bal.tab.formula {cobalt} | R Documentation |
Balance Statistics for Data Sets
Description
Generates balance statistics for unadjusted, matched, weighted, or stratified data using either a data.frame
or formula interface.
Usage
## 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,
...
)
## 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 'matrix'
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,
...
)
Arguments
x |
either a |
data |
an optional data frame containing variables named in other arguments. For some input object types, this is required. |
stats |
|
int |
|
poly |
|
distance |
an optional formula or data frame containing distance values (e.g., propensity scores) or a character vector containing their names. If a formula or variable names are specified, |
addl |
an optional formula or data frame containing additional covariates for which to present balance or a character vector containing their names. If a formula or variable names are specified, |
continuous |
whether mean differences for continuous variables should be standardized ( |
binary |
whether mean differences for binary variables (i.e., difference in proportion) should be standardized ( |
s.d.denom |
|
thresholds |
a named vector of balance thresholds, where the name corresponds to the statistic (i.e., in |
weights |
a vector, list, or |
cluster |
either a vector containing cluster membership for each unit or a string containing the name of the cluster membership variable in |
imp |
either a vector containing imputation indices for each unit or a string containing the name of the imputation index variable in |
pairwise |
whether balance should be computed for pairs of treatments or for each treatment against all groups combined. See |
s.weights |
Optional; either a vector containing sampling weights for each unit or a string containing the name of the sampling weight variable in |
abs |
|
subset |
a |
quick |
|
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 multi-category treatments are used. See |
... |
for some input types, other arguments that are required or allowed. Otherwise, further arguments to control display of output. See display options for details. |
treat |
either a vector containing treatment status values for each unit or a string containing the name of the treatment variable in |
Details
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 class-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()
.
Value
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 bal.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 class-bal.tab.imp
for details.
If multi-category 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 class-bal.tab.cluster
for details.
See Also
-
bal.tab()
for details of calculations. -
class-bal.tab.cluster
for more information on clustered data. -
class-bal.tab.imp
for more information on multiply imputed data. -
bal.tab.multi()
for more information on multi-category treatments.
Examples
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$p.score,
1/(1-lalonde$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")