The following vignette describes the `geom_lineribbon()`

family of stats and geoms in `ggdist`

, a family of stats and geoms for creating line+ribbon plots: for example, plots with a fit line and one or more uncertainty bands. This vignette also describes the `curve_interval()`

function for calculating curvewise (joint) intervals for lineribbon plots.

The following libraries are required to run this vignette:

```
library(dplyr)
library(tidyr)
library(purrr)
library(ggdist)
library(ggplot2)
library(distributional)
library(cowplot)
theme_set(theme_ggdist())
```

The lineribbon family follows the same naming scheme as the larger family of slabinterval geoms (see `vignette("slabinterval")`

). It has three members: `geom_lineribbon()`

, `stat_lineribbon()`

, and `stat_dist_lineribbon()`

.

`geom_lineribbon()`

is meant to be used on data already summarized into points and intervals.`stat_lineribbon()`

is meant to be used on sample data; e.g. draws from a posterior distribution of lines/curves, a bootstrap sampling distribution of lines/curves, an ensemble distribution, or any other distribution, really. This stat computes relevant summaries (points and intervals) before forwarding the summaries to`geom_lineribbon()`

.`stat_dist_lineribbon()`

can be used to create lineribbon geoms for analytical distributions. It takes either distributional objects or distribution names (the`dist`

aesthetic) and arguments (the`args`

aesthetic or`arg1`

, …`arg9`

aesthetics) and compute the relevant slabs and intervals.

All lineribbon geoms can be plotted horizontally or vertically. Depending on how aesthetics are mapped, they will attempt to automatically determine the orientation; if this does not produce the correct result, the orientation can be overridden by setting `orientation = "horizontal"`

or `orientation = "vertical"`

.

The base lineribbon geometry can only be applied to already-summarized data: data frames where each row contains a point and the upper and lower bounds of an interval. For the purposes of this example, we will first look at how to generate such a data frame from a data frame of sample data; such data could include Bayesian posterior distributions (for examples of this usage, see vignettes in tidybayes) or bootstrap sampling distributions. For the simple example here, we’ll just generate a distribution of lines manually:

```
set.seed(1234)
= 5000
n
= tibble(
df .draw = 1:n,
intercept = rnorm(n, 3, 1),
slope = rnorm(n, 1, 0.25),
x = list(-4:5),
y = map2(intercept, slope, ~ .x + .y * -4:5)
%>%
) unnest(c(x, y))
```

`df`

is a 50,000-row data frame with a sample of 5000 y values for each x value. It represents a sample of 5000 lines. Here is a subsample of 100 of the lines:

```
%>%
df filter(.draw %in% 1:100) %>%
ggplot(aes(x = x, y = y, group = .draw)) +
geom_line(alpha = 0.25)
```

We can summarize the data frame at each x position using `median_qi()`

(or any other function in the `point_interval()`

family):

```
%>%
df group_by(x) %>%
median_qi(y)
```

x | y | .lower | .upper | .width | .point | .interval |
---|---|---|---|---|---|---|

-4 | -1.0077977 | -3.709267 | 1.610913 | 0.95 | median | qi |

-3 | -0.0149326 | -2.382834 | 2.300754 | 0.95 | median | qi |

-2 | 0.9814098 | -1.140094 | 3.066840 | 0.95 | median | qi |

-1 | 1.9952129 | 0.000809 | 3.968098 | 0.95 | median | qi |

0 | 2.9940488 | 1.072151 | 4.942736 | 0.95 | median | qi |

1 | 3.9923913 | 2.036765 | 5.996592 | 0.95 | median | qi |

2 | 4.9852137 | 2.879074 | 7.141744 | 0.95 | median | qi |

3 | 5.9984769 | 3.584397 | 8.443761 | 0.95 | median | qi |

4 | 6.9951571 | 4.248730 | 9.809548 | 0.95 | median | qi |

5 | 8.0110761 | 4.885812 | 11.161546 | 0.95 | median | qi |

Given data summarized like that above, we can then construct a line+ribbon chart using `geom_lineribbon()`

:

```
%>%
df group_by(x) %>%
median_qi(y) %>%
ggplot(aes(x = x, y = y, ymin = .lower, ymax = .upper)) +
geom_lineribbon(fill = "gray65")
```

`geom_lineribbon()`

automatically pulls in the `.width`

column and maps it onto the `fill`

aesthetic so that intervals of different widths get different colors. However, the result with just one interval is not great, as the default color scheme is quite dark with just one color. Let’s make two changes to improve the chart:

- We will use the
`.width`

argument of`median_qi()`

to generate multiple uncertainty bands (a 50%, 80%, and 95% band). - We will use
`scale_fill_brewer()`

to get a nicer color scheme.

The result looks like this:

```
%>%
df group_by(x) %>%
median_qi(y, .width = c(.50, .80, .95)) %>%
ggplot(aes(x = x, y = y, ymin = .lower, ymax = .upper)) +
geom_lineribbon() +
scale_fill_brewer()
```

To apply lineribbons to sample data, we can also use `stat_lineribbon()`

instead of summarizing the data first using `median_qi()`

or `point_interval()`

. This function does the summarization internally for you.

Taking the previous example, we can simply removing the lines that summarize the data first, and omit the aesthetic mappings onto `ymin`

and `ymax`

, which are automatically set by `stat_lineribbon()`

. This simplifies the previous example considerably:

```
%>%
df ggplot(aes(x = x, y = y)) +
stat_lineribbon() +
scale_fill_brewer()
```

The default `.width`

setting of `stat_lineribbon()`

is `c(.50, .80, .95)`

, as can be seen in the results above. We can change this as well; for example:

```
%>%
df ggplot(aes(x = x, y = y)) +
stat_lineribbon(.width = c(.66, .95)) +
scale_fill_brewer()
```

You can also create gradient-like plots with lineribbons by passing a large number of probability levels to the `.width`

parameter. Calling `ppoints(n)`

generates `n`

values between `0`

and `1`

(exclusive), which can be used as interval widths. We must also override the default `fill`

aesthetic to use the `.width`

generated variable, which is continuous (by default lineribbons map `level`

onto the `fill`

aesthetic, which is a transformation of `.width`

into a factor—and produces illegible legends when many values are used). This also requires us to use a continuous fill scale (e.g. `scale_fill_distiller()`

) instead of a discrete one (e.g. `scale_fill_brewer()`

):

```
%>%
df ggplot(aes(x = x, y = y)) +
stat_lineribbon(aes(fill = stat(.width)), .width = ppoints(50)) +
scale_fill_distiller()
```

To get a gradient that ramps down to the background color, you could also use the `fill_ramp`

aesthetic provided by `ggdist`

(see `help("scale_fill_ramp")`

). It is necessary to invert the range (`range = c(1, 0)`

) so that the gradient is dark in the middle (rather than the outside):

```
%>%
df ggplot(aes(x = x, y = y)) +
stat_lineribbon(aes(fill_ramp = stat(.width)), .width = ppoints(50), fill = "#2171b5") +
scale_fill_ramp_continuous(range = c(1, 0))
```

If you are using a different background color than `"white"`

, pass that color to the `from`

argument of `scale_fill_ramp_continuous()`

.

One may also want to plot multiple lineribbons on the same plot, perhaps corresponding to separate groups. This might require applying a different color or fill to differentiate the lineribbons. Consider these data:

```
= rbind(
df_2groups mutate(df, g = "a"),
mutate(df, g = "b", y = (y - 2) * 0.5)
)
```

The naive approach to plotting will make the lines hard to distinguish:

```
%>%
df_2groups ggplot(aes(x = x, y = y, color = g)) +
stat_lineribbon() +
scale_fill_brewer()
```

Instead, we could change the fill color and allow the lines the be semi-transparent:

```
%>%
df_2groups ggplot(aes(x = x, y = y, fill = g)) +
stat_lineribbon(alpha = 1/4)
```

We could also use the `fill_ramp`

aesthetic provided by `ggdist`

to similar effect (see `help("scale_fill_ramp")`

), though this tends to work best when the lines do not overlap:

```
%>%
df_2groups ggplot(aes(x = x, y = y, fill = g)) +
stat_lineribbon(aes(fill_ramp = stat(level)))
```

Lineribbons can also be applied to analytical distributions. This use case often arises with confidence distributions describing uncertainty in a fit line; for an example, see the end of `vignette("freq-uncertainty-vis")`

. Here, we will look at a simpler example where we construct the distributions manually; here we’ll assume some variable `y`

that is normally distributed conditional on `x`

with mean `y_mean`

and standard deviation `y_sd`

:

```
= tibble(
analytical_df x = -4:5,
y_mean = 3 + x,
y_sd = sqrt(x^2/10 + 1),
) analytical_df
```

x | y_mean | y_sd |
---|---|---|

-4 | -1 | 1.612451 |

-3 | 0 | 1.378405 |

-2 | 1 | 1.183216 |

-1 | 2 | 1.048809 |

0 | 3 | 1.000000 |

1 | 4 | 1.048809 |

2 | 5 | 1.183216 |

3 | 6 | 1.378405 |

4 | 7 | 1.612451 |

5 | 8 | 1.870829 |

We can visualize this conditional distribution with `stat_dist_lineribbon()`

using `distributional::dist_normal()`

:

```
%>%
analytical_df ggplot(aes(x = x, dist = dist_normal(y_mean, y_sd))) +
stat_dist_lineribbon() +
scale_fill_brewer()
```

For more examples of lineribbons, including multiple lineribbons in the same plot, see the examples at the end of `vignette("freq-uncertainty-vis")`

.

The above examples all calculate *conditional* intervals, either using `point_interval()`

(directly or indirectly) or using quantiles of an analytical distribution. However, you may not always want conditional intervals.

Where `point_interval()`

calculates *pointwise* intervals, or intervals *conditional* on each group, `curve_interval()`

calculates *joint* or *curvewise* intervals. In the literature these are also called *curve boxplots* (Mirzargar *et al.* 2014, Juul *et al.* 2020).

An example will help illustrate the difference between the two types of intervals. Consider the following set of curves, where each curve is assumed to be a “draw” from some distribution of curves, \(\mathbf{y} = f(\mathbf{x})\), where \(\mathbf{x}\) and \(\mathbf{y}\) are vectors:

```
= 11 # number of curves
k = 501
n = tibble(
df .draw = 1:k,
mean = seq(-5,5, length.out = k),
x = list(seq(-15,15,length.out = n)),
%>%
) unnest(x) %>%
mutate(y = dnorm(x, mean, 3)/max(dnorm(x, mean, 3)))
%>%
df ggplot(aes(x = x, y = y)) +
geom_line(aes(group = .draw), alpha=0.2)
```

If one used one of the `point_interval()`

functions to summarize this curve (such as `median_qi()`

, `mean_qi()`

, etc), it would calculate *pointwise* intervals:

```
%>%
df group_by(x) %>%
median_qi(y, .width = .5) %>%
ggplot(aes(x = x, y = y)) +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
geom_line(aes(group = .draw), alpha=0.15, data = df) +
scale_fill_brewer() +
ggtitle("50% pointwise intervals with point_interval()")
```

The 50% *pointwise* interval calculated at (say) \(x = 1\) would contain 50% of the draws from \(y|x=1\). At a different value of \(x\), say \(x = 2\), the 50% pointwise interval would also contain 50% of the draws from \(y|x = 2\). However, the specific draws contained in the interval for \(y|x=2\) might be *different* draws from those contained in the interval for \(x|y=1\): if you trace any of the underlying curves, you will notice that each curve is included in some intervals and not included in others. Thus, the set of intervals—the ribbon—may not fully contain 50% of curves. Indeed, inspecting the above plot, the 50% ribbon contains **none** of the curves!

Depending on what type of inference we care about, this might be sufficient for our purposes: maybe we are interested just in what the outcome is likely to be at a given x value (a conditional inference), but we are not interested in joint inferences (e.g., what is the shape of the curve likely to look like?). However, if we *are* interested in such joint inferences, pointwise intervals can be misleading. The shape of the median curve, for example, looks nothing like any of the possible outcomes. The interval also does not include the maximum value of *any* of the underlying curves, which might cause us to conclude (incorrectly) that a value close to 1 is unlikely, when the exact opposite is the case (every curve touches 1).

One solution I like for such situations is to show spaghetti plots: just plot the underlying curves. This is a so-called *frequency framing* uncertainty visualization, and it tends to work fairly well. However, in some cases you may want a visual summary using intervals, in which case curvewise intervals could help. Using `curve_interval()`

instead of `point_interval()`

or `median_qi()`

calculates these:

```
%>%
df group_by(x) %>%
curve_interval(y, .width = .5) %>%
ggplot(aes(x = x, y = y)) +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
geom_line(aes(group = .draw), alpha=0.15, data = df) +
scale_fill_brewer() +
ggtitle("50% curvewise intervals with curve_interval()")
```

Note how the 50% *curvewise* interval now contains half of the underlying curves, and the median curve *is* one of the underlying curves (so it is more representative of the curve shape we should expect). These intervals also cover the peaks of the curves, where the pointwise intervals did not.

An X% *curvewise* interval is calculated across all the curves by taking the top X% closest curves to the central curve, for some definition of “close” and “central”. The `curve_interval()`

function currently orders curves by mean halfspace depth, which is basically how close each curve is to the pointwise median in percentiles, on average.

Given the above, let’s see what more realistic curvewise intervals of the above example might look like by using a larger number of draws:

```
= 1000 # number of curves
k = tibble(
large_df .draw = 1:k,
mean = seq(-5,5, length.out = k),
x = list(seq(-15,15,length.out = n)),
%>%
) unnest(x) %>%
mutate(y = dnorm(x, mean, 3)/max(dnorm(x, mean, 3)))
= large_df %>%
pointwise_plot group_by(x) %>%
median_qi(y, .width = c(.5, .8, .95)) %>%
ggplot(aes(x = x, y = y)) +
geom_hline(yintercept = 1, color = "gray75", linetype = "dashed") +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
scale_fill_brewer() +
ggtitle("point_interval()")
= large_df %>%
curvewise_plot group_by(x) %>%
curve_interval(y, .width = c(.5, .8, .95)) %>%
ggplot(aes(x = x, y = y)) +
geom_hline(yintercept = 1, color = "gray75", linetype = "dashed") +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
scale_fill_brewer() +
ggtitle("curve_interval()")
plot_grid(nrow = 2,
pointwise_plot, curvewise_plot )
```

Notice how the pointwise intervals miss out on the peaks of this distribution of curves. Even the 95% ribbon, which appears to reach up to the peaks, in fact falls slightly short. While this is a bit of a pathological example, it does demonstrate the potential shortcomings of pointwise intervals.

One challenge with curvewise intervals is that they can tend to be very conservative, especially at moderate-to-large intervals widths. Let’s bootstrap some LOESS fits to horsepower versus MPG in the `mtcars`

dataset:

```
set.seed(1234)
= 4000
n = seq(min(mtcars$mpg), max(mtcars$mpg), length.out = 100)
mpg
= tibble(
mtcars_boot .draw = 1:n,
m = map(.draw, ~ loess(
~ mpg,
hp span = 0.9,
# this lets us predict outside the range of the data
control = loess.control(surface = "direct"),
data = slice_sample(mtcars, prop = 1, replace = TRUE)
)),hp = map(m, predict, newdata = tibble(mpg)),
mpg = list(mpg)
%>%
) select(-m) %>%
unnest(c(hp, mpg))
```

This is a pretty naive approach, and definitely not a great way of analyzing this data, but it will illustrate the problems of some kinds of problems we might get with joint intervals. Let’s look at a spaghetti plot of just 400 draws from this bootstrap distribution first:

```
%>%
mtcars_boot filter(.draw < 400) %>%
ggplot(aes(x = mpg, y = hp)) +
geom_line(aes(group = .draw), alpha = 1/10) +
geom_point(data = mtcars) +
coord_cartesian(ylim = c(0, 400))
```

Now, pointwise intervals:

```
%>%
mtcars_boot ggplot(aes(x = mpg, y = hp)) +
stat_lineribbon(.width = c(.5, .7, .9)) +
geom_point(data = mtcars) +
scale_fill_brewer() +
coord_cartesian(ylim = c(0, 400))
```

Finally, curvewise intervals:

```
%>%
mtcars_boot group_by(mpg) %>%
curve_interval(hp, .width = c(.5, .7, .9)) %>%
ggplot(aes(x = mpg, y = hp)) +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
geom_point(data = mtcars) +
scale_fill_brewer() +
coord_cartesian(ylim = c(0, 400))
```

Notice how noisy the curvewise intervals are. In addition, because a number of curves tend to start low and end high (or vice versa), above 50%, the bands rapidly expand to cover almost all of the curves in the sample, regardless of coverage level. You can try different methods to sometimes get improved bands; e.g. using the `"bd-mbd"`

method per Sun and Genton (2011) works better on this dataset:

```
%>%
mtcars_boot group_by(mpg) %>%
curve_interval(hp, .width = c(.5, .7, .9), .interval = "bd-mbd") %>%
ggplot(aes(x = mpg, y = hp)) +
geom_lineribbon(aes(ymin = .lower, ymax = .upper)) +
geom_point(data = mtcars) +
scale_fill_brewer() +
coord_cartesian(ylim = c(0, 400))
```

In general I have found that there is no one method that consistently works well on all datasets. No matter the method, intervals often become problematic above 50%, hence the default `.width`

for `curve_interval()`

is `0.5`

(unlike the default for `point_interval()`

, which is `0.95`

). In any case, caution when using these intervals is advised.