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Before diving into this vignette, we recommend reading the vignettes Introduction to LaMa, Inhomogeneous HMMs, Periodic HMMs and LaMa and RTMB.

This vignette explores how LaMa can be used to fit models involving nonparameteric components, represented by penalised splines. The main idea here is that it may be useful to represent some relationships in our model by smooth functions for which the functional form is not pre-specified, but flexibly estimated from the data. For HMM-like models, this is particularly valuable, as the latent nature of the state process makes modelling choices more difficult. For example, choosing an appropriate parametric family for the state-dependent distributions may be difficult, as we cannot do state-specific EDA before fitting the model. Also very difficult is the dependence of transition probabilities on covariates, as the transitions are not directly observed. Hence, the obvious alternative is to model these kind of relationships flexibly using splines but imposing a penalty on the smoothness of the estimated functions. This leads us to penalised splines.

LaMa contains helper functions that build design and penalty matrices for given formulas (using mgcv under the hood) and also functions to estimate models involving penalised splines in a random effects framework. For the latter to work, the penalised negative log-likelihood needs to be compatible with the R package RTMB to allow for automatic differentiation (AD). For more information on RTMB, see the vignette LaMa and RTMB or check out its documentation. For more information on penalised splines, we recommend Wood (2017).

Smooth transition probabilites

We will start by investigating a 2-state HMM for the trex data set, containing hourly step lengths and turning angles of a Tyrannosaurus rex living 66 million years ago. The transition probabilities are modelled as smooth functions of the time of day using cyclic P-Splines. The relationship can then be summarised as

logit(γij(t))=β0(ij)+sij(t), \text{logit}(\gamma_{ij}^{(t)}) = \beta_0^{(ij)} + s_{ij}(t), where sij(t)s_{ij}(t) is a smooth periodic function of time of day. We model the T-rex’s step lengths and turning angles using state-dependent gamma and von Mises distributions.

To ease with model specification, LaMa provides the function make_matrices() which creates design and penalty matrices for regression settings based on the R package mgcv. The user only needs to specify the right side of a formula using mgcv syntax and provide data. Here, we use s(tod, by = "cp") to create the matrices for cyclic P-splines (cp). This results in a cubic B-Spline basis, that is wrapped at boundary of the support (0 and 24). We then append both resulting matrices to the dat list.

library(LaMa)
#> Loading required package: RTMB

head(trex)
#>   tod      step     angle state
#> 1   9 0.3252437        NA     1
#> 2  10 0.2458265  2.234562     1
#> 3  11 0.2173252 -2.262418     1
#> 4  12 0.5114665 -2.958732     1
#> 5  13 0.3828494  1.811840     1
#> 6  14 0.4220099  1.834668     1
modmat = make_matrices(~ s(tod, bs = "cp"), # formula
                       data = data.frame(tod = 1:24), # data
                       knots = list(tod = c(0, 24))) # where to wrap the cyclic basis
Z = modmat$Z # spline design matrix
S = modmat$S # penalty matrix

We can now specify the penalised negative log-likelihood function. We can compute the transition probability matrix the regular way using tpm_g(). In the last line we need to add the curvature penalty based on S, which we can conveniently do using penalty().

pnll = function(par) {
  getAll(par, dat)
  # cbinding intercept and spline coefs, because intercept is not penalised
  Gamma = tpm_g(Z, cbind(beta0, betaSpline))
  # computing all periodically stationary distributions for easy access later
  Delta = stationary_p(Gamma); REPORT(Delta)
  # parameter transformations
  mu = exp(logmu); REPORT(mu)
  sigma = exp(logsigma); REPORT(sigma)
  kappa = exp(logkappa); REPORT(kappa)
  # calculating all state-dependent densities
  allprobs = matrix(1, nrow = length(step), ncol = N)
  ind = which(!is.na(step) & !is.na(angle)) # only for non-NA obs.
  for(j in 1:N){
    allprobs[ind,j] = dgamma2(step[ind],mu[j],sigma[j]) * dvm(angle[ind],0,kappa[j])
  }
  -forward_g(Delta[tod[1],], Gamma[,,tod], allprobs) + # regular forward algorithm
    penalty(betaSpline, S, lambda) # this does all the penalisation work
}

We also have to append a lambda vector to our dat list which is the initial penalty strength parameter vector. In this case it is of length two because our coefficient matrix has two rows.

If you are wondering why lambda is not added to the par list, this is because for penalised likelihood estimation, it is a hyperparameter, hence not a true parameter in the sense of the other parameters in par. One could, at his point, just use the above penalised likelihood function to do penalised ML for a fixed penalty strength lambda.

par = list(logmu = log(c(0.3, 2.5)), # state-dependent mean step
           logsigma = log(c(0.2, 1.5)), # state-dependent sd step
           logkappa = log(c(0.2, 1.5)), # state-dependent concentration angle
           beta0 = c(-2, 2), # state process intercepts
           betaSpline = matrix(rep(0, 2*(ncol(Z)-1)), nrow = 2)) # spline coefs

dat = list(step = trex$step, # observed steps
           angle = trex$angle, # observed angle
           N = 2, # number of states
           tod = trex$tod, # time of day (used for indexing)
           Z = Z, # spline design matrix
           S = S, # penalty matrix
           lambda = rep(100, 2)) # initial penalty strength

The model fit can then be conducted by using the qreml() function contained in LaMa. The quasi restricted maximum likelihood algorithm finds a good penalty strength parameter lambda by treating the spline coefficients as random effects. Under the hood, qreml() also constructs an AD function with RTMB but uses the qREML algorithm (Koslik 2024) to fit the model. We have to tell the qreml() function which parameters are spline coefficients by providing the name of the corresponding list element of par.

There are some rules to follow when using qreml():

  1. The likelihood function needs to be RTMB-compatible, i.e. have the same structure as the likelihood functions in the vignette LaMa and RTMB. Most importantly, it should be a function of the parameter list only.

  2. The penalty strength vector lambda needs its length to correspond to the total number of spline coefficient vectors used. In our case, this is the number of rows of betaSpline, but if we additionally had a different spline coefficient (with a different name) in our parameter list, possibly with a different length and a different penalty matrix, we would have needed more elements in lambda.

  3. The penalty() function can only be called once in the likelihood. If several spline coefficients are penalised, penalty() expects a list of coefficient matrices or vectors and a list of penalty matrices. This is shown in the third example in this vignette.

  4. When we summarise multiple spline coefficients in a matrix in our parameter list – which is very useful when these are of same lengths and have the same penalty matrix – this matrix must be arranged by row, i.e. each row is one spline coefficient vector. If it is arranged by column, qreml() will fail.

  5. By default, qreml() assmes that the penalisation hyperparameter in the dat object is called lambda. You can use a different name for dat (of course than changing it in your pnll as well), but if you want to use a different name for the penalisation hyperparameter, you have to specify it as a character string in the qreml() call using the psname argument.

system.time(
  mod1 <- qreml(pnll, par, dat, random = "betaSpline")
)
#> Creating AD function
#> Initializing with lambda: 100 100 
#> outer 1 - lambda: 2.93 2.612 
#> outer 2 - lambda: 0.417 0.423 
#> outer 3 - lambda: 0.311 0.162 
#> outer 4 - lambda: 0.308 0.121 
#> outer 5 - lambda: 0.308 0.113 
#> outer 6 - lambda: 0.308 0.113 
#> Converged
#> Final model fit with lambda: 0.308 0.113
#>    user  system elapsed 
#>   7.483   0.199   7.573

The mod object is now a list that contains everything that is reported by the likelihood function, but also the RTMB object created in the process. After fitting the model, we can also use the LaMa function pred_matrix(), that takes the modmat object we created earlier, to build a new interpolating design matrix using the exact same basis expansion specified above. This allows us to plot the estimated transition probabilities as a smooth function of time of day.

Gamma = mod1$Gamma
Delta = mod1$Delta

tod_seq = seq(0,24, length = 200)
Z_pred = pred_matrix(modmat, data.frame(tod = tod_seq))

Gamma_plot = tpm_g(Z_pred, mod1$beta) # interpolating transition probs

plot(tod_seq, Gamma_plot[1,2,], type = "l", lwd = 2, ylim = c(0,1),
     xlab = "time of day", ylab = "transition probability", bty = "n")
lines(tod_seq, Gamma_plot[2,1,], lwd = 2, lty = 3)
legend("topleft", lwd = 2, lty = c(1,3), bty = "n",
       legend = c(expression(gamma[12]^(t)), expression(gamma[21]^(t))))

plot(Delta[,2], type = "b", lwd = 2, pch = 16, xlab = "time of day", ylab = "Pr(active)", 
     col = "deepskyblue", bty = "n", xaxt = "n")
axis(1, at = seq(0,24,by=4), labels = seq(0,24,by=4))

Smooth density estimation

To demonstrate nonparametric estimation of the state-dependent densities, we will consider the nessi data set. It contains acceleration data of the Loch Ness Monster, specifically the overall dynamic body acceleration (ODBA). ODBA is strictily positive with some very extreme values, making direct analysis difficult. Hence, for our analysis we consider the logarithm of ODBA as our observed process.

head(nessi)
#>         ODBA   logODBA state
#> 1 0.03775025 -3.276763     2
#> 2 0.05417830 -2.915475     2
#> 3 0.03625247 -3.317248     2
#> 4 0.01310802 -4.334531     1
#> 5 0.05402441 -2.918319     3
#> 6 0.06133794 -2.791357     3
hist(nessi$logODBA, prob = TRUE, breaks = 50, bor = "white", 
     main = "", xlab = "log(ODBA)")

Clearly, there are at least three behavioural states in the data, and we start by fitting a simple 3-state Gaussian HMM with likelihood function:

nll = function(par){
    getAll(par, dat)
    sigma = exp(logsigma) # exp because strictly positive
    REPORT(mu); REPORT(sigma)
    Gamma = tpm(eta) # multinomial logit link
    delta = stationary(Gamma) # stationary dist of the homogeneous Markov chain
    allprobs = matrix(1, length(logODBA), N)
    ind = which(!is.na(logODBA))
    for(j in 1:N) allprobs[ind,j] = dnorm(logODBA[ind], mu[j], sigma[j])
    -forward(delta, Gamma, allprobs)
}

We then fit the model as explained in the vignette LaMa and RTMB.

# initial parameter list
par = list(mu = c(-4.5, -3.5, -2.5),
           logsigma = log(rep(0.5, 3)),
           eta = rep(-2, 6))

# data and hyperparameters
dat = list(logODBA = nessi$logODBA, N = 3)

# creating automatically differentiable objective function
obj = MakeADFun(nll, par, silent = TRUE)

# fitting the model
opt = nlminb(obj$par, obj$fn, obj$gr)

# reporting to get calculated quantities
mod = obj$report()

# visualising the results
color = c("orange", "deepskyblue", "seagreen3")
hist(nessi$logODBA, prob = TRUE, breaks = 50, bor = "white",
     main = "", xlab = "log(ODBA)")
for(j in 1:3) curve(mod$delta[j] * dnorm(x, mod$mu[j], mod$sigma[j]), 
                    add = TRUE, col = color[j], lwd = 2, n = 500)

We see a clear lack-of-fit due to the inflexibility of the Gaussian state-dependent densities. Thus, we now fit a model with state-dependent densities based on P-Splines.

In a first step, this requires us to prepare the design and penalty matrices needed using buildSmoothDens(). This function can take multiple data streams and a set of initial parameters (specifying initial means and standard deviations) for each data stream. It then builds the P-Spline design and penalty matrices for each data stream as well as a matrix of initial spline coefficients based on the provided parameters. The basis functions are standardised such that they integrate to one, which is needed for density estimation.

modmat = buildSmoothDens(nessi["logODBA"], # only one data stream here
                         k = 25, # number of basis functions
                         par = list(logODBA = list(mean = c(-4, -3.3, -2.8),
                                                   sd = c(0.3, 0.2, 0.5))))
#> logODBA 
#> Leaving out last column of the penalty matrix, fix the last spline coefficient at zero for identifiability!
#> Parameter matrix excludes the last column. Fix this column at zero!

# par is nested named list: top layer: each data stream
# for each data stream: initial means and standard deviations for each state

# objects for model fitting
Z = modmat$Z$logODBA # spline design matrix for logODBA
S = modmat$S$logODBA # penalty matrix for logODBA
beta = modmat$coef$logODBA # initial spline coefficients

# objects for prediction
Z_pred = modmat$Z_predict$logODBA # prediction design matrix
xseq = modmat$xseq$logODBA # prediction sequence of logODBA values

Then, we can specify the penalised negative log-likelihood function. The six lines in the middle are needed for P-Spline-based density estimation. The coefficient matrix beta provided by buildSmoothDens() has one column less than the number of basis functions, which is also printed when calling buildSmoothDens(). This is because the last column, i.e. the last coefficient for each state, needs to be fixed to zero for identifiability which we do by using cbind(beta, 0). Then, we transform the unconstrained parameter matrix to non-negative weights that sum to one (called alpha) for each state using the inverse multinomial logistic link (softmax). The columnns of the allprobs matrix are then computed as linear combinations of the columns of Z and the weights alpha. Lastly, we penalise the unconstrained coefficients beta (not the constrained alpha’s) using the penalty() function.

pnll = function(par){
  getAll(par, dat)
  # regular stationary HMM stuff
  Gamma = tpm(eta)
  delta = stationary(Gamma)
  # smooth state-dependent densities
  alpha = exp(cbind(beta, 0))
  alpha = alpha / rowSums(alpha) # multinomial logit link
  REPORT(alpha)
  allprobs = matrix(1, nrow(Z), N)
  ind = which(!is.na(Z[,1])) # only for non-NA obs.
  allprobs[ind,] = Z[ind,] %*% t(alpha)
  # forward algorithm + penalty
  -forward(delta, Gamma, allprobs) + 
    penalty(beta, S, lambda)
}

Now we specify the initial parameter and data list and fit the model. In this case, we actually don’t need to add the observations to our dat list anymore, as all the information is contained in the design matrix Z.

par = list(beta = beta, # spline coefficients prepared by buildSmoothDens()
           eta = rep(-2, 6)) # initial transition matrix on logit scale

dat = list(N = 3, # number of states
           Z = Z, # spline design matrix
           S = S, # spline penalty matrix
           lambda = rep(10, 3)) # initial penalty strength vector

# fitting the model using qREML
system.time(
  mod2 <- qreml(pnll, par, dat, random = "beta")
)
#> Creating AD function
#> Initializing with lambda: 10 10 10 
#> outer 1 - lambda: 2.258 1.645 3.555 
#> outer 2 - lambda: 1.718 1.411 2.705 
#> outer 3 - lambda: 1.627 1.381 2.475 
#> outer 4 - lambda: 1.611 1.376 2.401 
#> outer 5 - lambda: 1.608 1.375 2.377 
#> outer 6 - lambda: 1.608 1.375 2.368 
#> outer 7 - lambda: 1.608 1.375 2.367 
#> outer 8 - lambda: 1.608 1.375 2.367 
#> Converged
#> Final model fit with lambda: 1.608 1.375 2.367
#>    user  system elapsed 
#>  19.256   2.703  18.597

After fitting the model, we can easily visualise the smooth densities using the prepared prediction objects. We already have access to all reported quanitites because qreml() automatically runs the reporting after model fitting.

sDens = Z_pred %*% t(mod2$alpha) # all three state-dependent densities on a grid

hist(nessi$logODBA, prob = TRUE, breaks = 50, bor = "white", main = "", xlab = "log(ODBA)")
for(j in 1:3) lines(xseq, mod2$delta[j] * sDens[,j], col = color[j], lwd = 2)
lines(xseq, colSums(mod2$delta * t(sDens)), col = "black", lwd = 2, lty = 2)

The P-Spline model results in a very good fit to the empirical distribution. This is beause the first state has a skewed distribution, the second state has a high kurtosis and the third state has a funny right tail. The P-Spline model can capture all of these features where the parametric model failed to do so.

Markov-switching GAMLSS

Lastly, we want to demonstrate how one can easily fit Markov-switching regression models where the state-dependent means and potentially other parameters depend on covariates via smooth functions. For this, we consider the energy data set contained in the R package MSwM. It comprises 1784 daily observations of energy prices (in Cents per kWh) in Spain which we want to explain using the daily oil prices (in Euros per barrel) also provided in the data. Specifically, we consider a 2-state MS-GAMLSS defined by pricet{St=i}N(μt(i),(σt(i))2), \text{price}_t \mid \{ S_t = i \} \sim N \bigl(\mu_t^{(i)}, (\sigma_t^{(i)})^2 \bigr), μt(i)=β0,μ(i)+sμ(i)(oilt),log(σt(i))=β0,σ(i)+sσ(i)(oilt),i=1,2, \mu_t^{(i)} = \beta_{0,\mu}^{(i)} + s_{\mu}^{(i)}(\text{oil}_t), \quad \text{log}(\sigma_t^{(i)}) = \beta_{0, \sigma}^{(i)} + s_{\sigma}^{(i)}(\text{oil}_t), \quad i = 1,2, not covering other potential explanatory covariates for the sake of simplicity.

data(energy, package = "MSwM")
head(energy)
#>      Price      Oil      Gas     Coal   EurDol Ibex35   Demand
#> 1 3.188083 22.43277 14.40099 38.35157 1.134687 8.3976 477.3856
#> 2 4.953667 22.27263 19.02747 38.35157 1.106439 8.3771 609.1261
#> 3 4.730917 22.65383 18.48417 38.35157 1.106684 8.5547 650.3715
#> 4 4.531000 23.67657 18.30143 38.35157 1.116819 8.4631 647.0499
#> 5 5.141875 23.67209 14.55602 38.35157 1.122965 8.1773 627.9698
#> 6 6.322083 23.60534 15.22485 38.35157 1.122460 8.1866 693.2467

Similar to the first example, we can prepare the model matrices using make_matrices():

modmat = make_matrices(~ s(Oil, k = 12, bs = "ps"), energy)
Z = modmat$Z # design matrix
S = modmat$S # penalty matrix (list)

Then, we specify the penalised negative log-likelihood function. It differs from the first example as the state-dependent distributions, as opposed to the state process parameters, depend on the covariate. Additionally, we now have two completely separated spline-coefficient matrices/ random effects called betaSpline and alphaSpline for the state-dependent means and standard deviations respectively. Thus, we need to pass them as a list to the penalty() function.

We also pass the penalty matrix list S that is provided by make_matrices(). This could potentially be a list of length two if the two spline coefficient matrices were penalised differently (e.g. by us using a different spline basis). In this case, however, they are the same and we only pass the list of length one. It does not matter to penalty() if we pass a list of length one or just one matrix.

pnll = function(par) {
  getAll(par, dat)
  Gamma = tpm(eta) # computing the tpm
  delta = stationary(Gamma) # stationary distribution

  # regression parameters for mean and sd
  beta = cbind(beta0, betaSpline); REPORT(beta) # mean parameter matrix
  alpha = cbind(alpha0, alphaSpline); REPORT(alpha) # sd parameter matrix

  # calculating all covariate-dependent means and sds
  Mu = Z %*% t(beta) # mean
  Sigma = exp(Z %*% t(alpha)) # sd

  allprobs = cbind(dnorm(price, Mu[,1], Sigma[,1]),
                   dnorm(price, Mu[,2], Sigma[,2])) # state-dependent densities
  
  - forward(delta, Gamma, allprobs) +
    penalty(list(betaSpline, alphaSpline), S, lambda)
}

From this point on, the model fit is now basically identical to the previous two examples. We specify initial parameters and include an inital penalty strength parameter in the dat list.

# initial parameter list
par = list(eta = rep(-4, 2), # state process intercepts
           beta0 = c(2, 5), # state-dependent mean intercepts
           betaSpline = matrix(0, nrow = 2, ncol = 11), # mean spline coef
           alpha0 = c(0, 0), # state-dependent sd intercepts
           alphaSpline = matrix(0, nrow = 2, ncol = 11)) # sd spline coef

# data, model matrices and initial penalty strength
dat = list(price = energy$Price, 
           Z = Z, 
           S = S, 
           lambda = rep(1e3, 4))

# model fit
system.time(
  mod3 <- qreml(pnll, par, dat, random = c("betaSpline", "alphaSpline"))
)
#> Creating AD function
#> Initializing with lambda: 1000 1000 1000 1000 
#> outer 1 - lambda: 238.423 106.067 189.083 42.362 
#> outer 2 - lambda: 70.263 17.577 55.015 7.935 
#> outer 3 - lambda: 36.315 8.566 26.41 4.88 
#> outer 4 - lambda: 27.185 7.426 15.297 4.336 
#> outer 5 - lambda: 24.236 7.257 10.88 4.214 
#> outer 6 - lambda: 23.193 7.225 9.211 4.182 
#> outer 7 - lambda: 22.808 7.217 8.602 4.173 
#> outer 8 - lambda: 22.661 7.215 8.384 4.17 
#> outer 9 - lambda: 22.593 7.212 8.306 4.169 
#> outer 10 - lambda: 22.578 7.213 8.278 4.168 
#> outer 11 - lambda: 22.575 7.212 8.276 4.168 
#> outer 12 - lambda: 22.575 7.212 8.276 4.168 
#> Converged
#> Final model fit with lambda: 22.575 7.212 8.276 4.168
#>    user  system elapsed 
#>   7.528   3.541   6.787

Having fitted the model, we can visualise the results. We first decode the most probable state sequence and then plot the estimated state-dependent densities as a function of the oil price, as well as the decoded time series. For the former, we create a fine grid of oil price values and use the pred_matrix() function to build the associated interpolating design matrix.

xseq = seq(min(energy$Oil), max(energy$Oil), length = 200) # sequence for prediction
Z_pred = pred_matrix(modmat, newdata = data.frame(Oil = xseq)) # prediction design matrix

mod3$states = viterbi(mod3$delta, mod3$Gamma, mod3$allprobs) # decoding most probable state sequence

Mu_plot = Z_pred %*% t(mod3$beta)
Sigma_plot = exp(Z_pred %*% t(mod3$alpha))

library(scales) # to make colors semi-transparent

par(mfrow = c(1,2))

# state-dependent distribution as a function of oil price
plot(energy$Oil, energy$Price, pch = 20, bty = "n", col = alpha(color[mod3$states], 0.1),
     xlab = "oil price", ylab = "energy price")
for(j in 1:2) lines(xseq, Mu_plot[,j], col = color[j], lwd = 3) # means
qseq = qnorm(seq(0.5, 0.95, length = 4)) # sequence of quantiles
for(i in qseq){ for(j in 1:2){
  lines(xseq, Mu_plot[,j] + i * Sigma_plot[,j], col = alpha(color[j], 0.7), lty = 2)
  lines(xseq, Mu_plot[,j] - i * Sigma_plot[,j], col = alpha(color[j], 0.7), lty = 2)
}}
legend("topright", bty = "n", legend = paste("state", 1:2), col = color, lwd = 3)

# decoded time series
plot(NA, xlim = c(0, nrow(energy)), ylim = c(1,10), bty = "n",
     xlab = "time", ylab = "energy price")
segments(x0 = 1:(nrow(energy)-1), x1 = 2:nrow(energy),
         y0 = energy$Price[-nrow(energy)], y1 = energy$Price[-1], 
         col = color[mod3$states[-1]], lwd = 0.5)

References

Koslik, Jan-Ole. 2024. “Efficient Smoothness Selection for Nonparametric Markov-Switching Models via Quasi Restricted Maximum Likelihood.” https://arxiv.org/abs/2411.11498.
Wood, Simon. 2017. Generalized Additive Models: An Introduction with r. chapman; hall/CRC.