Description Usage Arguments Details Value Author(s) See Also Examples
Generates balance statistics for matchit
objects from MatchIt.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  ## S3 method for class 'matchit'
bal.tab(x,
method,
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,
...)

x 
a 
method 
a character vector containing the method of adjustment. Ignored unless subclassification was used in the original call to 
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, 
data 
an optional data frame containing variables that might be named in arguments to 
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 named list containing additional weights on which to assess balance. Each entry can be a vector of weights, the name of a variable in 
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 between the treatment groups 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 

... 
further arguments to control display of output. See display options for details. 
bal.tab.matchit()
generates a list of balance summaries for the matchit
object given, and functions similarly to MatchIt::summary.matchit()
. 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.
The threshold
argument controls whether extra columns should be inserted into the Balance table describing whether the balance statistics in question exceeded or were within the threshold. Including these thresholds also creates summary tables tallying the number of variables that exceeded and were within the threshold and displaying the variables with the greatest imbalance on that balance measure. When subclassification is used, the extra threshold columns are placed within the balance tables for each subclass as well as in the aggregate balance table, and the summary tables display balance for each subclass.
If subclassification is used and method
is set to "subclassification"
, an object of class "bal.tab.subclass"
containing balance summaries within and across subclasses. See bal.tab.subclass
for details.
If matching is used and clusters are not specified, an object of class "bal.tab"
containing balance summaries for the matchit
object. See \funbal.tab 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 details of calculations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  library(MatchIt); data("lalonde", package = "cobalt")
## Nearest Neighbor matching
m.out1 < matchit(treat ~ age + educ + race +
married + nodegree + re74 + re75,
data = lalonde, method = "nearest")
bal.tab(m.out1, un = TRUE, m.threshold = .1,
v.threshold = 2)
## Subclassification
m.out2 < matchit(treat ~ age + educ + race +
married + nodegree + re74 + re75,
data = lalonde, method = "subclass")
bal.tab(m.out2, disp.subclass = TRUE)

cobalt (Version 4.2.4, Build Date: 20201105 17:30:21 UTC)
Attaching package: ‘MatchIt’
The following object is masked from ‘package:cobalt’:
lalonde
Call
matchit(formula = treat ~ age + educ + race + married + nodegree +
re74 + re75, data = lalonde, method = "nearest")
Balance Measures
Type Diff.Un V.Ratio.Un Diff.Adj M.Threshold V.Ratio.Adj
distance Distance 1.7941 0.9211 0.9739 0.7566
age Contin. 0.3094 0.4400 0.0718 Balanced, <0.1 0.4568
educ Contin. 0.0550 0.4959 0.1290 Not Balanced, >0.1 0.5721
race_black Binary 0.6404 . 0.3730 Not Balanced, >0.1 .
race_hispan Binary 0.0827 . 0.1568 Not Balanced, >0.1 .
race_white Binary 0.5577 . 0.2162 Not Balanced, >0.1 .
married Binary 0.3236 . 0.0216 Balanced, <0.1 .
nodegree Binary 0.1114 . 0.0703 Balanced, <0.1 .
re74 Contin. 0.7211 0.5181 0.0505 Balanced, <0.1 1.3289
re75 Contin. 0.2903 0.9563 0.0257 Balanced, <0.1 1.4956
V.Threshold
distance Balanced, <2
age Not Balanced, >2
educ Balanced, <2
race_black
race_hispan
race_white
married
nodegree
re74 Balanced, <2
re75 Balanced, <2
Balance tally for mean differences
count
Balanced, <0.1 5
Not Balanced, >0.1 4
Variable with the greatest mean difference
Variable Diff.Adj M.Threshold
race_black 0.373 Not Balanced, >0.1
Balance tally for variance ratios
count
Balanced, <2 4
Not Balanced, >2 1
Variable with the greatest variance ratio
Variable V.Ratio.Adj V.Threshold
age 0.4568 Not Balanced, >2
Sample sizes
Control Treated
All 429 185
Matched 185 185
Unmatched 244 0
Call
matchit(formula = treat ~ age + educ + race + married + nodegree +
re74 + re75, data = lalonde, method = "subclass")
Balance by subclass
   Subclass 1   
Type Diff.Adj
distance Distance 0.2785
age Contin. 0.4024
educ Contin. 0.1142
race_black Binary 0.0823
race_hispan Binary 0.1492
race_white Binary 0.2315
married Binary 0.2877
nodegree Binary 0.0003
re74 Contin. 0.5864
re75 Contin. 0.1729
   Subclass 2   
Type Diff.Adj
distance Distance 0.1873
age Contin. 0.7473
educ Contin. 0.1183
race_black Binary 0.0094
race_hispan Binary 0.0094
race_white Binary 0.0000
married Binary 0.2473
nodegree Binary 0.0121
re74 Contin. 0.0352
re75 Contin. 0.0970
   Subclass 3   
Type Diff.Adj
distance Distance 0.0140
age Contin. 0.0524
educ Contin. 0.1372
race_black Binary 0.0000
race_hispan Binary 0.0000
race_white Binary 0.0000
married Binary 0.3550
nodegree Binary 0.2191
re74 Contin. 0.2669
re75 Contin. 0.0970
   Subclass 4   
Type Diff.Adj
distance Distance 0.0003
age Contin. 0.0499
educ Contin. 0.1436
race_black Binary 0.0000
race_hispan Binary 0.0000
race_white Binary 0.0000
married Binary 0.1116
nodegree Binary 0.0417
re74 Contin. 0.0073
re75 Contin. 0.0801
   Subclass 5   
Type Diff.Adj
distance Distance 0.0224
age Contin. 0.2640
educ Contin. 0.2977
race_black Binary 0.0000
race_hispan Binary 0.0000
race_white Binary 0.0000
married Binary 0.0000
nodegree Binary 0.0376
re74 Contin. 0.0190
re75 Contin. 0.1233
   Subclass 6   
Type Diff.Adj
distance Distance 0.0143
age Contin. 0.5245
educ Contin. 0.2781
race_black Binary 0.0000
race_hispan Binary 0.0000
race_white Binary 0.0000
married Binary 0.0000
nodegree Binary 0.1290
re74 Contin. 0.0152
re75 Contin. 0.2407
Balance measures across subclasses
Type Diff.Adj
distance Distance 0.0744
age Contin. 0.0609
educ Contin. 0.0323
race_black Binary 0.0154
race_hispan Binary 0.0234
race_white Binary 0.0388
married Binary 0.0533
nodegree Binary 0.0097
re74 Contin. 0.1466
re75 Contin. 0.0940
Sample sizes by subclass
1 2 3 4 5 6 All
Control 346 24 17 21 18 3 429
Treated 31 31 29 32 31 31 185
Total 377 55 46 53 49 34 614
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