Inhomogeneous HMMs

Before diving into this vignette, we recommend reading the vignette Introduction to LaMa.

This vignette explains how to fit inhomogeneous HMMs, i.e. models that depend on external covariates with LaMa. Such inhomogeneity in HMMs can result from covariates affecting the transition probabilities of the underlying Markov chain, or covariates affecting the state-dependent distributions, which would then be called Markov-switching regression. We will begin with effects in the state process

Covariate effects in the state process

If covariates affect the transition probabilities, this implies that we model the transition probability matrix as a function of said covariates. Let z_t be a vector of covariates of length p + 1 for t = 1, …, T, where the first entry is always equal to 1 to include an intercept. Moreover, let β_ij be a vector of regression parameters, also of length p + 1 for each off-diagonal element (i ≠ j) of the transition probability matrix. First, consider linear predictors η_ij^(t) = β_ij^′z_t, for t = 1, …, T. As the transition probabilities need to be in lie in the interval (0, 1) and each row of the transition matrix needs to sum to one, we obtain the transition probabilities via the inverse multinomial logistic link as $$ \gamma_{ij}^{(t)} = \Pr(S_t = j \mid S_{t-1} = i) = \frac{\exp(\eta_{ij}^{(t)})}{\sum_{k=1}^N \exp(\eta_{ik}^{(t)})}, $$ where η_ii is set to zero for i = 1, …, N for identifiability and N is the number of hidden states. The function tpm_g() conducts this calculation for all elements of the t.p.m. and all time points efficiently in C++.

At this point we want to point out that the definition of the transition probabilities is not necessarily unique. Indeed for data points at times 1, …, T we only need T − 1 transition probability matrices. The definition above means that the transition probability between t − 1 and t depends on the covariate values at time point t, but we could also have defined γ_ij^(t) = Pr (S_t + 1 = j ∣ S_t = i). We want to point out that these two specifications are not equivalent. For HMMs there is no established convention, so this choice needs to be made by users and can be important when the exact timing of the covariate effect is relevant. In LaMa this comes down to either passing the design matrix excluding its first or last row to tpm_g(), where we use the first option in this vignette. If you forget to exclude the first or the last row of the design matrix when calculating all transition matrices, and pass an array of dimension c(N,N,T) to forward_g() for likelihood evaluation, the function will revert to the first option by just ignoring the first slice of the array.

Simulation example

We begin by simulating data from the above specified model, assuming 2 states and Gaussian state-dependent distributions. The covariate effects for the state process are fully specified by a parameter matrix beta of dimension c(N*(N-1), p+1). By default the function tpm_g() will fill the off-diagonal elements of each transition matrix by column, which can be changed by setting byrow = TRUE. The latter is useful, as popular HMM packages like moveHMM or momentuHMM return the parameter matrix such that the t.p.m. needs to be filled by row.

# loading the package
library(LaMa)

# parameters
mu = c(5, 20)   # state-dependent means
sigma = c(4, 5) # state-dependent standard deviations

# state process regression parameters
beta = matrix(c(-2, -2,       # intercepts
                -1, 0.5,      # linear effects
                0.25, -0.25), # quadratic effects
              nrow = 2)

n = 1000 # number of observations
set.seed(123)
z = rnorm(n) # in practice there will be n covariate values.
# However, we only have n-1 transitions, thererfore we only need n-1 values:
Z = cbind(z, z^2) # quadratic effect of z
Gamma = tpm_g(Z = Z[-1,], beta) # of dimension c(2, 2, n-1)
delta = c(0.5, 0.5) # non-stationary initial distribution

color = c("orange", "deepskyblue")

oldpar = par(mfrow = c(1,2))
zseq = seq(-2,2,by = 0.01)
Gamma_seq = tpm_g(Z = cbind(zseq, zseq^2), beta)
plot(zseq, Gamma_seq[1,2,], type = "l", lwd = 3, bty = "n", ylim = c(0,1), 
     xlab = "z", ylab = "gamma_12", col = color[1])
plot(zseq, Gamma_seq[2,1,], type = "l", lwd = 3, bty = "n", ylim = c(0,1),
     xlab = "z", ylab = "gamma_21", col = color[2])

par(oldpar)

Let’s now simulate synthetic data from the above specified model.

s = rep(NA, n)
s[1] = sample(1:2, 1, prob = delta) # sampling first state from initial distr.
for(t in 2:n){
  # sampling next state conditional on previous one with tpm at that time point
  s[t] = sample(1:2, 1, prob = Gamma[s[t-1],,t-1])
}
# sampling observations conditional on the states
x = rnorm(n, mu[s], sigma[s])

plot(x[1:200], bty = "n", pch = 20, ylab = "x", 
     col = c(color[1], color[2])[s[1:200]])

We now model the transition probabilities parametrically, where we have a paramter for the intercept, the linear effect and the quadratic effect for each off-diagonal element of the t.p.m.

Writing the negative log-likelihood function

Here we specify the likelihood function and pretend we know the polynomial degree of the effect of z on the transition probabilities.

nll = function(par, x, Z){
  beta = matrix(par[1:6], nrow = 2) # matrix of coefficients
  Gamma = tpm_g(Z[-1,], beta) # excluding the first covariate value -> n-1 tpms
  delta = c(1, exp(par[7]))
  delta = delta / sum(delta)
  mu = par[8:9]
  sigma = exp(par[10:11])
  # calculate all state-dependent probabilities
  allprobs = matrix(1, length(x), 2)
  for(j in 1:2) allprobs[,j] = dnorm(x, mu[j], sigma[j])
  # forward algorithm
  -forward_g(delta, Gamma, allprobs)
}

Fitting an HMM to the data

par = c(beta = c(-2, -2, rep(0,4)), # initialising with homogeneous tpm
        logitdelta = 0, # starting value for initial distribution
        mu = c(4, 14), # initial state-dependent means
        sigma = c(log(3),log(5))) # initial state-dependents sds
system.time(
  mod <- nlm(nll, par, x = x, Z = Z)

)
#>    user  system elapsed 
#>   3.212   0.000   0.813

Really fast!

Visualising results

Again, we use tpm_g() and stationary() to tranform the parameters.

# transform parameters to working
beta_hat = matrix(mod$estimate[1:6], nrow = 2)
Gamma_hat = tpm_g(Z = Z[-1,], beta_hat)
delta_hat = c(1, exp(mod$estimate[7]))
delta_hat = delta_hat / sum(delta_hat)
mu_hat = mod$estimate[8:9]
sigma_hat = exp(mod$estimate[10:11])

# we calculate the average state distribution overall all covariate values
zseq = seq(-2, 2, by = 0.01)
Gamma_seq = tpm_g(Z = cbind(zseq, zseq^2), beta_hat)
Prob = matrix(nrow = length(zseq), ncol = 2)
for(i in 1:length(zseq)){ Prob[i,] = stationary(Gamma_seq[,,i]) }
prob = apply(Prob, 2, mean)

hist(x, prob = TRUE, bor = "white", breaks = 20, main = "")
curve(prob[1]*dnorm(x, mu_hat[1], sigma_hat[1]), add = TRUE, lwd = 3, 
      col = color[1], n=500)
curve(prob[2]*dnorm(x, mu_hat[2], sigma_hat[2]), add = TRUE, lwd = 3, 
      col = color[2], n=500)
curve(prob[1]*dnorm(x, mu_hat[1], sigma_hat[1])+
        prob[2]*dnorm(x, mu[2], sigma_hat[2]),
      add = TRUE, lwd = 3, lty = "dashed", n = 500)
legend("topright", col = c(color[1], color[2], "black"), lwd = 3, bty = "n",
       lty = c(1,1,2), legend = c("state 1", "state 2", "marginal"))


oldpar = par(mfrow = c(1,2))
plot(zseq, Gamma_seq[1,2,], type = "l", lwd = 3, bty = "n", ylim = c(0,1), 
     xlab = "z", ylab = "gamma_12_hat", col = color[1])
plot(zseq, Gamma_seq[2,1,], type = "l", lwd = 3, bty = "n", ylim = c(0,1),
     xlab = "z", ylab = "gamma_21_hat", col = color[2])

par(mfrow = c(1,1))
plot(zseq, Prob[,1], type = "l", lwd = 3, bty = "n", ylim = c(0,1), xlab = "z", 
     ylab = "Pr(state 1)", col = color[1])

par(oldpar)

Covariate effects in the state-dependent process

We now look at a setting where covariates influence the mean of the state-dependent distribution, while the state switching is controlled by a homogeneous Markov chain. This is often called Markov-switching regression. Assuming the observation process to be conditionally normally distributed, this means

X_t ∣ S_t = j ∼ N(β_j^′z_t, σ_j²), j = 1, …, N.

Simulation example

First we specify parameters for the simulation. The important change here is that beta now contains the regression coefficients for the state-dependent regressions.

sigma = c(1, 1) # state-dependent standard deviations (homoscedasticity)

# parameter matrix
# each row contains parameter vector for the corresponding state
beta = matrix(c(8, 10,             # intercepts
                -2, 1, 0.5, -0.5), # slopes
              nrow = 2)
n = 1000 # number of observations
set.seed(123)
z = rnorm(n)
Z = cbind(z, z^2) # quadratic effect of z

Gamma = matrix(c(0.9, 0.1, 0.05, 0.95), 
               nrow = 2, byrow = TRUE) # homogeneous t.p.m.
delta = stationary(Gamma) # stationary Markov chain

Simulation

In the simulation code, the state-dependent mean now is not fixed anymore, but changes accoring to the covariate values in Z.

s = x = rep(NA, n)
s[1] = sample(1:2, 1, prob = delta)
x[1] = rnorm(1, beta[s[1],]%*%c(1, Z[1,]), # state-dependent regression
                    sigma[s[1]])
for(t in 2:n){
  s[t] = sample(1:2, 1, prob = Gamma[s[t-1],])
  x[t] = rnorm(1, beta[s[t],]%*%c(1, Z[t,]), # state-dependent regression
                      sigma[s[t]])
}

oldpar = par(mfrow = c(1,2))
plot(x[1:400], bty = "n", pch = 20, ylab = "x", 
     col = c(color[1], color[2])[s[1:400]])

plot(z[which(s==1)], x[which(s==1)], pch = 16, col = color[1], bty = "n", 
     ylim = c(0,15), xlab = "z", ylab = "x")
points(z[which(s==2)], x[which(s==2)], pch = 16, col = color[2])

par(oldpar)

Writing the negative log-likelihood function

In the likelihood function, we also add the state-dependent regression in the loop calculating the state-dependent probabilities. The code cbind(1,Z) %*% beta[j,] computes the linear predictor for the j-th state.

nllMSR = function(par, x, Z){
  Gamma = tpm(par[1:2]) # homogeneous tpm
  delta = stationary(Gamma) # stationary Markov chain
  beta = matrix(par[2 + 1:(2 + 2*2)], nrow = 2) # parameter matrix
  sigma = exp(par[2 + 2 + 2*2 + 1:2])
  # calculate all state-dependent probabilities
  allprobs = matrix(1, length(x), 2)
  # state-dependent regression
  for(j in 1:2) allprobs[,j] = dnorm(x, cbind(1,Z) %*% beta[j,], sigma[j])
  # forward algorithm
  -forward(delta, Gamma, allprobs)
}

Fitting a Markov-switching regression model

par = c(logitgamma = c(-2, -3),      # starting values state process
        beta = c(8, 10, rep(0,4)),   # starting values for regression
        logsigma = c(log(1),log(1))) # starting values for sigma
system.time(
  mod_reg <- nlm(nllMSR, par, x = x, Z = Z)
)
#>    user  system elapsed 
#>   0.229   0.000   0.228

Visualising results

To visualise the results, be transform the parameters to working parameters and add the two estimated state-specific regressions to the scatter plot.

Gamma_hat_reg = tpm(mod_reg$estimate[1:2]) # calculating all tpms
delta_hat_reg = stationary(Gamma_hat_reg)
beta_hat_reg = matrix(mod_reg$estimate[2+1:(2*2+2)], nrow = 2)
sigma_hat_reg = exp(mod_reg$estimate[2+2*2+2 +1:2])

# we have some label switching
plot(z, x, pch = 16, bty = "n", xlab = "z", ylab = "x", col = color[s])
points(z, x, pch = 20)
curve(beta_hat_reg[1,1] + beta_hat_reg[1,2]*x + beta_hat_reg[1,3]*x^2, 
      add = TRUE, lwd = 4, col = color[2])
curve(beta_hat_reg[2,1] + beta_hat_reg[2,2]*x + beta_hat_reg[2,3]*x^2, 
      add = TRUE, lwd = 4, col = color[1])

- Covariate effects in the state process
- Covariate effects in the state-dependent process