for class 'formula' bal.tab(x, data = NULL, stats, int = FALSE, poly = 1, distance = NULL, addl = NULL, continuous, binary, s.d.denom, thresholds = NULL, weights = NULL, cluster = NULL, imp = NULL, pairwise = TRUE, s.weights = NULL, abs = FALSE, subset = NULL, quick = TRUE, subclass = NULL, match.strata = NULL, method, estimand = NULL, focal = NULL, ...)
x 
either a 
treat 
either a vector containing treatment status values for each unit or a string containing the name of the treatment variable in 
stats, int, poly, distance, addl, data, continuous, binary, thresholds, weights, cluster, imp, pairwise, s.weights, abs, subset, quick, ... 
see \funbal.tab for details. See below for a special note on the 
subclass 
optional; either a vector containing subclass membership for each unit or a string containing the name of the subclass variable in 
match.strata 
optional; either a vector containing matching stratum membership for each unit or a string containing the name of the matching stratum variable in 
method 

estimand 

focal 
the name of the focal treatment when multicategory treatments are used. See 
The following argument has a special note when used with data.frame
or formula
input objects:
s.d.denom 
if weights are supplied, each set of weights should have a corresponding entry to 
bal.tab.data.frame()
generates a list of balance summaries for the covariates and treatment status values given. bal.tab.formula()
does the same but uses a formula interface instead. When the formula interface is used, the formula and data are reshaped into a treatment vector and data.frame
of covariates and then simply passed through the data.frame
method.
If weights
, subclass
and match.strata
are all NULL
, balance information will be presented only for the unadjusted sample.
The argument to match.strata
corresponds to a factor vector containing the name or index of each pair/stratum for units conditioned through matching, for example, using the optmatch package. If more than one of weights
, subclass
, or match.strata
are specified, bal.tab()
will attempt to figure out which one to apply. Currently only one of these can be applied ta a time. bal.tab()
behaves differently depending on whether subclasses are used in conditioning or not. If they are used, bal.tab()
creates balance statistics for each subclass and for the sample in aggregate. See bal.tab.subclass
for more information.
Multiple sets of weights can be supplied simultaneously by entering a data.frame
or a character vector containing the names of weight variables found in data
or a list of weights vectors or names. The arguments to method
, s.d.denom
, and estimand
, if any, must be either the same length as the number of sets of weights or of length one, where the sole entry is applied to all sets. When standardized differences are computed for the unadjusted group, they are done using the first entry to s.d.denom
or estimand
. When only one set of weights is supplied, the output for the adjusted group will simply be called "Adj"
, but otherwise will be named after each corresponding set of weights. Specifying multiple sets of weights will also add components to other outputs of bal.tab()
.
For point treatments, if clusters and imputations are not specified, an object of class "bal.tab"
containing balance summaries for the specified treatment and covariates. See \funbal.tab for details.
If imputations are specified, an object of class "bal.tab.imp"
containing balance summaries for each imputation and a summary of balance across imputations. See bal.tab.imp
for details.
If multicategory treatments are used, an object of class "bal.tab.multi"
containing balance summaries for each pairwise treatment comparison. See bal.tab.multi
for details.
If clusters are specified, an object of class "bal.tab.cluster"
containing balance summaries within each cluster and a summary of balance across clusters. See bal.tab.cluster
for details.
Noah Greifer
bal.tab for output and details of calculations.
bal.tab.cluster
for more information on clustered data.
bal.tab.imp
for more information on multiply imputed data.
bal.tab.multi
for more information on multicategory treatments.
data("lalonde", package = "cobalt") lalonde$p.score < glm(treat ~ age + educ + race, data = lalonde, family = "binomial")$fitted.values covariates < subset(lalonde, select = c(age, educ, race)) ## Propensity score weighting using IPTW lalonde$iptw.weights < ifelse(lalonde$treat==1, 1/lalonde$p.score, 1/(1lalonde$p.score)) # data frame interface: bal.tab(covariates, treat = "treat", data = lalonde, weights = "iptw.weights", s.d.denom = "pooled") # Formula interface: bal.tab(treat ~ age + educ + race, data = lalonde, weights = "iptw.weights", s.d.denom = "pooled") ## Propensity score subclassification lalonde$subclass < findInterval(lalonde$p.score, quantile(lalonde$p.score, (0:6)/6), all.inside = TRUE) # data frame interface: bal.tab(covariates, treat = "treat", data = lalonde, subclass = "subclass", disp.subclass = TRUE, s.d.denom = "pooled") # Formula interface: bal.tab(treat ~ age + educ + race, data = lalonde, subclass = "subclass", disp.subclass = TRUE, s.d.denom = "pooled")
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