---

output: github_document

knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "#>",
  fig.path = "man/figures/",
  fig.width = 6.5,
  fig.height = 4,
  render = knitr::normal_print,
  # dev.args=list(type="cairo"),
  # dev = "CairoPNG",
  dpi = 175,
  retina = 1
)
library(jtools)

jtools

This package consists of a series of functions created by the author (Jacob)
to automate otherwise tedious research tasks. At this juncture, the unifying
theme is the more efficient presentation of regression analyses. There are
a number of functions for other programming and statistical purposes as
well. Support for the survey package’s svyglm objects as well as weighted
regressions is a common theme throughout.

Notice: As of jtools version 2.0.0, all functions dealing with
interactions (e.g., interact_plot(), sim_slopes(), johnson_neyman()) have
been moved to a new package, aptly named
interactions.

Installation

For the most stable version, simply install from CRAN.

install.packages("jtools")

If you want the latest features and bug fixes then you can download from
Github. To do that you will need to have devtools installed if you don’t
already:

install.packages("devtools")

Then install the package from Github.

devtools::install_github("jacob-long/jtools")

To see what features are on the roadmap, check the issues
section of the repository, especially the “enhancement” tag. Closed issues
may be of interest, too, since they may be fixed in the Github version but not
yet submitted to CRAN.

Usage

Here’s a synopsis of the current functions in the package:

Console regression summaries (summ())

summ() is a replacement for summary() that provides the user several options
for formatting regression summaries. It supports glm, svyglm, and merMod
objects as input as well. It supports calculation and reporting of
robust standard errors via the sandwich package.

Basic use:

data(movies)
fit <- lm(metascore ~ budget + us_gross + year, data = movies)
summ(fit)

It has several conveniences, like re-fitting your model with
scaled variables (scale = TRUE). You have the option to leave the
outcome variable in its original scale (transform.response = TRUE),
which is the default for scaled models. I’m a fan of Andrew Gelman’s 2
SD standardization method, so you can specify by how many standard deviations
you would like to rescale (n.sd = 2).

You can also get variance inflation factors (VIFs) and partial/semipartial
(AKA part) correlations. Partial correlations are only available for OLS
models. You may also substitute confidence intervals in place of standard
errors and you can choose whether to show p values.

summ(fit, scale = TRUE, vifs = TRUE, part.corr = TRUE, confint = TRUE, pvals = FALSE)

Cluster-robust standard errors:

data("PetersenCL", package = "sandwich")
fit2 <- lm(y ~ x, data = PetersenCL)
summ(fit2, robust = "HC3", cluster = "firm")

Of course, summ() like summary() is best-suited for interactive use. When
it comes to sharing results with others, you want sharper output and probably
graphics. jtools has some options for that, too.

LaTeX-, Word-, and RMarkdown-friendly regression summary tables (export_summs())

For tabular output, export_summs() is an interface to the huxtable
package’s huxreg() function that preserves the niceties of summ(),
particularly its facilities for robust standard errors and standardization.
It also concatenates multiple models into a single table.

fit <- lm(metascore ~ log(budget), data = movies)
fit_b <- lm(metascore ~ log(budget) + log(us_gross), data = movies)
fit_c <- lm(metascore ~ log(budget) + log(us_gross) + runtime, data = movies)
coef_names <- c("Budget" = "log(budget)", "US Gross" = "log(us_gross)",
                "Runtime (Hours)" = "runtime", "Constant" = "(Intercept)")
export_summs(fit, fit_b, fit_c, robust = "HC3", coefs = coef_names)

# huxtable doesn't render right for the github_document, seems to thinks it's
# in the console
fit <- lm(metascore ~ log(budget), data = movies)
fit_b <- lm(metascore ~ log(budget) + log(us_gross), data = movies)
fit_c <- lm(metascore ~ log(budget) + log(us_gross) + runtime, data = movies)
coef_names <- c("Budget" = "log(budget)", "US Gross" = "log(us_gross)",
                "Runtime (Hours)" = "runtime", "Constant" = "(Intercept)")
e <- export_summs(fit, fit_b, fit_c, robust = "HC3", coefs = coef_names)
huxtable::print_html(e)

In RMarkdown documents, using export_summs() and the chunk option
results = 'asis' will give you nice-looking tables in HTML and PDF
output. Using the to.word = TRUE argument will create a Microsoft Word
document with the table in it.

Plotting regression summaries (plot_coefs() and plot_summs())

Another way to get a quick gist of your regression analysis is to
plot the values of the coefficients and their corresponding uncertainties
with plot_summs() (or the closely related plot_coefs()).
Like with export_summs(), you can still get your scaled
models and robust standard errors.

coef_names <- coef_names[1:3] # Dropping intercept for plots
plot_summs(fit, fit_b, fit_c, robust = "HC3", coefs = coef_names)

And since you get a ggplot object in return, you can tweak and theme as
you wish.

Another way to visualize the uncertainty of your coefficients is via the
plot.distributions argument.

plot_summs(fit_c, robust = "HC3", coefs = coef_names, plot.distributions = TRUE)

These show the 95% interval width of a normal distribution for each estimate.

plot_coefs() works much the same way, but without support for summ()
arguments like robust and scale. This enables a wider range of
models that have support from the broom package but not for summ().

Plotting model predictions (effect_plot())

Sometimes the best way to understand your model is to look at the predictions
it generates. Rather than look at coefficients, effect_plot() lets you plot
predictions across values of a predictor variable alongside the observed data.

effect_plot(fit_c, pred = runtime, interval = TRUE, plot.points = TRUE)

And a new feature in version 2.0.0 lets you plot partial residuals
instead of the raw observed data, allowing you to assess model quality after
accounting for effects of control variables.

effect_plot(fit_c, pred = runtime, interval = TRUE, partial.residuals = TRUE)

Categorical predictors, polynomial terms, (G)LM(M)s, weighted data, and much
more are supported.

Other stuff

There are several other things that might interest you.

• gscale(): Scale and/or mean-center data, including svydesign objects
• scale_mod() and center_mod(): Re-fit models with scaled and/or
  mean-centered data
• wgttest() and pf_sv_test(), which are combined in weights_tests():
  Test the ignorability of sample weights in regression models
• svycor(): Generate correlation matrices from svydesign objects
• theme_apa(): A mostly APA-compliant ggplot2 theme
• theme_nice(): A nice ggplot2 theme
• add_gridlines() and drop_gridlines(): ggplot2 theme-changing
  convenience functions
• make_predictions(): an easy way to generate hypothetical predicted data
  from your regression model for plotting or other purposes.

Details on the arguments can be accessed via the R documentation
(?functionname).
There are now vignettes documenting just about everything you can do as well.

Contributing

I’m happy to receive bug reports, suggestions, questions, and (most of all)
contributions to fix problems and add features. I prefer you use the Github
issues system over trying to reach out to me in other ways. Pull requests for
contributions are encouraged. If you are considering writing up a bug fix
or new feature, please check out the contributing guidelines.

Please note that this project is released with a
Contributor Code of Conduct. By participating in this project
you agree to abide by its terms.

License

This package is licensed under the
GPLv3 license or any
later version.