R: Balance Statistics for MatchIt Objects

bal.tab.matchit {cobalt}

R Documentation

Balance Statistics for MatchIt Objects

Description

Generates balance statistics for matchit objects from MatchIt. 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 '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,
    abs = FALSE,
    subset = NULL,
    quick = TRUE, 
    ...)

Arguments

`x`	a `matchit` object; the output of a call to `matchit()` from the MatchIt package.
`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. Note that the distance measure 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 first in the argument to `data`, if specified, and next in the `matchit` object.
`data`	an optional data frame containing variables that might be named in arguments to `distance`, `addl`, `cluster`, and `imp`. Can also be `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.
`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`. This argument is used to set `std` in `col_w_smd`.
`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`. This argument is used to set `std` in `col_w_smd`.
`s.d.denom`	`character`; how the denominator for standardized mean differences should be calculated, if requested. See `col_w_smd` for allowable options. The default is "treated" when matching without caliper and "pooled" when matching with a caliper. When subclassification is used and `sub.by` is specified in the call to `matchit()`, the corresponding value will be used (except for `sub.by = "all"`, which triggers "pooled"). 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 `matchit` 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 `matchit()`. 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 `matchit()`. `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.
`...`	further arguments to control display of output. See display options for details.

Details

bal.tab.matchit() generates a list of balance summaries for the matchit object given, and functions similarly to summary.matchit() in 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 bal.tab.subclass for more information.

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.

Value

If subclassification is used, an object of class "bal.tab.subclass" containing balance summaries within and across subclasses. See 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 bal.tab.cluster for details.

Note

When a user-supplied distance measure or Mahalanobis distance matching (method = "mahalanobis") are used with matchit(), full covariate information is not stored in the matchit output object. In particular, categorical variables are not retained as such, and so the dummies are treated as their own variables. The consequence of this is that for all but the first factor variable supplied to matchit(), the first category will be absent. In addition, full compatibility with functions that rely on variable names (like love.plot) will be hampered. To get around this issue, users can supply the original data frame to bal.tab() through the data argument, and the variables will be processed correctly.

Author(s)

Noah Greifer

Examples

library(MatchIt); data("lalonde", package = "cobalt")

## 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)

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

[Package cobalt version 4.2.2 Index]