Plot prior or posterior model draws on top of data. Use plot_pars to plot individual parameter estimates.
# S3 method for mcpfit plot( x, facet_by = NULL, lines = 25, geom_data = "point", cp_dens = TRUE, q_fit = FALSE, q_predict = FALSE, rate = TRUE, prior = FALSE, which_y = "ct", arma = TRUE, nsamples = 2000, scale = "response", ... )Arguments x
An mcpfit object
String. Name of a varying group.
linesPositive integer or FALSE. Number of lines (posterior draws). FALSE or lines = 0 plots no lines. Note that lines always plot fitted values - not predicted. For prediction intervals, see the q_predict argument.
String. One of "point" (default), "line" (good for time-series), or FALSE (don not plot).
cp_densTRUE/FALSE. Plot posterior densities of the change point(s)? Currently does not respect facet_by. This will be added in the future.
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 (default): 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.
Same as q_fit, but for the prediction interval.
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.
TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")
What to plot on the y-axis. One of
"ct": The central tendency which is often the mean after applying the link function (default).
"sigma": The variance
"ar1", "ar2", etc. depending on which order of the autoregressive effects you want to plot.
Whether to include autoregressive effects.
TRUE: Compute autoregressive residuals. Requires the response variable in newdata.
FALSE: Disregard the autoregressive effects. For family = gaussian(), predict() just use sigma for residuals.
Integer or NULL. Number of samples to return/summarise. If there are varying effects, this is the number of samples from each varying group. NULL means "all". Ignored if both are FALSE. More samples trade speed for accuracy.
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).
Currently ignored.
ValueA ggplot2 object.
Detailsplot() uses fit$simulate() on posterior samples. These represent the (joint) posterior distribution.
# Typical usage. ex_fit is an mcpfit object.
plot(ex_fit)
# \donttest{
plot(ex_fit, prior = TRUE) # The prior
plot(ex_fit, lines = 0, q_fit = TRUE) # 95% HDI without lines
plot(ex_fit, q_predict = c(0.1, 0.9)) # 80% prediction interval
plot(ex_fit, which_y = "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(ex_fit) + theme_bw(15) + ggtitle("Great plot!")
# }
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