ssm_ung  R Documentation 
Construct an object of class ssm_ung
by directly defining the
corresponding terms of the model.
ssm_ung(
y,
Z,
T,
R,
a1 = NULL,
P1 = NULL,
distribution,
phi = 1,
u,
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 
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. 
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. 
distribution 
Distribution of the observed time series. Possible
choices are 
phi 
Additional parameter relating to the nonGaussian distribution.
For negative binomial distribution this is the dispersion term, for gamma
distribution this is the shape parameter, and for other distributions this
is ignored. Should an object of class 
u 
A vector of positive constants for nonGaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials. 
init_theta 
Initial values for the unknown hyperparameters theta (i.e. unknown variables excluding latent state variables). 
D 
Intercept terms 
C 
Intercept terms 
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 univariate nonGaussian model is defined using the following observational and state equations:
p(y_t  D_t + Z_t \alpha_t), (\textrm{observation equation})
\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t,
(\textrm{transition equation})
where \eta_t \sim N(0, I_k)
and
\alpha_1 \sim N(a_1, P_1)
independently of each other,
and p(y_t  .)
is either Poisson, binomial, gamma, or
negative binomial distribution.
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
, phi
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_ung
,
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.
An object of class ssm_ung
.
data("drownings", package = "bssm")
model < ssm_ung(drownings[, "deaths"], Z = 1, T = 1, R = 0.2,
a1 = 0, P1 = 10, distribution = "poisson", u = drownings[, "population"])
# approximate results based on Gaussian approximation
out < smoother(model)
ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2)
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