Skip to content
Tools for summarizing/visualizing regressions and other helpful stuff
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github/ISSUE_TEMPLATE Add issue templates Feb 16, 2019
.vscode Update minutiae Feb 7, 2019
R Handle start values in pR2 calculation Jun 5, 2019
data-raw Add work in progress raw data Feb 7, 2019
inst Prep for CRAN submission Apr 7, 2019
man Accept list as input to plot_coefs, switch from broom to generics pac… Jun 5, 2019
misc Fix download counter Aug 12, 2018
revdep Add revdep Feb 8, 2019
tests Shrink brms test model Apr 8, 2019
vignettes
.Rbuildignore Add .github and .vs folders to .Rbuildignore Apr 7, 2019
.gitignore Update minutiae Feb 7, 2019
.travis.yml Fix rstan build issues on Travis Jan 23, 2019
CONDUCT.md 0.9.3 prep Jan 28, 2018
CONTRIBUTING.md Add CONTRIBUTING guidelines Feb 16, 2019
DESCRIPTION Accept list as input to plot_coefs, switch from broom to generics pac… Jun 5, 2019
LICENSE Bumped version, added conduct, fixed license Feb 13, 2017
NAMESPACE Accept list as input to plot_coefs, switch from broom to generics pac… Jun 5, 2019
NEWS.md Update NEWS.md Apr 7, 2019
README.Rmd Update README Mar 3, 2019
README.md Update README Mar 3, 2019
_pkgdown.yml Update pkgdown Feb 8, 2019
appveyor.yml Adding appveyor CI Jan 2, 2017
brmfit.rds Shrink brms test model Apr 8, 2019
cran-comments.md Update cran-comments.md Apr 7, 2019

README.md

jtools

CRAN_Status_Badge Total Downloads CII Best Practices Build Status AppVeyor Build Status codecov

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")

You should also check out the dev branch of this repository for the latest and greatest changes, but also the latest and greatest bugs. To see what features are on the roadmap, check the issues section of the repository, especially the “enhancement” tag.

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:

fit <- lm(mpg ~ hp + wt, data = mtcars)
summ(fit)
#> MODEL INFO:
#> Observations: 32
#> Dependent Variable: mpg
#> Type: OLS linear regression 
#> 
#> MODEL FIT:
#> F(2,29) = 69.21, p = 0.00
#> R² = 0.83
#> Adj. R² = 0.81 
#> 
#> Standard errors: OLS
#> ------------------------------------------------
#>                      Est.   S.E.   t val.      p
#> ----------------- ------- ------ -------- ------
#> (Intercept)         37.23   1.60    23.28   0.00
#> hp                  -0.03   0.01    -3.52   0.00
#> wt                  -3.88   0.63    -6.13   0.00
#> ------------------------------------------------

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)
#> MODEL INFO:
#> Observations: 32
#> Dependent Variable: mpg
#> Type: OLS linear regression 
#> 
#> MODEL FIT:
#> F(2,29) = 69.21, p = 0.00
#> R² = 0.83
#> Adj. R² = 0.81 
#> 
#> Standard errors: OLS
#> ------------------------------------------------------------------------------
#>                      Est.    2.5%   97.5%   t val.    VIF   partial.r   part.r
#> ----------------- ------- ------- ------- -------- ------ ----------- --------
#> (Intercept)         20.09   19.15   21.03    43.82                            
#> hp                  -2.18   -3.44   -0.91    -3.52   1.77       -0.55    -0.27
#> wt                  -3.79   -5.06   -2.53    -6.13   1.77       -0.75    -0.47
#> ------------------------------------------------------------------------------
#> 
#> Continuous predictors are mean-centered and scaled by 1 s.d.

Cluster-robust standard errors:

data("PetersenCL", package = "sandwich")
fit2 <- lm(y ~ x, data = PetersenCL)
summ(fit2, robust = "HC3", cluster = "firm")
#> MODEL INFO:
#> Observations: 5000
#> Dependent Variable: y
#> Type: OLS linear regression 
#> 
#> MODEL FIT:
#> F(1,4998) = 1310.74, p = 0.00
#> R² = 0.21
#> Adj. R² = 0.21 
#> 
#> Standard errors: Cluster-robust, type = HC3
#> -----------------------------------------------
#>                     Est.   S.E.   t val.      p
#> ----------------- ------ ------ -------- ------
#> (Intercept)         0.03   0.07     0.44   0.66
#> x                   1.03   0.05    20.36   0.00
#> -----------------------------------------------

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(mpg ~ hp + wt, data = mtcars)
fit_b <- lm(mpg ~ hp + wt + disp, data = mtcars)
fit_c <- lm(mpg ~ hp + wt + disp + drat, data = mtcars)
coef_names <- c("Horsepower" = "hp", "Weight (tons)" = "wt",
                "Displacement" = "disp", "Rear axle ratio" = "drat",
                "Constant" = "(Intercept)")
export_summs(fit, fit_b, fit_c, scale = TRUE, transform.response = TRUE, coefs = coef_names)

Model 1

Model 2

Model 3

Horsepower

-0.36 ** 

-0.35 * 

-0.40 **

(0.10)   

(0.13)  

(0.13)  

Weight (tons)

-0.63 ***

-0.62 **

-0.56 **

(0.10)   

(0.17)  

(0.18)  

Displacement

       

-0.02   

0.08   

       

(0.21)  

(0.22)  

Rear axle ratio

       

      

0.16   

       

      

(0.12)  

Constant

0.00    

0.00   

0.00   

(0.08)   

(0.08)  

(0.08)  

N

32       

32      

32      

R2

0.83    

0.83   

0.84   

*** p < 0.001; ** p < 0.01; * p < 0.05.

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[1:4] # Dropping intercept for plots
plot_summs(fit, fit_b, fit_c, scale = TRUE, 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, scale = TRUE, 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 = hp, 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 = hp, 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

The source code of this package is licensed under the MIT License.

You can’t perform that action at this time.