Balance Statistics for MatchIt Objects

Generates balance statistics for matchit objects from MatchIt.

Usage

# S3 method for matchit
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,
  imp = NULL,
  pairwise = TRUE,
  s.weights = NULL,
  abs = FALSE,
  subset = NULL,
  quick = TRUE,
  method,
  ...
)

Arguments

x

a matchit object; the output of a call to MatchIt::matchit().

stats

character; which statistic(s) should be reported. See stats for allowable options. For binary and multi-category treatments, "mean.diffs" (i.e., mean differences) is the default. For continuous treatments, "correlations" (i.e., treatment-covariate Pearson correlations) is the default. Multiple options are allowed.

int

logical or numeric; whether or not to include 2-way interactions of covariates included in covs and in addl. If numeric, will be passed to poly as well.

poly

numeric; the highest polynomial of each continuous covariate to display. For example, if 2, squares of each continuous covariate will be displayed (in addition to the covariate itself); if 3, squares and cubes of each continuous covariate will be displayed, etc. If 1, the default, only the base covariate will be displayed. If int is numeric, poly will take on the value of int.

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, bal.tab() will look in the argument to data, if specified. The distance measure (e.g., propensity score) generated by matchit() is automatically included and named "distance".

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, bal.tab() will look in the arguments to the input object, covs, and data, if specified. For longitudinal treatments, can be a list of allowable arguments, one for each time point.

data

an optional data frame containing variables named in other arguments. For some input object types, this is required.

continuous

whether mean differences for continuous variables should be standardized ("std") or raw ("raw"). Default "std". Abbreviations allowed. This option can be set globally using set.cobalt.options().

binary

whether mean differences for binary variables (i.e., difference in proportion) should be standardized ("std") or raw ("raw"). Default "raw". Abbreviations allowed. This option can be set globally using set.cobalt.options().

s.d.denom

character; how the denominator for standardized mean differences should be calculated, if requested. See col_w_smd() for allowable options. Abbreviations allowed. If not specified, bal.tab() will figure out which one is best based on the estimand of the matchit object: if ATT, "treated"; if ATC, "control", otherwise "pooled".

thresholds

a named vector of balance thresholds, where the name corresponds to the statistic (i.e., in stats) that the threshold applies to. For example, to request thresholds on mean differences and variance ratios, one can set thresholds = c(m = .05, v = 2). Requesting a threshold automatically requests the display of that statistic. When specified, extra columns are inserted into the Balance table describing whether the requested balance statistics exceeded the threshold or not. 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 are added to the output.

weights

a vector, list, or data.frame containing weights for each unit, or a string containing the names of the weights variables in data, or an object with a get.w() method or a list thereof. The weights can be, e.g., inverse probability weights or matching weights resulting from a matching algorithm.

cluster

either a vector containing cluster membership for each unit or a string containing the name of the cluster membership variable in data or the input object. See class-bal.tab.cluster for details.

imp

either a vector containing imputation indices for each unit or a string containing the name of the imputation index variable in data or the input object. See class-bal.tab.imp for details. Not necessary if data is a mids object.

pairwise

whether balance should be computed for pairs of treatments or for each treatment against all groups combined. See bal.tab.multi() for details. This can also be used with a binary treatment to assess balance with respect to the full sample.

s.weights

Optional; either a vector containing sampling weights for each unit or a string containing the name of the sampling weight variable in data. These function like regular weights except that both the adjusted and unadjusted samples will be weighted according to these weights if weights are used. If s.weights was specified in the call to matchit(), they will automatically be included and do not need be specified again (though there is no harm if they are).

abs

logical; whether displayed balance statistics should be in absolute value or not.

subset

a logical or numeric vector denoting whether each observation should be included or which observations should be included. If logical, it should have length equal to the number of units. NAs will be treated as FALSE. This can be used as an alternative to cluster to examine balance on subsets of the data.

quick

logical; if TRUE, will not compute any values that will not be displayed. Set to FALSE if computed values not displayed will be used later.

method

a character vector containing the method of adjustment. Ignored unless subclassification was used in the original call to matchit(). If "weighting", the subclassification weights will be used and subclasses will be ignored. If "subclassification", balance will be assessed using the subclasses (see class-bal.tab.subclass for details). Abbreviations allowed.

...

for some input types, other arguments that are required or allowed. Otherwise, further arguments to control display of output. See display options for details.

Value

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 class-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 bal.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 class-bal.tab.cluster for details.

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 class-bal.tab.subclass for more information.

See also

• bal.tab() for details of calculations.

Examples

library(MatchIt); data("lalonde", package = "cobalt")
#> 
#> Attaching package: ‘MatchIt’
#> The following object is masked _by_ ‘.GlobalEnv’:
#> 
#>     lalonde
#> The following object is masked from ‘package:cobalt’:
#> 
#>     lalonde

## 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)
#> 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

## Subclassification
m.out2 <- matchit(treat ~ age + educ + race + 
                      married + nodegree + re74 + re75, 
                  data = lalonde, method = "subclass")

bal.tab(m.out2, disp.subclass = TRUE)
#> 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
#>