Compare the empirical distribution of censored data `y`

to the
distributions of simulated/replicated data `yrep`

from the posterior
predictive distribution. See the **Plot Descriptions** section, below, for
details.

Although some of the other bayesplot plots can be used with censored
data, `ppc_km_overlay()`

is currently the only plotting function designed
*specifically* for censored data. We encourage you to suggest or contribute
additional plots at
github.com/stan-dev/bayesplot.

```
ppc_km_overlay(y, yrep, ..., status_y, size = 0.25, alpha = 0.7)
ppc_km_overlay_grouped(y, yrep, group, ..., status_y, size = 0.25, alpha = 0.7)
```

- y
A vector of observations. See

**Details**.- yrep
An

`S`

by`N`

matrix of draws from the posterior (or prior) predictive distribution. The number of rows,`S`

, is the size of the posterior (or prior) sample used to generate`yrep`

. The number of columns,`N`

is the number of predicted observations (`length(y)`

). The columns of`yrep`

should be in the same order as the data points in`y`

for the plots to make sense. See the**Details**and**Plot Descriptions**sections for additional advice specific to particular plots.- ...
Currently only used internally.

- status_y
The status indicator for the observations from

`y`

. This must be a numeric vector of the same length as`y`

with values in {0, 1} (0 = right censored, 1 = event).- size, alpha
Passed to the appropriate geom to control the appearance of the

`yrep`

distributions.- group
A grouping variable of the same length as

`y`

. Will be coerced to factor if not already a factor. Each value in`group`

is interpreted as the group level pertaining to the corresponding observation.

A ggplot object that can be further customized using the **ggplot2** package.

`ppc_km_overlay()`

Empirical CCDF estimates of each dataset (row) in

`yrep`

are overlaid, with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for`y`

itself on top (and in a darker shade). This is a PPC suitable for right-censored`y`

. Note that the replicated data from`yrep`

is assumed to be uncensored.`ppc_km_overlay_grouped()`

The same as

`ppc_km_overlay()`

, but with separate facets by`group`

.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari,
A., and Rubin, D. B. (2013). *Bayesian Data Analysis.* Chapman & Hall/CRC
Press, London, third edition. (Ch. 6)

Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation
from incomplete observations.
*Journal of the American Statistical Association*. 53(282), 457--481.
doi:10.1080/01621459.1958.10501452.

```
color_scheme_set("brightblue")
y <- example_y_data()
# For illustrative purposes, (right-)censor values y > 110:
status_y <- as.numeric(y <= 110)
y <- pmin(y, 110)
# In reality, the replicated data (yrep) would be obtained from a
# model which takes the censoring of y properly into account. Here,
# for illustrative purposes, we simply use example_yrep_draws():
yrep <- example_yrep_draws()
dim(yrep)
#> [1] 500 434
# \donttest{
ppc_km_overlay(y, yrep[1:25, ], status_y = status_y)
# }
# With separate facets by group:
group <- example_group_data()
# \donttest{
ppc_km_overlay_grouped(y, yrep[1:25, ], group = group, status_y = status_y)
# }
```