# PPC-scatterplots

##### PPC scatterplots

Scatterplots of the observed data `y`

vs. simulated/replicated data
`yrep`

from the posterior predictive distribution. See the **Plot
Descriptions** and **Details** sections, below.

##### Usage

`ppc_scatter(y, yrep, ..., size = 2.5, alpha = 0.8)`ppc_scatter_avg(y, yrep, ..., size = 2.5, alpha = 0.8)

ppc_scatter_avg_grouped(y, yrep, group, ..., size = 2.5, alpha = 0.8)

##### Arguments

- y
A vector of observations. See

**Details**.- yrep
An \(S\) by \(N\) matrix of draws from the posterior predictive distribution, where \(S\) is the size of the posterior sample (or subset of the posterior sample used to generate

`yrep`

) and \(N\) is the number of observations (the length of`y`

). The columns of`yrep`

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

for the plots to make sense. See**Details**for additional instructions.- ...
Currently unused.

- size, alpha
Arguments passed to

`geom_point`

to control the appearance of the points.- group
A grouping variable (a vector or factor) the same length as

`y`

. Each value in`group`

is interpreted as the group level pertaining to the corresponding value of`y`

.

##### Details

For Binomial data, the plots will typically be most useful if
`y`

and `yrep`

contain the "success" proportions (not discrete
"success" or "failure" counts).

##### Value

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

##### Plot Descriptions

`ppc_scatter`

For each dataset (row) in

`yrep`

a scatterplot is generated showing`y`

against that row of`yrep`

. For this plot`yrep`

should only contain a small number of rows.`ppc_scatter_avg`

A scatterplot of

`y`

against the average values of`yrep`

, i.e., the points`(mean(yrep[, n]), y[n])`

, where each`yrep[, n]`

is a vector of length equal to the number of posterior draws.`ppc_scatter_avg_grouped`

The same as

`ppc_scatter_avg`

, but a separate plot is generated for each level of a grouping variable.

##### References

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)

##### See Also

Other PPCs: `PPC-discrete`

,
`PPC-distributions`

,
`PPC-errors`

, `PPC-intervals`

,
`PPC-loo`

, `PPC-overview`

,
`PPC-test-statistics`

##### Examples

```
# NOT RUN {
y <- example_y_data()
yrep <- example_yrep_draws()
p1 <- ppc_scatter_avg(y, yrep)
p1
p2 <- ppc_scatter(y, yrep[20:23, ], alpha = 0.5, size = 1.5)
p2
# give x and y axes the same limits
lims <- ggplot2::lims(x = c(0, 160), y = c(0, 160))
p1 + lims
p2 + lims
group <- example_group_data()
ppc_scatter_avg_grouped(y, yrep, group, alpha = 0.7) + lims
# }
```

*Documentation reproduced from package bayesplot, version 1.6.0, License: GPL (>= 3)*