what about this? Any suggestion for different themes / color scales? You need latest performance and see from GitHub...

library(lme4)
#> Loading required package: Matrix
library(see)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
library(performance)

data(sleepstudy)
sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
sleepstudy$mysubgrp <- NA
for (i in 1:5) {
  filter_group <- sleepstudy$mygrp == i
  sleepstudy$mysubgrp[filter_group] <-
    sample(1:30, size = sum(filter_group), replace = TRUE)
}

sleepstudy$x1 <- rnorm(nrow(sleepstudy))
sleepstudy$x2 <- runif(nrow(sleepstudy))

m <- lmer(
  Reaction ~ Days + x1 + x2 + (1 | mygrp / mysubgrp) + (1 | Subject),
  data = sleepstudy
)
#> boundary (singular) fit: see ?isSingular

check_model(m)

^{Created on 2019-06-03 by the reprex package (v0.3.0)}

I would say tags and tags_labels is redundant. Why not directly setting labels via tags, and if tags = NULL. no tags are shown.

What does transform() actually do? (see here: https://github.com/easystats/see/blob/master/R/geom_violinhalf.R#L73)

Can we replace it by dplyr::mutate()?

It works like a charm without labels but fails with :(

library(see)
params <- parameters::model_parameters(psych::fa(mtcars, nfactors = 3, rotate = "none"))
plot(params)

params <- parameters::model_parameters(psych::fa(mtcars, nfactors = 3, rotate = "none"), labels = as.character(1:ncol(mtcars)))
plot(params)
#> Error in abs(.data$y): non-numeric argument to mathematical function

^{Created on 2019-11-18 by the reprex package (v0.3.0)}

With the current structure, which makes the plotting function per se quite small and transparent (often consisting of just the ggplot chunk, as the bulk of the work is done in data_plot), it should be rather fairly easy to add a how_to_plot() method, that would actually show (print) the plot code. Then the user could copy this code and tweak it to his needs.

How does that sound?

@mattansb could you please add the description to the arguments for the bayesfactor_models-plot in data_plot.R?

https://github.com/easystats/see/blob/master/R/data_plot.R

should be similar to p_direction, but with rope bands

"Never use more dimensions than your data" - thus, a point-estimate should simply be a point. And then I would add error bars / CI to the plot, so you end up having forest plots.

Originally posted by @strengejacke in easystats/parameters#65 (comment)

Based on this discussion.

@strengejacke feel free to change the name etc., I've decided to push the creation of this package to add in a few things that I need for a paper I am writing. Hope it's ok with you 😉

Even though this is currently a relatively small and preliminary package, maybe we could think about releasing it on CRAN. Thus, we could start using it in vignettes etc. What do you think?

Not sure why this now happens, but when I run

library(rstanarm)
library(bayestestR)

model <- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris)
result <- rope(model, ci = c(0.9, 0.95))

plot(result, data = model, rope_color = "red") +
  theme_modern()

I get an error:

Error: Discrete value supplied to continuous scale

The y-value for the plot-data is not numeric, but "Posterior". I think the source of the error is somewhere here:
https://github.com/easystats/see/blob/master/R/plot.hdi.R#L10

@DominiqueMakowski any ideas?

https://twitter.com/hadleywickham/status/1160712230645784579?s=20

Autoscaling the dots would be awesome!

I like this idea. Maybe something like: which_estimates(c("All", "Median", "Mean", "MAP"))

We could derive this from bayestestR::point_estimate(), where this argement is named centrality. We could then save the option as attribute, and reading that attribute in the plot-method.

(0502ac0)

Minor, but what do you think of a white (instead of black) default colour (i.e., contour) for the pies? 😁

Now that priors can easily be extracted as distributions using simulate_priors, I wonder if it would be tough to give the option to add them in the posterior plots (so you can see for all parameters, the prior and the posterior)...

There is no hurry, but I think we have added all necessary stuff for forthcoming updates of our other packages (https://github.com/easystats/see/blob/master/NEWS.md), so I think, in general the next release of see is ready.

Any preferences regarding time frame, any stuff missing? @DominiqueMakowski @mattansb @pdwaggoner

This will most likely not pass CRAN checks:
ggplot2:::ggname
(https://github.com/easystats/see/blob/master/R/geom_violinhalf.R#L85)

Describe the bug
can't reproduce many of the plots from: https://easystats.github.io/see/articles/bayestestR.html, eg, priors = TRUE doesn't work; p_significance not working either, yields: Error in plot.window(...) : need finite 'xlim' values

To Reproduce
Steps to reproduce the behaviour:

1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error

Expected behaviour
I expected to be able to reproduce the [see?] plots but cannot

Screenshots

Specifiations (please complete the following information):
everything is 100% up to date

Currently in plots() we use ncol, but in plot.bayestestR_hdi we use n_columns. We might wanna harmonize this. For what it's worth, I think ncol and nrow are the most commonly used in R.

• ci
• equivalence_test
• estimate_density
• hdi
• rope
• p_direction

This would also mean that the plotting functions in see need to be updated to use as.data.frame(as.matrix(as.mcmc.emmGrid(x, names = FALSE))), right?

^{From here}

In bayestestR I mentioned Wagenmakers' pizza analogy for the BFs.

It's not a secret that my favourite dish is pizza.

I thought there a package for everything in R. Thus I looked for some pizza themes for pie charts...

didn't find one

Then I thought, this is something for see!

@mattansb what do you say about plot methods for the BFs objects that would return pizza pies (pie charts with a theme_pizza)?? Wouldn't that be the coolest thing in the world?

I think this is doable, and also preferable. This is rather a slow process, with small steps.

library(see)
library(bayestestR)
library(rstanarm)

junk <- capture.output(model <- rstanarm::stan_glm(mpg ~ wt + gear + cyl + disp, data = mtcars))
plot(equivalence_test(model, ci=0.5))
#> Warning: Possible multicollinearity between disp and wt (r = 0.63), disp
#> and cyl (r = 0.71). This might lead to inappropriate results. See 'Details'
#> in '?equivalence_test'.
#> Picking joint bandwidth of 0.0467
#> Warning: Removed 7996 rows containing non-finite values
#> (stat_density_ridges).

^{Created on 2019-05-17 by the reprex package (v0.2.1)}

Currently, if I understand, we start by keeping the samples of the posterior within the CI. Then, these remaining samples are passed to density_ridges which computes the density and plots it.

Unfortunately, the density estimation of a subsample done by density_ridges gives a distorted vision (especially in the tails, which is usually of interest) of the actual distribution, even compared to a regular geom_density (which is already distorted):

library(see)
library(bayestestR)
library(rstanarm)
library(ggplot2)
library(ggridges)

df <- data.frame(x = c(rnorm(10000), rnorm(10000)), 
                y = rep(c("a", "b"), each=10000))

df <- df[df$x < 1.5 & df$x > -1.5, ] # Let's say this is my HDI bounds
       

ggplot(df, aes(x = x, y=y)) +
  geom_density_ridges()
#> Picking joint bandwidth of 0.109

ggplot(df[df$y == "a",], aes(x = x)) +
  geom_density()

^{Created on 2019-05-17 by the reprex package (v0.2.1)}

One of the ways to address it is to do the density estimation beforehand, then select the HDI section, and pass that to geom_ridgeline:

library(see)
library(bayestestR)
library(rstanarm)
library(ggplot2)
library(ggridges)al
library(dplyr)


df <- data.frame(x = c(rnorm(10000), rnorm(10000)), 
                 y = rep(c("a", "b"), each=10000))

df2 <- rbind(
  bayestestR::estimate_density(df[df$y == "a", "x"]) %>% 
    dplyr::mutate(group="a"),
  bayestestR::estimate_density(df[df$y == "b", "x"]) %>% 
    dplyr::mutate(group="b")
)
df2 <- df2[df2$x < 1.5 & df2$x > -1.5, ] # Let's say this is my HDI bounds

ggplot(df2, aes(x = x, y=group, height=y)) +
  geom_ridgeline()

^{Created on 2019-05-17 by the reprex package (v0.2.1)}

• #39
• #35

Hi,
thanks for the package, I especially like the color layout and plot themes.
However, I ran into problems using geom_point2

When I run this code

library(ggplot2)
library(see)

p1 <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) + 
  geom_point() + theme_modern()

p2 <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) + 
  geom_point2() + theme_modern()

p3 <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species, shape = Species)) + 
  geom_point() + theme_modern()

p4 <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species, shape = Species)) + 
  geom_point2() + theme_modern()


plots(p1, p2, p3, p4, ncol = 2, tags = T)

it produces the following plot

You can see that plot B produces more diamond like points, compared with A.
Also, geom_point2 seems to not support shapes (plot D). Regulard geom_point works fine (plot C).

> sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252    LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                     LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] see_0.1.0     ggplot2_3.1.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1       rstudioapi_0.10  magrittr_1.5     tidyselect_0.2.5 insight_0.3.0    munsell_0.5.0    colorspace_1.4-1 R6_2.4.0         rlang_0.3.4      plyr_1.8.4       dplyr_0.8.1     
[12] tools_3.5.2      grid_3.5.2       gtable_0.3.0     withr_2.1.2      digest_0.6.19    lazyeval_0.2.2   assertthat_0.2.1 tibble_2.1.1     crayon_1.3.4     gridExtra_2.3    purrr_0.3.2     
[23] ggridges_0.5.1   glue_1.3.1       labeling_0.3     compiler_3.5.2   pillar_1.4.0     scales_1.0.0     pkgconfig_2.0.2

library(see)
plot(bayestestR::hdi(rnorm(1000)))

^{Created on 2019-10-02 by the reprex package (v0.2.1)}

Would be nice in these cases to have a regular distribution area (that touches the x axis). That would affect all the bayestestR plotting so I am not sure which is the most convenient way of doing it

library(bayestestR)
m <- insight::download_model("brms_zi_3")
#> Registered S3 method overwritten by 'xts':
#>   method     from
#>   as.zoo.xts zoo
p <- pd(m, component = "all")
plot(p)

^{Created on 2019-09-10 by the reprex package (v0.3.0)}

Maybe based on this:
https://strengejacke.github.io/sjPlot/reference/sjp.pca.html

See https://github.com/easystats/see/projects/3

@DominiqueMakowski and @strengejacke the X axis labels in figures 1 and 2 under Multiple Plots in the README look a bit off. Not sure where these are stored (presumably they are png or eps files...). Could be worth a look to correct for interested users.

For model_parameters(exponentiate = TRUE), the plot should transform scale to Odds Ratios.

Hi @strengejacke - apologies for my delay here. Yeah, I have some MWE for each. First, for the ePCP (expected proportion correctly predicted), we are looking for a higher value, bounded between 0 and 1, where 1 = a model that perfectly predicted all actual observations. Here is some sample code using mtcars:

library(OOmisc) # for ePCP
library(ggplot2)

logitmod <- glm(vs ~ mpg, data = mtcars); summary(logitmod)
logitmod2 <- glm(vs ~ mpg + cyl, data = mtcars); summary(logitmod2)

y <- mtcars$vs
pred1 <- predict(logitmod, type="response")
pred2 <- predict(logitmod2, type="response")

# ePCP, expected proportion of correct prediction
epcp1 <- ePCP(pred1, y, alpha = 0.05) # define observed values and obtain predicted values
epcp2 <- ePCP(pred2, y, alpha = 0.05)

epcpdata <- data.frame(rbind(epcp1, epcp2))
epcpdata$model <- c(1,2)
epcpdata$count <- factor(c(1,2), label = c("Model1","Model2"))

# Now the plot
ggplot(epcpdata, aes(x=model,y=ePCP,colour=count)) +
  geom_bar(position=position_dodge(), stat="identity", fill = "darkgray") +
  geom_errorbar(aes(ymin=lower, ymax=upper),
                width=.2,
                position=position_dodge(.9)) +
  labs(x="Model Specification",
       y="Expected Proportion of Correct Prediction",
       title = "Comparing ePCP between Model 1 and Model 2",
       colour="Model Specification") +
  theme_bw()

And next, for the heatmapFit, which is a little less flexible, the goal is to compare model predictions with smoothed empirical predictions. The 45-degree line references a perfect fit. Any deviance from that line suggests a loss in goodness of fit. The “p-value” legend shows if any deviance is statistically significant (dark color means statistical significance). Using the same models as an example from mtcars, it would look something like:

## heatmapfit
library(heatmapFit) # for heat maps

# quick rescale to bound predictions between 0 and 1
range <- function(x){
  (x-min(x))/(max(x)-min(x))
  }

pred1 <- range(pred1)
pred2 <- range(pred2)

heatmap.fit(y, pred1, reps = 1000, legend = TRUE)
heatmap.fit(y, pred2, reps = 1000, legend = TRUE)

** You can copy and paste this code to see the figures. Pretty clean, efficient renderings of model fit.

** And of course, ROC curves are always great, simple model fit checks. Let me know thoughts if you have them. Hope this helps a bit!

Originally posted by @pdwaggoner in easystats/performance#38 (comment)

Testing plotting is not straightforward. This issue to discuss some ideas and gather information.

• https://stackoverflow.com/questions/31038709/how-to-write-a-test-for-a-ggplot-plot

• Add median/mean difference
• Enable the plotting of individual draws as segments instead of CIs

Context

Plotting pairwise contrasts is tricky because the "significance" of the contrasts is often quite poorly reflected by the CIs of the estimated means.

Russ (from emmeans) suggested the following:

• The way SAS does it is to plot each pair of means in a 2D plot, along with the identity line and CIs (scaled by sqrt(1/2)) for the differences of the means going perpendicular to the identity line, centered at each point. A difference is statistically significant if the interval does not cross the identity line. I programmed that up, but I find it very hard to label the plot effectively and don't care much for it.
• You can try emmeans::plot(emm, comparisons = TRUE) where emm is the result of an emmeans() call. This adds red arrows to the plot which indicate significant differences when the arrows don't overlap. This uses an ad hoc algorithm, and it is not guaranteed to be possible, especially when the SEs of the differences vary widely. Feedback from users has not been all that enthusiastic, and I'm not sure I like it so much either, but to me, when it works, it's better than the SAS approach.
• If you do emmeans::CLD(emm), it adds a column to the summary of emm showing a compact letter display. Two means that share the same letter are significantly different. This works, but it is very black-and-white: with alpha = .05, P values slightly above or below .05 make a difference, but there's no difference between a P value of .051 and one of .987, or between .049 and .00001 [note that recently Russ decided not to support this method anymore]

Here's how emmeans is currently doing it:

The blue bars are confidence intervals for the EMMs, and the red arrows are for the comparisons among them. If an arrow from one mean overlaps an arrow from another group, the difference is not significant, based on the adjust setting (which defaults to "tukey"). (Note: Don’t ever use confidence intervals for EMMs to perform comparisons; they can be very misleading.)

Proposition

However, I am not entirely satisfied with these ways of representing contrasts, which I found quite unintuitive or complex. I feel like there is a gap in the available ways of representing estimated contrasts. As I am often using them, I tried to imagine different ways of representing the differences between factor levels.

I currently have an idea in mind, but I can't even say if it's a good solution, as I have troubles making the plot I have in mind in the first place. Get ready for some paint 🎨

The idea would be to represent at the same time means and intervals (via pointrange). Then, each pairwise contrast would be shown as a "ray of light" (a triangle basically) starting from the lefthand level, and spanning over the CI of the difference + the mean of the left level.

For the contrast between levels A and B, there would be a triangle with one corner located at the mean of A, and it would "project" over the interval of the CI of the difference at B.

In dark red are the estimated means and their CIs and in light green would be the CI_high and CI_low of the difference (added to the mean of A). The dark green line would be the "mean" difference.

This could be interpreted as: for one this given value of A, B would be in the green area. I.e., independently of the variability of the means, B would be always (in this case) bigger than A.

For multiple levels, I have the means:

library(tidyverse)
library(rstanarm)
library(estimate)
library(see)

model <- stan_glm(Sepal.Width ~ Species, data=iris)
contrasts <- estimate_contrasts(model)
means <- estimate_means(model)

means %>%
  ggplot() +
  geom_pointrange(data=means, aes(x=Species, y=Median, ymin=CI_low, ymax=CI_high))

^{Created on 2019-04-17 by the reprex package (v0.2.1)}

But then I have no clear idea how to approach something like this:

(values are arbitrary)

I don't if the best way to achieve this is through geom_polygon or geom_ribbon.

Does this idea makes any sense?

Hi @DominiqueMakowski and @strengejacke - There was a brand new journal just announced called "Journal of Data Science, Statistics, and Visualisation". I found out about it through the ASA listserv. They announced a call for the first round of papers to be published. There are no APCs and its open source! What do you guys think about preparing and submitting a paper covering an introduction and implementation of see? It could be a really cool thing to get easystats in on the ground level of a new journal related to what we all do.

Here is the homepage: https://jdssv.org/index.php/jdssv

Let me know if you're on board. If so, I would be happy to take the lead on the paper and start drafting. If not, no worries.

• Remove annoying tableGrobs textual output
• Improve alignment (?)

@mattansb @DominiqueMakowski

Currently, plot.see_bayesfactor_models() uses within() to create variables. The help-file, however, states:

For programming however, i.e., in one's functions, more care is needed, and typically one should refrain from using with(), as, e.g., variables in data may accidentally override local variables, see the reference.

Should we re-write the code in a less beatufiul, non-piped way to avoid with() / within()?

I think some of the plot-functions can be described in more details. Even me as package maintainer is struggling with some of the arguments ;-) especially for plot.bayesfactor_models().

See example here:

https://easystats.github.io/see/articles/bayestestR.html#probability-of-direction-pd

intercept for zi is not remove, for cond it is.

Something like ggthemes::theme_solarized(light = FALSE), but with a more contrastful color for axis labels, and maybe more beatiful legend?

Here's the ggthemes-theme:

library(easystats)
model <- rstanarm::stan_glm(mpg ~ wt + gear + cyl + disp, data = mtcars)
result <- equivalence_test(model)

plot(result) + ggthemes::theme_solarized(light = F) + scale_fill_material()

^{Created on 2019-05-16 by the reprex package (v0.2.1)}

@DominiqueMakowski what would you say? Can you quickly copy/re-use the code from the dark-theme?

Per the other issue, here is my simple solution, at least to get the ball rolling on this option. Happy to update address (or abandon) accordingly, depending on your preferences, @DominiqueMakowski and @strengejacke :

## Note the change in the function call as well as in the code at the end of the function:

theme_modern <- function(plot.title.size=15, plot.title.face="plain", plot.title.space=20, legend.position = "right", axis.title.space = 20, legend.title.size = 13, legend.text.size = 12, axis.title.size = 13, axis.title.face = "plain", axis.text.size = 12, tags.size=15, tags.face="bold", rotate=TRUE) {
  
  
  # Remove legend title if necessary
  if (is.null(plot.title.size)) {
    plot.title.size <- element_text(size = plot.title.size, face = plot.title.face, margin=margin(0,0,plot.title.space,0))
  } else if (plot.title.size == "none") {
    plot.title.size <- element_blank()
  } else {
    plot.title.size <- element_text(size = plot.title.size, face = plot.title.face, margin=margin(0,0,plot.title.space,0))
  }
  
  # Remove legend title if necessary
  if (is.null(legend.title.size)) {
    legend.title.size <- element_text(size = legend.title.size)
  } else if (legend.title.size == "none") {
    legend.title.size <- element_blank()
  } else {
    legend.title.size <- element_text(size = legend.title.size)
  }
  
  # Remove axis title if necessary
  if (is.null(axis.title.size)) {
    axis.title.size <- element_text(size = axis.title.size, face = axis.title.face)
  } else if (axis.title.size == "none") {
    axis.title.size <- element_blank()
  } else {
    axis.title.size <- element_text(size = axis.title.size, face = axis.title.face)
  }
  
  # Remove axis text if necessary
  if (is.null(axis.text.size)) {
    axis.text.size <- element_text(size = axis.text.size)
  } else if (axis.text.size == "none") {
    axis.text.size <- element_blank()
  } else {
    axis.text.size <- element_text(size = axis.text.size)
  }
  
  # rotate x axis labels or not?
  if (rotate == TRUE) {
    axis.text.x <- element_text(angle=45, vjust=0.5, size=9)
  } else {
    axis.text.x <- element_text(angle=0, vjust=0.5, size=9)
  }
  
  theme_classic() +
    theme(
      plot.title = plot.title.size,
      legend.position = legend.position,
      legend.text = element_text(size = legend.text.size),
      legend.title = legend.title.size,
      legend.key = element_blank(),
      legend.spacing.x = unit(2, "pt"),
      axis.title = axis.title.size,
      axis.title.y = element_text(margin = margin(t = 0, r = axis.title.space, b = 0, l = 0)),
      axis.title.x = element_text(margin = margin(t = axis.title.space, r = 0, b = 0, l = 0)),
      axis.text = axis.text.size,
      axis.ticks = element_blank(),
      plot.tag = element_text(size = tags.size, face = tags.face),
      strip.background = element_blank(),
      strip.text = element_text(face="bold"),
      axis.text.x = axis.text.x
    )
}


## WORKING EXAMPLE
library(ggplot2)

p1.rotated.default <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + 
  geom_boxplot() + theme_modern() 
p1.rotated.default

p2.not.rotated <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + 
  geom_boxplot() + theme_modern(rotate=FALSE) 
p2.not.rotated

Remove \dontrun{}.

@DominiqueMakowski estimate_fit() was renamed to estimate_response(), if I'm not mistaken. However, the plot now looks a bit different (I guess this is intended).

Could you please check https://github.com/easystats/see if plots are OK?

we could (and should?) actually add tests for how_to_plot() in the future...

Originally posted by @strengejacke in #9 (comment)

This is some great stuff - I really like the new check_* family. In this vein, I wrote a function to check for influential observations via cook's distance. Its in a similar flavor as your measures @strengejacke , and also includes options to print names of outliers, drop outliers (and write a new df), and plot cook's distance.

I would welcome any thoughts here or whether this is even a desired "check". Thanks all!

check_outliers <- function(model, print_outliers = TRUE, drop_outliers = FALSE, plot = TRUE) {
  data <- insight::get_data(model)
  n <- nrow(data)
  
  #calculate cook's distance
  cook <- stats::cooks.distance(model)
  o <- length(as.numeric(names(cook)[(cook > (4/n))]))
  outliers <- ifelse(o >= 1, 1, 0)
  
  # check for outliers via cook's distance
  if (outliers >= 1) {
    insight::print_color(sprintf("Warning: Outliers detected.\n"), 'red')
  } else insight::print_color(sprintf("OK: No outliers detected.\n"), 'green')
  
  #option to print names of outliers
  if (print_outliers == TRUE) {
    print((names(cook)[(cook > (4 / n))]))
  }
  
  # option to drop outliers and write a new data frame (- outliers)
  if (drop_outliers == TRUE) {
    data_dropped <- data[!(cook > (4 / n)), ]
    return(data_dropped)
  }
  
  # option to plot cook's distance values, with line denoting likely outliers
  if (plot == TRUE){
    plot(cook, pch = 19, 
         main = "Check for Influential Observations\n(Cook's Distance)",
         ylab = "Cook's Distance Value")
    abline(h = (4 / n), 
           col = "red",
           cex = 4)
    text(x = 1:length(cook) + 1, 
         y = cook, 
         labels = ifelse(cook > (4 / n), 
                         names(cook), ""), 
         col = "red")
  }
}

# example
mt1 <- mtcars[, c(1,3)]  # select only mpg and disp (continuous)
oo <- data.frame(mpg = c(37, 40), # create some fake outliers
                 disp = c(300, 400))
mt2 <- rbind(mt1, oo)  # attach outliers to main df
model <- lm(disp ~ mpg, data = mt2) # fit model with outliers

# call the function
check_outliers(model, print_outliers = TRUE, drop_outliers = FALSE, plot = TRUE)
#> Warning: Outliers detected.
#> [1] "1" "2"

^{Created on 2019-06-03 by the reprex package (v0.3.0)}

Originally posted by @pdwaggoner in easystats/performance#31 (comment)

What about creating a DOI for this so it can be properly cited in bayestestR's paper (see here)?

library(rstanarm)
library(see)
library(bayestestR)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
x <- point_estimate(model, "all")
plot(x)

^{Created on 2019-07-30 by the reprex package (v0.3.0)}

library(bayestestR)
library(see)

posterior <- distribution_beta(100, 0.4, 0.2)
plot(point_estimate(posterior))
#> Error in unit(rep(1, nrow), "null"): 'x' and 'units' must have length > 0

^{Created on 2020-01-03 by the reprex package (v0.3.0)}

😢