ar1_prec_consecutive: Sparse precision matrix for a stationary Gaussian AR(1)...
In irregulAR1: Functions for Irregularly Sampled AR(1) Processes
Description
Usage
Arguments
Value
Examples
Description
Creates the precision (inverse covariance) matrix of an AR(1) process with
parameters rho and sigma, observed at n consecutive
time points. The process is assumed to be in stationarity and to have
Gaussian errors. The matrix is a tridiagonal band matrix and thus sparse.
Usage
1
ar1_prec_consecutive(n, rho, sigma)
Arguments
n
An integer greater than or equal to 1.
rho
A real number strictly less than 1 in absolute value.
sigma
A positive real number.
Value
A matrix with n rows and n columns.
Examples
1
2
3
4
5
library(Matrix)
n <- 5
rho <- 0.5
sigma <- 1
ar1_prec_consecutive(n, rho, sigma)
irregulAR1 documentation built on May 2, 2019, 8:49 a.m.
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Description Usage Arguments Value Examples
Description
Creates the precision (inverse covariance) matrix of an AR(1) process with
parameters rho and sigma, observed at n consecutive
time points. The process is assumed to be in stationarity and to have
Gaussian errors. The matrix is a tridiagonal band matrix and thus sparse.
Usage
1 | ar1_prec_consecutive(n, rho, sigma)
|
Arguments
n |
An integer greater than or equal to 1. |
rho |
A real number strictly less than 1 in absolute value. |
sigma |
A positive real number. |
Value
A matrix with n rows and n columns.
Examples
1 2 3 4 5 | library(Matrix)
n <- 5
rho <- 0.5
sigma <- 1
ar1_prec_consecutive(n, rho, sigma)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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