MatchThem Objectsbal.tab.mimids.RdGenerates balance statistics for mimids and wimids objects from MatchThem.
# S3 method for mimids
bal.tab(x,
stats,
int = FALSE,
poly = 1,
distance = NULL,
addl = NULL,
data = NULL,
continuous,
binary,
s.d.denom,
thresholds = NULL,
weights = NULL,
cluster = NULL,
pairwise = TRUE,
s.weights = NULL,
abs = FALSE,
subset = NULL,
quick = TRUE,
...)a mimids or wimids object; the output of a call to MatchThem::matchthem() or MatchThem::weightthem().
see bal.tab() for details.
See below for special notes on the distance, s.d.denom, and imp arguments. Note the imp argument is ignored because the imputation identifers are already present in the input object.
The following arguments have special notes when used with mimids and wimids objects:
the distance measure generated by matchthem() or weightthem() is automatically included and named "distance" or "prop.score", respectively.
the defaults depend on the options specified in the original function calls; see bal.tab.matchit() and bal.tab.weightit() for details on the defaults.
bal.tab.mimids() and bal.tab.wimids() generate a list of balance summaries for the mimids or wimids object given.
If clusters are not 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 clusters are specified, an object of class "bal.tab.imp.cluster" containing summaries between and across all clusters and imputations.
bal.tab() for details of calculations.
library(mice)
#>
#> Attaching package: ‘mice’
#> The following object is masked from ‘package:stats’:
#>
#> filter
#> The following objects are masked from ‘package:base’:
#>
#> cbind, rbind
library(MatchThem)
#>
#> Attaching package: ‘MatchThem’
#> The following objects are masked from ‘package:mice’:
#>
#> cbind, pool
#> The following object is masked from ‘package:base’:
#>
#> cbind
data("lalonde_mis", package = "cobalt")
#Imputing the missing data
imp <- mice(lalonde_mis, m = 5)
#>
#> iter imp variable
#> 1 1 married re74 re75
#> 1 2 married re74 re75
#> 1 3 married re74 re75
#> 1 4 married re74 re75
#> 1 5 married re74 re75
#> 2 1 married re74 re75
#> 2 2 married re74 re75
#> 2 3 married re74 re75
#> 2 4 married re74 re75
#> 2 5 married re74 re75
#> 3 1 married re74 re75
#> 3 2 married re74 re75
#> 3 3 married re74 re75
#> 3 4 married re74 re75
#> 3 5 married re74 re75
#> 4 1 married re74 re75
#> 4 2 married re74 re75
#> 4 3 married re74 re75
#> 4 4 married re74 re75
#> 4 5 married re74 re75
#> 5 1 married re74 re75
#> 5 2 married re74 re75
#> 5 3 married re74 re75
#> 5 4 married re74 re75
#> 5 5 married re74 re75
#Matching using within-imputation propensity scores
mt.out1 <- matchthem(treat ~ age + educ + race +
married + nodegree + re74 + re75,
data = imp, approach = "within")
#>
#> Matching Observations | dataset: #1
#> #2
#> #3
#> #4
#> #5
#>
bal.tab(mt.out1)
#> Balance summary across all imputations
#> Type Min.Diff.Adj Mean.Diff.Adj Max.Diff.Adj
#> distance Distance 0.9666 0.9697 0.9713
#> age Contin. 0.0008 0.0391 0.0589
#> educ Contin. -0.1452 -0.1151 -0.0538
#> race_black Binary 0.3730 0.3730 0.3730
#> race_hispan Binary -0.1730 -0.1654 -0.1568
#> race_white Binary -0.2162 -0.2076 -0.2000
#> married Binary -0.0108 -0.0054 0.0000
#> nodegree Binary 0.0432 0.0681 0.0865
#> re74 Contin. -0.0829 -0.0622 -0.0218
#> re75 Contin. -0.0801 -0.0445 -0.0254
#>
#> Average sample sizes across imputations
#> Control Treated
#> All 429 185
#> Matched 185 185
#> Unmatched 244 0
#Matching using across-imputation average propensity scores
mt.out2 <- matchthem(treat ~ age + educ + race +
married + nodegree + re74 + re75,
data = imp, approach = "across")
#> Estimating distances | dataset: #1
#> #2
#> #3
#> #4
#> #5
#>
#> Matching Observations | dataset: #1
#> #2
#> #3
#> #4
#> #5
#>
bal.tab(mt.out2)
#> Balance summary across all imputations
#> Type Min.Diff.Adj Mean.Diff.Adj Max.Diff.Adj
#> distance Distance 0.9745 0.9745 0.9745
#> age Contin. 0.0665 0.0665 0.0665
#> educ Contin. -0.1264 -0.1264 -0.1264
#> race_black Binary 0.3730 0.3730 0.3730
#> race_hispan Binary -0.1676 -0.1676 -0.1676
#> race_white Binary -0.2054 -0.2054 -0.2054
#> married Binary -0.0216 -0.0151 -0.0108
#> nodegree Binary 0.0595 0.0595 0.0595
#> re74 Contin. -0.1128 -0.0734 -0.0305
#> re75 Contin. -0.0586 -0.0466 -0.0286
#>
#> Average sample sizes across imputations
#> Control Treated
#> All 429 185
#> Matched 185 185
#> Unmatched 244 0
#Weighting using within-imputation propensity scores
wt.out <- weightthem(treat ~ age + educ + race +
married + nodegree + re74 + re75,
data = imp, approach = "within",
estimand = "ATT")
#> Estimating weights | dataset: #1
#> #2
#> #3
#> #4
#> #5
#>
bal.tab(wt.out)
#> Balance summary across all imputations
#> Type Min.Diff.Adj Mean.Diff.Adj Max.Diff.Adj
#> prop.score Distance -0.0337 -0.0328 -0.0300
#> age Contin. 0.0874 0.0958 0.1109
#> educ Contin. -0.0480 -0.0344 -0.0274
#> race_black Binary -0.0033 -0.0032 -0.0031
#> race_hispan Binary 0.0004 0.0005 0.0008
#> race_white Binary 0.0025 0.0027 0.0028
#> married Binary 0.0164 0.0208 0.0250
#> nodegree Binary 0.0152 0.0177 0.0216
#> re74 Contin. -0.0115 0.0124 0.0259
#> re75 Contin. -0.0060 0.0017 0.0116
#>
#> Average effective sample sizes across imputations
#> Control Treated
#> Unadjusted 429. 185
#> Adjusted 100.5 185