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README.md
In equatiomatic: Transform Models into 'LaTeX' Equations
equatiomatic 
The goal of equatiomatic is to reduce the pain associated with
writing LaTeX code from a fitted model. The package aims to support any
model supported by
broom. See the
introduction to
equatiomatic
for currently supported models.
Installation
Install from CRAN with
install.packages("equatiomatic")
Or get the development version from GitHub with
remotes::install_github("datalorax/equatiomatic")
Basic usage
The gif above shows the basic functionality.
To convert a model to LaTeX, feed a model object to extract_eq():
library(equatiomatic)
# Fit a simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
# Give the results to extract_eq
extract_eq(mod1)
#> $$
#> \operatorname{mpg} = \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \epsilon
#> $$
The model can be built in any standard way. It can handle shortcut
syntax:
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)
#> $$
#> \operatorname{mpg} = \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp}) + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs}) + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> $$
When using categorical variables, it will include the levels of the
variables as subscripts. Here, we use data from the
{palmerpenguins}
dataset.
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)
#> $$
#> \operatorname{body\_mass\_g} = \alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}}) + \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}}) + \epsilon
#> $$
It helpfully preserves the order the variables are supplied in the
formula:
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 ~ cont1 + cat2 + cont2 + cat1, d)
extract_eq(mod4)
#> $$
#> \operatorname{out} = \alpha + \beta_{1}(\operatorname{cont1}) + \beta_{2}(\operatorname{cat2}_{\operatorname{B}}) + \beta_{3}(\operatorname{cat2}_{\operatorname{C}}) + \beta_{4}(\operatorname{cont2}) + \beta_{5}(\operatorname{cat1}_{\operatorname{b}}) + \beta_{6}(\operatorname{cat1}_{\operatorname{c}}) + \epsilon
#> $$
Appearance
You can wrap the equations so that a specified number of terms appear on
the right-hand side of the equation using terms_per_line (defaults to
4):
extract_eq(mod2, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp})\ + \\
#> &\quad \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs})\ + \\
#> &\quad \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
extract_eq(mod2, wrap = TRUE, terms_per_line = 6)
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp}) + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt})\ + \\
#> &\quad \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs}) + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
When wrapping, you can change whether the lines end with trailing math
operators like + (the default), or if they should begin with them
using operator_location = "end" or operator_location = "start":
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, operator_location = "start")
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp})\\
#> &\quad + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs})\\
#> &\quad + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
By default, all text in the equation is wrapped in \operatorname{}.
You can optionally have the variables themselves be italicized (i.e. not
be wrapped in \operatorname{}) with ital_vars = TRUE:
extract_eq(mod2, wrap = TRUE, ital_vars = TRUE)
#> $$
#> \begin{aligned}
#> mpg &= \alpha + \beta_{1}(cyl) + \beta_{2}(disp) + \beta_{3}(hp)\ + \\
#> &\quad \beta_{4}(drat) + \beta_{5}(wt) + \beta_{6}(qsec) + \beta_{7}(vs)\ + \\
#> &\quad \beta_{8}(am) + \beta_{9}(gear) + \beta_{10}(carb) + \epsilon
#> \end{aligned}
#> $$
R Markdown and previewing
If you include extract_eq() in an R Markdown chunk, knitr will
render the equation. If you’d like to see the LaTeX code wrap the call
in print().
You can also use the tex_preview() function from the
texPreview package
to preview the equation in RStudio:
tex_preview(extract_eq(mod1))
Both extract_eq() and tex_preview() work with magrittr pipes, so
you can do something like this:
library(magrittr) # or library(tidyverse) or any other package that exports %>%
extract_eq(mod1) %>%
tex_preview()
Extra options
There are several extra options you can enable with additional arguments
to extract_eq().
Actual coefficients
You can return actual numeric coefficients instead of Greek letters with
use_coefs = TRUE:
extract_eq(mod1, use_coefs = TRUE)
#> $$
#> \operatorname{\widehat{mpg}} = 34.66 - 1.59(\operatorname{cyl}) - 0.02(\operatorname{disp})
#> $$
By default, it will remove doubled operators like “+ -”, but you can
keep those in (which is often useful for teaching) with
fix_signs = FALSE:
extract_eq(mod1, use_coefs = TRUE, fix_signs = FALSE)
#> $$
#> \operatorname{\widehat{mpg}} = 34.66 + -1.59(\operatorname{cyl}) + -0.02(\operatorname{disp})
#> $$
This works in longer wrapped equations:
extract_eq(mod2, wrap = TRUE, terms_per_line = 3,
use_coefs = TRUE, fix_signs = FALSE)
#> $$
#> \begin{aligned}
#> \operatorname{\widehat{mpg}} &= 12.3 + -0.11(\operatorname{cyl}) + 0.01(\operatorname{disp})\ + \\
#> &\quad -0.02(\operatorname{hp}) + 0.79(\operatorname{drat}) + -3.72(\operatorname{wt})\ + \\
#> &\quad 0.82(\operatorname{qsec}) + 0.32(\operatorname{vs}) + 2.52(\operatorname{am})\ + \\
#> &\quad 0.66(\operatorname{gear}) + -0.2(\operatorname{carb})
#> \end{aligned}
#> $$
Beyond lm()
You’re not limited to just lm models! equatiomatic supports many
other models, including logistic regression, probit regression, and
ordered logistic regression (with MASS::polr()).
Logistic regression with glm()
model_logit <- glm(sex ~ bill_length_mm + species,
data = penguins, family = binomial(link = "logit"))
extract_eq(model_logit, wrap = TRUE, terms_per_line = 3)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{sex} = \operatorname{male} ) }{ 1 - P( \operatorname{sex} = \operatorname{male} ) } \right] &= \alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\quad \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})
#> \end{aligned}
#> $$
Probit regression with glm()
model_probit <- glm(sex ~ bill_length_mm + species,
data = penguins, family = binomial(link = "probit"))
extract_eq(model_probit, wrap = TRUE, terms_per_line = 3)
#> $$
#> \begin{aligned}
#> P( \operatorname{sex} = \operatorname{male} ) &= \Phi[\alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\qquad\ \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})]
#> \end{aligned}
#> $$
Ordered logistic regression with MASS::polr()
set.seed(1234)
df <- data.frame(outcome = factor(rep(LETTERS[1:3], 100),
levels = LETTERS[1:3],
ordered = TRUE),
continuous_1 = rnorm(300, 100, 1),
continuous_2 = rnorm(300, 50, 5))
model_ologit <- MASS::polr(outcome ~ continuous_1 + continuous_2,
data = df, Hess = TRUE, method = "logistic")
model_oprobit <- MASS::polr(outcome ~ continuous_1 + continuous_2,
data = df, Hess = TRUE, method = "probit")
extract_eq(model_ologit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
extract_eq(model_oprobit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
Ordered regression (logit and probit) with ordinal::clm()
set.seed(1234)
df <- data.frame(outcome = factor(rep(LETTERS[1:3], 100),
levels = LETTERS[1:3],
ordered = TRUE),
continuous_1 = rnorm(300, 1, 1),
continuous_2 = rnorm(300, 5, 5))
model_ologit <- ordinal::clm(outcome ~ continuous_1 + continuous_2,
data = df, link = "logit")
model_oprobit <- ordinal::clm(outcome ~ continuous_1 + continuous_2,
data = df, link = "probit")
extract_eq(model_ologit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
$$
\begin{aligned}
\log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2}) \
\log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2})
\end{aligned}
$$
extract_eq(model_oprobit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
$$
\begin{aligned}
P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2})] \
P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2})]
\end{aligned}
$$
Extension
If you would like to contribute to this package, we’d love your help! We
are particularly interested in extending to more models. We hope to
support any model supported by
broom in the future.
Code of Conduct
Please note that the ‘equatiomatic’ project is released with a
Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
A note of appreciation
We’d like to thank the authors of the
{palmerpenguins}
dataset for generously allowing us to incorporate the penguins dataset
in our package for example usage.
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago
(Antarctica) penguin data. R package version 0.1.0.
https://allisonhorst.github.io/palmerpenguins/
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equatiomatic
The goal of equatiomatic is to reduce the pain associated with writing LaTeX code from a fitted model. The package aims to support any model supported by broom. See the introduction to equatiomatic for currently supported models.
Installation
Install from CRAN with
install.packages("equatiomatic")
Or get the development version from GitHub with
remotes::install_github("datalorax/equatiomatic")
Basic usage
The gif above shows the basic functionality.
To convert a model to LaTeX, feed a model object to extract_eq():
library(equatiomatic)
# Fit a simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
# Give the results to extract_eq
extract_eq(mod1)
#> $$
#> \operatorname{mpg} = \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \epsilon
#> $$
The model can be built in any standard way. It can handle shortcut syntax:
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)
#> $$
#> \operatorname{mpg} = \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp}) + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs}) + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> $$
When using categorical variables, it will include the levels of the variables as subscripts. Here, we use data from the {palmerpenguins} dataset.
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)
#> $$
#> \operatorname{body\_mass\_g} = \alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}}) + \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}}) + \epsilon
#> $$
It helpfully preserves the order the variables are supplied in the formula:
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 ~ cont1 + cat2 + cont2 + cat1, d)
extract_eq(mod4)
#> $$
#> \operatorname{out} = \alpha + \beta_{1}(\operatorname{cont1}) + \beta_{2}(\operatorname{cat2}_{\operatorname{B}}) + \beta_{3}(\operatorname{cat2}_{\operatorname{C}}) + \beta_{4}(\operatorname{cont2}) + \beta_{5}(\operatorname{cat1}_{\operatorname{b}}) + \beta_{6}(\operatorname{cat1}_{\operatorname{c}}) + \epsilon
#> $$
Appearance
You can wrap the equations so that a specified number of terms appear on
the right-hand side of the equation using terms_per_line (defaults to
4):
extract_eq(mod2, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp})\ + \\
#> &\quad \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs})\ + \\
#> &\quad \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
extract_eq(mod2, wrap = TRUE, terms_per_line = 6)
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp}) + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt})\ + \\
#> &\quad \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs}) + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
When wrapping, you can change whether the lines end with trailing math
operators like + (the default), or if they should begin with them
using operator_location = "end" or operator_location = "start":
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, operator_location = "start")
#> $$
#> \begin{aligned}
#> \operatorname{mpg} &= \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \beta_{3}(\operatorname{hp})\\
#> &\quad + \beta_{4}(\operatorname{drat}) + \beta_{5}(\operatorname{wt}) + \beta_{6}(\operatorname{qsec}) + \beta_{7}(\operatorname{vs})\\
#> &\quad + \beta_{8}(\operatorname{am}) + \beta_{9}(\operatorname{gear}) + \beta_{10}(\operatorname{carb}) + \epsilon
#> \end{aligned}
#> $$
By default, all text in the equation is wrapped in \operatorname{}.
You can optionally have the variables themselves be italicized (i.e. not
be wrapped in \operatorname{}) with ital_vars = TRUE:
extract_eq(mod2, wrap = TRUE, ital_vars = TRUE)
#> $$
#> \begin{aligned}
#> mpg &= \alpha + \beta_{1}(cyl) + \beta_{2}(disp) + \beta_{3}(hp)\ + \\
#> &\quad \beta_{4}(drat) + \beta_{5}(wt) + \beta_{6}(qsec) + \beta_{7}(vs)\ + \\
#> &\quad \beta_{8}(am) + \beta_{9}(gear) + \beta_{10}(carb) + \epsilon
#> \end{aligned}
#> $$
R Markdown and previewing
If you include extract_eq() in an R Markdown chunk, knitr will
render the equation. If you’d like to see the LaTeX code wrap the call
in print().
You can also use the tex_preview() function from the
texPreview package
to preview the equation in RStudio:
tex_preview(extract_eq(mod1))
Both extract_eq() and tex_preview() work with magrittr pipes, so
you can do something like this:
library(magrittr) # or library(tidyverse) or any other package that exports %>%
extract_eq(mod1) %>%
tex_preview()
Extra options
There are several extra options you can enable with additional arguments
to extract_eq().
Actual coefficients
You can return actual numeric coefficients instead of Greek letters with
use_coefs = TRUE:
extract_eq(mod1, use_coefs = TRUE)
#> $$
#> \operatorname{\widehat{mpg}} = 34.66 - 1.59(\operatorname{cyl}) - 0.02(\operatorname{disp})
#> $$
By default, it will remove doubled operators like “+ -”, but you can
keep those in (which is often useful for teaching) with
fix_signs = FALSE:
extract_eq(mod1, use_coefs = TRUE, fix_signs = FALSE)
#> $$
#> \operatorname{\widehat{mpg}} = 34.66 + -1.59(\operatorname{cyl}) + -0.02(\operatorname{disp})
#> $$
This works in longer wrapped equations:
extract_eq(mod2, wrap = TRUE, terms_per_line = 3,
use_coefs = TRUE, fix_signs = FALSE)
#> $$
#> \begin{aligned}
#> \operatorname{\widehat{mpg}} &= 12.3 + -0.11(\operatorname{cyl}) + 0.01(\operatorname{disp})\ + \\
#> &\quad -0.02(\operatorname{hp}) + 0.79(\operatorname{drat}) + -3.72(\operatorname{wt})\ + \\
#> &\quad 0.82(\operatorname{qsec}) + 0.32(\operatorname{vs}) + 2.52(\operatorname{am})\ + \\
#> &\quad 0.66(\operatorname{gear}) + -0.2(\operatorname{carb})
#> \end{aligned}
#> $$
Beyond lm()
You’re not limited to just lm models! equatiomatic supports many
other models, including logistic regression, probit regression, and
ordered logistic regression (with MASS::polr()).
Logistic regression with glm()
model_logit <- glm(sex ~ bill_length_mm + species,
data = penguins, family = binomial(link = "logit"))
extract_eq(model_logit, wrap = TRUE, terms_per_line = 3)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{sex} = \operatorname{male} ) }{ 1 - P( \operatorname{sex} = \operatorname{male} ) } \right] &= \alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\quad \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})
#> \end{aligned}
#> $$
Probit regression with glm()
model_probit <- glm(sex ~ bill_length_mm + species,
data = penguins, family = binomial(link = "probit"))
extract_eq(model_probit, wrap = TRUE, terms_per_line = 3)
#> $$
#> \begin{aligned}
#> P( \operatorname{sex} = \operatorname{male} ) &= \Phi[\alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\qquad\ \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})]
#> \end{aligned}
#> $$
Ordered logistic regression with MASS::polr()
set.seed(1234)
df <- data.frame(outcome = factor(rep(LETTERS[1:3], 100),
levels = LETTERS[1:3],
ordered = TRUE),
continuous_1 = rnorm(300, 100, 1),
continuous_2 = rnorm(300, 50, 5))
model_ologit <- MASS::polr(outcome ~ continuous_1 + continuous_2,
data = df, Hess = TRUE, method = "logistic")
model_oprobit <- MASS::polr(outcome ~ continuous_1 + continuous_2,
data = df, Hess = TRUE, method = "probit")
extract_eq(model_ologit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
extract_eq(model_oprobit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
Ordered regression (logit and probit) with ordinal::clm()
set.seed(1234)
df <- data.frame(outcome = factor(rep(LETTERS[1:3], 100),
levels = LETTERS[1:3],
ordered = TRUE),
continuous_1 = rnorm(300, 1, 1),
continuous_2 = rnorm(300, 5, 5))
model_ologit <- ordinal::clm(outcome ~ continuous_1 + continuous_2,
data = df, link = "logit")
model_oprobit <- ordinal::clm(outcome ~ continuous_1 + continuous_2,
data = df, link = "probit")
extract_eq(model_ologit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
$$ \begin{aligned} \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{A} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{A} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2}) \ \log\left[ \frac { P( \operatorname{outcome} \leq \operatorname{B} ) }{ 1 - P( \operatorname{outcome} \leq \operatorname{B} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2}) \end{aligned} $$
extract_eq(model_oprobit, wrap = TRUE)
#> $$
#> \begin{aligned}
#> P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
$$ \begin{aligned} P( \operatorname{outcome} \leq \operatorname{A} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2})] \ P( \operatorname{outcome} \leq \operatorname{B} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous_1}) + \beta_{2}(\operatorname{continuous_2})] \end{aligned} $$
Extension
If you would like to contribute to this package, we’d love your help! We are particularly interested in extending to more models. We hope to support any model supported by broom in the future.
Code of Conduct
Please note that the ‘equatiomatic’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
A note of appreciation
We’d like to thank the authors of the
{palmerpenguins}
dataset for generously allowing us to incorporate the penguins dataset
in our package for example usage.
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/
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