Package 'tidysmd'

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

.df	A data frame.
...	matchit objects returned by the MatchIt package. They can be named or unnamed.

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

linewidth	The line size, passed to ggplot2::geom_line().
line_size	Deprecated. Please use linewidth.
point_size	The point size, passed to ggplot2::geom_point().
vline_xintercept	The X intercept, passed to ggplot2::geom_vline().
vline_color	The vertical line color, passed to ggplot2::geom_vline().
vlinewidth	The vertical line size, passed to ggplot2::geom_vline().
vline_size	Deprecated. Please use vlinewidth.
.df	a data frame produced by tidy_smd()

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

---

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

.df	A data frame
.vars	Variables for which to calculate SMD
.group	Grouping variable
.wts	Variables to use for weighting the SMD calculation. These can be, for instance, propensity score weights or a binary indicator signaling whether or not a participant was included in a matching algorithm.
include_observed	Logical. If using .wts, also calculate the unweighted SMD?
include_unweighted	Deprecated. Please use include_observed.
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.
make_dummy_vars	Logical. Transform categorical variables to dummy variables using model.matrix()? By default, smd::smd uses a summary value based on the Mahalanobis distance distance to approximate the SMD of categorical variables. An alternative approach is to transform categorical variables to a set of dummy variables.

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)
)

---