bal.tab.df.formula: Balance Statistics for Data Sets

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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## S3 method for class 'data.frame'
bal.tab(x, 
    treat, 
    data = NULL, 
    weights = NULL, 
    subclass = NULL, 
    match.strata = NULL, 
    method, 
    stats,
    int = FALSE, 
    poly = 1, 
    distance = NULL,
    addl = NULL, 
    continuous,  
    binary, 
    s.d.denom, 
    thresholds = NULL,
    cluster = NULL, 
    imp = NULL, 
    pairwise = TRUE, 
    focal = NULL, 
    s.weights = NULL, 
    estimand = NULL, 
    abs = FALSE,
    subset = NULL,
    quick = TRUE, 
    ...)

## S3 method for class 'formula'
bal.tab(x, 
    data = 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.

treat

Either a vector containing treatment status values for each unit or a string containing the name of the treatment variable in data.

data

Optional; a data.frame containing variables with the names used in formula, treat, weights, distance, addl, subclass, match.strata, cluster, and/or imp if any. Can also be a mids object, the output of a call to \pkgfunmicemice from the mice package, containing multiply imputed data sets. In this case, imp is automatically supplied using the imputation variable created from processing the mids object.

weights

Optional; a vector, list, or data.frame containing weights for each unit or a string containing the names of the weights variables in data. These can be weights generated by, e.g., inverse probability weighting or matching weights resulting from a matching algorithm. This must be specified in method. If weights=NULL, subclass=NULL and match.strata=NULL, balance information will be presented only for the unadjusted sample.

subclass

Optional; either a vector containing subclass membership for each unit or a string containing the name of the subclass variable in data. If weights=NULL, subclass=NULL and match.strata=NULL, balance information will be presented only for the unadjusted sample.

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. If weights=NULL, subclass=NULL and match.strata=NULL, balance information will be presented only for the unadjusted sample.

method

A character vector containing 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.

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.

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 covs and data, if specified.

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 \funset.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 \funset.cobalt.options.

s.d.denom

character; how the denominator for standardized mean differences should be calculated, if requested. See \funcol_w_smd for allowable options. If weights are supplied, each set of weights should have a corresponding entry to s.d.denom. Abbreviations allowed. If left blank and weights, subclasses, or matching strata are supplied, bal.tab() will figure out which one is best based on the 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. See Details.

cluster

either a vector containing cluster membership for each unit or a string containing the name of the cluster membership variable in data. See 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. See 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.

focal

The name of the focal treatment when multiple categorical treatments are used. See bal.tab.multi for details.

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.

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.

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 be the same length as the treatment and covariates. 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.

...

For bal.tab.formula(), other arguments to be passed to bal.tab.data.frame(). Otherwise, further arguments to control display of output. See display options 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.

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 bal.tab.subclass for more information.

All balance statistics are calculated whether they are displayed by print or not, unless quick = TRUE. 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.

The inputs (if any) to covs and data must be a data.frame; if more than one variable is included, this is straightforward (i.e., because data[,c("v1", "v2")] is already a data.frame), but if only one variable is used with the matrix subsetting syntax (e.g., data[,"v1"]), R will coerce it to a vector, thus making it unfit for input. To avoid this, make sure to use the list subsetting syntax (e.g., data["v1"]) if only one variable is to be added (this can also be used for multiple variables and is good practice in general). Again, when more than one variable is included, the input is generally already a data.frame and nothing needs to be done.

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 output 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 \funbal.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 bal.tab.imp for details.

If multiple categorical 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 bal.tab.cluster for details.

Author(s)

Noah Greifer

See Also

\fun

bal.tab for output and details of calculations. bal.tab.cluster for more information on clustered data. bal.tab.imp for more information on multiply imputed data. bal.tab.multi for more information on multiple categorical treatments.

Examples

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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")

Example output

 cobalt (Version 4.2.4, Build Date: 2020-11-05 17:30:21 UTC)
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
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
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

Balance measures across subclasses
               Type Diff.Adj
age         Contin.  -0.0193
educ        Contin.  -0.0184
race_black   Binary   0.0576
race_hispan  Binary  -0.0714
race_white   Binary   0.0138

Sample sizes by subclass
          1   2   3   4   5   6 All
Control 100  93  96  73  24  43 429
Treated   3   9   6  29  78  60 185
Total   103 102 102 102 102 103 614
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

Balance measures across subclasses
               Type Diff.Adj
age         Contin.  -0.0193
educ        Contin.  -0.0184
race_black   Binary   0.0576
race_hispan  Binary  -0.0714
race_white   Binary   0.0138

Sample sizes by subclass
          1   2   3   4   5   6 All
Control 100  93  96  73  24  43 429
Treated   3   9   6  29  78  60 185
Total   103 102 102 102 102 103 614

cobalt documentation built on March 30, 2021, 5:12 p.m.