This vignette shows how to combine the `ggdist`

geoms with
output from the `broom`

package to enable visualization of
uncertainty from frequentist models. The general idea is to use
`xdist`

and `ydist`

aesthetics supported by
`ggdist`

stats to visualize *confidence distributions*
instead of visualizing posterior distributions as we might from a
Bayesian model. For more information on the families of geoms and stats
in ggdist, see `vignette("slabinterval")`

,
`vignette("dotsinterval")`

, and
`vignette("lineribbon")`

.

*Confidence distributions* are a way of unifying the notion of
sampling distributions, bootstrap distributions, and several other
concepts in frequentist inference. They are a convenient tool for
visualizing uncertainty in a way that generalizes across Bayesian and
frequentist frameworks: where in a Bayesian framework we might visualize
a *probability* distribution, in the frequentist framework we
visualize a *confidence* distribution. This gives us a way to use
the same geometries for uncertainty visualization in either
framework.

For more on confidence distributions, see: Xie, Min‐ge, and Kesar
Singh. Confidence
distribution, the frequentist distribution estimator of a parameter: A
review. *International Statistical Review* 81.1 (2013):
3-39.

The following libraries are required to run this vignette:

```
library(dplyr)
library(tidyr)
library(ggdist)
library(ggplot2)
library(broom)
library(modelr)
library(distributional)
theme_set(theme_ggdist())
```

We’ll start with an ordinary least squares (OLS) linear regression analysis of this simple dataset:

```
set.seed(5)
= 10
n = 5
n_condition =
ABC tibble(
condition = rep(c("A","B","C","D","E"), n),
response = rnorm(n * 5, c(0,1,2,1,-1), 0.5)
)
```

This is a typical tidy format data frame: one observation per row. Graphically:

```
%>%
ABC ggplot(aes(x = response, y = condition)) +
geom_point(alpha = 0.5) +
ylab("condition")
```

And a simple linear regression of the data is fit as follows:

`= lm(response ~ condition, data = ABC) m_ABC `

The default summary is not great from an uncertainty communication perspective:

`summary(m_ABC)`

```
##
## Call:
## lm(formula = response ~ condition, data = ABC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9666 -0.4084 -0.1053 0.4104 1.2331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1816 0.1732 1.048 0.30015
## conditionB 0.8326 0.2450 3.399 0.00143 **
## conditionC 1.6930 0.2450 6.910 1.38e-08 ***
## conditionD 0.8456 0.2450 3.452 0.00122 **
## conditionE -1.1168 0.2450 -4.559 3.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5478 on 45 degrees of freedom
## Multiple R-squared: 0.7694, Adjusted R-squared: 0.7489
## F-statistic: 37.53 on 4 and 45 DF, p-value: 8.472e-14
```

So let’s try half-eye plots instead. The basic idea is that we need
to get the three parameters for the sampling distribution of each
parameter and then use `stat_halfeye()`

to plot them. The
confidence distribution for parameter \(i\), \(\tilde\beta_i\), from an `lm`

model is a scaled-and-shifted t distribution:

\[ \tilde\beta_i \sim \textrm{student_t}\left(\nu, \hat\beta_i, \sigma_{\hat\beta_i}\right) \]

With:

- \(\nu\):
*degrees of freedom*, equal to`df.residual(m_ABC)`

- \(\hat\beta_i\):
*location*, equal to the point estimate of the parameter (`estimate`

column from`broom::tidy()`

) - \(\sigma_{\hat\beta_i}\):
*scale*, equal to the standard error of the parameter estimate (`std.error`

column from`broom::tidy()`

)

We can get the estimates and standard errors easily by using
`broom::tidy()`

:

`tidy(m_ABC)`

term | estimate | std.error | statistic | p.value |
---|---|---|---|---|

(Intercept) | 0.1815842 | 0.1732360 | 1.048190 | 0.3001485 |

conditionB | 0.8326303 | 0.2449927 | 3.398593 | 0.0014276 |

conditionC | 1.6929997 | 0.2449927 | 6.910410 | 0.0000000 |

conditionD | 0.8455952 | 0.2449927 | 3.451513 | 0.0012237 |

conditionE | -1.1168101 | 0.2449927 | -4.558545 | 0.0000394 |

Finally, we can construct vectors of probability distributions using
functions like `distributional::dist_student_t()`

from the distributional
package. The `stat_slabinterval()`

family of functions
supports these objects.

Putting everything together, we have:

```
%>%
m_ABC tidy() %>%
ggplot(aes(y = term)) +
stat_halfeye(
aes(xdist = dist_student_t(df = df.residual(m_ABC), mu = estimate, sigma = std.error))
)
```

If we would rather see uncertainty in conditional means, we can
instead use `modelr::data_grid()`

along with
`broom::augment()`

(similar to how we can use
`modelr::data_grid()`

with
`tidybayes::add_fitted_draws()`

for Bayesian models). Here we
want the confidence distribution for the mean in condition \(c\), \(\tilde\mu_c\):

\[ \tilde\mu_c \sim \textrm{student_t}\left(\nu, \hat\mu_c, \sigma_{\hat\mu_c} \right) \]

With:

- \(\nu\):
*degrees of freedom*, equal to`df.residual(m_ABC)`

- \(\hat\mu_c\):
*location*, equal to the point estimate of the mean in condition \(c\) (`.fitted`

column from`broom::augment()`

) - \(\sigma_{\hat\mu_c}\):
*scale*, equal to the standard error of the mean in condition \(c\) (`.se.fit`

column from`broom::augment(..., se_fit = TRUE)`

)

Putting everything together, we have:

```
%>%
ABC data_grid(condition) %>%
augment(m_ABC, newdata = ., se_fit = TRUE) %>%
ggplot(aes(y = condition)) +
stat_halfeye(
aes(xdist = dist_student_t(df = df.residual(m_ABC), mu = .fitted, sigma = .se.fit)),
scale = .5
+
) # we'll add the data back in too (scale = .5 above adjusts the halfeye height so
# that the data fit in as well)
geom_point(aes(x = response), data = ABC, pch = "|", size = 2, position = position_nudge(y = -.15))
```

Of course, this works with the entire
`stat_slabinterval()`

family. Here are gradient plots
instead:

```
%>%
ABC data_grid(condition) %>%
augment(m_ABC, newdata = ., se_fit = TRUE) %>%
ggplot(aes(y = condition)) +
stat_gradientinterval(
aes(xdist = dist_student_t(df = df.residual(m_ABC), mu = .fitted, sigma = .se.fit)),
scale = .5, fill_type = "gradient"
)
```

**Note:** The example above uses the
**experimental** `fill_type = "gradient"`

option. This can be omitted if your system does not support it; see
further discussion in the section on gradient plots in
`vignette("slabinterval")`

.

Or complementary cumulative distribution function (CCDF) bar plots:

```
%>%
ABC data_grid(condition) %>%
augment(m_ABC, newdata = ., se_fit = TRUE) %>%
ggplot(aes(y = condition)) +
stat_ccdfinterval(
aes(xdist = dist_student_t(df = df.residual(m_ABC), mu = .fitted, sigma = .se.fit))
)
```

We can also create quantile dotplots by using the `dots`

family of geoms. Quantile dotplots show quantiles from a distribution
(in this case, the sampling distribution), employing a *frequency
framing* approach to uncertainty communication that can be easier
for people to interpret (Kay et al. 2016, Fernandes et
al. 2018):

```
%>%
ABC data_grid(condition) %>%
augment(m_ABC, newdata = ., se_fit = TRUE) %>%
ggplot(aes(y = condition)) +
stat_dots(
aes(xdist = dist_student_t(df = df.residual(m_ABC), mu = .fitted, sigma = .se.fit)),
quantiles = 100
)
```

See `vignette("slabinterval")`

and
`vignette("dotsinterval")`

for more examples of uncertainty
geoms and stats in the slabinterval family.

The same principle of reconstructing the confidence distribution
allows us to use `stat_lineribbon()`

to construct uncertainty
bands around regression fit lines. Here we’ll reconstruct an example
with the `mtcars`

dataset from
`vignette("tidy-brms", package = "tidybayes")`

, but using
`lm()`

instead:

`= lm(mpg ~ hp * cyl, data = mtcars) m_mpg `

Again we’ll use `modelr::data_grid()`

with
`broom::tidy()`

, but now we’ll employ
`stat_lineribbon()`

:

```
%>%
mtcars group_by(cyl) %>%
data_grid(hp = seq_range(hp, n = 101)) %>%
augment(m_mpg, newdata = ., se_fit = TRUE) %>%
ggplot(aes(x = hp, fill = ordered(cyl), color = ordered(cyl))) +
stat_lineribbon(
aes(ydist = dist_student_t(df = df.residual(m_mpg), mu = .fitted, sigma = .se.fit)),
alpha = 1/4
+
) geom_point(aes(y = mpg), data = mtcars) +
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Dark2") +
labs(
color = "cyl",
fill = "cyl",
y = "mpg"
)
```

Another alternative to using `alpha`

to create gradations
of lineribbon colors in different groups is to use the
`fill_ramp`

aesthetic provided by `ggdist`

to
“ramp” the fill color of the ribbons from `"white"`

to their
full color (see `help("scale_fill_ramp")`

). Here we’ll
“whiten” the fill color of each band according to its `level`

(the `level`

variable is computed by
`stat_lineribbon()`

and is an ordered factor version of
`.width`

):

```
%>%
mtcars group_by(cyl) %>%
data_grid(hp = seq_range(hp, n = 101)) %>%
augment(m_mpg, newdata = ., se_fit = TRUE) %>%
ggplot(aes(x = hp, color = ordered(cyl))) +
stat_lineribbon(aes(
ydist = dist_student_t(df = df.residual(m_mpg), mu = .fitted, sigma = .se.fit),
fill = ordered(cyl),
fill_ramp = stat(level)
+
)) geom_point(aes(y = mpg), data = mtcars) +
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Dark2") +
labs(
color = "cyl",
fill = "cyl",
y = "mpg"
)
```

One way to avoid problems created by overlapping ribbons (besides
using `alpha`

, as above) is to use commutative blending modes
available in R > 4.2. The ggblend package enables
support for such blending modes, and its documentation includes examples
of their use with `stat_lineribbon()`

.

For more examples of using lineribbons, see
`vignette("lineribbon")`

.