plot.ctStanModel: Prior plotting

In ctsem: Continuous Time Structural Equation Modelling

Prior plotting

Description

Plots priors for free model parameters in a ctStanModel.

Usage

## S3 method for class 'ctStanModel'
plot(
  x,
  rows = "all",
  wait = FALSE,
  nsamples = 1e+06,
  rawpopsd = "marginalise",
  inddifdevs = c(-1, 1),
  inddifsd = 0.1,
  plot = TRUE,
  ...
)

Arguments

x	ctStanModel object as generated by ctModel with type='stanct' or 'standt'.
rows	vector of integers denoting which rows of ctstanmodel$pars to plot priors for. Character string 'all' plots all rows with parameters to be estimated.
wait	If true, user is prompted to continue before plotting next graph.
nsamples	Numeric. Higher values increase fidelity (smoothness / accuracy) of density plots, at cost of speed.
rawpopsd	Either 'marginalise' to sample from the specified (in the ctstanmodel) prior distribution for the raw population standard deviation, or a numeric value to use for the raw population standard deviation for all subject level prior plots - the plots in dotted blue or red.
inddifdevs	numeric vector of length 2, setting the means for the individual differences distributions.
inddifsd	numeric, setting the standard deviation of the population means used to generate individual difference distributions.
plot	If FALSE, ouputs list of GGplot objects that can be further modified.
...	not used.

Details

Plotted in black is the prior for the population mean. In red and blue are the subject level priors that result given that the population mean is estimated as 1 std deviation above the mean of the prior, or 1 std deviation below. The distributions around these two points are then obtained by marginalising over the prior for the raw population std deviation - so the red and blue distributions do not represent any specific subject level prior, but rather characterise the general amount and shape of possible subject level priors at the specific points of the population mean prior.

Examples

model <- ctModel(type='stanct',
manifestNames='sunspots', 
latentNames=c('ss_level', 'ss_velocity'),
LAMBDA=matrix(c( 1, 'ma1' ), nrow=1, ncol=2),
DRIFT=matrix(c(0, 1,   'a21', 'a22'), nrow=2, ncol=2, byrow=TRUE),
MANIFESTMEANS=matrix(c('m1'), nrow=1, ncol=1),
# MANIFESTVAR=matrix(0, nrow=1, ncol=1),
CINT=matrix(c(0, 0), nrow=2, ncol=1),
DIFFUSION=matrix(c(
  0, 0,
  0, "diffusion"), ncol=2, nrow=2, byrow=TRUE))
  
plot(model,rows=8)

ctsem documentation built on Sept. 11, 2024, 9:06 p.m.