Minor release.

Fixes:

- When Satterthwaite or Kenward-Roger degrees of freedom were used to calculate
*p*values for linear`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. - When the deprecated
`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. - Fixed an error in
`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:

- Added
`%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`

:

`interact_plot()`

`cat_plot()`

`sim_slopes()`

`johnson_neyman()`

`probe_interaction()`

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.- The function that prints those tables mentioned above is called
`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`

)- The
`model.check`

argument in`summ()`

has been removed. - A function called
`get_colors`

is now available to users. It retrieves the color palettes used in`jtools`

functions. - Plots made by
`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)- Logical variables (with values of
`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.- 2 vignettes are now made with
`rmdformats`

rather than the base`rmarkdown`

template. - S3 methods for S3 generics that aren’t imported by the package (
`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.

- Suppressed a number of warning messages caused by a
`broom`

update when using`export_summs()`

and`plot_coefs()`

. - Fixed an error with
`plot_coefs()`

arising from the latest update to`ggplot2`

. - Fixed a new bug introduced in 1.0.0 wherein the points of weighted data were not being sized according to their weight.
- Fixed an issue with pseudo-R^2 calculation in non-interactive use. [#34]
- Pseudo-R^2 is now included in the
`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.

- For
`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.- Users may now label values in
`sim_slopes()`

in a way similar to`interact_plot()`

. [#35] - Users may provide their own labels for preset moderator values with
`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.- All
`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'`

.

- All interaction functions now use
`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.

- There is now a
`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()`

. - For those who have
`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

- Major speed gains (15% faster for small
`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. - More model types are supported. In the past, some models failed because the update method was not defined correctly or there was more information needed to refit the model than what can be provided by these functions.
- More complicated formula input is supported, with a caveat. If you have, for instance, log-transformed a predictor (with
`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 `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`

Using the `crayon`

package as a backend, console output is now formatted for most `jtools`

functions for better readability on supported systems. Feedback on this is welcome since this might look better or worse in certain editors/setups.

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.- An upcoming version of R will change a common warning to an error, causing a need to change the internals of
`gscale`

. - The default model names in
`export_summs()`

had an extra space (e.g.,`( 1)`

) due to changes in`huxtable`

. The defaults are now just single numbers.

Bugfix:

- Johnson-Neyman plots misreported the alpha level if
`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:

- An update to
`huxtable`

broke`export_summs()`

, but that has been fixed.

Feature updates:

- You can now manually provide colors to
`interact_plot()`

and`cat_plot()`

by providing a vector of colors (any format that`ggplot2`

accepts) for the`color.class`

argument. - Noah Greifer wrote up a tweak to
`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.

`johnson_neyman()`

:

- A new
`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. - A
`line.thickness`

argument has been added after Heidi Jacobs pointed out that it cannot be changed after the fact. - The construction of the multiple plots when using
`sim_slopes()`

for 3-way interactions is much-improved. - The critical test statistic used by default has been slightly altered. It previously used a normal approximation; i.e., if
`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.

`interact_plot()`

:

- More improvements to
`plot.points`

(see 0.8.1 for more). You can now plot observed data with 3-way interactions. - Another pre-set
`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. - A new
`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. - When the model used weights, like survey sampling weights, the observed data points are resized according to the observation’s weight when
`plot.points = TRUE`

. - New
`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.

`summ()`

:

- Users are now informed why the function is taking so long if
`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:

- When
`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. - Much better support for plotting observed data in
`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.- Confidence intervals for
`merMod`

objects were not respecting the user-requested confidence level and that has been fixed. - Confidence intervals for
`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. * confidence intervals are now properly supported with `export_summs()`

* changes made to `export_summs()`

for compatibility with huxtable 1.0.0 changes

Important bugfix:

- When standardize was set to TRUE using
`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`

:

- With the
`part.corr = TRUE`

argument for a linear model, partial and semipartial correlations for each variable are reported.

More tweaks to `summ.merMod`

:

- Default behavior with regard to p values depends on model type (
`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`

. - There is an
`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.- At long last,
`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.- I’ve added a warning to
`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. - By default,
`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 predictor

Bug 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:

- Set a default number of digits to print for all jtools functions with the option
`"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.- Related to the prior point,
`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()`

:

- Added support for models with a weights parameter in
`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. - Added support for two-level factor predictors in
`interact_plot()`

. Previously, factor variables had to be a moderator. - When predictor in
`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. - Offsets are now supported (especially useful for Poisson GLMs), but only if specified via the offset argument rather than included in the model formula. You can (and should) specify the offset used for the plot using the
`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.- Improved printing features of
`wgttest()`

.

Bug fixes:

- R 3.4 introduced a change that caused warning messages when return objects are created in a certain way. This was first addressed in jtools 0.4.5, but a few instances slipped through the cracks. Thanks to Kim Henry for pointing out one such instance.
- When
`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.

- Added better support for plotting nonlinear interactions with
`interact_plot()`

, providing an option to plot on original (nonlinear) scale. `interact_plot()`

can now plot fixed effects interactions from`merMod`

objects- Fixed warning messages when using
`j_summ()`

with R 3.4.x - Added preliminary
`merMod`

support for`j_summ()`

. Still needs convergence warnings, some other items.

- Under the hood changes to
`j_summ()`

- Cleaned up examples
- Added
`wgttest()`

function, which runs a test to assess need for sampling weights in linear regression

- No matter what you do, there’s nothing like seeing your package on CRAN to open your eyes to all the typos, etc. you’ve put into your package.

- This is the first CRAN release. Compared to 0.4.1, the prior Github release, dependencies have been removed and several functions optimized for speed.