bayesplot (version 1.6.0)

PPC-scatterplots: PPC scatterplots

Description

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.

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.

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).

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

# }