plot.mcpfit  R Documentation 
Plot prior or posterior model draws on top of data. Use plot_pars
to
plot individual parameter estimates.
## S3 method for class '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", ... )
x 
An 
facet_by 
String. Name of a varying group. 
lines 
Positive integer or 
geom_data 
String. One of "point", "line" (good for timeseries), or FALSE (don not plot). 
cp_dens 
TRUE/FALSE. Plot posterior densities of the change point(s)?
Currently does not respect 
q_fit 
Whether to plot quantiles of the posterior (fitted value).

q_predict 
Same as 
rate 
Boolean. For binomial models, plot on raw data ( 
prior 
TRUE/FALSE. Plot using prior samples? Useful for 
which_y 
What to plot on the yaxis. One of

arma 
Whether to include autoregressive effects.

nsamples 
Integer or 
scale 
One of

... 
Currently ignored. 
plot()
uses fit$simulate()
on posterior samples. These represent the
(joint) posterior distribution.
A ggplot2 object.
Jonas Kristoffer Lindeløv jonas@lindeloev.dk
# Typical usage. demo_fit is an mcpfit object. plot(demo_fit) plot(demo_fit, prior = TRUE) # The prior plot(demo_fit, lines = 0, q_fit = TRUE) # 95% HDI without lines plot(demo_fit, q_predict = c(0.1, 0.9)) # 80% prediction interval plot(demo_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(demo_fit) + theme_bw(15) + ggtitle("Great plot!")
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