NEWS.md
New:
movies
, has been added. I will be gradually updating examples to use movies
rather than R’s much-used, built-in data.Bugfixes:
effect_plot()
no longer ignores the int.width
argument. Thanks to Marco Giesselmann for reporting. (#82)brms
deprecating the bf_parse()
function.broom
0.7.0 release. (#83)make_predictions()
and its associated dependencies in this and other packages now handles scale()
’d variables correctly. (Issue #33 for the interactions
package)srvyr
package. Thanks to Mark White for reporting. (#84)Hotfix release:
ggplot2
(3.3.0) broke theme_apa()
. That’s fixed.theme_nice()
now takes advantage of a new ggplot2
feature that aligns captions in a better way.effect_plot()
no longer ignores the colors
argument.effect_plot()
and make_predictions()
now work properly with multivariate and distributional brms
models in which there is a different set of predictors in the different parts of the model.New features:
effect_plot()
and plot_coefs()
has enhanced support for brms
models. You may now use the resp
and dpar
arguments to select dependent variables in multivariate models as well as distributional parameters in distributional models.get_formula()
. This is mostly an internal helper function designed to extract formulas from objects like brmsfit
that have their own class of formula that will break other internal functions.Minor release.
Fixes:
effect_plot()
no longer silently ignores the at =
argument.make_new_data()
no longer tries to calculate control values for variables included in data
but not the model.Other changes:
plot_coefs()
and plot_summs()
can now accept a list of models as its input. (#64)make_predictions()
and functions that depend on it (e.g., effect_plot()
) now use fitted()
to get predicted values from brmsfit
objects, which provide a smoother predicted line as would be expected.Minor release.
Fixes:
merMod
(i.e., lmerMod
) models with summ
, the p values reported were one-tailed — half their actual value. t statistics and standard errors were correct.odds.ratio
argument was given to summ()
, users were correctly warned that it is a deprecated argument but the exponentiated coefficients were not returned as they should have been.make_new_data()
/make_predictions()
/effect_plot()
when offsets are specified in a formula or a variable is included more than once in a formula.make_predictions()
and partialize()
handle missing data more gracefully, especially when the original data are a tibble
.Other changes:
%just.list%
and %not.list%
S3 methods.%just%
now sorts the matches on the left-hand side in the order they occur on the right-hand side.summ()
(and md_table()
) now rely on pander
to produce plain-text tables and use pander
’s "multiline"
format by default. Check out "grid"
for another option. You can change the default using table.format
in set_summ_defaults()
.stars
(i.e., significance stars) are no longer available from summ()
. This is partially due to the change to printing tables via pander
but also in keeping with statistical best practices.predict_merMod()
, which is used for generating confidence intervals for merMod
model predictions in make_predictions()
and effect_plot()
, is now a user-accessible function.stop_wrap()
, warn_wrap()
, and msg_wrap()
now interface with the rlang
package equivalents rather than the base stop()
and so on. End users may also take advantage of the rlang
sub-classing abilities through these functions.summ()
now passes extra arguments to center_mod()
/scale_mod()
, allowing you to use those functions’ more advanced options.Big changes.
interactions
To reduce the complexity of this package and help people understand what they are getting, I have removed all functions that directly analyze interaction/moderation effects and put them into a new package, interactions
. There are still some functions in jtools
that support interactions
, but some users may find that everything they ever used jtools
for has now moved to interactions
. The following functions have moved to interactions
:
Hopefully moving these items to a separate package called interactions
will help more people discover those functions and reduce confusion about what both packages are for.
make_predictions()
and removal of plot_predictions()
In the jtools
1.0.0 release, I introduced make_predictions()
as a lower-level way to emulate the functionality of effect_plot()
, interact_plot()
, and cat_plot()
. This would return a list object with predicted data, the original data, and a bunch of attributes containing information about how to plot it. One could then take this object, with class predictions
, and use it as the main argument to plot_predictions()
, which was another new function that creates the plots you would see in effect_plot()
et al.
I have simplified make_predictions()
to be less specific to those plotting functions and eliminated plot_predictions()
, which was ultimately too complex to maintain and caused problems for separating the interaction tools into a separate package. make_predictions()
by default simply creates a new data frame of predicted values along a pred
variable. It no longer accepts modx
or mod2
arguments. Instead, it accepts an argument called at
where a user can specify any number of variables and values to generate predictions at. This syntax is designed to be similar to the predictions
/margins
packages. See the documentation for more info on this revised syntax.
make_new_data()
is a new function that supports make_predictions()
by creating the data frame of hypothetical values to which the predictions will be added.
I have added a new function, partialize()
, that creates partial residuals for the purposes of plotting (e.g., with effect_plot()
). One negative when visualizing predictions alongside original data with effect_plot()
or similar tools is that the observed data may be too spread out to pick up on any patterns. However, sometimes your model is controlling for the causes of this scattering, especially with multilevel models that have random intercepts. Partial residuals include the effects of all the controlled-for variables and let you see how well your model performs with all of those things accounted for.
You can plot partial residuals instead of the observed data in effect_plot()
via the argument partial.residuals = TRUE
or get the data yourself using partialize()
. It is also integrated into make_predictions()
.
In keeping with the “tools” focus of this package, I am making available some of the programming tools that previously had only been used internally inside the jtools
package.
%nin%
, %not%
, and %just%
Many are familiar with how handy the %in%
operator is, but sometimes we want everything except the values in some object. In other words, we might want !(x %in% y)
instead of x %in% y
. This is where %nin%
(“not in”) acts as a useful shortcut. Now, instead of !(x %in% y)
, you can just use x %nin% y
. Note that the actual implementation of %nin%
is slightly different to produce the same results but more quickly for large data. You may run into some other packages that also have a %nin%
function and they are, to my knowledge, functionally the same.
One of my most common uses of both %in% and %nin% is when I want to subset an object. For instance, assume x
is 1 through 5, y is 3 through 7, and I want only the instances of x
that are not in y
. Using %nin%
, I would write x[x %nin% y]
, which leaves you with 1 and 2. I really don’t like having to write the object’s name twice in a row like that, so I created something to simplify further: %not%
. You can now subset x
to only the parts that are not in y
like this: x %not% y
. Conversely, you can do the equivalent of x[x %in% y]
using the %just%
operator: x %just% y
.
As special cases for %not%
and %just%
, if the left-hand side is a matrix or data frame, it is assumed that the right hand side are column indices (if numeric) or column names (if character). For example, if I do mtcars %just% c("mpg", "qsec")
, I get a data frame that is just the “mpg” and “qsec” columns of mtcars
. It is an S3 method so support can be added for additional object types by other developers.
wrap_str()
, msg_wrap()
, warn_wrap()
, and stop_wrap()
An irritation when writing messages/warnings/errors to users is breaking up the long strings without unwanted line breaks in the output. One problem is not knowing how wide the user’s console is. wrap_str()
takes any string and inserts line breaks at whatever the “width” option is set to, which automatically changes according to the actual width in RStudio and in some other setups. This means you can write the error message in a single string across multiple, perhaps indented, lines without those line breaks and indentations being part of the console output. msg_wrap()
, warn_wrap()
, and stop_wrap()
are wrap_str()
wrappers (pun not intended) around message()
, warning()
, and stop()
, respectively.
summ()
no longer prints coefficient tables as data frames because this caused RStudio notebook users issues with the output not being printed to the console and having the notebook format them in less-than-ideal ways. The tables now have a markdown format that might remind you of Stata’s coefficient tables.md_table()
and can be used by others if they want. It is based on knitr
’s kable
function.summ()
no longer prints significance stars by default. This can be enabled with the stars = TRUE
argument or by setting the "summ-stars"
option to TRUE
(also available via set_summ_defaults
)model.check
argument in summ()
has been removed.get_colors
is now available to users. It retrieves the color palettes used in jtools
functions.jtools
now have a new theme, which you can use yourself, called theme_nice()
. The previous default, theme_apa()
, is still available but I don’t like it as a default since I don’t think the APA has defined the nicest-looking design guidelines for general use.effect_plot()
now can plot categorical predictors, picking up a functionality previously provided by cat_plot()
.effect_plot()
now uses tidy evaluation for the pred
argument (#37). This means you can pass a variable that contains the name of pred
, which is most useful if you are creating a function, for loop, etc. If using a variable, put a !!
from the rlang
package before it (e.g., pred = !! variable
). For most users, these changes will not affect their usage.make_predictions()
(and by extension effect_plot()
and plotting functions in the interactions
package) now understands dependent variable transformations better. For instance, there shouldn’t be issues if your response variable is log(y)
instead of y
. When returning the original data frame, these functions will append a transformed (e.g., log(y)
) column as needed.lme4
has a bug when generating predictions in models with offsets — it ignores them when the offset is specified via the offset =
argument. I have created a workaround for this.This is a minor release.
plot_predictions()
had an incorrect default value for interval
, causing an error if you used the default arguments with make_predictions()
. The default is now FALSE
. (#39)interact_plot()
, cat_plot()
, and effect_plot()
would have errors when the models included covariates (not involved in the interaction, if any) that were non-numeric. That has been corrected. (#41)TRUE
or FALSE
) were not handled by the plotting functions appropriately, causing them to be treated as numeric. They are now preserved as logical. (#40).sim_slopes()
gave inaccurate results when factor moderators did not have treatment coding ("contr.treatment"
) but are now recoded to treatment coding.summ()
output in RMarkdown documents is now powered by kableExtra
, which (in my opinion) offers more attractive HTML output and seems to have better luck with float placement in PDF documents. Your mileage may vary.rmdformats
rather than the base rmarkdown
template.tidy
and glance
from broom
, knit_print
from knitr
, as_huxtable
from huxtable
) will now have conditional namespace registration for users of R 3.6. This shouldn’t have much effect on end users.This release was initially intended to be a bugfix release, but enough other things came up to make it a minor release.
broom
update when using export_summs()
and plot_coefs()
.plot_coefs()
arising from the latest update to ggplot2
.export_summs()
output for glm
models. [#36]interact_plot()
no longer errors if there are missing observations in the original data and quantiles are requested.summ.merMod
, the default p-value calculation is now via the Satterthwaite method if you have lmerTest
installed. The old default, Kenward-Roger, is used by request or when pbkrtest
is installed but not lmerTest
. It now calculates a different degrees of freedom for each predictor and also calculates a variance-covariance matrix for the model, meaning the standard errors are adjusted as well. It is not the default largely because the computation takes too long for too many models.johnson_neyman()
now allows you to specify your own critical t value if you are using some alternate method to calculate it.johnson_neyman()
now allows you to specify the range of moderator values you want to plot as well as setting a title.sim_slopes()
in a way similar to interact_plot()
. [#35]interact_plot()
(e.g., when modx.values = "plus-minus"
). [#31]plot_coefs()
/plot_summs()
now supports facetting the coefficients based on user-specified groupings. See ?plot_summs
for details.summ()
variants now have pretty output in RMarkdown documents if you have the huxtable
package installed. This can be disabled with the chunk option render = 'normal_print'
.modx.values
, mod2.values
, and pred.values
in place of modxvals
, mod2vals
, and predvals
. Don’t go running to change your code, though; those old argument names will still work, but these new ones are clearer and preferred in new code.plot()
method for sim_slopes
objects. Just save your sim_slopes()
call to an object and call the plot()
function on that object to see what happens. Basically, it’s plot_coefs()
for sim_slopes()
.huxtable
installed, you can now call as_huxtable
on a sim_slopes()
object to get a publication-style table. The interface is comparable to export_summs()
.This release has several big changes embedded within, side projects that needed a lot of work to implement and required some user-facing changes. Overall these are improvements, but in some edge cases they could break old code. The following sections are divided by the affected functions. Some of the functions are discussed in more than one section.
interact_plot()
, cat_plot()
, and effect_plot()
These functions no longer re-fit the inputted model to center covariates, impose labels on factors, and so on. This generally has several key positives, including
lm
models, 60% for svyglm
, and 80% for merMod
in my testing). The speed gains increase as the models become more complicated and the source data become larger.log
) in the formula, the function would previously would have a lot of trouble and usually have errors. Now this is supported, provided you input the data used to fit the model via the data
argument. You’ll receive a warning if the function thinks this is needed to work right.As noted, there is a new data
argument for these functions. You do not normally need to use this if your model is fit with a y ~ x + z
type of formula. But if you start doing things like y ~ factor(x) + z
, then you need to provide the source data frame. Another benefit is that this allows for fitting polynomials with effect_plot()
or even interactions with polynomials with interact_plot()
. For instance, if my model was fit using this kind of formula — y ~ poly(x, 2) + z
— I could then plot the predicted curve with effect_plot(fit, pred = x, data = data)
substituting fit
with whatever my model is called and data
with whatever data frame I used is called.
There are some possible drawbacks for these changes. One is that no longer are factor predictors supported in interact_plot()
and effect_plot()
, even two-level ones. This worked before by coercing them to 0/1 continuous variables and re-fitting the model. Since the model is no longer re-fit, this can’t be done. To work around it, either transform the predictor to numeric before fitting the model or use cat_plot()
. Relatedly, two-level factor covariates are no longer centered and are simply set to their reference value.
Robust confidence intervals: Plotting robust standard errors for compatible models (tested on lm
, glm
). Just use the robust
argument like you would for sim_slopes()
or summ()
.
Preliminary support for confidence intervals for merMod
models: You may now get confidence intervals when using merMod
objects as input to the plotting functions. Of importance, though, is the uncertainty is only for the fixed effects. For now, a warning is printed. See the next section for another option for merMod
confidence intervals.
Rug plots in the margins: So-called “rug” plots can be included in the margins of the plots for any of these functions. These show tick marks for each of the observed data points, giving a non-obtrusive impression of the distribution of the pred
variable and (optionally) the dependent variable. See the documentation for interact_plot()
and effect_plot()
and the rug
/rug.sides
arguments.
Facet by the modx
variable: Some prefer to visualize the predicted lines on separate panes, so that is now an option available via the facet.modx
argument. You can also use plot.points
with this, though the division into groups is not straightforward is the moderator isn’t a factor. See the documentation for more on how that is done.
make_predictions()
and plot_predictions()
: New tools for advanced plottingTo let users have some more flexibility, jtools
now lets users directly access the (previously internal) functions that make effect_plot()
, cat_plot()
, and interact_plot()
work. This should make it easier to tailor the outputs for specific needs. Some features may be implemented for these functions only to keep the _plot
functions from getting any more complicated than they already are.
The simplest use of the two functions is to use make_predictions()
just like you would effect_plot()
/interact_plot()
/cat_plot()
. The difference is, of course, that make_predictions()
only makes the data that would be used for plotting. The resulting predictions
object has both the predicted and original data as well as some attributes describing the arguments used. If you pass this object to plot_predictions()
with no further arguments, it should do exactly what the corresponding _plot
function would do. However, you might want to do something entirely different using the predicted data which is part of the reason these functions are separate.
One such feature specific to make_predictions()
is bootstrap confidence intervals for merMod
models.
You may no longer use these tools to scale the models. Use scale_mod()
, save the resulting object, and use that as your input to the functions if you want scaling.
All these tools have a new default centered
argument. They are now set to centered = "all"
, but "all"
no longer means what it used to. Now it refers to all variables not included in the interaction, including the dependent variable. This means that in effect, the default option does the same thing that previous versions did. But instead of having that occur when centered = NULL
, that’s what centered = "all"
means. There is no NULL
option any longer. Note that with sim_slopes()
, the focal predictor (pred
) will now be centered — this only affects the conditional intercept.
sim_slopes()
This function now supports categorical (factor) moderators, though there is no option for Johnson-Neyman intervals in these cases. You can use the significance of the interaction term(s) for inference about whether the slopes differ at each level of the factor when the moderator is a factor.
You may now also pass arguments to summ()
, which is used internally to calculate standard errors, p values, etc. This is particularly useful if you are using a merMod
model for which the pbkrtest
-based p value calculation is too time-consuming.
gscale()
The interface has been changed slightly, with the actual numbers always provided as the data
argument. There is no x
argument and instead a vars
argument to which you can provide variable names. The upshot is that it now fits much better into a piping workflow.
The entire function has gotten an extensive reworking, which in some cases should result in significant speed gains. And if that’s not enough, just know that the code was an absolute monstrosity before and now it’s not.
There are two new functions that are wrappers around gscale()
: standardize()
and center()
, which call gscale()
but with n.sd = 1
in the first case and with center.only = TRUE
in the latter case.
summ()
Tired of specifying your preferred configuration every time you use summ()
? Now, many arguments will by default check your options so you can set your own defaults. See ?set_summ_defaults
for more info.
Rather than having separate scale.response
and center.response
arguments, each summ()
function now uses transform.response
to collectively cover those bases. Whether the response is centered or scaled depends on the scale
and center
arguments.
The robust.type
argument is deprecated. Now, provide the type of robust estimator directly to robust
. For now, if robust = TRUE
, it defaults to "HC3"
with a warning. Better is to provide the argument directly, e.g., robust = "HC3"
. robust = FALSE
is still fine for using OLS/MLE standard errors.
Whereas summ.glm
, summ.svyglm
, and summ.merMod
previously offered an odds.ratio
argument, that has been renamed to exp
(short for exponentiate) to better express the quantity.
vifs
now works when there are factor variables in the model.
One of the first bugs summ()
ever had occurred when the function was given a rank-deficient model. It is not straightforward to detect, especially since I need to make a space for an almost empty row in the outputted table. At long last, this release can handle such models gracefully.
Like the rest of R, when summ()
rounded your output, items rounded exactly to zero would be treated as, well, zero. But this can be misleading if the original value was actually negative. For instance, if digits = 2
and a coefficient was -0.003
, the value printed to the console was 0.00
, suggesting a zero or slightly positive value when in fact it was the opposite. This is a limitation of the round
(and trunc
) function. I’ve now changed it so the zero-rounded value retains its sign.
summ.merMod
now calculates pseudo-R^2 much, much faster. For only modestly complex models, the speed-up is roughly 50x faster. Because of how much faster it now is and how much less frequently it throws errors or prints cryptic messages, it is now calculated by default. The confidence interval calculation is now “Wald” for these models (see confint.merMod
for details) rather than “profile”, which for many models can take a very long time and sometimes does not work at all. This can be toggled with the conf.method
argument.
summ.glm
/summ.svyglm
now will calculate pseudo-R^2 for quasibinomial and quasipoisson families using the value obtained from refitting them as binomial/poisson. For now, I’m not touching AIC/BIC for such models because the underlying theory is a bit different and the implementation more challenging.
summ.lm
now uses the t-distribution for finding critical values for confidence intervals. Previously, a normal approximation was used.
The summ.default
method has been removed. It was becoming an absolute terror to maintain and I doubted anyone found it useful. It’s hard to provide the value added for models of a type that I do not know (robust errors don’t always apply, scaling doesn’t always work, model fit statistics may not make sense, etc.). Bug me if this has really upset things for you.
One new model type has been supported: rq
models from the quantreg
package. Please feel free to provide feedback for the output and support of these models.
scale_lm()
and center_lm()
are now scale_mod()
/center_mod()
To better reflect the capabilities of these functions (not restricted to lm
objects), they have been renamed. The old names will continue to work to preserve old code.
However, scale.response
and center.response
now default to FALSE
to reflect the fact that only OLS models can support transformations of the dependent variable in that way.
There is a new vars =
argument for scale_mod()
that allows you to only apply scaling to whichever variables are included in that character vector.
I’ve also implemented a neat technical fix that allows the updated model to itself be updated while not also including the actual raw data in the model call.
plot_coefs()
and plot_summs()
A variety of fixes and optimizations have been added to these functions. Now, by default, there are two confidence intervals plotted, a thick line representing (with default settings) the 90% interval and a thinner line for the 95% intervals. You can set inner_ci_level
to NULL
to get rid of the thicker line.
With plot_summs()
, you can also set per-model summ()
arguments by providing the argument as a vector (e.g., robust = c(TRUE, FALSE)
). Length 1 arguments are applied to all models. plot_summs()
will now also support models not accepted by summ()
by just passing those models to plot_coefs()
without using summ()
on them.
Another new option is point.shape
, similar to the model plotting functions. This is most useful for when you are planning to distribute your output in grayscale or to colorblind audiences (although the new default color scheme is meant to be colorblind friendly, it is always best to use another visual cue).
The coolest is the new plot.distributions
argument, which if TRUE will plot normal distributions to even better convey the uncertainty. Of course, you should use this judiciously if your modeling or estimation approach doesn’t produce coefficient estimates that are asymptotically normally distributed. Inspiration comes from https://twitter.com/BenJamesEdwards/status/979751070254747650.
Minor fixes: broom
’s interface for Bayesian methods is inconsistent, so I’ve hacked together a few tweaks to make brmsfit
and stanreg
models work with plot_coefs()
.
You’ll also notice vertical gridlines on the plots, which I think/hope will be useful. They are easily removable (see drop_x_gridlines()
) with ggplot2’s built-in theming options.
export_summs()
Changes here are not too major. Like plot_summs()
, you can now provide unsupported model types to export_summs()
and they are just passed through to huxreg
. You can also provide different arguments to summ()
on a per-model basis in the way described under the plot_summs()
heading above.
There are some tweaks to the model info (provided by glance
). Most prominent is for merMod
models, for which there is now a separate N for each grouping factor.
theme_apa()
plus new functions add_gridlines()
, drop_gridlines()
New arguments have been added to theme_apa()
: remove.x.gridlines
and remove.y.gridlines
, both of which are TRUE
by default. APA hates giving hard and fast rules, but the norm is that gridlines should be omitted unless they are crucial for interpretation. theme_apa()
is also now a “complete” theme, which means specifying further options via theme
will not revert theme_apa()
’s changes to the base theme.
Behind the scenes the helper functions add_gridlines()
and drop_gridlines()
are used, which do what they sound like they do. To avoid using the arguments to those functions, you can also use add_x_gridlines()
/add_y_gridlines()
or drop_x_gridlines()
/drop_y_gridlines()
which are wrappers around the more general functions.
weights_tests()
— wgttest()
and pf_sv_test()
— now handle missing data in a more sensible and consistent way.
There is a new default qualitative palette, based on Color Universal Design (designed to be readable by the colorblind) that looks great to all. There are several other new palette choices as well. These are all documented at ?jtools_colors
This release is limited to dealing with the huxtable
package’s temporary removal from CRAN, which in turn makes this package out of compliance with CRAN policies regarding dependencies on non-CRAN packages.
Look out for jtools
1.0.0 coming very soon!
Bugfixes:
johnson_neyman()
and sim_slopes()
were both encountering errors with merMod
input. Thanks to Seongho Bae for reporting these issues and testing out development versions.gscale
.export_summs()
had an extra space (e.g., ( 1)
) due to changes in huxtable
. The defaults are now just single numbers.Bugfix:
control.fdr
was TRUE
. It was reporting alpha * 2
in the legend, but now it is accurate again.Feature update:
johnson_neyman()
now handles multilevel models from lme4
.Bugfix update:
Jonas Kunst helpfully pointed out some odd behavior of interact_plot()
with factor moderators. No longer should there be occasions in which you have two different legends appear. The linetype and colors also should now be consistent whether there is a second moderator or not. For continuous moderators, the darkest line should also be a solid line and it is by default the highest value of the moderator.
Other fixes:
huxtable
broke export_summs()
, but that has been fixed.Feature updates:
interact_plot()
and cat_plot()
by providing a vector of colors (any format that ggplot2
accepts) for the color.class
argument.summ()
that formats the output in a way that lines up the decimal points. It looks great.This may be the single biggest update yet. If you downloaded from CRAN, be sure to check the 0.8.1 update as well.
New features are organized by function.
control.fdr
option is added to control the false discovery rate, building on new research. This makes the test more conservative but less likely to be a Type 1 error.line.thickness
argument has been added after Heidi Jacobs pointed out that it cannot be changed after the fact.sim_slopes()
for 3-way interactions is much-improved.alpha = .05
the critical test statistic was always 1.96. Now, the residual degrees of freedom are used with the t distribution. You can do it the old way by setting df = "normal"
or any arbitrary number.plot.points
(see 0.8.1 for more). You can now plot observed data with 3-way interactions.modxvals
and mod2vals
specification has been added: "terciles"
. This splits the observed data into 3 equally sized groups and chooses as values the mean of each of those groups. This is especially good for skewed data and for second moderators.linearity.check
option for two-way interactions. This facets by each level of the moderator and lets you compare the fitted line with a loess smoothed line to ensure that the interaction effect is roughly linear at each level of the (continuous) moderator.plot.points = TRUE
.jitter
argument added for those using plot.points
. If you don’t want the points jittered, you can set jitter = 0
. If you want more or less, you can play with the value until it looks right. This applies to effect_plot()
as well.r.squared
or pbkrtest
are slowing things down. r.squared
is now set to FALSE by default.New functions!
plot_summs()
: A graphic counterpart to export_summs()
, which was introduced in the 0.8.0 release. This plots regression coefficients to help in visualizing the uncertainty of each estimate and facilitates the plotting of nested models alongside each other for comparison. This allows you to use summ()
features like robust standard errors and scaling with this type of plot that you could otherwise create with some other packages.
plot_coefs()
: Just like plot_summs()
, but no special summ()
features. This allows you to use models unsupported by summ()
, however, and you can provide summ()
objects to plot the same model with different summ()
argument alongside each other.
cat_plot()
: This was a long time coming. It is a complementary function to interact_plot()
, but is designed to deal with interactions between categorical variables. You can use bar plots, line plots, dot plots, and box and whisker plots to do so. You can also use the function to plot the effect of a single categorical predictor without an interaction.
Thanks to Kim Henry who reported a bug with johnson_neyman()
in the case that there is an interval, but the entire interval is outside of the plotted area: When that happened, the legend wrongly stated the plotted line was non-significant.
Besides that bugfix, some new features:
johnson_neyman()
fails to find the interval (because it doesn’t exist), it no longer quits with an error. The output will just state the interval was not found and the plot will still be created.interact_plot()
has been added. Previously, if the moderator was a factor, you would get very nicely colored plotted points when using plot.points = TRUE
. But if the moderator was continuous, the points were just black and it wasn’t very informative beyond examining the main effect of the focal predictor. With this update, the plotted points for continuous moderators are shaded along a gradient that matches the colors used for the predicted lines and confidence intervals.Not many user-facing changes since 0.7.4, but major refactoring internally should speed things up and make future development smoother.
Bugfixes:
interact_plot()
and effect_plot()
would trip up when one of the focal predictors had a name that was a subset of a covariate (e.g., pred = “var” but a covariate is called “var_2”). That’s fixed.merMod
objects were not respecting the user-requested confidence level and that has been fixed.merMod
objects were throwing a spurious warning on R 3.4.2.interact_plot()
was mis-ordering secondary moderators. That has been fixed.export_summs()
had a major performance problem when providing extra arguments which may have also caused it to wrongly ignore some arguments. That has been fixed and it is much faster.Enhancements:
interact_plot()
now gives more informative labels for secondary moderators when the user has defined the values but not the labels.export_summs()
export_summs()
for compatibility with huxtable 1.0.0 changesImportant bugfix:
summ()
, the model was not mean-centered as the output stated. This has been fixed. I truly regret the error—double-check any analyses you may have run with this feature.New function: export_summs()
.
This function outputs regression models supported by summ()
in table formats useful for RMarkdown output as well as specific options for exporting to Microsoft Word files. This is particularly helpful for those wanting an efficient way to export regressions that are standardized and/or use robust standard errors.
The documentation for j_summ()
has been reorganized such that each supported model type has its own, separate documentation. ?j_summ
will now just give you links to each supported model type.
More importantly, j_summ()
will from now on be referred to as, simply, summ()
. Your old code is fine; j_summ()
will now be an alias for summ()
and will run the same underlying code. Documentation will refer to the summ()
function, though. That includes the updated vignette.
One new feature for summ.lm
:
part.corr = TRUE
argument for a linear model, partial and semipartial correlations for each variable are reported.More tweaks to summ.merMod
:
lmer()
vs. glmer()
/nlmer()
) and, in the case of linear models, whether the pbkrtest
package is installed. If it is, p values are calculated based on the Kenward-Roger degrees of freedom calculation and printed. Otherwise, p values are not shown by default with lmer()
models. p values are shown with glmer()
models, since that is also the default behavior of lme4
.r.squared
option, which for now is FALSE by default. It adds runtime since it must fit a null model for comparison and sometimes this also causes convergence issues.Returning to CRAN!
A very strange bug on CRAN’s servers was causing jtools updates to silently fail when I submitted updates; I’d get a confirmation that it passed all tests, but a LaTeX error related to an Indian journal I cited was torpedoing it before it reached CRAN servers.
The only change from 0.7.0 is fixing that problem, but if you’re a CRAN user you will want to flip through the past several releases as well to see what you’ve missed.
New features:
j_summ()
can now provide cluster-robust standard errors for lm models.j_summ()
output now gives info about missing observations for supported models.j_summ()
/scale_lm()
/center_lm()
can standardize/center models with logged terms and other functions applied.interact_plot()
and effect_plot()
will now also support predictors that have functions applied to them.j_summ()
now supports confidence intervals at user-specified widths.j_summ()
now allows users to not display p-values if requested.j_summ()
output with merMod objects, since it provides p-values calculated on the basis of the estimated t-values. These are not to be interpreted in the same way that OLS and GLM p-values are, since with smaller samples mixed model t-values will give inflated Type I error rates.j_summ()
will not show p-values for merMod
objects.Bug fix:
scale_lm()
did not have its center argument implemented and did not explain the option well in its documentation.johnson_neyman()
got confused when a factor variable was given as a predictorBug fix release:
wgttest()
acted in a way that might be unexpected when providing a weights variable name but no data argument. Now it should work as expected by getting the data frame from the model call.gscale()
had a few situations in which it choked on missing data, especially when weights were used. This in turn affected j_summ()
, scale_lm()
, and center_lm()
, which each rely on gscale()
for standardization and mean-centering. That’s fixed now.gscale()
wasn’t playing nicely with binary factors in survey designs, rendering the scaling incorrect. If you saw a warning, re-check your outputs after this update.A lot of changes!
New functions:
effect_plot()
: If you like the visualization of moderation effects from interact_plot()
, then you should enjoy effect_plot()
. It is a clone of interact_plot()
, but shows a single regression line rather than several. It supports GLMs and lme4 models and can plot original, observed data points.pf_sv_test()
: Another tool for survey researchers to test whether it’s okay to run unweighted regressions. Named after Pfeffermann and Sverchkov, who devised the test.weights_tests()
: Like probe_interaction()
does for the interaction functions, weights_tests()
will run the new pf_sv_test()
as well as wgttest()
simultaneously with a common set of arguments.Enhancements:
"jtools-digits"
.wgttest()
now accepts and tests GLMs and may work for other regression models.Bug fixes:
j_summ()
would print significance stars based on the rounded p value, sometimes resulting in misleading output. Now significance stars are based on the non-rounded p values.probe_interaction()
did not pass an “alpha” argument to sim_slopes()
, possibly confusing users of johnson_neyman()
. The argument sim_slopes()
is looking for is called "jnalpha"
. Now probe_interaction will pass "alpha"
arguments as "jn_alpha"
.interact_plot()
would stop on an error when the model included a two-level factor not involved in the interaction and not centered. Now those factors in that situation are treated like other factors.interact_plot()
sometimes gave misleading output when users manually defined moderator labels. It is now more consistent with the ordering the labels and values and will not wrongly label them when the values are provided in an odd order.wgttest()
now functions properly when a vector of weights is provided to the weights argument rather than a column name.gscale()
now works properly on tibbles, which requires a different style of column indexing than data frames.j_summ()
/standardize_lm()
/center_lm()
now work properly on models that were originally fit with tibbles in the data argument.sim_slopes()
would fail for certain weighted lm
objects depending on the way the weights were specified in the function call. It should now work for all weighted lm
objects.More goodies for users of interact_plot()
:
interact_plot()
. It would work previously, but didn’t use a weighted mean or SD in calculating values of the moderator(s) and for mean-centering other predictors. Now it does.interact_plot()
. Previously, factor variables had to be a moderator.interact_plot()
has only two unique values (e.g., dummy variables that have numeric class), by default only those two values have tick marks on the x-axis. Users may use the pred.labels
argument to specify labels for those ticks.set.offset
argument. By default it is 1 so that the y-axis represents a proportion.Other feature changes:
sim_slopes()
now supports weights (from the weights argument rather than a svyglm
model). Previously it used unweighted mean and standard deviation for non-survey models with weights.wgttest()
.Bug fixes:
sim_slopes()
called johnson_neyman()
while the robust
argument was set to TRUE, the robust.type
argument was not being passed (causing the default of “HC3” to be used). Now it is passing that argument correctly.interact_plot()
, providing an option to plot on original (nonlinear) scale.interact_plot()
can now plot fixed effects interactions from merMod
objectsj_summ()
with R 3.4.xmerMod
support for j_summ()
. Still needs convergence warnings, some other items.