Package 'equatiomatic' reference manual

Title:	Transform Models into 'LaTeX' Equations
Description:	The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model.
Authors:	Daniel Anderson [aut] , Andrew Heiss [aut] , Jay Sumners [aut], Joshua Rosenberg [ctb] , Jonathan Sidi [ctb] , Ellis Hughes [ctb] , Thomas Fung [ctb] , Reza Norouzian [ctb] , Indrajeet Patil [ctb] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Quinn White [ctb] , David Kane [ctb], Philippe Grosjean [cre]
Maintainer:	Philippe Grosjean <[email protected]>
License:	CC BY 4.0
Version:	0.3.4
Built:	2024-12-22 05:05:08 UTC
Source:	https://github.com/datalorax/equatiomatic

Arrest data from Gelman & Hill

Description

Arrest data from Gelman & Hill's book, used in Chapter 6 (and others). The data have been aggregated by precinct and race/ethnicity, with the sum of prior arrests and stops calculated. You can download the original data here: http://www.stat.columbia.edu/~gelman/arm/examples/police/

Usage

arrests
arrests

Format

A tibble with 225 rows and 4 variables:

precinct: An integer denoting the precinct identification number.
eth: A factor with the coded race/ethnicity
stops: The number of police stops
arrests: The number of prior arrests (this is used as an offset variable in the book)

'LaTeX' equation for R models

Description

Extract the variable names from a model to produce a 'LaTeX' equation. Supports any model where there is a broom::tidy() method. This is a generic function with methods for lmerMod objects obtained with lme4::lmer(), glmerMod objects with lme4::glmer(), forecast_ARIMA with forecast::Arima() and default, with the later further covering most "base" R models implemented in broom::tidy() like lm objects with stats::lm(), glm objects with stats::glm() or polr objects with MASS::polr(). The default method also supports clm objects obtained with ordinal::clm().

Usage

extract_eq(
  model,
  intercept = "alpha",
  greek = "beta",
  greek_colors = NULL,
  subscript_colors = NULL,
  var_colors = NULL,
  var_subscript_colors = NULL,
  raw_tex = FALSE,
  swap_var_names = NULL,
  swap_subscript_names = NULL,
  ital_vars = FALSE,
  label = NULL,
  index_factors = FALSE,
  show_distribution = FALSE,
  wrap = FALSE,
  terms_per_line = 4,
  operator_location = "end",
  align_env = "aligned",
  use_coefs = FALSE,
  coef_digits = 2,
  fix_signs = TRUE,
  font_size = NULL,
  mean_separate = NULL,
  return_variances = FALSE,
  se_subscripts = FALSE,
  ...
)
extract_eq(
  model,
  intercept = "alpha",
  greek = "beta",
  greek_colors = NULL,
  subscript_colors = NULL,
  var_colors = NULL,
  var_subscript_colors = NULL,
  raw_tex = FALSE,
  swap_var_names = NULL,
  swap_subscript_names = NULL,
  ital_vars = FALSE,
  label = NULL,
  index_factors = FALSE,
  show_distribution = FALSE,
  wrap = FALSE,
  terms_per_line = 4,
  operator_location = "end",
  align_env = "aligned",
  use_coefs = FALSE,
  coef_digits = 2,
  fix_signs = TRUE,
  font_size = NULL,
  mean_separate = NULL,
  return_variances = FALSE,
  se_subscripts = FALSE,
  ...
)

Arguments

`model`	A fitted model
`intercept`	How should the intercept be displayed? Default is `"alpha"`, but can also accept `"beta"`, in which case the it will be displayed as beta zero.
`greek`	What notation should be used for coefficients? Currently only accepts `"beta"` (with plans for future development). Can be used in combination with `raw_tex` to use any notation, e.g., `"\hat{\beta}"`.
`greek_colors`	The colors of the greek notation in the equation. Must be a single color (named or HTML hex code) or a vector of colors (which will be recycled if smaller than the number of terms in the model). When rendering to PDF, I suggest using HTML hex codes, as not all named colors are recognized by LaTeX, but equatiomatic will internally create the color definitions for you if HTML codes are supplied. Note that this is not yet implemented for mixed effects models (lme4).
`subscript_colors`	The colors of the subscripts for the greek notation. The argument structure is equivalent to `greek_colors` (i.e., see above for more detail).
`var_colors`	The color of the variable names. This takes a named vector of the form `c("variable" = "color")`. For example `c("bill_length_mm" = "#00d4fa", "island" = "#00fa85")`. Colors can be names (e.g., `"red"`) or HTML hex codes, as shown in the example.
`var_subscript_colors`	The colors of the factor subscripts for categorical variables. The interface for this is equivalent to `var_colors`, and all subscripts for a given variable will be displayed in the provided color. For example, the code `c("island" = "green")` would result in the subscripts for "Dream" and "Torgersen" being green (assuming "Biscoe" was the reference group).
`raw_tex`	Logical. Is the greek code being passed to denote coefficients raw tex code?
`swap_var_names`	A vector of the form c("old_var_name" = "new name"). For example: c("bill_length_mm" = "Bill Length (MM)").
`swap_subscript_names`	A vector of the form c("old_subscript_name" = "new name"). For example: c("f" = "Female").
`ital_vars`	Logical, defaults to `FALSE`. Should the variable names not be wrapped in the `\operatorname{}` command?
`label`	A label for the equation, which can then be used for in-text references. See example here. Note that this only works for PDF output. The in-text references also must match the label exactly, and must be formatted as `\ref{eq: label}`, where `label` is a place holder for the specific label. Notice the space after the colon before the label. This also must be there, or the cross-reference will fail.
`index_factors`	Logical, defaults to `FALSE`. Should the factors be indexed, rather than using subscripts to display all levels?
`show_distribution`	Logical. When fitting a logistic or probit regression, should the binomial distribution be displayed? Defaults to `FALSE`.
`wrap`	Logical, defaults to `FALSE`. Should the terms on the right-hand side of the equation be split into multiple lines? This is helpful with models with many terms.
`terms_per_line`	Integer, defaults to 4. The number of right-hand side terms to include per line. Used only when `wrap` is `TRUE`.
`operator_location`	Character, one of “end” (the default) or “start”. When terms are split across multiple lines, they are split at mathematical operators like `+`. If set to “end”, each line will end with a trailing operator (`+` or `-`). If set to “start”, each line will begin with an operator.
`align_env`	TeX environment to wrap around equation. Must be one of `aligned`, `aligned`, `align`, or `align`. Defaults to `aligned`.
`use_coefs`	Logical, defaults to `FALSE`. Should the actual model estimates be included in the equation instead of math symbols?
`coef_digits`	Integer, defaults to 2. The number of decimal places to round to when displaying model estimates.
`fix_signs`	Logical, defaults to `TRUE`. If disabled, coefficient estimates that are negative are preceded with a "+" (e.g. `5(x) + -3(z)`). If enabled, the "+ -" is replaced with a "-" (e.g. `5(x) - 3(z)`).
`font_size`	The font size of the equation. Defaults to default of the output format. Takes any of the standard LaTeX arguments (see here).
`mean_separate`	Currently only support for `lmer` models. Should the mean structure be inside or separated from the normal distribution? Defaults to `NULL`, in which case it will become `TRUE` if there are more than three fixed-effect parameters. If `TRUE`, the equation will be displayed as, for example, outcome ~ N(mu, sigma); mu = alpha + beta_1(wave). If `FALSE`, this same equation would be outcome ~ N(alpha + beta, sigma).
`return_variances`	Logical. When `use_coefs = TRUE` with a mixed effects model (e.g., `lme4::lmer()`), should the variances and co-variances be returned? If `FALSE` (the default) standard deviations and correlations are returned instead.
`se_subscripts`	Logical. If `se_subscripts = TRUE` then the equation will include the standard errors below each coefficient. This is supported for lm and glm models.
`...`	Additional arguments (for future development; not currently used).

Details

The different methods all use the same arguments, but not all arguments are suitable to all models. Check here above to determine if a feature is implemented for a given model.

Value

A character of class “equation”.

Examples

# Simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
extract_eq(mod1)

# Include all variables
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)

# Works for categorical variables too, putting levels as subscripts
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)

set.seed(8675309)
d <- data.frame(
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5),
  out = rnorm(300, 10, 0.5)
)
mod4 <- lm(out ~ ., d)
extract_eq(mod4)

# Don't italicize terms
extract_eq(mod1, ital_vars = FALSE)

# Wrap equations in an "aligned" environment
extract_eq(mod2, wrap = TRUE)

# Wider equation wrapping
extract_eq(mod2, wrap = TRUE, terms_per_line = 4)

# Include model estimates instead of Greek letters
extract_eq(mod2, wrap = TRUE, terms_per_line = 2, use_coefs = TRUE)

# Don't fix doubled-up "+ -" signs
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, use_coefs = TRUE, fix_signs = FALSE)

# Use indices for factors instead of subscripts
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, index_factors = TRUE)

# Use other model types, like glm
set.seed(8675309)
d <- data.frame(
  out = sample(0:1, 100, replace = TRUE),
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5)
)
mod5 <- glm(out ~ ., data = d, family = binomial(link = "logit"))
extract_eq(mod5, wrap = TRUE)
# Simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
extract_eq(mod1)

# Include all variables
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)

# Works for categorical variables too, putting levels as subscripts
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)

set.seed(8675309)
d <- data.frame(
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5),
  out = rnorm(300, 10, 0.5)
)
mod4 <- lm(out ~ ., d)
extract_eq(mod4)

# Don't italicize terms
extract_eq(mod1, ital_vars = FALSE)

# Wrap equations in an "aligned" environment
extract_eq(mod2, wrap = TRUE)

# Wider equation wrapping
extract_eq(mod2, wrap = TRUE, terms_per_line = 4)

# Include model estimates instead of Greek letters
extract_eq(mod2, wrap = TRUE, terms_per_line = 2, use_coefs = TRUE)

# Don't fix doubled-up "+ -" signs
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, use_coefs = TRUE, fix_signs = FALSE)

# Use indices for factors instead of subscripts
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, index_factors = TRUE)

# Use other model types, like glm
set.seed(8675309)
d <- data.frame(
  out = sample(0:1, 100, replace = TRUE),
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5)
)
mod5 <- glm(out ~ ., data = d, family = binomial(link = "logit"))
extract_eq(mod5, wrap = TRUE)

Format 'LaTeX' equations

Description

Format 'LaTeX' equations built with extract_eq.

Usage

## S3 method for class 'equation'
format(x, ..., latex = knitr::is_latex_output())
## S3 method for class 'equation'
format(x, ..., latex = knitr::is_latex_output())

Arguments

`x`	'LaTeX' equation built with `extract_eq`
`...`	not used
`latex`	Logical, whether the output is LaTeX or not. The default value uses `knitr::is_latex_output()` to determine the current output format.

Value

A character string with the equation formatted either as proper LaTeX code, or as a display equation tag (surrounded by ⁠$$...$$⁠) for R Markdown or Quarto documents.

A subset of the full 1982 High School and Beyond Survey

Description

This is the dataset used throughout Raudenbush & Bryk (2002).

Usage

hsb
hsb

Format

A tibble with 7185 rows and 8 variables:

sch.id: An integer denoting the school identification number. There are 160 unique schools.
math: Individual students' math score.
size: The number of students in the school.
sector: A dummy variable (integer) denoting whether the school is public (sector = 0) or catholic (sector = 1). There are 90 public schools and 70 catholic.
meanses: A group-mean centered SES variable at the school level
minority: A dummy variable indicating if the student was coded as white (minority = 0) or not (minority = 1).
female: A dummy variable indicating if the student was coded as female (female = 1) or not (female = 0).
ses: A student-level composite variable indicating the students' socio-economic status.

Print 'LaTeX' equations in R Markdown environments

Description

Print 'LaTeX' equations built with extract_eq nicely in R Markdown environments.

Usage

## S3 method for class 'equation'
knit_print(
  x,
  ...,
  tex_packages = "\\renewcommand*\\familydefault{\\rmdefault}"
)
## S3 method for class 'equation'
knit_print(
  x,
  ...,
  tex_packages = "\\renewcommand*\\familydefault{\\rmdefault}"
)

Arguments

`x`	'LaTeX' equation built with `extract_eq`
`...`	not used
`tex_packages`	A string with LaTeX code to include in the header, usually to include LaTeX packages in the output.

Value

A string with the equation formatted according to R Markdown's output format (different output for HTML, PDF, docx, gfm, markdown_strict). The format is detected automatically, so, you do not have to worry about it.

Size measurements for adult foraging penguins near Palmer Station, Antarctica

Description

Data originally from palmerpenguins. Includes measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

Usage

penguins
penguins

Format

A tibble with 344 rows and 8 variables:

species: a factor denoting penguin species (Adélie, Chinstrap and Gentoo)
island: a factor denoting island in Palmer Archipelago, Antarctica (Biscoe, Dream or Torgersen)
bill_length_mm: a number denoting bill length (millimeters)
bill_depth_mm: a number denoting bill depth (millimeters)
flipper_length_mm: an integer denoting flipper length (millimeters)
body_mass_g: an integer denoting body mass (grams)
sex: a factor denoting penguin sex (female, male)
year: an integer denoting the study year (2007, 2008, or 2009)

Source

Adélie penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Adélie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative doi:10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f

Gentoo penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Gentoo penguin (Pygoscelis papua) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative doi:10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689

Chinstrap penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Chinstrap penguin (Pygoscelis antarcticus) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 6. Environmental Data Initiative doi:10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e

Originally published in: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081

The polls data from Gelman and Hill ()

Description

This is the dataset used in Gelman & Hill's book, Data Analysis Using Regression and Multilevel/Hierarchical Models. They are polling data on the presidential election from 1988, collected one week before the election. You can download all the data from the book here: http://www.stat.columbia.edu/~gelman/arm/examples/ARM_Data.zip. Note that this is only a few of the variables from the original data supplied with the book.

Usage

polls
polls

Format

A tibble with 13,544 rows and 7 variables:

state: An integer denoting the state identification number.
edu: An ordered factor stating the education level of the respondent
age: An unordered factor stating the age of range of the respondent
female: A dummy variable (integer) denoting whether the respondent was coded as male (female = 0) or female (female = 1).
black: A dummy variable (integer) denoting whether the respondent was coded as Black (black = 1) or not Black (black = 0).
weight: A sampling weight
bush: Whether the respondent stated they were in favor of voting for George Bush Sr.

Print 'LaTeX' equations

Description

Print 'LaTeX' equations built with extract_eq.

Usage

## S3 method for class 'equation'
print(x, ...)
## S3 method for class 'equation'
print(x, ...)

Arguments

`x`	'LaTeX' equation built with `extract_eq`
`...`	not used

Value

The unmodified object 'x' is returned invisibly. The function is used for its side effect of printing the equation.

Display equations in shiny apps

Description

These are a set of functions designed to help render equations in shiny applications (see the vignette about Shiny).

Usage

renderEq(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())

eqOutput(outputId)
renderEq(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())

eqOutput(outputId)

Arguments

`expr`	An R expression, specifically a call to `extract_eq()`
`env`	The environment
`quoted`	Is the expresion quoted?
`outputArgs`	list of output arguments
`outputId`	The identifier of the output from the server. Should be passed as a string.

Value

Render the equation in a suitable way for Shiny for renderEq() in an eqOutput() equation output element that can be included in a panel.

Functions

renderEq(): Rendering function
eqOutput(): Output function

Simulated longitudinal data

Description

Data are simulated to be similar to longitudinal data collected within schools/districts.

Usage

sim_longitudinal
sim_longitudinal

Format

A tibble with 1000 rows and 8 variables:

sid: An integer denoting the individual student. There are 100 students.
school: An integer denoting the school There are 15 schools.
district: An integer denoting the school district. There are 5 districts.
group: A character variable denoting the instructional level of the student, low, medium, or high.
treatment: A factor indicating whether the student received the intervention treatment (0 = no treatment received; 1 = treatment received).
prop_low: The proportion of student in the school in the low instructional group.
wave: The assessment wave. Each student has nine waves of data collection
score: The individual students' score at the given wave.

Simple simulated time series data

Description

Output from set.seed(42); simple_ts <- ts(rnorm(1000),freq = 4). This is included primarily for unit testing.

Usage

simple_ts
simple_ts

Format

A tibble with 1000 rows and 8 variables:

Qtr1: First quarter simulated values.
Qtr2: Second quarter simulated values.
Qtr3: Third quarter simulated values.
Qtr4: Fourth quarter simulated values.

Simulated data for time-series regression

Description

Output from set.seed(42); ts_reg_list <- list(x1 = rnorm(1000), x2 = rnorm(1000), ts_rnorm = rnorm(1000)).

Usage

ts_reg_list
ts_reg_list

Format

A tibble with 1000 rows and 8 variables:

x1: Random normal simulated data.
x2: Random normal simulated data.
ts_rnorm: Random normal simulated data.

Package 'equatiomatic'

Help Index

Arrest data from Gelman & Hill

Description

Usage

Format

'LaTeX' equation for R models

Description

Usage

Arguments

Details

Value

Examples

Format 'LaTeX' equations

Description

Usage

Arguments

Value

A subset of the full 1982 High School and Beyond Survey

Description

Usage

Format

Print 'LaTeX' equations in R Markdown environments

Description

Usage

Arguments

Value

Size measurements for adult foraging penguins near Palmer Station, Antarctica

Description

Usage

Format

Source

The polls data from Gelman and Hill ()

Description

Usage

Format

Print 'LaTeX' equations

Description

Usage

Arguments

Value

Display equations in shiny apps

Description

Usage

Arguments

Value

Functions

Simulated longitudinal data

Description

Usage

Format

Simple simulated time series data

Description

Usage

Format

Simulated data for time-series regression

Description

Usage

Format