# SS.stst: Steady State In SSsimple: State Space Models

## Description

Find steady state of system, i.e., locate when Kalman gain converges

## Usage

 `1` ```SS.stst(F, H, Q, R, P0, epsilon, verbosity=0) ```

## Arguments

 `F` The state matrix. A scalar, or vector of length d, or a d x d matrix. When scalar, `F` is constant diagonal. When a vector, `F` is diagonal. `H` The measurement matrix. Must be n x d. `Q` The state variance. A scalar, or vector of length d, or a d x d matrix. When scalar, `Q` is constant diagonal. When a vector, `Q` is diagonal. `R` The measurement variance. A scalar, or vector of length n, or a n x n matrix. When scalar, `R` is constant diagonal. When a vector, `R` is diagonal. `P0` Initial a priori prediction error. `epsilon` A small scalar number. `verbosity` 0, 1 or 2.

## Details

Note: The test for convergence has been (very, very slightly) modified since v0.5.1. The current test has been implemented for rigor. Users who have results based on earlier releases may observe infinitesimal differences in the resulting prediction error.

## Value

A named list.

 `P.apri` A d x d matrix giving a priori prediction variance. `P.apos` A d x d matrix giving a posteriori prediction variance.

## Examples

 ```1 2 3``` ```H <- matrix(1) SS.stst(1, H, 1, 1, P0=10^5, epsilon=10^(-14), verbosity=1) ```

SSsimple documentation built on Dec. 7, 2019, 9:06 a.m.