The goal of tidysmd is to easily create tidy data frames of SMDs. tidysmd wraps the smd package to easily calculate SMDs across many variables and using several weights in order to easily compare different adjustment strategies.
You can install the most recent version of tidysmd from CRAN with:
Alternatively, you can install the development version of tidysmd from GitHub with:
tidy_smd() supports both unweighted SMDs and weighted SMDs.
library(tidysmd)
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 0.177nhefs_weights contains several types of propensity score weights for which we can calculate SMDs. Unweighted SMDs are also included by default.
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 0.00934Having SMDs in a tidy format makes it easy to work with the estimates, for instance in creating Love plots:
library(ggplot2)
plot_df <- tidy_smd(
nhefs_weights,
race:active,
.group = qsmk,
.wts = starts_with("w_")
)
ggplot(
data = plot_df,
mapping = aes(x = abs(smd), y = variable, group = weights, color = weights)
) +
geom_line(orientation = "y") +
geom_point() +
geom_vline(xintercept = 0.1, color = "black", size = 0.1)