PPCscatterplots {bayesplot}  R Documentation 
Scatterplots of the observed data y
vs. simulated/replicated data
yrep
from the posterior predictive distribution. See the
Plot Descriptions and Details sections, below.
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)
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 
... 
Currently unused. 
size, alpha 
Arguments passed to 
group 
A grouping variable (a vector or factor) the same length as

For Binomial data, the plots will typically be most useful if
y
and yrep
contain the "success" proportions (not discrete
"success" or "failure" counts).
A ggplot object that can be further customized using the ggplot2 package.
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.
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:
PPCcensoring
,
PPCdiscrete
,
PPCdistributions
,
PPCerrors
,
PPCintervals
,
PPCloo
,
PPCoverview
,
PPCteststatistics
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