PPCerrors {bayesplot}  R Documentation 
Various plots of predictive errors y  yrep
. See the
Details and Plot Descriptions sections, below.
ppc_error_hist(
y,
yrep,
...,
facet_args = list(),
binwidth = NULL,
breaks = NULL,
freq = TRUE
)
ppc_error_hist_grouped(
y,
yrep,
group,
...,
facet_args = list(),
binwidth = NULL,
breaks = NULL,
freq = TRUE
)
ppc_error_scatter(y, yrep, ..., facet_args = list(), size = 2.5, alpha = 0.8)
ppc_error_scatter_avg(y, yrep, ..., size = 2.5, alpha = 0.8)
ppc_error_scatter_avg_grouped(
y,
yrep,
group,
...,
facet_args = list(),
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,
...,
facet_args = list(),
bins = NULL,
size = 1,
alpha = 0.25
)
ppc_error_data(y, yrep, group = NULL)
y 
A vector of observations. See Details. 
yrep 
An 
... 
Currently unused. 
facet_args 
A named list of arguments (other than 
binwidth 
Passed to 
breaks 
Passed to 
freq 
For histograms, 
group 
A grouping variable of the same length as 
size , alpha 
For scatterplots, arguments passed to

x 
A numeric vector the same length as 
bins 
For 
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.
A ggplot object that can be further customized using the ggplot2 package.
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
yaxis and a predictor variable x
is plotted on the xaxis.
ppc_error_binned()
Intended for use with binomial data. A separate binned error plot (similar
to arm::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.
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
,
PPCintervals
,
PPCloo
,
PPCoverview
,
PPCscatterplots
,
PPCteststatistics
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 yaxis
table(group) # more obs in GroupB, can set freq=FALSE to show density on yaxis
(p2 < ppc_error_hist_grouped(y, yrep[1:3, ], group, freq = FALSE))
p2 + yaxis_text()
# 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)
## Not run:
# binned error plot with binomial model from rstanarm
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, ])
## End(Not run)