PPC-censoring {bayesplot} | R Documentation |
Compare the empirical distribution of censored data y
to the
distributions of simulated/replicated data yrep
from the posterior
predictive distribution. See the Plot Descriptions section, below, for
details.
Although some of the other bayesplot plots can be used with censored
data, ppc_km_overlay()
is currently the only plotting function designed
specifically for censored data. We encourage you to suggest or contribute
additional plots at
github.com/stan-dev/bayesplot.
ppc_km_overlay(y, yrep, ..., status_y, size = 0.25, alpha = 0.7)
ppc_km_overlay_grouped(y, yrep, group, ..., status_y, size = 0.25, alpha = 0.7)
y |
A vector of observations. See Details. |
yrep |
An |
... |
Currently only used internally. |
status_y |
The status indicator for the observations from |
size , alpha |
Passed to the appropriate geom to control the appearance of
the |
group |
A grouping variable of the same length as |
A ggplot object that can be further customized using the ggplot2 package.
ppc_km_overlay()
Empirical CCDF estimates of each dataset (row) in yrep
are overlaid,
with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for y
itself on
top (and in a darker shade). This is a PPC suitable for right-censored
y
. Note that the replicated data from yrep
is assumed to be
uncensored.
ppc_km_overlay_grouped()
The same as ppc_km_overlay()
, but with separate facets by group
.
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)
Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association. 53(282), 457–481. doi:10.1080/01621459.1958.10501452.
Other PPCs:
PPC-discrete
,
PPC-distributions
,
PPC-errors
,
PPC-intervals
,
PPC-loo
,
PPC-overview
,
PPC-scatterplots
,
PPC-test-statistics
color_scheme_set("brightblue")
y <- example_y_data()
# For illustrative purposes, (right-)censor values y > 110:
status_y <- as.numeric(y <= 110)
y <- pmin(y, 110)
# In reality, the replicated data (yrep) would be obtained from a
# model which takes the censoring of y properly into account. Here,
# for illustrative purposes, we simply use example_yrep_draws():
yrep <- example_yrep_draws()
dim(yrep)
ppc_km_overlay(y, yrep[1:25, ], status_y = status_y)
# With separate facets by group:
group <- example_group_data()
ppc_km_overlay_grouped(y, yrep[1:25, ], group = group, status_y = status_y)