Title:	Tidy Standardized Mean Differences
Version:	0.2.0
Description:	Tidy standardized mean differences ('SMDs'). 'tidysmd' uses the 'smd' package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.
License:	MIT + file LICENSE
URL:	https://github.com/r-causal/tidysmd, https://r-causal.github.io/tidysmd/
BugReports:	https://github.com/r-causal/tidysmd/issues
Depends:	R (≥ 2.10)
Imports:	dplyr, purrr, rlang, smd, stats, tidyr, tidyselect, utils
Suggests:	covr, ggplot2, MatchIt, spelling, testthat (≥ 3.0.0), vdiffr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2023-05-26 17:15:47 UTC; malcolmbarrett
Author:	Malcolm Barrett [aut, cre]
Maintainer:	Malcolm Barrett <malcolmbarrett@gmail.com>
Repository:	CRAN
Date/Publication:	2023-05-26 17:30:02 UTC

tidysmd: Tidy Standardized Mean Differences

Description

Tidy standardized mean differences ('SMDs'). 'tidysmd' uses the 'smd' package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.

Author(s)

Maintainer: Malcolm Barrett malcolmbarrett@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/r-causal/tidysmd

• https://r-causal.github.io/tidysmd/

• Report bugs at https://github.com/r-causal/tidysmd/issues

Bind Match Indicator Columns to a Data Frame

Description

Given a data frame .df, the function bind_matches creates binary indicator variables for each match returned by the MatchIt library and binds the resulting columns to .df. In other words, the result is the original data frame plus a column for however many matches you want to bind.

Usage

bind_matches(.df, ...)

Arguments

Value

.df with addition columns for every element of ...

Create a Love plot

Description

geom_love() and love_plot() are helper functions to create Love plots in ggplot2. Love plots are a diagnostic approach to assessing balance before and after weighting. Many researchers use 0.1 on the absolute SMD scale to evaluate if a variable is well-balanced between groups, although this is just a rule of thumb. geom_love() is a simple wrapper around ggplot2::geom_point(), ggplot2::geom_line(), and ggplot2::geom_vline(). It also adds default aesthetics via ggplot2::aes(). love_plot() is a quick plotting function that further wraps geom_love(). For more complex Love plots, we recommend using ggplot2 directly.

Usage

geom_love(
  linewidth = 0.8,
  line_size = NULL,
  point_size = 1.85,
  vline_xintercept = 0.1,
  vline_color = "grey70",
  vlinewidth = 0.6,
  vline_size = NULL
)

love_plot(
  .df,
  linewidth = 0.8,
  line_size = NULL,
  point_size = 1.85,
  vline_xintercept = 0.1,
  vline_color = "grey70",
  vlinewidth = 0.6,
  vline_size = NULL
)

Arguments

Value

a list of geoms or a ggplot

Examples

plot_df <- tidy_smd(
  nhefs_weights,
  race:active,
  .group = qsmk,
  .wts = starts_with("w_")
)

love_plot(plot_df)

# or use ggplot2 directly
library(ggplot2)
ggplot(
  plot_df,
  aes(
    x = abs(smd),
    y = variable,
    group = method,
    color = method,
    fill = method
  )
) +
  geom_love()

NHEFS with various propensity score weights

Description

A dataset containing various propensity score weights for causaldata::nhefs_complete.

Usage

nhefs_weights

Format

A data frame with 1566 rows and 14 variables:

qsmk

Quit smoking

race

Race

age

Age

education

Education level

smokeintensity

Smoking intensity

smokeyrs

Number of smoke-years

exercise

Exercise level

active

Daily activity level

wt71

Participant weight in 1971 (baseline)

w_ate

ATE weight

w_att

ATT weight

w_atc

ATC weight

w_atm

ATM weight

w_ato

ATO weight

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

tidyselect

contains, ends_with, everything, last_col, matches, num_range, one_of, peek_vars, starts_with

Tidy Standardized Mean Differences

Description

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, method, 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. Additionally, the .wts argument supports matched datasets where the variable supplied to .wts is an binary variable indicating whether the row was included in the match. If you're using MatchIt, the helper function bind_matches() will bind these indicators to the original dataset, making it easier to compare across matching specifications.

Usage

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

Arguments

Value

a tibble

Examples

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

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