PPC-errors: PPC errors
In bayesplot: Plotting for Bayesian Models

PPC-errors

R Documentation

PPC errors

Description

Various plots of predictive errors y - yrep. See the Details and Plot Descriptions sections, below.

Usage

ppc_error_hist(
  y,
  yrep,
  ...,
  facet_args = list(),
  binwidth = NULL,
  bins = NULL,
  breaks = NULL,
  freq = TRUE
)

ppc_error_hist_grouped(
  y,
  yrep,
  group,
  ...,
  facet_args = list(),
  binwidth = NULL,
  bins = 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)

Arguments

`y`	A vector of observations. See Details.
`yrep`	An `S` by `N` matrix of draws from the posterior (or prior) predictive distribution. The number of rows, `S`, is the size of the posterior (or prior) sample used to generate `yrep`. The number of columns, `N` is the number of predicted observations (`length(y)`). The columns of `yrep` should be in the same order as the data points in `y` for the plots to make sense. See the Details and Plot Descriptions sections for additional advice specific to particular plots.
`...`	Currently unused.
`facet_args`	A named list of arguments (other than `facets`) passed to `ggplot2::facet_wrap()` or `ggplot2::facet_grid()` to control faceting. Note: if `scales` is not included in `facet_args` then bayesplot may use `scales="free"` as the default (depending on the plot) instead of the ggplot2 default of `scales="fixed"`.
`binwidth`	Passed to `ggplot2::geom_histogram()` to override the default binwidth.
`bins`	For `ppc_error_binned()`, the number of bins to use (approximately).
`breaks`	Passed to `ggplot2::geom_histogram()` as an alternative to `binwidth`.
`freq`	For histograms, `freq=TRUE` (the default) puts count on the y-axis. Setting `freq=FALSE` puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the `yaxis_text()` convenience function.)
`group`	A grouping variable of the same length as `y`. Will be coerced to factor if not already a factor. Each value in `group` is interpreted as the group level pertaining to the corresponding observation.
`size`, `alpha`	For scatterplots, arguments passed to `ggplot2::geom_point()` to control the appearance of the points. For the binned error plot, arguments controlling the size of the outline and opacity of the shaded region indicating the 2-SE bounds.
`x`	A numeric vector the same length as `y` to use as the x-axis variable.

Details

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.

Value

A ggplot object that can be further customized using the ggplot2 package.

Plot descriptions

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 y-axis and a predictor variable x is plotted on the x-axis.
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.

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()
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 y-axis

table(group) # more obs in GroupB, can set freq=FALSE to show density on y-axis
(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)

bayesplot documentation built on May 29, 2024, 2:27 a.m.