R: PPC distributions

PPC-distributions {bayesplot}

R Documentation

PPC distributions

Description

Compare the empirical distribution of the data y to the distributions of simulated/replicated data yrep from the posterior predictive distribution. See the Plot Descriptions section, below, for details.

Usage

ppc_hist(y, yrep, ..., binwidth = NULL, freq = TRUE)

ppc_boxplot(y, yrep, ..., notch = TRUE, size = 0.5, alpha = 1)

ppc_freqpoly(y, yrep, ..., binwidth = NULL, freq = TRUE, size = 0.25,
  alpha = 1)

ppc_freqpoly_grouped(y, yrep, group, ..., binwidth = NULL, freq = TRUE,
  size = 0.25, alpha = 1)

ppc_dens(y, yrep, ..., trim = FALSE, size = 0.5, alpha = 1)

ppc_dens_overlay(y, yrep, ..., trim = FALSE, size = 0.25, alpha = 0.7)

ppc_ecdf_overlay(y, yrep, ..., pad = TRUE, size = 0.25, alpha = 0.7)

ppc_violin_grouped(y, yrep, group, ..., probs = c(0.1, 0.5, 0.9), size = 1,
  alpha = 1, y_draw = c("violin", "points", "both"), y_size = 1,
  y_alpha = 1, y_jitter = 0.1)

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.
`binwidth`	An optional value used as the `binwidth` argument to `geom_histogram` to override the default 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.)
`notch`	A logical scalar passed to `geom_boxplot`. Unlike for `geom_boxplot`, the default is `notch=TRUE`.
`size, alpha`	Passed to the appropriate geom to control the appearance of the `yrep` distributions.
`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`.
`trim`	A logical scalar passed to `geom_density`.
`pad`	A logical scalar passed to `stat_ecdf`.
`probs`	A numeric vector passed to `geom_violin`'s `draw_quantiles` argument to specify at which quantiles to draw horizontal lines. Set to `NULL` to remove the lines.
`y_draw`	For `ppc_violin_grouped`, a string specifying how to draw `y`: `"violin"` (default), `"points"` (jittered points), or `"both"`.
`y_jitter, y_size, y_alpha`	For `ppc_violin_grouped`, if `y_draw` is `"points"` or `"both"` then `y_size`, `y_alpha`, and `y_jitter` are passed to to the `size`, `alpha`, and `width` arguments of `geom_jitter` to control the appearance of `y` points. The default of `y_jitter=NULL` will let ggplot2 determine the amount of jitter.

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_hist, ppc_freqpoly, ppc_dens, ppc_boxplot: A separate histogram, shaded frequency polygon, smoothed kernel density estimate, or box and whiskers plot is displayed for y and each dataset (row) in yrep. For these plots yrep should therefore contain only a small number of rows. See the Examples section.
ppc_freqpoly_grouped: A separate frequency polygon is plotted for each level of a grouping variable for y and each dataset (row) in yrep. For this plot yrep should therefore contain only a small number of rows. See the Examples section.
ppc_dens_overlay, ppc_ecdf_overlay: Kernel density or empirical CDF estimates of each dataset (row) in yrep are overlaid, with the distribution of y itself on top (and in a darker shade).
ppc_violin_grouped: The density estimate of yrep within each level of a grouping variable is plotted as a violin with horizontal lines at notable quantiles. y is overlaid on the plot either as a violin, points, or both, depending on the y_draw argument.

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

color_scheme_set("brightblue")
y <- example_y_data()
yrep <- example_yrep_draws()
dim(yrep)
ppc_dens_overlay(y, yrep[1:25, ])

ppc_ecdf_overlay(y, yrep[sample(nrow(yrep), 25), ])


# for ppc_hist,dens,freqpoly,boxplot definitely use a subset yrep rows so
# only a few (instead of nrow(yrep)) histograms are plotted
ppc_hist(y, yrep[1:8, ])


color_scheme_set("red")
ppc_boxplot(y, yrep[1:8, ])

# wizard hat plot
color_scheme_set("blue")
ppc_dens(y, yrep[200:202, ])


ppc_freqpoly(y, yrep[1:3,], alpha = 0.1, size = 1, binwidth = 5)

# if groups are different sizes then the 'freq' argument can be useful
group <- example_group_data()
ppc_freqpoly_grouped(y, yrep[1:3,], group) + yaxis_text()

ppc_freqpoly_grouped(y, yrep[1:3,], group, freq = FALSE) + yaxis_text()


# don't need to only use small number of rows for ppc_violin_grouped
# (as it pools yrep draws within groups)
color_scheme_set("gray")
ppc_violin_grouped(y, yrep, group, size = 1.5)

ppc_violin_grouped(y, yrep, group, alpha = 0)

# change how y is drawn
ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "points", y_size = 1.5)
ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "both",
                   y_size = 1.5, y_alpha = 0.5, y_jitter = 0.33)

[Package bayesplot version 1.4.0 Index]