# PPC-errors

##### PPC errors

Various plots of predictive errors `y`

- `yrep`

. See the
**Details** and **Plot Descriptions** sections, below.

##### Usage

`ppc_error_hist(y, yrep, ..., binwidth = NULL, breaks = NULL, freq = TRUE)`ppc_error_hist_grouped(y, yrep, group, ..., binwidth = NULL, breaks = NULL,
freq = TRUE)

ppc_error_scatter(y, yrep, ..., size = 2.5, alpha = 0.8)

ppc_error_scatter_avg(y, yrep, ..., size = 2.5, alpha = 0.8)

ppc_error_scatter_avg_vs_x(y, yrep, x, ..., size = 2.5, alpha = 0.8)

ppc_error_binned(y, yrep, ..., size = 1, alpha = 0.25)

##### 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.

- binwidth
Passed to

`geom_histogram`

to override the default binwidth.- breaks
Passed to

`geom_histogram`

as an alternative to`binwidth`

.- freq
For histograms,

`freq=TRUE`

(the default) puts count on the y-axis. Setting`freq=FALSE`

puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the`yaxis_text`

convenience function.)- 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`

.- size, alpha
For scatterplots, arguments passed to

`geom_point`

to control the appearance of the points. For the binned error plot, arguments controlling the size of the outline and opacity of the shaded region indicating the 2-SE bounds.- x
A numeric vector the same length as

`y`

to use as the x-axis variable.

##### Details

All of these functions (aside from the `*_scatter_avg`

functions)
compute and plot predictive errors for each row of the matrix `yrep`

, so
it is usually a good idea for `yrep`

to contain only a small number of
draws (rows). See **Examples**, below.

For binomial and Bernoulli data the `ppc_error_binned`

function can be
used to generate binned error plots. Bernoulli data can be input as a vector
of 0s and 1s, whereas for binomial data `y`

and `yrep`

should
contain "success" proportions (not counts). See the **Examples**
section, below.

##### Value

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

##### Plot descriptions

`ppc_error_hist`

A separate histogram is plotted for the predictive errors computed from

`y`

and each dataset (row) in`yrep`

. For this plot`yrep`

should have only a small number of rows.`ppc_error_hist_grouped`

Like

`ppc_error_hist`

, except errors are computed within levels of a grouping variable. The number of histograms is therefore equal to the product of the number of rows in`yrep`

and the number of groups (unique values of`group`

).`ppc_error_scatter`

A separate scatterplot is displayed for

`y`

vs. the predictive errors computed from`y`

and each dataset (row) in`yrep`

. For this plot`yrep`

should have only a small number of rows.`ppc_error_scatter_avg`

A single scatterplot of

`y`

vs. the average of the errors computed from`y`

and each dataset (row) in`yrep`

. For each individual data point`y[n]`

the average error is the average of the errors for`y[n]`

computed over the the draws from the posterior predictive distribution.`ppc_error_scatter_avg_vs_x`

Same as

`ppc_error_scatter_avg`

, except the average is plotted on the \(y\)-axis and a a predictor variable`x`

is plotted on the \(x\)-axis.`ppc_error_binned`

Intended for use with binomial data. A separate binned error plot (similar to

`binnedplot`

) is generated for each dataset (row) in`yrep`

. For this plot`y`

and`yrep`

should contain proportions rather than counts, and`yrep`

should have only a small number of rows.

##### 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-intervals`

, `PPC-loo`

,
`PPC-overview`

,
`PPC-scatterplots`

,
`PPC-test-statistics`

##### Examples

```
# NOT RUN {
y <- example_y_data()
yrep <- example_yrep_draws()
ppc_error_hist(y, yrep[1:3, ])
# errors within groups
group <- example_group_data()
(p1 <- ppc_error_hist_grouped(y, yrep[1:3, ], group))
p1 + yaxis_text() # defaults to showing counts on y-axis
# }
# NOT RUN {
table(group) # more obs in GroupB, can set freq=FALSE to show density on y-axis
(p2 <- ppc_error_hist_grouped(y, yrep[1:3, ], group, freq = FALSE))
p2 + yaxis_text()
# }
# NOT RUN {
# scatterplots
ppc_error_scatter(y, yrep[10:14, ])
ppc_error_scatter_avg(y, yrep)
x <- example_x_data()
ppc_error_scatter_avg_vs_x(y, yrep, x)
# ppc_error_binned with binomial model from rstanarm
# }
# NOT RUN {
library(rstanarm)
example("example_model", package = "rstanarm")
formula(example_model)
# get observed proportion of "successes"
y <- example_model$y # matrix of "success" and "failure" counts
trials <- rowSums(y)
y_prop <- y[, 1] / trials # proportions
# get predicted success proportions
yrep <- posterior_predict(example_model)
yrep_prop <- sweep(yrep, 2, trials, "/")
ppc_error_binned(y_prop, yrep_prop[1:6, ])
# }
# NOT RUN {
# }
```

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