开源软件名称（OpenSource Name）： datalorax/equatiomatic开源软件地址(OpenSource Url)： https://github.com/datalorax/equatiomatic开源编程语言(OpenSource Language)：
R
100.0%
开源软件介绍(OpenSource Introduction)：
The goal of equatiomatic is to reduce the pain associated with
writing LaTeX code from a fitted model. In the future, the package aims
to support any model supported by
broom introduction to
equatiomatic
for currently supported models.
Install from CRAN with
install.packages(" equatiomatic" Or get the development version from GitHub with
remotes :: install_github(" datalorax/equatiomatic"
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 modelmod1 <- lm(mpg ~ cyl + disp , mtcars )
# Give the results to extract_eqmod1 ) #> $$
#> \operatorname{mpg} = \alpha + \beta_{1}(\operatorname{cyl}) + \beta_{2}(\operatorname{disp}) + \epsilon
#> $$
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 ) #> $$
#> \operatorname{out} = \alpha + \beta_{1}(\operatorname{cont1}) + \beta_{2}(\operatorname{cat2}_{\operatorname{B}}) + \beta_{3}(\operatorname{cat2}_{\operatorname{C}}) + \beta_{4}(\operatorname{cont2}) + \beta_{5}(\operatorname{cat1}_{\operatorname{b}}) + \beta_{6}(\operatorname{cat1}_{\operatorname{c}}) + \epsilon
#> $$
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 ) #> $$
#> \begin{aligned}
#> mpg &= \alpha + \beta_{1}(cyl) + \beta_{2}(disp) + \beta_{3}(hp)\ + \\
#> &\quad \beta_{4}(drat) + \beta_{5}(wt) + \beta_{6}(qsec) + \beta_{7}(vs)\ + \\
#> &\quad \beta_{8}(am) + \beta_{9}(gear) + \beta_{10}(carb) + \epsilon
#> \end{aligned}
#> $$
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
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 %>%mod1 ) %> %
tex_preview() Extra options
There are several extra options you can enable with additional arguments
to extract_eq()
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 ) #> $$
#> \begin{aligned}
#> \operatorname{\widehat{mpg}} &= 12.3 + -0.11(\operatorname{cyl}) + 0.01(\operatorname{disp})\ + \\
#> &\quad -0.02(\operatorname{hp}) + 0.79(\operatorname{drat}) + -3.72(\operatorname{wt})\ + \\
#> &\quad 0.82(\operatorname{qsec}) + 0.32(\operatorname{vs}) + 2.52(\operatorname{am})\ + \\
#> &\quad 0.66(\operatorname{gear}) + -0.2(\operatorname{carb})
#> \end{aligned}
#> $$
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()).
glm()model_logit <- glm(sex ~ bill_length_mm + species ,
data = penguins , family = binomial(link = " logit" model_logit , wrap = TRUE , terms_per_line = 3 )#> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{sex} = \operatorname{male} ) }{ 1 - P( \operatorname{sex} = \operatorname{male} ) } \right] &= \alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\quad \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})
#> \end{aligned}
#> $$
glm()model_probit <- glm(sex ~ bill_length_mm + species ,
data = penguins , family = binomial(link = " probit" model_probit , wrap = TRUE , terms_per_line = 3 )#> $$
#> \begin{aligned}
#> P( \operatorname{sex} = \operatorname{male} ) &= \Phi[\alpha + \beta_{1}(\operatorname{bill\_length\_mm}) + \beta_{2}(\operatorname{species}_{\operatorname{Chinstrap}})\ + \\
#> &\qquad\ \beta_{3}(\operatorname{species}_{\operatorname{Gentoo}})]
#> \end{aligned}
#> $$
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" model_ologit , wrap = TRUE ) #> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{A} \geq \operatorname{B} ) }{ 1 - P( \operatorname{A} \geq \operatorname{B} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{B} \geq \operatorname{C} ) }{ 1 - P( \operatorname{B} \geq \operatorname{C} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
extract_eq(model_oprobit , wrap = TRUE ) #> $$
#> \begin{aligned}
#> P( \operatorname{A} \geq \operatorname{B} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{B} \geq \operatorname{C} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
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" model_ologit , wrap = TRUE ) #> $$
#> \begin{aligned}
#> \log\left[ \frac { P( \operatorname{A} \geq \operatorname{B} ) }{ 1 - P( \operatorname{A} \geq \operatorname{B} ) } \right] &= \alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2}) \\
#> \log\left[ \frac { P( \operatorname{B} \geq \operatorname{C} ) }{ 1 - P( \operatorname{B} \geq \operatorname{C} ) } \right] &= \alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})
#> \end{aligned}
#> $$
extract_eq(model_oprobit , wrap = TRUE ) #> $$
#> \begin{aligned}
#> P( \operatorname{A} \geq \operatorname{B} ) &= \Phi[\alpha_{1} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})] \\
#> P( \operatorname{B} \geq \operatorname{C} ) &= \Phi[\alpha_{2} + \beta_{1}(\operatorname{continuous\_1}) + \beta_{2}(\operatorname{continuous\_2})]
#> \end{aligned}
#> $$
This project is brand new. If you would like to contribute, we’d love
your help! We are particularly interested in extending to more models.
We hope to support any model supported by
broom
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.
We’d like to thank the authors of the
{palmerpenguin}
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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