Balance Statistics for WeightIt Objects — bal.tab.weightit • cobalt

Generates balance statistics for weightit and weightitMSM objects from WeightIt.

Usage

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

Arguments

x: a weightit or weightitMSM object; the output of a call to WeightIt::weightit() or WeightIt::weightitMSM().
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. Propensity scores generated by weightit() and weightitMSM() are automatically included and named "prop.score".
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.
data: an optional data frame containing variables named in other arguments. For some input object types, this is required.
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 not specified, bal.tab() will figure out which one is best based on the estimand of the weightit object: if ATT, "treated"; if ATC, "control"; otherwise "pooled".
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. If s.weights was supplied in the call to weightit() or weightitMSM(), they will automatically be included and do not need be specified again (though there is no harm if they are).
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.
...: for some input types, other arguments that are required or allowed. Otherwise, further arguments to control display of output. See display options for details.

Value

For point treatments, if clusters and imputations are not specified, an object of class "bal.tab" containing balance summaries for the weightit object. 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 weightit() is used with multi-category treatments, an object of class "bal.tab.multi" containing balance summaries for each pairwise treatment comparison. See bal.tab.multi() for details.

If weightitMSM() is used for longitudinal treatments, an object of class "bal.tab.msm" containing balance summaries for each time period. See class-bal.tab.msm 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.weightit() generates a list of balance summaries for the weightit object given.

Examples

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

## Basic propensity score weighting
w.out1 <- weightit(treat ~ age + educ + race + 
                       married + nodegree + re74 + re75, 
                   data = lalonde, method = "glm")
bal.tab(w.out1, un = TRUE, 
        thresholds = c(m = .1, v = 2))
#> Balance Measures
#>                 Type Diff.Un V.Ratio.Un Diff.Adj        M.Threshold V.Ratio.Adj
#> prop.score  Distance  1.7569     0.9211   0.1360                         0.9758
#> age          Contin. -0.2419     0.4400  -0.1676 Not Balanced, >0.1      0.3689
#> educ         Contin.  0.0448     0.4959   0.1296 Not Balanced, >0.1      0.5657
#> race_black    Binary  0.6404          .   0.0499     Balanced, <0.1           .
#> race_hispan   Binary -0.0827          .   0.0047     Balanced, <0.1           .
#> race_white    Binary -0.5577          .  -0.0546     Balanced, <0.1           .
#> married       Binary -0.3236          .  -0.0944     Balanced, <0.1           .
#> nodegree      Binary  0.1114          .  -0.0547     Balanced, <0.1           .
#> re74         Contin. -0.5958     0.5181  -0.2740 Not Balanced, >0.1      0.8208
#> re75         Contin. -0.2870     0.9563  -0.1579 Not Balanced, >0.1      0.9562
#>                  V.Threshold
#> prop.score      Balanced, <2
#> age         Not Balanced, >2
#> educ            Balanced, <2
#> race_black                  
#> race_hispan                 
#> race_white                  
#> married                     
#> nodegree                    
#> re74            Balanced, <2
#> re75            Balanced, <2
#> 
#> Balance tally for mean differences
#>                    count
#> Balanced, <0.1         5
#> Not Balanced, >0.1     4
#> 
#> Variable with the greatest mean difference
#>  Variable Diff.Adj        M.Threshold
#>      re74   -0.274 Not Balanced, >0.1
#> 
#> Balance tally for variance ratios
#>                  count
#> Balanced, <2         4
#> Not Balanced, >2     1
#> 
#> Variable with the greatest variance ratio
#>  Variable V.Ratio.Adj      V.Threshold
#>       age      0.3689 Not Balanced, >2
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.    185.  
#> Adjusted    329.01   58.33

## Weighting with a multi-category treatment
w.out2 <- weightit(race ~ age + educ + married + 
                       nodegree + re74 + re75, 
                   data = lalonde, method = "glm",
                   estimand = "ATE")
bal.tab(w.out2, un = TRUE)
#> Balance summary across all treatment pairs
#>             Type Max.Diff.Un Max.Diff.Adj
#> age      Contin.      0.3065       0.0557
#> educ     Contin.      0.5861       0.1140
#> married   Binary      0.3430       0.0297
#> nodegree  Binary      0.2187       0.0546
#> re74     Contin.      0.6196       0.1527
#> re75     Contin.      0.3442       0.1491
#> 
#> Effective sample sizes
#>             black hispan  white
#> Unadjusted 243.    72.   299.  
#> Adjusted   138.38  54.99 259.59
bal.tab(w.out2, un = TRUE, pairwise = FALSE)
#> Balance summary across all treatment pairs
#>             Type Max.Diff.Un Max.Diff.Adj
#> age      Contin.      0.1532       0.0792
#> educ     Contin.      0.4641       0.0868
#> married   Binary      0.1931       0.0313
#> nodegree  Binary      0.1336       0.0298
#> re74     Contin.      0.3390       0.1531
#> re75     Contin.      0.1744       0.1468
#> 
#> Effective sample sizes
#>             black hispan  white
#> Unadjusted 243.    72.   299.  
#> Adjusted   138.38  54.99 259.59

## IPW for longitudinal treatments
data("msmdata", package = "WeightIt")

wmsm.out <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                        A_2 ~ X1_1 + X2_1 +
                            A_1 + X1_0 + X2_0,
                        A_3 ~ X1_2 + X2_2 +
                            A_2 + X1_1 + X2_1 +
                            A_1 + X1_0 + X2_0),
                   data = msmdata,
                   method = "glm")
bal.tab(wmsm.out)
#> Balance summary across all time points
#>        Times    Type Max.Diff.Adj
#> X1_0 1, 2, 3 Contin.       0.0342
#> X2_0 1, 2, 3  Binary       0.0299
#> X1_1    2, 3 Contin.       0.0657
#> X2_1    2, 3  Binary       0.0299
#> A_1     2, 3  Binary       0.0262
#> X1_2       3 Contin.       0.0643
#> X2_2       3  Binary       0.0096
#> A_2        3  Binary       0.0054
#> 
#> Effective sample sizes
#>  - Time 1
#>            Control Treated
#> Unadjusted 3306.    4194. 
#> Adjusted    845.79   899.4
#>  - Time 2
#>            Control Treated
#> Unadjusted 3701.   3799.  
#> Adjusted    912.87  829.87
#>  - Time 3
#>            Control Treated
#> Unadjusted 4886.   2614.  
#> Adjusted   1900.26  600.12

Balance Statistics for `WeightIt` Objects

Usage

Arguments

Value

Details

See also

Examples