ssm_mlg {bssm}  R Documentation 
Construct an object of class ssm_mlg
by directly defining the
corresponding terms of the model.
ssm_mlg( 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 multivariate time series or matrix with dimensions n x p. 
Z 
System matrix Z of the observation equation as p x m matrix or p x m x n array. 
H 
Lower triangular matrix H of the observation. 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. 
R 
Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array. 
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 (i.e. unknown variables excluding latent state variables). 
D 
Intercept terms for observation equation, given as a p x n matrix. 
C 
Intercept terms for state equation, given as m x n matrix. 
state_names 
A character vector defining the names of the states. 
update_fn 
A 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 
A function which returns log of prior density given input vector theta. 
The general multivariate 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, I_p), η_t \sim N(0, I_k) and α_1 \sim N(a_1, P_1) independently of each other. Here p is the number of time series and 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_mlg
,
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
An object of class ssm_mlg
.
data("GlobalTemp", package = "KFAS") model_temp < ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10, state_names = "temperature", # using default values, but being explicit for testing purposes D = matrix(0, 2, 1), C = matrix(0, 1, 1)) ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3)