bayesplot: PPC-scatterplots – R documentation

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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 `ggplot2::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)

Examples

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

bayesplot

Plotting for Bayesian Models

v1.8.0

GPL (>= 3)

Authors

Jonah Gabry [aut, cre], Tristan Mahr [aut], Paul-Christian Bürkner [ctb], Martin Modrák [ctb], Malcolm Barrett [ctb], Frank Weber [ctb], Eduardo Coronado Sroka [ctb], Aki Vehtari [ctb]

Initial release

2021-01-07