R: Balance Statistics for 'Matching', 'optmatch', 'ebal', and...

bal.tab.Match {cobalt} | R Documentation

## Balance Statistics for `Matching`, `optmatch`, `ebal`, and `designmatch` Objects

### Description

Generates balance statistics for output objects from Matching, optmatch, ebal, and designmatch. Note that several arguments that used to be documented here are now documented in display options. They are still available.

### Usage

    ## S3 method for class 'Match'
    bal.tab(x,
        formula = NULL,
        data = NULL,
        treat = NULL,
        covs = NULL,
        stats,
        int = FALSE,
        poly = 1,
        distance = NULL,
        addl = NULL,
        continuous,
        binary,
        s.d.denom,
        thresholds = NULL,
        weights = NULL,
        cluster = NULL,
        imp = NULL,
        abs = FALSE,
        subset = NULL,
        quick = TRUE,
        ...)

    ## S3 method for class 'optmatch'
    bal.tab(x, ...)

    ## S3 method for class 'ebalance'
    bal.tab(x, ...)

    ## S3 method for class 'designmatch'
    bal.tab(x, ...)

### Arguments

`x` | either a `Match` object (the output of a call to `Match()` or `Matchby()` from the Matching package), an `optmatch` object (the output of a call to `pairmatch()` or `fullmatch()` from the optmatch package), an `ebalance` object (the output of a call to `ebalance()` or `ebalance.trim()` from the ebal package), or the output of a call to `bmatch()` or related wrapper functions from the designmatch package.
`formula` | a `formula` with the treatment variable as the response and the covariates for which balance is to be assessed as the predictors. All named variables must be in `data`. See Details.
`data` | Optional; a `data.frame` containing variables with the names used in `formula`, `treat`, `weights`, `distance`, `addl`, `cluster`, and/or `imp` if any. Can also be a `mids` object, the output of a call to `mice()` 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. See Details.
`treat` | a vector of treatment statuses. See Details.
`covs` | a data frame of covariate values for which to check balance. See Details.
`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. 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. In older versions of cobalt, setting `int = TRUE` displayed squares of covariates; to replicate this behavior, set `int = 2`.
`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 `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. If not specified, for `Match` objects, `bal.tab()` will use "treated" if the estimand of the call to `Match()` is the ATT, "pooled" if the estimand is the ATE, and "control" if the estimand is the ATC; for `optmatch`, `ebal`, and `designmatch` objects, `bal.tab()` will determine which value makes the most sense based on the input. Abbreviations allowed.
`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.
`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 `data` that contains weights, or an object with a `get.w` method.
`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 CBPS object. 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` or the original data set used in the call to `weightit()`. See `bal.tab.imp` for details. Not necessary if `data` is a `mids` object.
`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 variables in the original call to the conditioning function. `NA`s 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.optmatch()`, `bal.tab.ebalance()`, and `bal.tab.designmatch()`, the same arguments as those passed to `bal.tab.Match()`. Otherwise, further arguments to control display of output. See display options for details.

### Details

`bal.tab()` generates a list of balance summaries for the object given, and function similarly to `MatchBalance()` in Matching and `meantab()` in designmatch. Note that output objects from designmatch do not have their own class; `bal.tab()` first checks whether the object meets the criteria to be treated as a `designmatch` object before dispatching the correct method. In particular, renaming or removing items from the output object can create unintended consequences.

The input to `bal.tab.Match()`, `bal.tab.optmatch()`, `bal.tab.ebalance()`, and `bal.tab.designmatch()` must include either both `formula` and `data` or both `covs` and `treat`. Using the `formula` + `data` inputs mirrors how `MatchBalance()` is used in Matching, and using the `covs` + `treat` input mirrors how `meantab()` is used in designmatch. (Note that to see identical results to `meantb()`, `s.d.denom` must be set to `"pooled"`, though this is not recommended.) For `optmatch` output objects, specifying a treatment is not required.

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.

The inputs (if any) to `covs` 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 (e.g., `data[,"v1"]`), R will coerce it to a vector, thus making it unfit for input. To avoid this, simply wrap the input to `covs` in `data.frame()` or use `subset()` if only one variable is to be added. Again, when more than one variable is included, the input is general already a data frame and nothing needs to be done.

### Value

For point treatments, if clusters and imputations are not specified, an object of class `"bal.tab"` containing balance summaries for the given 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 `bal.tab.cluster` for details.

### Author(s)

Noah Greifer

### See Also

`bal.tab` for details of calculations.

### Examples

    ########## Matching ##########
    library(Matching); data("lalonde", package = "cobalt")

    p.score <- glm(treat ~ age + educ + race +
                married + nodegree + re74 + re75,
                data = lalonde, family = "binomial")$fitted.values
    Match.out <- Match(Tr = lalonde$treat, X = p.score)

    ## Using formula and data
    bal.tab(Match.out, treat ~ age + educ + race +
            married + nodegree + re74 + re75, data = lalonde)

    ########## optmatch ##########
    library("optmatch"); data("lalonde", package = "cobalt")

    lalonde$prop.score <- glm(treat ~ age + educ + race +
                married + nodegree + re74 + re75,
                data = lalonde, family = binomial)$fitted.values
    pm <- pairmatch(treat ~ prop.score, data = lalonde)

    ## Using formula and data
    bal.tab(pm, treat ~ age + educ + race +
            married + nodegree + re74 + re75, data = lalonde,
            distance = "prop.score")

    ########## ebal ##########
    library("ebal"); data("lalonde", package = "cobalt")

    covariates <- subset(lalonde, select = -c(re78, treat, race))
    e.out <- ebalance(lalonde$treat, covariates)

    ## Using treat and covs
    bal.tab(e.out, treat = lalonde$treat, covs = covariates)

    ## Not run:
    ########## designmatch ##########
    library("designmatch"); data("lalonde", package = "cobalt")

    covariates <- as.matrix(lalonde[c("age", "educ", "re74", "re75")])
    dmout <- bmatch(lalonde$treat,
                    total_groups = sum(lalonde$treat == 1),
                    mom = list(covs = covariates,
                               tols = absstddif(covs, treat, .05))
                    )

    ## Using treat and covs
    bal.tab(dmout, treat = lalonde$treat, covs = covariates)

    ## End(Not run)

[Package cobalt version 4.2.2 Index]