Simulate from the Ornstein-Uhlenbeck process

We use the common Ornstein-Uhlenbeck process to showcase the use of likelihood.

\[ \mathrm{d}X_{t} = \theta (\mu - X_{t}) \, \mathrm{d}t \, + \sigma_{X} \, \mathrm{d}B_{t} \]

\[ Y_{t_{k}} = X_{t_{k}} + e_{t_{k}}, \qquad e_{t_{k}} \sim \mathcal{N}\left(0,\sigma_{Y}^{2}\right) \] We first create data by simulating the process

# Simulate data using Euler Maruyama
set.seed(10)
theta=10; mu=1; sigma_x=1; sigma_y=1e-1
# 
dt.sim = 1e-3
t.sim = seq(0,1,by=dt.sim)
dw = rnorm(length(t.sim)-1,sd=sqrt(dt.sim))
x = 3
for(i in 1:(length(t.sim)-1)) {
  x[i+1] = x[i] + theta*(mu-x[i])*dt.sim + sigma_x*dw[i]
}

# Extract observations and add noise
dt.obs = 1e-2
t.obs = seq(0,1,by=dt.obs)
y = x[t.sim %in% t.obs] + sigma_y * rnorm(length(t.obs))

# Create data
.data = data.frame(
  t = t.obs,
  y = y
)

Construct model object

We now construct the ctsmTMB model object

# Create model object
obj = ctsmTMB$new()

# Add system equations
obj$addSystem(
  dx ~ theta * (mu-x) * dt + sigma_x*dw
)

# Add observation equations
obj$addObs(
  y ~ x
)

# Set observation equation variances
obj$setVariance(
  y ~ sigma_y^2
)

# Specify algebraic relations
obj$setAlgebraics(
  theta ~ exp(logtheta),
  sigma_x ~ exp(logsigma_x),
  sigma_y ~ exp(logsigma_y)
)

# Specify parameter initial values and lower/upper bounds in estimation
obj$setParameter(
  logtheta   = log(c(initial = 5,    lower = 0,    upper = 20)),
  mu         = c(    initial = 0,    lower = -10,  upper = 10),
  logsigma_x = log(c(initial = 1e-1, lower = 1e-5, upper = 5)),
  logsigma_y = log(c(initial = 1e-1, lower = 1e-5, upper = 5))
)

# Set initial state mean and covariance
obj$setInitialState(list(x[1], 1e-1*diag(1)))

Estimation

We are in principle ready to call the estimate method to run the optimization scheme using the built-in optimization which uses stats::nlminb i.e.

fit = obj$estimate(.data)

## Building model...

## Checking data...

## Constructing objective function and derivative tables...

## ...took: 0.043 seconds.

## Minimizing the negative log-likelihood...

##   0:     936.11682:  1.60944  0.00000 -2.30259 -2.30259
##   1:     87.083269:  1.05839 0.612612 -1.83165 -1.98751
##   2:     30.831804:  1.42872 0.571166 -1.47012 -1.13285
##   3:     27.093246:  1.22027  1.42418 -1.16175 -1.49867
##   4:    0.42802599:  1.21559  1.07263 -0.807822 -1.53233
##   5:    -29.837043:  1.68587 0.793054 0.591933 -2.65227
##   6:    -32.378284:  1.71155 0.797170 0.422376 -2.67022
##   7:    -33.322237:  1.80730 0.816713 0.294476 -2.60826
##   8:    -35.484031:  2.11436 0.813309 0.261892 -2.45452
##   9:    -36.943433:  2.20642 0.984187 0.199473 -2.38963
##  10:    -38.269996:  2.39678  1.01007 0.128045 -2.32821
##  11:    -38.662773:  2.46559  1.03990 0.118501 -2.31645
##  12:    -39.208765:  2.61559  1.05578 0.0954213 -2.30526
##  13:    -39.271267:  2.67270  1.09444 0.113168 -2.30196
##  14:    -39.341842:  2.73859  1.07795 0.123778 -2.32079
##  15:    -39.346399:  2.71897  1.07807 0.124258 -2.33025
##  16:    -39.347572:  2.71957  1.07662 0.127387 -2.32720
##  17:    -39.347641:  2.72307  1.07756 0.127613 -2.32681
##  18:    -39.347669:  2.72144  1.07762 0.127756 -2.32677
##  19:    -39.347672:  2.72159  1.07745 0.127677 -2.32686
##  20:    -39.347672:  2.72164  1.07749 0.127690 -2.32684
##  21:    -39.347672:  2.72163  1.07749 0.127690 -2.32684

##   Optimization finished!:
##             Elapsed time: 0.004 seconds.
##             The objective value is: -3.934767e+01
##             The maximum gradient component is: 8.4e-06
##             The convergence message is: relative convergence (4)
##             Iterations: 21
##             Evaluations: Fun: 30 Grad: 22
##             See stats::nlminb for available tolerance/control arguments.

## Returning results...

## Finished!

Inside the package we optimise the objective function with respect to the fixed parameters using the construction function handlers from TMB::MakeADFun and parsing them to stats::nlminb i.e.

nll = TMB::MakeADFun(...)
opt = stats::nlminb(start=nll$par, objective=nll$fn, grad=nll$gr, hessian=nll$he)

Extract function handlers

The likelihood method allows you to retrieve the nll object that holds the negative log-likelihood, and its derivatives. The method takes arguments similar to those of estimate.

nll = obj$likelihood(.data)

## Checking data...

## Succesfully returned function handlers

The initial parameters (supplied by the user) are stored here

nll$par

##   logtheta         mu logsigma_x logsigma_y 
##   1.609438   0.000000  -2.302585  -2.302585

The objective function can be evaluated by

nll$fn(nll$par)

## [1] 936.1168

The gradient can be evaluated by

nll$gr(nll$par)

##          [,1]      [,2]      [,3]     [,4]
## [1,] 1430.881 -1590.748 -1222.864 -818.151

The hessian can be evaluated by

nll$he(nll$par)

##           [,1]       [,2]       [,3]       [,4]
## [1,]  2348.091 -2949.2028 -1700.6171 -1167.7123
## [2,] -2949.203  1691.7601  2308.7901   874.6161
## [3,] -1700.617  2308.7901   938.3781  1516.2869
## [4,] -1167.712   874.6161  1516.2869   311.1072

We can now use these to optimize the function using e.g. stats::optim instead.

Extract parameter lower/upper bounds

You can extract the parameter bounds specified when calling setParameter() method by using the getParameters method (note that nll$par and pars$initial are identical).

pars = obj$getParameters()
print(pars)

##            type   estimate   initial     lower     upper
## logtheta   free  2.7216294  1.609438      -Inf  2.995732
## mu         free  1.0774882  0.000000 -10.00000 10.000000
## logsigma_x free  0.1276898 -2.302585 -11.51293  1.609438
## logsigma_y free -2.3268411 -2.302585 -11.51293  1.609438

Optimize manually using stats::optim

We supply the initial parameter values, objective function handler and gradient handler, and parameter bounds to optim.

opt = stats::optim(par=nll$par, 
                   fn=nll$fn, 
                   gr=nll$gr, 
                   method="L-BFGS-B", 
                   lower=pars$lower, 
                   upper=pars$upper)

Compare results between the two optimizers

Lets compare the results from using stats::optim with the extracted function handler versus the internal optimisation that uses stats::nlminb stored in fit:

# Estimated parameters
data.frame(external=opt$par, internal=fit$par.fixed)

##              external   internal
## logtheta    2.7216300  2.7216294
## mu          1.0774878  1.0774882
## logsigma_x  0.1276904  0.1276898
## logsigma_y -2.3268419 -2.3268411

# Neg. Log-Likelihood
data.frame(external=opt$value, internal=fit$nll)

##    external  internal
## 1 -39.34767 -39.34767

# Gradient components
data.frame(external=t(nll$gr(opt$par)), internal=t(nll$gr(fit$par.fixed)))

##        external      internal
## 1  7.587709e-06 -8.417709e-06
## 2 -5.872425e-05  8.215885e-06
## 3  1.062722e-05  4.106731e-06
## 4 -1.917425e-05  1.643487e-06