Balance Statistics for Data Sets

Generates balance statistics for unadjusted, matched, weighted, or stratified data using either a data.frame or formula interface.

Usage

# S3 method for 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 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 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.frame containing covariate values for each unit or a formula with the treatment variable as the response and the covariates for which balance is to be assessed as the terms. If a formula is supplied, all terms must be present as variable names in data or the global environment.
data: an optional data frame containing variables named in other arguments. For some input object types, this is required.
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. For longitudinal treatments, can be a list of allowable arguments, one for each time point.
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.
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 weights are supplied, each set of weights should have a corresponding entry to s.d.denom; a single entry will be recycled to all sets of weights. If left blank and one of weights, subclass, or match.strata are supplied, bal.tab() will figure out which one is best based on estimand, if given (for ATT, "treated"; for ATC, "control"; otherwise "pooled") and other clues if not.
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.
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.
subclass: optional; either a vector containing subclass membership for each unit or a string containing the name of the subclass variable in data.
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 data. See Details.
method: character; the method of adjustment, if any. If weights are specified, the user can specify either "matching" or "weighting"; "weighting" is the default. If multiple sets of weights are used, each must have a corresponding value for method, but if they are all of the same type, only one value is required. If subclass is specified, "subclassification" is the default. Abbreviations allowed. The only distinction between "matching" and "weighting" is how sample sizes are displayed.
estimand: character; whether the desired estimand is the "ATT", "ATC", or "ATE" for each set of weights. This argument can be used in place of s.d.denom to specify how standardized differences are calculated.
focal: the name of the focal treatment when multi-category treatments are used. See bal.tab.multi() for details.
...: 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 data. Required for the data.frame method.

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.

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().

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")
#> Balance Measures
#>                Type Diff.Adj
#> age         Contin.  -0.1242
#> educ        Contin.   0.0727
#> race_black   Binary   0.0053
#> race_hispan  Binary  -0.0025
#> race_white   Binary  -0.0029
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.    185.  
#> Adjusted    344.33   65.47

# Formula interface:
bal.tab(treat ~ age + educ + race, data = lalonde, 
        weights = "iptw.weights", s.d.denom = "pooled")
#> Balance Measures
#>                Type Diff.Adj
#> age         Contin.  -0.1242
#> educ        Contin.   0.0727
#> race_black   Binary   0.0053
#> race_hispan  Binary  -0.0025
#> race_white   Binary  -0.0029
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.    185.  
#> Adjusted    344.33   65.47

## 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")
#> Balance by subclass
#>  - - - Subclass 1 - - - 
#>                Type Diff.Adj
#> age         Contin.  -1.2029
#> educ        Contin.  -0.2551
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 2 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.4108
#> educ        Contin.   0.3005
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 3 - - - 
#>                Type Diff.Adj
#> age         Contin.  -0.1400
#> educ        Contin.   0.0295
#> race_black   Binary   0.0000
#> race_hispan  Binary  -0.0833
#> race_white   Binary   0.0833
#> 
#>  - - - Subclass 4 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.2294
#> educ        Contin.  -0.4409
#> race_black   Binary   0.3467
#> race_hispan  Binary  -0.3467
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 5 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.4675
#> educ        Contin.   0.3427
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 6 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.1293
#> educ        Contin.  -0.0838
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 

# Formula interface:
bal.tab(treat ~ age + educ + race, data = lalonde, 
        subclass = "subclass", disp.subclass = TRUE, 
        s.d.denom = "pooled")
#> Balance by subclass
#>  - - - Subclass 1 - - - 
#>                Type Diff.Adj
#> age         Contin.  -1.2029
#> educ        Contin.  -0.2551
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 2 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.4108
#> educ        Contin.   0.3005
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 3 - - - 
#>                Type Diff.Adj
#> age         Contin.  -0.1400
#> educ        Contin.   0.0295
#> race_black   Binary   0.0000
#> race_hispan  Binary  -0.0833
#> race_white   Binary   0.0833
#> 
#>  - - - Subclass 4 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.2294
#> educ        Contin.  -0.4409
#> race_black   Binary   0.3467
#> race_hispan  Binary  -0.3467
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 5 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.4675
#> educ        Contin.   0.3427
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#> 
#>  - - - Subclass 6 - - - 
#>                Type Diff.Adj
#> age         Contin.   0.1293
#> educ        Contin.  -0.0838
#> race_black   Binary   0.0000
#> race_hispan  Binary   0.0000
#> race_white   Binary   0.0000
#>

Usage

Arguments

Value

Details

See also

Examples