Tidy Standardized Mean Differences

Source: R/tidysmd.R

tidy_smd() calculates the standardized mean difference (SMD) for variables in a dataset between groups. Optionally, you may also calculate weighted SMDs. tidy_smd() wraps smd::smd(), returning a tidy dataframe with the columns variable, weights, and smd, as well as fourth column the contains the level of .group the SMD represents. You may also supply multiple weights to calculate multiple weighted SMDs, useful when comparing different types of weights.

tidy_smd(
  .df,
  .vars,
  .group,
  .wts = NULL,
  include_unweighted = TRUE,
  na.rm = FALSE,
  gref = 1L,
  std.error = FALSE
)

Arguments

.df

A data frame
.vars

Variables for which to calculate SMD
.group

Grouping variable
.wts

Variables to use for weighting the SMD calculation
include_unweighted

Logical. If using .wts, also calculate the unweighted SMD?
na.rm

Remove NA values from x? Defaults to FALSE.
gref

an integer indicating which level of g to use as the reference group. Defaults to 1.
std.error

Logical indicator for computing standard errors using compute_smd_var. Defaults to FALSE.

Value

a tibble

Examples


tidy_smd(nhefs_weights, c(age, education, race), .group = qsmk)

#> # A tibble: 3 × 4
#>   variable  weights    qsmk     smd
#>   <chr>     <chr>      <chr>  <dbl>
#> 1 age       unweighted 1     -0.282
#> 2 education unweighted 1      0.196
#> 3 race      unweighted 1      0.177
tidy_smd(nhefs_weights, c(age, education), .group = qsmk, std.error = TRUE)

#> # A tibble: 2 × 5
#>   variable  weights    qsmk     smd std.error
#>   <chr>     <chr>      <chr>  <dbl>     <dbl>
#> 1 age       unweighted 1     -0.282    0.0580
#> 2 education unweighted 1      0.196    0.0579

tidy_smd(
  nhefs_weights,
  c(age, race, education),
  .group = qsmk,
  .wts = c(w_ate, w_att, w_atm)
)

#> # A tibble: 12 × 4
#>    variable  weights    qsmk       smd
#>    <chr>     <chr>      <chr>    <dbl>
#>  1 age       unweighted 1     -0.282  
#>  2 race      unweighted 1      0.177  
#>  3 education unweighted 1      0.196  
#>  4 age       w_ate      1     -0.00585
#>  5 race      w_ate      1      0.00664
#>  6 education w_ate      1      0.0347 
#>  7 age       w_att      1     -0.0120 
#>  8 race      w_att      1      0.00365
#>  9 education w_att      1      0.0267 
#> 10 age       w_atm      1     -0.00184
#> 11 race      w_atm      1      0.00113
#> 12 education w_atm      1      0.00934