Introduction to LaMa • LaMa

The R package LaMa provides convenient functions for fitting a variety of latent Markov models (Mews, Koslik, and Langrock 2024), including hidden Markov models (HMMs), hidden semi-Markov models (HSMMs), state space models (SSMs) and continuous-time variants via direct numerical maximum likelihood estimation. The core idea is that the user defines their own negative log-likelihood function for numerical optimisation, but can rely on package functions for convenience and speed.

The main families of functions are forward, tpm and stationary and we showcasse the simplest versions in the following introductory example.

Introductory example: Homogeneous HMM

In this vignette, we start from the most simple HMM we can think of. Such a basic $N$ -state HMM is a doubly stochastic process in discrete time. Observations are generated by one of $N$ possible distributions $f_j(x_t)$ , $j = 1, \dots N$ with an unobserved $N$ -state Markov chain selecting which distribution is active at any given time point. Hence, HMMs can be interpreted as temporally dependent mixture models and are very popular accross a wide range of disciplines like ecology, sports and finance where time-series data with underlying sequential dependencies are to be analysed. They statements above already hint at the two main assumptions in such a model, namely

$f(s_t \mid s_{t-1}, s_{t-2}, \dots, s_1) = f(s_t \mid s_{t-1})$ (Markov assumption)
$f(x_t \mid x_1, \dots, x_{t-1}, x_{t-1}, x_T, s_1, \dots, s_T) = f(x_t \mid s_t)$ (conditional independence assumption).

The hidden state process is described by a Markov chain, as such a stochastic process can easily be characterised by its initial distribution $\delta^{(1)} = (\Pr(S_1 = 1), \dots, \Pr(S_1 = N))$ and the one-step transition probabilities $\gamma_{ij} = \Pr(S_t = j \mid S_{t-1} = i), \quad i,j = 1, \dotsc, N$ which are typically summarised in the so-called transition probability matrix (t.p.m.) $\Gamma = (\gamma_{ij})_{i,j = 1, \dots, N}$ where row $i$ is the conditional one-step ahead distribution of the state process given that the current state is $i$ . Such a matrix is most-conveniently parametrised by an unconstrained parameter vector for the $N (N-1)$ off-diagonal elements. Each row can then be computed via the inverse multinomial logistic link (also known as softmax). This can be done using the function tpm():

(Gamma = tpm(c(-2, -3))) # 2 states -> 2*(1-2) = 2 off-diagonal entries
#>           [,1]       [,2]
#> [1,] 0.9525741 0.04742587
#> [2,] 0.1192029 0.88079708

For HMMs with such homogeneous transition probabilities, we often assume stationarity of the underlying Markov chain, as well-behaved Markov chains converge to a unique stationary distribution. When we e.g. observe an animial and model its behavioral states by a Markov chain, it is reasonable to assume that the chain has been running for a long time prior to our observation and thus already converged to its stationary distribution. This distribution (which we call $\delta$ ) can be computed by solving the system of equations $\delta \Gamma = \delta, \quad \text{s.t.} \; \sum_{j=1}^N \delta_j = 1,$ which is implemented in the function stationary(). For stationary HMMs, we then replace the initial distribution $\delta^{(1)}$ by this stationary distribution. We can easily compute the stationary distribution associated with the above t.p.m. using

(delta = stationary(Gamma))
#>   state 1   state 2 
#> 0.7153801 0.2846199

This stationary distribution can be interpreted as the log-run-time proportion of time spent in each state.

For the conditional distributions of the observations $f_j(x_t)$ , a typical choice would be some kind of parametric family like normal or gamma distributions with state-specific means and standard deviations. For a more exhaustive description of such models see Zucchini, MacDonald, and Langrock (2016).

Generating data from a 2-state HMM

We start by simulating some data from a simple 2-state HMM with Gaussian state-dependent distributions, to get some intuition. Here we can again use stationary() to compute the stationary distribution.

# parameters
mu = c(0, 6)    # state-dependent means
sigma = c(2, 4) # state-dependent standard deviations
Gamma = matrix(c(0.95, 0.05, 0.15, 0.85), # transition probability matrix
               nrow = 2, byrow = TRUE)
delta = stationary(Gamma) # stationary distribution

# simulation
n = 1000
set.seed(123)
s = rep(NA, n)
s[1] = sample(1:2, 1, prob = delta) # sampling first state from delta
for(t in 2:n){
  # drawing the next state conditional on the last one
  s[t] = sample(1:2, 1, prob = Gamma[s[t-1],]) 
}
# drawing the observation conditional on the states
x = rnorm(n, mu[s], sigma[s])

color = c("orange", "deepskyblue")
plot(x[1:200], bty = "n", pch = 20, ylab = "x", 
     col = color[s[1:200]])

Inference by direct numerical maximum likelihood estimation

Inference for HMMs is more difficult compared to e.g. regression modelling as the observations are not independent. We want to estimate model parameters via maximum likelihood estimation, due to the nice properties possessed by the maximum likelihood estimator. However, computing the HMM likelihood for observed data points $x_1, \dots, x_T$ is a non-trivial task as we do not observe the underlying states. We thus need to sum out all possible state sequences which would be infeasible for general state processes. We can, however, exploit the Markov property and thus calculate the likelihood recursively as a matrix product using the so-called forward algorithm. In closed form, the HMM likelihood then becomes

$L(\theta) = \delta^{(1)} P(x_1) \Gamma P(x_2) \Gamma \dots \Gamma P(x_T) 1^t,$ where $\delta^{(1)}$ and $\Gamma$ are as defined above, $P(x_t)$ is a diagonal matrix with state-dependent densities or probability mass functions $f_j(x_t) = f(x_t \mid S_t = j)$ on its diagonal and $1$ is a row vector of ones with length $N$ . All model parameters are here summarised in the vector $\theta$ . Being able to evaluate the likelihood function, it can be numerically maximised by popular optimisers like nlm() or optim().

The algorithm explained above suffers from numerical underflow and for $T$ only moderately large the likelihood is rounded to zero. Thus, one can use a scaling strategy, detailed by Zucchini, MacDonald, and Langrock (2016), to avoid this and calculate the log-likelihood recursively. This version of the forward algorithm is implemented in LaMa and written in C++.

Additionally, for HMMs we often need to constrain the domains of several of the model parameters in $\theta$ (i.e. positive standard deviations or a transition probability matrix with elements between 0 and 1 and rows that sum to one). One could now resort to contrained numerical optimisation but in practice the better option is to maximise the likelihood w.r.t. a transformed version (to the real number line) of the model parameters by using suitable invertible and differenentiable link functions (denoted by par in the code). For example we use the log-link for parameters that need to be strictly positive and the multinomial logistic link for the transition probability matrix. While the former can easily be coded by hand, the latter is implemented in the functions of the tpm family for convenience and computational speed.

For efficiency, it is also advisable to evaluate the state-dependent densities (or probability mass functions) vectorised outside the recursive forward algorithm. This results in a matrix containing the state-dependent likelihoods for each data point (rows), conditional on each state (columns), which, throughout the package, we call the allprobs matrix.

In this example, within the negative log-likelihood function we build the homogeneous transition probability matrix using the tpm() function and compute the stationary distribution of the Markov chain using stationary(). We then build the allprobs matrix and calculate the log-likelihood using forward() in the last line. It is returned negative such that the function can be numerically minimised by e.g. nlm().

nll = function(par, x){
  # parameter transformations for unconstrained optimisation
  Gamma = tpm(par[1:2]) # multinomial logistic link
  delta = stationary(Gamma) # stationary initial distribution
  mu = par[3:4] # no transformation needed
  sigma = exp(par[5:6]) # strictly positive
  # calculating all state-dependent probabilities outside the forward algorithm
  allprobs = matrix(1, length(x), 2)
  for(j in 1:2) allprobs[,j] = dnorm(x, mu[j], sigma[j])
  # return negative for minimisation
  -forward(delta, Gamma, allprobs)
}

Fitting an HMM to the data

par = c(logitGamma = qlogis(c(0.05, 0.05)),
        mu = c(1,4),
        logsigma = c(log(1),log(3)))
# initial transformed parameters: not chosen too well
system.time(
  mod <- nlm(nll, par, x = x)
)
#>    user  system elapsed 
#>   0.093   0.003   0.097

We see that implementation of the forward algorithm in C++ leads to really fast estimation speeds.

Visualising results

After model estimation, we need to retransform the unconstrained parameters according to the code inside the likelihood:

# transform parameters to working
Gamma = tpm(mod$estimate[1:2])
delta = stationary(Gamma) # stationary HMM
mu = mod$estimate[3:4]
sigma = exp(mod$estimate[5:6])

hist(x, prob = TRUE, bor = "white", breaks = 40, main = "")
curve(delta[1]*dnorm(x, mu[1], sigma[1]), add = TRUE, lwd = 2, col = "orange", n=500)
curve(delta[2]*dnorm(x, mu[2], sigma[2]), add = TRUE, lwd = 2, col = "deepskyblue", n=500)
curve(delta[1]*dnorm(x, mu[1], sigma[1])+delta[2]*dnorm(x, mu[2], sigma[2]),
      add = TRUE, lwd = 2, lty = "dashed", n=500)
legend("topright", col = c(color, "black"), lwd = 2, bty = "n",
       lty = c(1,1,2), legend = c("state 1", "state 2", "marginal"))

We can also decode the most probable state sequence with the viterbi() function, when first computing the allprobs matrix:

allprobs = matrix(1, length(x), 2)
for(j in 1:2) allprobs[,j] = dnorm(x, mu[j], sigma[j])

states = viterbi(delta, Gamma, allprobs)

plot(x, pch = 20, bty = "n", col = color[states])
legend("topright", pch = 20, legend = c("state 1", "state 2"), 
       col = color, box.lwd = 0)

Lastly, we can do some model checking using pseudo-residuals. First, we need to compute the local state probabilities of our observations:

probs = stateprobs(delta, Gamma, allprobs)

Then, we can pass the observations, the state probabilities, the parametric family and the estimated parameters to the pseudo_res() function to get pseudo-residuals for model validation. These should be standard normally distributed if the model is correct.

pres = pseudo_res(x, # observations
                  probs, # local state probabilities
                  "norm", # parametric distribution to use
                  list(mean = mu, sd = sigma)) # parameters for that distribution

oldpar = par(mfrow = c(1,2))
hist(pres, prob = TRUE, bor = "white")
curve(dnorm(x), lty = 2, add = TRUE)
qqnorm(pres, pch = 16, col = "#00000020", bty = "n")
qqline(pres, col = "orange")

par(oldpar)

In this case, our model looks really good – as it should as we simulated from the exact same model.

References

Mews, Sina, Jan-Ole Koslik, and Roland Langrock. 2024. “How to Build Your Latent Markov Model - the Role of Time and Space.” arXiv Preprint arXiv:2406.19157.

Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. 2016. Hidden Markov Models for Time Series: An Introduction Using R. Boca Raton: Chapman & Hall/CRC.