ssm_ulg {bssm}  R Documentation 
Construct an object of class ssm_ulg
by directly defining the
corresponding terms of the model.
ssm_ulg( y, Z, H, T, R, a1 = NULL, P1 = NULL, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn )
y 
Observations as time series (or vector) of length n. 
Z 
System matrix Z of the observation equation. Either a vector of length m, a m x n matrix, or object which can be coerced to such. 
H 
Vector of standard deviations. Either a scalar or a vector of length n. 
T 
System matrix T of the state equation. Either a m x m matrix or a m x m x n array, or object which can be coerced to such. 
R 
Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array, or object which can be coerced to such. 
a1 
Prior mean for the initial state as a vector of length m. 
P1 
Prior covariance matrix for the initial state as m x m matrix. 
init_theta 
Initial values for the unknown hyperparameters theta. 
D 
Intercept terms D_t for the observations equation, given as a scalar or vector of length n. 
C 
Intercept terms C_t for the state equation, given as a m times 1 or m times n matrix. 
state_names 
Names for the states. 
update_fn 
Function which returns list of updated model
components given input vector theta. This function should take only one
vector argument which is used to create list with elements named as

prior_fn 
Function which returns log of prior density given input vector theta. 
The general univariate linearGaussian model is defined using the following observational and state equations:
y_t = D_t + Z_t α_t + H_t ε_t, (\textrm{observation equation})
α_{t+1} = C_t + T_t α_t + R_t η_t, (\textrm{transition equation})
where ε_t \sim N(0, 1), η_t \sim N(0, I_k) and α_1 \sim N(a_1, P_1) independently of each other. Here k is the number of disturbance terms which can be less than m, the number of states.
The update_fn
function should take only one
vector argument which is used to create list with elements named as
Z
, H
T
, R
, a1
, P1
, D
,
and C
,
where each element matches the dimensions of the original model.
If any of these components is missing, it is assumed to be constant wrt.
theta.
Note that while you can input say R as m x k matrix for ssm_ulg
,
update_fn
should return R as m x k x 1 in this case.
It might be useful to first construct the model without updating function
and then check the expected structure of the model components from the
output.
Object of class ssm_ulg
.
# Regression model with timevarying coefficients set.seed(1) n < 100 x1 < rnorm(n) x2 < rnorm(n) b1 < 1 + cumsum(rnorm(n, sd = 0.5)) b2 < 2 + cumsum(rnorm(n, sd = 0.1)) y < 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) Z < rbind(1, x1, x2) H < 0.1 T < diag(3) R < diag(c(0, 1, 0.1)) a1 < rep(0, 3) P1 < diag(10, 3) # updates the model given the current values of the parameters update_fn < function(theta) { R < diag(c(0, theta[1], theta[2])) dim(R) < c(3, 3, 1) list(R = R, H = theta[3]) } # prior for standard deviations as halfnormal(1) prior_fn < function(theta) { if(any(theta < 0)) { log_p < Inf } else { log_p < sum(dnorm(theta, 0, 1, log = TRUE)) } log_p } model < ssm_ulg(y, Z, H, T, R, a1, P1, init_theta = c(1, 0.1, 0.1), update_fn = update_fn, prior_fn = prior_fn, state_names = c("level", "b1", "b2"), # using default values, but being explicit for testing purposes C = matrix(0, 3, 1), D = numeric(1)) out < run_mcmc(model, iter = 10000) out sumr < summary(out, variable = "state") ts.plot(sumr$Mean, col = 1:3) lines(b1, col= 2, lty = 2) lines(b2, col= 3, lty = 2) # Perhaps easiest way to construct a general SSM for bssm is to use the # model building functionality of KFAS: library("KFAS") model_kfas < SSModel(log(drivers) ~ SSMtrend(1, Q = 5e4)+ SSMseasonal(period = 12, sea.type = "trigonometric", Q = 0) + log(PetrolPrice) + law, data = Seatbelts, H = 0.005) # use as_bssm function for conversion, kappa defines the # prior variance for diffuse states model_bssm < as_bssm(model_kfas, kappa = 100) # define updating function for parameter estimation # we can use SSModel and as_bssm functions here as well # (for large model it is more efficient to do this # "manually" by constructing only necessary matrices, # i.e., in this case a list with H and Q) updatefn < function(theta) { model_kfas < SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2) + log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) as_bssm(model_kfas, kappa = 100) } prior < function(theta) { if(any(theta < 0)) Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) } init_theta < rep(1e2, 3) c("sd_level", "sd_seasonal", "sd_y") model_bssm < as_bssm(model_kfas, kappa = 100, init_theta = init_theta, prior_fn = prior, update_fn = updatefn) out < run_mcmc(model_bssm, iter = 10000, burnin = 5000) out # Above the regression coefficients are modelled as # timeinvariant latent states. # Here is an alternative way where we use variable D so that the # coefficients are part of parameter vector theta: updatefn2 < function(theta) { # note no PetrolPrice or law variables here model_kfas2 < SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2), data = Seatbelts, H = theta[3]^2) X < model.matrix(~ 1 + law + log(PetrolPrice), data = Seatbelts) D < t(X %*% theta[4:5]) as_bssm(model_kfas2, D = D, kappa = 100) } prior2 < function(theta) { if(any(theta[1:3] < 0)) { Inf } else { sum(dnorm(theta[1:3], 0, 0.1, log = TRUE)) + sum(dnorm(theta[4:5], 0, 10, log = TRUE)) } } init_theta < c(rep(1e2, 3), 0, 0) names(init_theta) < c("sd_level", "sd_seasonal", "sd_y", "law", "Petrol") model_bssm2 < updatefn2(init_theta) model_bssm2$theta < init_theta model_bssm2$prior_fn < prior2 model_bssm2$update_fn < updatefn2 out2 < run_mcmc(model_bssm2, iter = 10000, burnin = 5000) out2