# PPC-scatterplots

0th

Percentile

##### 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)

Other PPCs: PPC-discrete, PPC-distributions, PPC-errors, PPC-intervals, PPC-loo, PPC-overview, PPC-test-statistics

##### Aliases
• PPC-scatterplots
• ppc_scatter
• ppc_scatter_avg
• ppc_scatter_avg_grouped
##### 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)

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