mixAR (version 0.22.8)

fit_mixARreg-methods: Fit time series regression models with mixture autoregressive residuals

Description

Estimate a linear regression model for time series with residuals from a mixture autoregressive process.

Usage

fit_mixARreg(x, y, mixARmodel, EMinit, ...)
mixARreg(x, y, mixARmodel, tol = 1e-6, niter = 200)

Value

reg

The summary output of the regression part of the model.

mixARmodel

Estimates of the mixture autoregressive part of the model.

niter

The number of iterations until convergence.

Arguments

x

the response time series (currently a numeric vector).

y

data.frame, matrix or numeric vector. If either of the first two, each column must contain one covariate (currently numeric). A check for matching lengths between x and y is done.

mixARmodel

An object inheriting from class "MixAR", giving initial values for EM-estimation of mixture autoregressive parameters. Currenlty only "MixARGaussian" is supported.

EMinit

starting values for EM estimation of MixAR parameters. If present, must be a named list, containing at least prob and scale as numeric vectors, and a list for arcoef.

tol

threshold for convergence criterion.

...

passed on to MixARreg.

niter

maximal number of iterations.

Methods

signature(x = "ANY", y = "data.frame", mixARmodel = "MixAR", EMinit = "missing")

Covariates y are supplied as data.frame: each column corresponds to one covariate. Initialization of MixAR paramters is done using input mixARmodel

signature(x = "ANY", y = "matrix", mixARmodel = "MixAR", EMinit = "missing")

Covariates y are supplied as matrix: each column corresponds to one covariate. Initialization of MixAR paramters is done using input mixARmodel

signature(x = "ANY", y = "numeric", mixARmodel = "MixAR", EMinit = "missing")

Covariates y is supplied as numeric: this method handles the simple regression case with a single covairate. Initialization of MixAR paramters is done using input mixARmodel

%\item{\code{signature(x = "ANY", y = "ANY", mixARmodel = "ANY", EMinit = "ANY")}}{}

signature(x = "ANY", y = "ANY", mixARmodel = "missing", EMinit = "list")

EMinit must be a named list (see 'Arguments').

signature(x = "ANY", y = "ANY", mixARmodel = "MixAR", EMinit = "list")

When both mixARmodel and EMinit are supplied, the second is ignored.

Author

Davide Ravagli and Georgi N. Boshnakov

Details

fit_mixARreg is a generic function. Currently there is no default method for fit_mixARreg. Arguments y, mixARmodel, EMinit can be given in a number of ways, see individual methods for details.

Argument mixARmodel gives the details of the the MixAR part of the model and initial values for the parameters. For fit_mixARreg this can alternatively be done with a list using argument EMinit. Currently, at least one of the two must be supplied, and if both are present EMinit is ignored.

mixARreg performs a two-step estimation of a linear regression model with mixture autoregressive residuals. It is the workhorse for fit_mixARreg which calls it to do the computations.

See Also

fit_mixAR

Examples

## Simulate covariates
set.seed(1234)
n <- 50 # for CRAN
y <- data.frame(rnorm(n, 7, 1), rt(n, 3), rnorm(n, 3, 2))

## Build mixAR part
model <- new("MixARGaussian", 
             prob   = exampleModels$WL_At@prob,      # c(0.5, 0.5)
             scale  = exampleModels$WL_At@scale,     # c(1, 2)        
             arcoef = exampleModels$WL_At@arcoef@a ) # list(-0.5, 1.1)

## Simulate from MixAR part
u <- mixAR_sim(model, n, 0)

x <- 10 + y[, 1] + 3 * y[, 2] + 2 * y[, 3] + u

## Fit model

## Using MixARGaussian
fit_mixARreg(x = x, y = y, mixARmodel = model, niter = 3)

## Using EMinit
EMinit <- list(prob = exampleModels$WL_At@prob, scale = exampleModels$WL_At@scale,
               arcoef = exampleModels$WL_At@arcoef@a)
fit_mixARreg(x = x, y = y, EMinit = EMinit, niter = 3)