KFAS: coef.SSModel – R documentation

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coef.SSModel

Smoothed Estimates or One-step-ahead Predictions of States

Description

Compute smoothed estimates or one-step-ahead predictions of states of SSModel object or extract them from output of KFS. For non-Gaussian models without simulation (nsim = 0), these are the estimates of conditional modes of states. For Gaussian models and non-Gaussian models with importance sampling, these are the estimates of conditional means of states.

Usage

## S3 method for class 'KFS'
coef(
  object,
  start = NULL,
  end = NULL,
  filtered = FALSE,
  states = "all",
  last = FALSE,
  ...
)

## S3 method for class 'SSModel'
coef(
  object,
  start = NULL,
  end = NULL,
  filtered = FALSE,
  states = "all",
  last = FALSE,
  nsim = 0,
  ...
)

Arguments

`object`	An object of class `KFS` or `SSModel`.
`start`	The start time of the period of interest. Defaults to first time point of the object.
`end`	The end time of the period of interest. Defaults to the last time point of the object.
`filtered`	Logical, return filtered instead of smoothed estimates of state vector. Default is `FALSE`.
`states`	Which states to extract? Either a numeric vector containing the indices of the corresponding states, or a character vector defining the types of the corresponding states. Possible choices are `"all"`, `"level"`, `"slope"`, `"trend"`, `"regression"`, `"arima"`, `"custom"`, `"cycle"` or `"seasonal"`, where `"trend"` extracts all states relating to trend. These can be combined. Default is `"all"`.
`last`	If `TRUE`, extract only the last time point as numeric vector (ignoring `start` and `end`). Default is `FALSE`.
`...`	Additional arguments to `KFS`. Ignored in method for object of class `KFS`.
`nsim`	Only for method for for non-Gaussian model of class `SSModel`. The number of independent samples used in importance sampling. Default is 0, which computes the approximating Gaussian model by `approxSSM` and performs the usual Gaussian filtering/smoothing so that the smoothed state estimates equals to the conditional mode of p(α[t]\|y). In case of `nsim = 0`, the mean estimates and their variances are computed using the Delta method (ignoring the covariance terms).

Value

Multivariate time series containing estimates states.

Examples

model <- SSModel(log(drivers) ~ SSMtrend(1, Q = list(1)) +
 SSMseasonal(period = 12, sea.type = "trigonometric") +
 log(PetrolPrice) + law, data = Seatbelts, H = 1)

coef(model, states = "regression", last = TRUE)
coef(model, start = c(1983, 12), end = c(1984, 2))
out <- KFS(model)
coef(out, states = "regression", last = TRUE)
coef(out, start = c(1983, 12), end = c(1984, 2))

KFAS

Kalman Filter and Smoother for Exponential Family State Space Models

v1.4.5

GPL (>= 2)

Authors

Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>)

Initial release

2021-05-10