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 visualizing and doing inference for interaction terms. Support for the
svyglm objects as well as weighted regressions is a common theme throughout.
Note: This is beta software. Bugs are possible, both in terms of code-breaking errors and more pernicious errors of mistaken computation.
For the most stable version, simply install from CRAN.
If you want the latest features and bug fixes (and perhaps the latest bugs, too) then you can download from Github. To do that you will need to have
devtools installed if you don’t already:
Then install the package from Github.
Here’s a brief synopsis of the current functions in the package:
summ is a replacement for
summary that provides the user several options for formatting regression summaries. It supports
merMod objects as input as well. It supports calculation and reporting of robust standard errors via the
#> MODEL INFO: #> Observations: 32 #> Dependent Variable: mpg #> #> MODEL FIT: #> F(2,29) = 69.21, p = 0 #> R-squared = 0.83 #> Adj. R-squared = 0.81 #> #> Standard errors: OLS #> Est. S.E. t val. p #> (Intercept) 37.23 1.6 23.28 0 *** #> hp -0.03 0.01 -3.52 0 ** #> wt -3.88 0.63 -6.13 0 ***
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 (
scale.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.
#> MODEL INFO: #> Observations: 32 #> Dependent Variable: mpg #> #> MODEL FIT: #> F(2,29) = 69.21, p = 0 #> R-squared = 0.83 #> Adj. R-squared = 0.81 #> #> Standard errors: OLS #> Est. 2.5% 97.5% t val. VIF partial.r part.r #> (Intercept) 20.09 19.19 20.99 43.82 #> hp -2.18 -3.39 -0.97 -3.52 1.77 -0.55 -0.27 #> wt -3.79 -5.01 -2.58 -6.13 1.77 -0.75 -0.47 #> #> All continuous predictors are mean-centered and scaled by 1 s.d.
Cluster-robust standard errors:
#> MODEL INFO: #> Observations: 5000 #> Dependent Variable: y #> #> MODEL FIT: #> F(1,4998) = 1310.74, p = 0 #> R-squared = 0.21 #> Adj. R-squared = 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 ***
summary is best-suited for interactive use. When it comes to share results with others, you want sharper output and probably graphics.
jtools has some options for that, too.
First, for tabular output,
export_summs is an interface to the
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.
|( 1)||( 2)||( 3)|
|hp||-0.36 **||-0.35 *||-0.40 **|
|wt||-0.63 ***||-0.62 **||-0.56 **|
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.
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
jtools has made some slight changes to
ggplot2 geoms to make everything look nice; and like with
export_summs, you can still get your scaled models and robust standard errors.
And since you get a
ggplot object in return, you can tweak and theme as you wish.
plot_coefs works much the same way, but without support for
summ arguments like
scale. This enables a wider range of models that have support from the
broom package but not for
summ. And you can give
summ objects to
plot_coefs since this package defines tidy methods for
For instance, I could compare the confidence bands with different robust standard error specifications using
plot_coefs by giving the
summ objects as arguments.
summ_fit_1 <- summ(fit_b, scale = TRUE) summ_fit_2 <- summ(fit_b, scale = TRUE, robust = TRUE, robust.type = "HC0") summ_fit_3 <- summ(fit_b, scale = TRUE, robust = TRUE, robust.type = "HC3") plot_coefs(summ_fit_1, summ_fit_2, summ_fit_3, model.names = c("OLS","HC0","HC3"), coefs = c("Horsepower" = "hp", "Weight (tons)" = "wt", "Displacement" = "disp"))
Unless you have a really keen eye and good familiarity with both the underlying mathematics and the scale of your variables, it can be very difficult to look at the ouput of regression model that includes an interaction and actually understand what the model is telling you.
This package contains several means of aiding understanding and doing statistical inference with interactions.
The “classic” way of probing an interaction effect is to calculate the slope of the focal predictor at different values of the moderator. When the moderator is binary, this is especially informative—e.g., what is the slope for men vs. women? But you can also arbitrarily choose points for continuous moderators.
With that said, the more statistically rigorous way to explore these effects is to find the Johnson-Neyman interval, which tells you the range of values of the moderator in which the slope of the predictor is significant vs. nonsignificant at a specified alpha level.
sim_slopes function will by default find the Johnson-Neyman interval and tell you the predictor’s slope at specified values of the moderator; by default either both values of binary predictors or the mean and the mean +/- one standard deviation for continuous moderators.
#> JOHNSON-NEYMAN INTERVAL #> #> The slope of hp is p < .05 when wt is OUTSIDE this interval: #> [3.69, 5.9] #> Note: The range of observed values of wt is [1.51, 5.42]
#> SIMPLE SLOPES ANALYSIS #> #> Slope of hp when wt = 4.2 (+ 1 SD): #> Est. S.E. p #> 0.00 0.01 0.76 #> #> Slope of hp when wt = 3.22 (Mean): #> Est. S.E. p #> -0.03 0.01 0.00 #> #> Slope of hp when wt = 2.24 (- 1 SD): #> Est. S.E. p #> -0.06 0.01 0.00
The Johnson-Neyman plot can really help you get a handle on what the interval is telling you, too. Note that you can look at the Johnson-Neyman interval directly with the
The above all generalize to three-way interactions, too.
This function plots two- and three-way interactions using
ggplot2 with a similar interface to the aforementioned
sim_slopes function. Users can customize the appearance with familiar
ggplot2 commands. It supports several customizations, like confidence intervals.
You can also plot the observed data for comparison:
The function also supports categorical moderators—plotting observed data in these cases can reveal striking patterns.
You may also combine the plotting and simple slopes functions by using
probe_interaction, which calls both functions simultaneously. Categorical by categorical interactions can be investigated using the
This will format your
ggplot2 graphics to make them (mostly) appropriate for APA style publications. There’s no drop-in, perfect way to get plots into APA format sight unseen, but this gets you close and returns a
ggplot object that can be further tweaked to your specification.
The plots produced by other functions in this package use
theme_apa, but use its options to position the plots and alter other details to make them more in line with
ggplot2 defaults than APA norms.
You might start with something like the above interaction plots and then use
theme_apa to tune it to APA specification. Note the
You may need to make further changes to please your publisher, of course. Since these are regular
ggplot theme changes, it shouldn’t be a problem.
This function extends the
survey package by calculating correlations with complex survey designs, a feature absent from
survey. Users may request significance tests, which are calculated via bootstrap by calling the
#> api00 api99 dnum #> api00 1 0.98* 0.25* #> api99 0.98* 1 0.24* #> dnum 0.25* 0.24* 1
In keeping with the package’s attention to users of survey data, I’ve implemented a couple of tests that help to check whether your model is specified correctly without survey weights. It goes without saying that you shouldn’t let statistical tests do your thinking for you, but they can provide useful info.
The first is
wgttest, which implements the DuMouchel-Duncan (1983) procedure and is meant in part to duplicate the user-written Stata procedure of the same name. It can both test whether the model fit overall is changed with the addition of weights as well as show you which coefficients are most affected.
The next is
pf_sv_test, short for Pfeffermann-Sverchkov (1999) test, which focuses on residual correlation with weights. You’ll need the
boot package for this one.
To run both at once, you can use
svysd each do some of the behind the scenes computation in the above functions, but could do well for end users as well. See the documentation for more.
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.
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.
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.
The source code of this package is licensed under the MIT License.