ctsmTMB (version 1.0.1)

plot.ctsmTMB.fit: 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 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,
  ...
)

Value

a (list of) ggplot residual plot(s)

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

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
if (FALSE) plot(fit)

# plot filtered states
if (FALSE) plot(fit, type="states")