# 5 Penalised splines

#### Jan-Ole Fischer

> Before diving into this vignette, we recommend reading the vignettes Introduction to LaMa, Automatic differentiation via RTMB and Extensions.

This vignette explores how `LaMa` can be used to fit models involving nonparametric 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 probabilities

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 be summarised as

\[ \text{logit}(\gamma_{ij}^{(t)}) = \beta_0^{(ij)} + s_{ij}(t), \] where \(s_{ij}(t)\) 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, bs = "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)

    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. The transition probability matrix can be computed the regular way using `tpm()`. 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(cbind(beta0, betaSpline), Z)
      # 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(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(0.3, 2)), # state-dependent sd step
               logkappa = log(c(0.1, 2)), # 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 with automatic smoothness selection 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. By default, `qreml()` assumes that the penalisation hyperparameter in the `dat` object is called `lambda`. You can use a different name for `dat` (of course then 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")
    )
    #> outer 1 - lambda: 32.077 31.825
    #> outer 2 - lambda: 10.571 10.745
    #> outer 3 - lambda: 3.667 3.974
    #> outer 4 - lambda: 1.414 1.588
    #> outer 5 - lambda: 0.671 0.693
    #> outer 6 - lambda: 0.427 0.347
    #> outer 7 - lambda: 0.347 0.21
    #> outer 8 - lambda: 0.321 0.154
    #> outer 9 - lambda: 0.312 0.13
    #> outer 10 - lambda: 0.31 0.12
    #> outer 11 - lambda: 0.309 0.116
    #> outer 12 - lambda: 0.309 0.114
    #> outer 13 - lambda: 0.309 0.114
    #>    user  system elapsed
    #>   3.581   0.039   3.648

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 use `predict()` with 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. We use `MCreport()` to draw samples from the approximate normal distribution of the MLE and propagate uncertainty into the plotted quantities.

    color = LaMaColors(2)

    tod_seq = seq(0, 24, length = 100)
    Z_p = predict(modmat, data.frame(tod = tod_seq))

    # MC samples: with report = TRUE, REPORT()ed quantities are sampled directly
    samples = MCreport(mod1$obj, report = TRUE)
    #> Evaluating Hessian...
    #> Computing reported quantities...

    # transition probability samples on the plotting grid
    g12 = sapply(samples$beta, function(b) tpm(b, Z_p)[1,2,])
    g21 = sapply(samples$beta, function(b) tpm(b, Z_p)[2,1,])
    g12.q = apply(g12, 1, quantile, probs = c(0.025, 0.975))
    g21.q = apply(g21, 1, quantile, probs = c(0.025, 0.975))

    # Delta[,2] samples available directly via report = TRUE
    Delta2.samples = sapply(samples$Delta, function(d) d[,2])
    Delta2.q = apply(Delta2.samples, 1, quantile, probs = c(0.025, 0.975))

    # MLE quantities
    Gamma_plot = tpm(mod1$beta, Z_p)
    Delta = mod1$Delta

    oldpar = par(mfrow = c(1, 2))

    # transition probabilities with 95% confidence bands
    plot(NA, bty = "n", ylim = c(0, 1), xlim = c(0, 24),
         xlab = "time of day", ylab = "transition probability")
    polygon(c(tod_seq, rev(tod_seq)), c(g12.q[1,], rev(g12.q[2,])),
            col = adjustcolor(color[1], 0.3), border = NA)
    lines(tod_seq, Gamma_plot[1,2,], lwd = 2, col = color[1])
    polygon(c(tod_seq, rev(tod_seq)), c(g21.q[1,], rev(g21.q[2,])),
            col = adjustcolor(color[2], 0.3), border = NA)
    lines(tod_seq, Gamma_plot[2,1,], lwd = 2, col = color[2])
    legend("topleft", lwd = 2, col = color, bty = "n",
           legend = c(expression(gamma[12]^(t)), expression(gamma[21]^(t))))

    # periodically stationary distribution with 95% confidence band
    plot(Delta[,2], type = "b", lwd = 2, pch = 16, ylim = c(0, 1),
         xlab = "time of day", ylab = "Pr(active)",
         col = "deepskyblue", bty = "n", xaxt = "n")
    polygon(c(1:24, 24:1), c(Delta2.q[1,], rev(Delta2.q[2,])),
            col = adjustcolor("deepskyblue", 0.3), border = NA)
    axis(1, at = seq(0, 24, by = 4), labels = seq(0, 24, by = 4))

    par(oldpar)

## 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 strictly 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 = report(obj)

    # visualising the results
    color = LaMaColors(3)
    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 `smooth_dens_construct()`. 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.

    # providing initial means and sds to initialise spline coefficients
    par0 = list(logODBA = list(mean = c(-4, -3.3, -2.8), sd = c(0.3, 0.2, 0.5)))

    # construct the smooth density objects
    modmat = smooth_dens_construct(nessi["logODBA"], # only one data stream here
                                   par = par0)
    #> Data stream: logODBA
    #> Leaving out last column of the penalty matrix, fix the last spline coefficient at zero for identifiability!
    #> Parameter matrix excludes the last column. Add zero column using 'cbind(coef, 0)' in your loss function!

    # 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_p = 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 `smooth_dens_construct()` 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 columns 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 smooth_dens_construct()
               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
    #> Initialising with lambda: 10 10 10
    #> outer 1 - lambda: 4.227 3.837 4.852
    #> outer 2 - lambda: 2.326 1.943 3.061
    #> outer 3 - lambda: 1.623 1.338 2.355
    #> outer 4 - lambda: 1.326 1.133 2.046
    #> outer 5 - lambda: 1.189 1.059 1.902
    #> outer 6 - lambda: 1.124 1.031 1.832
    #> outer 7 - lambda: 1.092 1.021 1.798
    #> outer 8 - lambda: 1.077 1.017 1.784
    #> outer 9 - lambda: 1.068 1.015 1.776
    #> outer 10 - lambda: 1.064 1.015 1.769
    #> outer 11 - lambda: 1.063 1.015 1.767
    #> outer 12 - lambda: 1.062 1.015 1.766
    #> outer 13 - lambda: 1.062 1.015 1.765
    #> Converged
    #> Final model fit with lambda: 1.062 1.015 1.765
    #>    user  system elapsed
    #>   5.392   0.043   5.462

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

    sDens = Z_p %*% 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 because 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 \[ \text{price}_t \mid \{ S_t = i \} \sim N \bigl(\mu_t^{(i)}, (\sigma_t^{(i)})^2 \bigr), \] \[ \mu_t^{(i)} = \beta_{0,\mu}^{(i)} + s_{\mu}^{(i)}(\text{oil}_t), \quad \text{log}(\sigma_t^{(i)}) = \beta_{0, \sigma}^{(i)} + s_{\sigma}^{(i)}(\text{oil}_t), \quad i = 1,2, \] 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 separate spline-coefficient matrices 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 intial 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
    #> Initialising with lambda: 1000 1000 1000 1000
    #> outer 1 - lambda: 467.043 374.341 432.55 329.665
    #> outer 2 - lambda: 250.35 143.737 200.469 113.054
    #> outer 3 - lambda: 126.091 57.843 99.212 42.159
    #> outer 4 - lambda: 72.858 26.197 56.919 18.27
    #> outer 5 - lambda: 48.024 14.493 35.934 9.767
    #> outer 6 - lambda: 35.993 10.082 24.165 6.518
    #> outer 7 - lambda: 29.872 8.366 17.321 5.192
    #> outer 8 - lambda: 26.632 7.685 13.361 4.626
    #> outer 9 - lambda: 24.864 7.41 11.105 4.375
    #> outer 10 - lambda: 23.881 7.297 9.836 4.263
    #> outer 11 - lambda: 23.325 7.25 9.13 4.213
    #> outer 12 - lambda: 23.007 7.23 8.74 4.189
    #> outer 13 - lambda: 22.822 7.22 8.525 4.178
    #> outer 14 - lambda: 22.719 7.216 8.406 4.173
    #> outer 15 - lambda: 22.655 7.214 8.342 4.171
    #> outer 16 - lambda: 22.618 7.213 8.305 4.17
    #> outer 17 - lambda: 22.594 7.213 8.288 4.169
    #> outer 18 - lambda: 22.586 7.212 8.279 4.169
    #> outer 19 - lambda: 22.584 7.212 8.277 4.169
    #> Converged
    #> Final model fit with lambda: 22.584 7.212 8.277 4.169
    #>    user  system elapsed
    #>   4.792   0.038   4.854

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 again create a fine grid of oil price values and use `predict()` to build the associated interpolating design matrix.

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

    energy$states = viterbi(mod = mod3) # decoding most probable state sequence

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

    library(scales) # to make colors semi-transparent

    oldpar = 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[energy$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[energy$states[-1]], lwd = 0.5)

    par(oldpar)

## 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.