Package 'halfmoon' reference manual

Title:	Techniques to Build Better Balance
Description:	Build better balance in causal inference models. 'halfmoon' helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. 'halfmoon' supports both weighting and matching techniques.
Authors:	Malcolm Barrett [aut, cre, cph]
Maintainer:	Malcolm Barrett <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0.9000
Built:	2025-02-03 19:26:45 UTC
Source:	https://github.com/r-causal/halfmoon

Add ESS Table Header

Description

This function replaces the counts in the default header of gtsummary::tbl_svysummary() tables to counts representing the Effective Sample Size (ESS). See ess() for details.

Usage

add_ess_header(
  x,
  header = "**{level}**  \nESS = {format(n, digits = 1, nsmall = 1)}"
)
add_ess_header(
  x,
  header = "**{level}**  \nESS = {format(n, digits = 1, nsmall = 1)}"
)

Arguments

`x`	(`tbl_svysummary`) Object of class `'tbl_svysummary'` typically created with `gtsummary::tbl_svysummary()`.
`header`	(`string`) String specifying updated header. Review `gtsummary::modify_header()` for details on use.

Value

a 'gtsummary' table

Examples


svy <- survey::svydesign(~1, data = nhefs_weights, weights = ~ w_ate)

gtsummary::tbl_svysummary(svy, include = c(age, sex, smokeyrs)) |>
  add_ess_header()
hdr <- paste0(
  "**{level}**  \n",
  "N = {n_unweighted}; ESS = {format(n, digits = 1, nsmall = 1)}"
)
gtsummary::tbl_svysummary(svy, by = qsmk, include = c(age, sex, smokeyrs)) |>
  add_ess_header(header = hdr)

svy <- survey::svydesign(~1, data = nhefs_weights, weights = ~ w_ate)

gtsummary::tbl_svysummary(svy, include = c(age, sex, smokeyrs)) |>
  add_ess_header()
hdr <- paste0(
  "**{level}**  \n",
  "N = {n_unweighted}; ESS = {format(n, digits = 1, nsmall = 1)}"
)
gtsummary::tbl_svysummary(svy, by = qsmk, include = c(age, sex, smokeyrs)) |>
  add_ess_header(header = hdr)

Calculate the Effective Sample Size (ESS)

Description

This function computes the effective sample size (ESS) given a vector of weights, using the classical $(\sum w)^2 / \sum(w^2)$ formula (sometimes referred to as "Kish's effective sample size").

Usage

ess(wts)
ess(wts)

Arguments

wts

A numeric vector of weights (e.g., from survey or inverse-probability weighting).

Details

The effective sample size (ESS) reflects how many observations you would have if all were equally weighted. If the weights vary substantially, the ESS can be much smaller than the actual number of observations. Formally:

$\mathrm{ESS} = \frac{\left(\sum_i w_i\right)^2}{\sum_i w_i^2}.$

Diagnostic Value:

Indicator of Weight Concentration: A large discrepancy between ESS and the actual sample size indicates that a few observations carry disproportionately large weights, effectively reducing the usable information in the dataset.
Variance Inflation: A small ESS signals that weighted estimates are more sensitive to a handful of observations, inflating the variance and standard errors.
Practical Guidance: If ESS is much lower than the total sample size, it is advisable to investigate why some weights are extremely large or small. Techniques like weight trimming or stabilized weights might be employed to mitigate the issue

Value

A single numeric value representing the effective sample size.

Examples

# Suppose we have five observations with equal weights
wts1 <- rep(1.2, 5)
# returns 5, because all weights are equal
ess(wts1)

# If weights vary more, smaller than 5
wts2 <- c(0.5, 2, 2, 0.1, 0.8)
ess(wts2)

# Suppose we have five observations with equal weights
wts1 <- rep(1.2, 5)
# returns 5, because all weights are equal
ess(wts1)

# If weights vary more, smaller than 5
wts2 <- c(0.5, 2, 2, 0.1, 0.8)
ess(wts2)

Calculate weighted and unweighted empirical cumulative distributions

Description

The empirical cumulative distribution function (ECDF) provides an alternative visualization of distribution. geom_ecdf() is similar to ggplot2::stat_ecdf() but it can also calculate weighted ECDFs.

Usage

geom_ecdf(
  mapping = NULL,
  data = NULL,
  geom = "step",
  position = "identity",
  ...,
  n = NULL,
  pad = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)
geom_ecdf(
  mapping = NULL,
  data = NULL,
  geom = "step",
  position = "identity",
  ...,
  n = NULL,
  pad = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`geom`	The geometric object to use to display the data for this layer. When using a `⁠stat_*()⁠` function to construct a layer, the `geom` argument can be used to override the default coupling between stats and geoms. The `geom` argument accepts the following: A `Geom` ggproto subclass, for example `GeomPoint`. A string naming the geom. To give the geom as a string, strip the function name of the `geom_` prefix. For example, to use `geom_point()`, give the geom as `"point"`. For more information and other ways to specify the geom, see the layer geom documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`...`	Other arguments passed on to `layer()`'s `params` argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the `position` argument, or aesthetics that are required can not be passed through `...`. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, `colour = "red"` or `linewidth = 3`. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the `params`. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a `⁠stat_()⁠` function, the `...` argument can be used to pass on parameters to the `geom` part of the layer. An example of this is `stat_density(geom = "area", outline.type = "both")`. The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a `⁠geom_()⁠` function, the `...` argument can be used to pass on parameters to the `stat` part of the layer. An example of this is `geom_area(stat = "density", adjust = 0.5)`. The stat's documentation lists which parameters it can accept. The `key_glyph` argument of `layer()` may also be passed on through `...`. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
`n`	if NULL, do not interpolate. If not NULL, this is the number of points to interpolate with.
`pad`	If `TRUE`, pad the ecdf with additional points (-Inf, 0) and (Inf, 1)
`na.rm`	If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.

Value

a geom

Aesthetics

In addition to the aesthetics for ggplot2::stat_ecdf(), geom_ecdf() also accepts:

weights

Examples

library(ggplot2)

ggplot(
  nhefs_weights,
  aes(x = smokeyrs, color = qsmk)
) +
  geom_ecdf(aes(weights = w_ato)) +
  xlab("Smoking Years") +
  ylab("Proportion <= x")

library(ggplot2)

ggplot(
  nhefs_weights,
  aes(x = smokeyrs, color = qsmk)
) +
  geom_ecdf(aes(weights = w_ato)) +
  xlab("Smoking Years") +
  ylab("Proportion <= x")

Create mirrored histograms

Description

Create mirrored histograms

Usage

geom_mirror_histogram(
  mapping = NULL,
  data = NULL,
  position = "stack",
  ...,
  binwidth = NULL,
  bins = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)
geom_mirror_histogram(
  mapping = NULL,
  data = NULL,
  position = "stack",
  ...,
  binwidth = NULL,
  bins = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`...`	Other arguments passed on to `layer()`'s `params` argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the `position` argument, or aesthetics that are required can not be passed through `...`. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, `colour = "red"` or `linewidth = 3`. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the `params`. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a `⁠stat_()⁠` function, the `...` argument can be used to pass on parameters to the `geom` part of the layer. An example of this is `stat_density(geom = "area", outline.type = "both")`. The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a `⁠geom_()⁠` function, the `...` argument can be used to pass on parameters to the `stat` part of the layer. An example of this is `geom_area(stat = "density", adjust = 0.5)`. The stat's documentation lists which parameters it can accept. The `key_glyph` argument of `layer()` may also be passed on through `...`. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
`binwidth`	The width of the bins. Can be specified as a numeric value or as a function that calculates width from unscaled x. Here, "unscaled x" refers to the original x values in the data, before application of any scale transformation. When specifying a function along with a grouping structure, the function will be called once per group. The default is to use the number of bins in `bins`, covering the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data. The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.
`bins`	Number of bins. Overridden by `binwidth`. Defaults to 30.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`orientation`	The orientation of the layer. The default (`NA`) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting `orientation` to either `"x"` or `"y"`. See the Orientation section for more detail.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.

Value

a geom

Examples

library(ggplot2)
ggplot(nhefs_weights, aes(.fitted)) +
  geom_mirror_histogram(
    aes(group = qsmk),
    bins = 50
  ) +
  geom_mirror_histogram(
    aes(fill = qsmk, weight = w_ate),
    bins = 50,
    alpha = 0.5
  ) +
  scale_y_continuous(labels = abs)
library(ggplot2)
ggplot(nhefs_weights, aes(.fitted)) +
  geom_mirror_histogram(
    aes(group = qsmk),
    bins = 50
  ) +
  geom_mirror_histogram(
    aes(fill = qsmk, weight = w_ate),
    bins = 50,
    alpha = 0.5
  ) +
  scale_y_continuous(labels = abs)

NHEFS with various propensity score weights

Description

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

Usage

nhefs_weights
nhefs_weights

Format

A data frame with 1566 rows and 14 variables:

qsmk: Quit smoking
race: Race
age: Age
sex: Sex
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
.fitted: Propensity score

Package 'halfmoon'

Help Index

Add ESS Table Header

Description

Usage

Arguments

Value

Examples

Calculate the Effective Sample Size (ESS)

Description

Usage

Arguments

Details

Value

Examples

Calculate weighted and unweighted empirical cumulative distributions

Description

Usage

Arguments

Value

Aesthetics

Examples

Create mirrored histograms

Description

Usage

Arguments

Value

Examples

NHEFS with various propensity score weights

Description

Usage

Format