Plot full fits

Plot prior or posterior model draws on top of data.

Usage

# S3 method for class 'mcpfit'
plot(
  x,
  q_fit = FALSE,
  q_predict = FALSE,
  facet_by = NULL,
  color_by = NULL,
  lines = 25,
  geom_data = "point",
  cp_dens = TRUE,
  rate = TRUE,
  prior = FALSE,
  arma = TRUE,
  ndraws = 1000,
  at = NULL,
  nsamples = lifecycle::deprecated(),
  ...
)

plot_dpar(
  x,
  dpar = "epred",
  q_fit = FALSE,
  facet_by = NULL,
  color_by = NULL,
  lines = 25,
  cp_dens = TRUE,
  prior = FALSE,
  arma = TRUE,
  ndraws = 1000,
  scale = "response",
  at = NULL,
  nsamples = lifecycle::deprecated(),
  ...
)

Arguments

x

An mcpfit object

q_fit

Whether to plot quantiles of the posterior (fitted value).

TRUE Add 2.5% and 97.5% quantiles. Corresponds to q_fit = c(0.025, 0.975).
FALSE No quantiles
A vector of quantiles. For example, quantiles = 0.5 plots the median and quantiles = c(0.2, 0.8) plots the 20% and 80% quantiles.

q_predict

Same as q_fit, but for the prediction interval.

facet_by

Character vector. Names of categorical data columns to split to facets. Can be grouping-factor or categorical predictor columns.

color_by

A character vector naming categorical or varying-effect data columns to color by. If both color_by and facet_by are omitted, a sole categorical predictor is colored automatically. Set color_by = NULL explicitly to disable this. Multiple columns are combined as an interaction. Curves and quantiles remain separate for grouping columns not mapped to color.

lines

Positive integer or FALSE. The number of fitted lines (draws). It is the number of joint posterior draws shown for every curve. FALSE or lines = 0 plots no lines. Note that lines always plot fitted values - not predicted. For prediction intervals, see the q_predict argument.

geom_data

String. One of "point", "line" (good for time-series), or FALSE (do not plot).

cp_dens

TRUE/FALSE. Plot posterior densities of the change point(s)? Currently does not respect facet_by. This will be added in the future.

rate

Boolean. For binomial models, plot on raw data (rate = FALSE) or response divided by number of trials (rate = TRUE). If FALSE, linear interpolation on trial number is used to infer trials at a particular x.

prior

TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")

arma

Whether to include AR and MA effects.

TRUE Compute the GARMA residual recurrence. Requires the response variable in newdata.
FALSE Disregard AR and MA effects. For family = gaussian(), predict() uses only sigma for residuals.

ndraws

Integer or NULL. Number of posterior draws to return/summarise. If there are varying effects, this is the number of draws from each varying group. NULL means "all". Ignored if both are FALSE. More samples trade speed for accuracy.

at

Named list setting additional continuous predictors to fixed values. They default to their observed means. Passed to interpolate_newdata().

nsamples

Deprecated. Use ndraws instead.

...

Currently ignored.

dpar

What distributional parameter to evaluate. This is only relevant when type == "fitted". E.g.,

"epred" (default): Expected value of the full model (or NULL for compatibility with brms etc.).
"mu": The central tendency which is often the mean after applying the link function.
"sigma": The standard deviation of the residuals.
"ar1", "ar2", "ma1", "ma2", etc. depending on which AR or MA coefficient you want to evaluate.

scale

One of

"response": return on the observed scale, i.e., after applying the inverse link function.
"linear": return on the parameter scale (where the linear trends are modelled). A linear scale is only applicable when type == "fitted" and dpar is not NULL.

Value

A ggplot2 object.

Details

plot() uses fit$simulate() on posterior samples. These represent the (joint) posterior distribution. Interval summaries use at most 1000 draws by default; use ndraws = NULL to use all draws. Change-point densities always use all available draws.

Functions

plot_dpar(): Plot distributional parameters

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

# Typical usage. demo_fit is an mcpfit object.
plot(demo_fit)

# \donttest{
plot(demo_fit, prior = TRUE)  # The prior


plot(demo_fit, lines = 0, q_fit = TRUE)  # 95% central interval without lines

plot(demo_fit, q_predict = c(0.1, 0.9))  # 80% prediction interval

plot_dpar(demo_fit, dpar = "sigma", lines = 100)  # The variance parameter on y


# Show a panel for each varying effect
# plot(fit, facet_by = "my_column")

# Customize plots using regular ggplot2
library(ggplot2)
plot(demo_fit) + theme_bw(15) + ggtitle("Great plot!")

# }