plot.ctsmTMB.fit: This function creates residual plots for an estimated ctsmTMB...

In ctsmTMB: Continuous Time Stochastic Modelling using Template Model Builder

This function creates residual plots for an estimated ctsmTMB object

Description

This function creates residual plots for an estimated ctsmTMB object

Usage

## S3 method for class 'ctsmTMB.fit'
plot(
  x,
  print.plot = 1,
  type = "residuals",
  state.type = "prior",
  against.obs = NULL,
  ggtheme = getggplot2theme(),
  ylims = c(NA, NA),
  residual.burnin = 0L,
  residual.vs.obs.and.inputs = FALSE,
  ...
)

Arguments

x	A R6 ctsmTMB fit object
print.plot	a single integer determining which element out of all states/observations (depending on the argument to type).
type	a character vector either 'residuals' or 'states' determining what to plot.
state.type	a character vector either 'prior', 'posterior' or 'smoothed' determining what kind of states to plot.
against.obs	name of an observation to plot state predictions against.
ggtheme	ggplot2 theme to use for creating the ggplot.
ylims	limits on the y-axis for residual time-series plot
residual.burnin	integer N to remove the first N residuals
residual.vs.obs.and.inputs	the residual plots also include a new window with time-series plots of residuals, associated observations and inputs
...	additional arguments

Value

a (list of) ggplot residual plot(s)

Examples

library(ctsmTMB)
model <- ctsmTMB$new()

# create model
model$addSystem(dx ~ theta * (mu+u-x) * dt + sigma_x*dw)
model$addObs(y ~ x)
model$setVariance(y ~ sigma_y^2)
model$addInput(u)
model$setParameter(
  theta   = c(initial = 1, lower=1e-5, upper=50),
  mu      = c(initial=1.5, lower=0, upper=5),
  sigma_x = c(initial=1, lower=1e-10, upper=30),
  sigma_y = 1e-2
)
model$setInitialState(list(1,1e-1))

# fit model to data
fit <- model$estimate(Ornstein)

# plot residuals
## Not run: plot(fit)

# plot filtered states
## Not run: plot(fit, type="states")

ctsmTMB documentation built on Aug. 28, 2025, 1:08 a.m.