R: Balance statistics for 'CBPS' Objects

bal.tab.CBPS {cobalt} | R Documentation

## Balance statistics for `CBPS` Objects

### Description

Generates balance statistics for `CBPS` and `CBMSM` objects from the CBPS package.

### Usage

    ## S3 method for class 'CBPS'
    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,
            ...)

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

### Arguments

`x` | a `CBPS` or `CBMSM` object; the output of a call to `CBPS::CBPS()` or `CBPS::CBMSM()`.
`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, distance.list` | 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 first in the argument to `data`, if specified, and next in the input object. Note that the propensity scores generated by `CBPS()` and `CBMSM()` are automatically included and named "prop.score". For `CBMSM` objects, can be a list of distance values as described above, with one list entry per time period. Each data set must have one row per individual, unlike the data frame in the original call to `CBMSM()`
`addl, addl.list` | 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 input object. For `CBMSM` objects, can be a list of additional covariate values as described above, with one list entry per time period. Each data set must have one row per individual, unlike the data frame in the original call to `CBMSM()`.
`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()`.
`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, `bal.tab()` will use "treated" if the estimand of the call to `CBPS()` is the ATT and "pooled" if the estimand is the ATE. 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 `stats`.
`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 `CBPS()` or `CBMSM()`. 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.
`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` or the CBPS object. If the original call to `CBPS()` included sampling weights, they should be specified again here to ensure correct computation of balance statistics and unadjusted values. See Details below.
`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 `CBPS()` or `CBMSM()`. `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.CBPS()` and `bal.tab.CBMSM()` generate a list of balance summaries for the `CBPS` or `CBMSM` object given and functions similarly to `CBPS::balance()`.

The `thresholds` 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 `CBPS` object does not return sampling weights even if they are used; rather, the weights returned already have the sampling weights combined within them. Because some of the checks and defaults in `bal.tab()` rely on patterns in these weights, using sampling weights in `CBPS()` without specifying them in `bal.tab()` can lead to incorrect results. If sampling weights are used in `CBPS()`, it is important that they are specified in `bal.tab()` as well.

### Value

For point treatments, if clusters are not specified, an object of class `"bal.tab"` containing balance summaries for the `CBPS` 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.

If `CBPS()` is used with multi-category treatments, an object of class `"bal.tab.multi"` containing balance summaries for each pairwise treatment comparison and a summary of balance across pairwise comparisons. See `bal.tab.multi` for details.

If `CBMSM()` is used for longitudinal treatments, an object of class `"bal.tab.msm"` containing balance summaries for each time period and a summary of balance across time periods. See `bal.tab.msm` for details.

### Author(s)

Noah Greifer

### See Also

`bal.tab()` for details of calculations. `bal.tab.cluster` for more information on clustered data. `bal.tab.multi` for more information on multi-category treatments. `bal.tab.msm` for more information on longitudinal treatments.

### Examples

    library(CBPS)
    data("lalonde", package = "cobalt")

    ## Using CBPS() for generating covariate balancing
    ## propensity score weights
    cbps.out <- CBPS(treat ~ age + educ + married + race +
                 nodegree + re74 + re75, data = lalonde)

    bal.tab(cbps.out)

[Package cobalt version 4.3.2 Index]