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). A value of 0 means not to plot anything.
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)
#> Checking model components...
#> Checking and setting data...
#> Constructing objective function and derivative tables...
#> Minimizing the negative log-likelihood...
#>   0:     160.35328:  1.00000  1.50000  1.00000
#>  10:     89.603625:  2.58436  2.91384  1.15608
#> 	 Optimization finished!:
#>             Elapsed time: 0.008 seconds.
#>             The objective value is: 7.387879e+01
#>             The maximum gradient component is: 9.9e-08
#>             The convergence message is: relative convergence (4)
#>             Iterations: 19
#>             Evaluations: Fun: 29 Grad: 20
#>             See stats::nlminb for available tolerance/control arguments.
#> Returning results...
#> Finished!

# plot residuals
if (FALSE) plot(fit) # \dontrun{}
plot(fit)

# plot filtered states
if (FALSE) plot(fit, type="states") # \dontrun{}