R/ts.model.R
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments
Defines functions
desc.to.ts.model
print.ts.model
`+.ts.model`
`*.ts.model`
SARIMA
SARMA
ARIMA
ARMA
MA
AR
DR
RW
WN
QN
GM
ARMA11
MA1
AR1
Documented in
AR
AR1
ARIMA
ARMA
ARMA11
desc.to.ts.model
DR
GM
MA
MA1
print.ts.model
QN
RW
SARIMA
SARMA
WN
#' Create an Autoregressive 1 [AR(1)] Process
#'
#' Setups the necessary backend for the AR1 process.
#' @param phi A \code{double} value for the \eqn{\phi}{phi} of an AR1 process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "AR1","SIGMA2"}
#' \item{theta}{\eqn{\phi}{phi}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"AR1"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' AR1()
#' AR1(phi=.32, sigma2=1.3)
AR1 = function(phi = NULL, sigma2 = 1) {
starting = FALSE;
if(is.null(phi)){
phi = 0;
sigma2 = 1;
starting = TRUE;
}
if(length(phi) != 1 & length(sigma2) != 1){
stop("Bad AR1 model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("AR1","SIGMA2"),
theta = c(phi,sigma2),
plength = 2,
desc = "AR1",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Moving Average 1 [MA(1)] Process
#'
#' Setups the necessary backend for the MA1 process.
#' @param theta A \code{double} value for the \eqn{\phi}{phi} of an MA1 process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "MA1","SIGMA2"}
#' \item{theta}{\eqn{\theta}{theta}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"MA1"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' MA1()
#' MA1(theta=.32, sigma2=1.3)
MA1 = function(theta = NULL, sigma2 = 1) {
starting = FALSE;
if(is.null(theta)){
theta = 0;
sigma2 = 1;
starting = TRUE;
}
if(length(theta) != 1 & length(sigma2) != 1){
stop("Bad MA1 model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("MA1","SIGMA2"),
theta = c(theta,sigma2),
plength = 2,
desc = "MA1",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Autoregressive Order 1 - Moving Average Order 1 (ARMA(1,1)) Process
#'
#' Sets up the necessary backend for the ARMA(1,1) process.
#' @param phi A \code{double} containing the coefficients for \eqn{\phi _1}{phi[1]}'s for the Autoregressive 1 (AR1) term.
#' @param theta A \code{double} containing the coefficients for \eqn{\theta _1}{theta[1]}'s for the Moving Average 1 (MA1) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR1}, \eqn{MA1}, \eqn{SIGMA2}}
#' \item{theta}{\eqn{\phi}{phi}, \eqn{\theta}{theta}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters: 3}
#' \item{obj.desc}{Depth of Parameters e.g. list(c(1,1,1))}
#' \item{starting}{Guess Starting values? \code{TRUE} or \code{FALSE} (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Creates an ARMA(1,1) process with predefined coefficients.
#' ARMA11(phi = .23, theta = .1, sigma2 = 1)
#'
#' # Creates an ARMA(1,1) process with values to be guessed on callibration.
#' ARMA11()
ARMA11 = function(phi = NULL, theta = NULL, sigma2 = 1.0) {
# Assume the user specified data
starting = FALSE
if(is.null(phi) || is.null(theta)){
phi = 0
theta = 0;
sigma2 = 1;
starting = TRUE;
}else if(length(phi) != 1 || length(theta) != 1){
stop("`phi` and `theta` must have only one value.")
}
out = structure(list(process.desc = c("AR1","MA1","SIGMA2"),
theta = c(phi, theta, sigma2),
plength = 3,
desc = "ARMA11",
obj.desc = list(c(1,1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create a Gauss-Markov (GM) Process
#'
#' Setups the necessary backend for the GM process.
#' @param beta A \code{double} value for the \eqn{\beta}{beta} of an GM process.
#' @param sigma2_gm A \code{double} value for the variance, \eqn{\sigma ^2_{gm}}{sigma^2[gm]}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "BETA","SIGMA2"}
#' \item{theta}{\eqn{\beta}{beta}, \eqn{\sigma ^2_{gm}}{sigma^2[gm]}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"GM"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' When supplying values for \eqn{\beta}{beta} and \eqn{\sigma ^2_{gm}}{sigma^2[gm]},
#' these parameters should be of a GM process and NOT of an AR1. That is,
#' do not supply AR1 parameters such as \eqn{\phi}{phi}, \eqn{\sigma^2}{sigma^2}.
#'
#' Internally, GM parameters are converted to AR1 using the `freq`
#' supplied when creating data objects (\link{imu}, \link{gts})
#' or specifying a `freq` parameter in \link{gmwm} or \link{gmwm.imu}.
#'
#' The `freq` of a data object takes precedence over the `freq` set when modeling.
#' @author JJB
#' @examples
#' GM()
#' GM(beta=.32, sigma2_gm=1.3)
GM = function(beta = NULL, sigma2_gm = 1) {
starting = FALSE;
if(is.null(beta)){
beta = 0;
sigma2_gm = 1;
starting = TRUE;
}
if(length(beta) != 1 & length(sigma2_gm) != 1){
stop("Bad GM model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("BETA","SIGMA2_GM"),
theta = c(beta,sigma2_gm),
plength = 2,
desc = "GM",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Quantisation Noise (QN) Process
#'
#' Sets up the necessary backend for the QN process.
#' @param q2 A \code{double} value for the \eqn{Q^2}{Q^2} of a QN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "QN"}
#' \item{theta}{\eqn{Q^2}{Q^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' QN()
#' QN(q2=3.4)
QN = function(q2 = NULL) {
starting = FALSE
if(is.null(q2)){
q2 = 2
starting = TRUE
}
if(length(q2) != 1){
stop("Bad QN model submitted. Must be a double that indicates the Q2 value.")
}
out = structure(list(process.desc = "QN",
theta = q2,
plength = 1,
desc = "QN",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an White Noise (WN) Process
#'
#' Sets up the necessary backend for the WN process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "WN"}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' WN()
#' WN(sigma2=3.4)
WN = function(sigma2 = NULL) {
starting = FALSE
if(is.null(sigma2)){
sigma2 = 3
starting = TRUE
}
if(length(sigma2) != 1){
stop("Bad WN model submitted. Must be a double that indicates the standard deviation.")
}
out = structure(list(process.desc = "WN",
theta = sigma2,
plength = 1,
desc = "WN",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Random Walk (RW) Process
#'
#' Sets up the necessary backend for the RW process.
#' @param gamma2 A \code{double} value for the variance \eqn{\gamma ^2}{gamma^2}
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "RW"}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' RW()
#' RW(gamma2=3.4)
RW = function(gamma2 = NULL) {
starting = FALSE
if(is.null(gamma2)){
gamma2 = 4
starting = TRUE
}
if(length(gamma2) != 1){
stop("Bad RW model submitted. Must be a double that indicates the standard deviation.")
}
out = structure(list(process.desc = "RW",
theta = gamma2,
plength = 1,
desc = "RW",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Drift (DR) Process
#'
#' Sets up the necessary backend for the DR process.
#' @param omega A \code{double} value for the slope of a DR process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "DR"}
#' \item{theta}{slope}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' DR()
#' DR(omega=3.4)
DR = function(omega = NULL) {
starting = FALSE
if(is.null(omega)){
omega = 5
starting = TRUE
}
if(length(omega) != 1){
stop("Bad Drift model submitted. Must be a double that indicates a slope.")
}
out = structure(list(process.desc = "DR",
theta = omega,
plength = 1,
desc = "DR",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Create an Autoregressive P [AR(P)] Process
#' @description Setups the necessary backend for the AR(P) process.
#' @param phi A \code{vector} with double values for the \eqn{\phi}{phi} of an AR(P) process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "AR-1","AR-2", ..., "AR-P", "SIGMA2"}
#' \item{theta}{\eqn{\phi_1}{phi[[1]]}, \eqn{\phi_2}{phi[[2]]}, ..., \eqn{\phi_p}{phi[[p]]}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"AR"}
#' \item{obj.desc}{Depth of Parameters e.g. list(p,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' AR(1) # Slower version of AR1()
#' AR(phi=.32, sigma=1.3) # Slower version of AR1()
#' AR(2) # Equivalent to ARMA(2,0).
AR = function(phi = NULL, sigma2 = 1) {
if(is.null(phi)){
stop("`phi` must either be a whole number or a vector of numbers for phi parameter in AR().")
}
out = SARIMA(ar = phi, i = 0, ma = 0, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' Create an Moving Average Q [MA(Q)] Process
#'
#' Setups the necessary backend for the MA(Q) process.
#' @param theta A \code{vector} with double values for the \eqn{\theta}{theta} of an MA(Q) process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "MA-1","MA-2", ..., "MA-Q", "SIGMA2"}
#' \item{theta}{\eqn{\theta_1}{theta[[1]]}, \eqn{\theta_2}{theta[[2]]}, ..., \eqn{\theta_q}{theta[[q]]}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"MA"}
#' \item{obj.desc}{Depth of Parameters e.g. list(q,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' MA(1) # One theta
#' MA(2) # Two thetas!
#'
#' MA(theta=.32, sigma=1.3) # 1 theta with a specific value.
#' MA(theta=c(.3,.5), sigma=.3) # 2 thetas with specific values.
MA = function(theta = NULL, sigma2 = 1) {
if(is.null(theta)){
stop("`theta` must be either a whole number or a vector of doubles for theta parameter in MA().")
}
out = SARIMA(ar = 0, i = 0, ma = theta, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create an Autoregressive Moving Average (ARMA) Process
#' @description Sets up the necessary backend for the ARMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Create an ARMA(1,2) process
#' ARMA(ar=1,2)
#' # Creates an ARMA(3,2) process with predefined coefficients.
#' ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))
#'
#' # Creates an ARMA(3,2) process with predefined coefficients and standard deviation
#' ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)
ARMA = function(ar = 1, ma = 1, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = 0, ma = ma, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create an Autoregressive Integrated Moving Average (ARIMA) Process
#' @description Sets up the necessary backend for the ARIMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param i An \code{integer} containing the number of differences to be done.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARIMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Create an ARMA(1,2) process
#' ARIMA(ar=1,2)
#' # Creates an ARMA(3,2) process with predefined coefficients.
#' ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))
#'
#' # Creates an ARMA(3,2) process with predefined coefficients and standard deviation
#' ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)
ARIMA = function(ar = 1, i = 0, ma = 1, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = i, ma = ma, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create a Seasonal Autoregressive Moving Average (SARMA) Process
#' @description Sets up the necessary backend for the SARMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Phi}{PHI}'s or the process number \eqn{P} for the Seasonal Autoregressive (SAR) term.
#' @param sma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Theta}{THETA}'s or the process number \eqn{Q} for the Seasonal Moving Average (SMA) term.
#' @param s A \code{integer} indicating the seasonal value of the data.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the SARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}, \eqn{SAR*P}{SAR x P}, \eqn{SMA*Q}{SMA x Q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation unlike R.
#' @author JJB
#' @examples
#' # Create an SARMA(1,2)x(1,1) process
#' SARMA(ar = 1, ma = 2,sar = 1, sma =1)
#'
#' # Creates an SARMA(1,1)x(1,1) process with predefined coefficients.
#' SARMA(ar=0.23, ma=0.4, sar = .3, sma = .3)
SARMA = function(ar = 1, ma = 1, sar = 1, sma = 1, s = 12, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = 0, ma = ma, sar = sar, si = 0, sma = sma, s = 12, sigma2 = sigma2)
invisible(out)
}
#' @title Create a Seasonal Autoregressive Integrated Moving Average (SARIMA) Process
#' @description Sets up the necessary backend for the SARIMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param i An \code{integer} containing the number of differences to be done.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Phi}{PHI}'s or the process number \eqn{P} for the Seasonal Autoregressive (SAR) term.
#' @param si An \code{integer} containing the number of seasonal differences to be done.
#' @param sma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Theta}{THETA}'s or the process number \eqn{Q} for the Seasonal Moving Average (SMA) term.
#' @param s An \code{integer} containing the seasonality.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the SARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}, \eqn{SAR*P}{SAR x P}, \eqn{SMA*Q}{SMA x Q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1, i, si) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation unlike R.
#' @author JJB
#' @examples
#' # Create an SARIMA(1,1,2)x(1,0,1) process
#' SARIMA(ar = 1, i = 1, ma = 2, sar = 1, si = 0, sma =1)
#'
#' # Creates an SARMA(1,0,1)x(1,1,1) process with predefined coefficients.
#' SARIMA(ar=0.23, i = 0, ma=0.4, sar = .3, sma = .3)
SARIMA = function(ar = 1, i = 0, ma = 1, sar = 1, si = 0, sma = 1, s = 12, sigma2 = 1.0) {
# Assume the user specified data
starting = FALSE
# Get initial parameters
p = length(ar)
q = length(ma)
P = length(sar)
Q = length(sma)
d = length(i)
ds = length(si)
if(d > 1 || ds > 1){stop("`i` or `si` must have a length of 1.")}
else if(!is.wholenumber(i) || !is.wholenumber(si)){stop("`i` or `si` must be integers.")}
# If P or Q == 1, this implies we might have a starting guess.
if( p == 1 || q == 1 || P == 1 || Q == 1 ){
if(p == 1){
if(is.wholenumber(ar) & ar != 0){
ar = rep(-1, ar)
starting = TRUE
}else if(ar == 0){
ar = numeric(0) # creates a size 0 vector
}
}
if(q == 1){
if(is.wholenumber(ma) & ma != 0){
ma = rep(-2, ma)
starting = TRUE
}else if(ma == 0){
ma = numeric(0)
}
}
if(P == 1){
if(is.wholenumber(sar) & sar != 0){
sar = rep(-1, sar)
starting = TRUE
}else if(sar == 0){
sar = numeric(0) # creates a size 0 vector
}
}
if(Q == 1){
if(is.wholenumber(sma) & sma != 0){
sma = rep(-2, sma)
starting = TRUE
}else if(sma == 0){
sma = numeric(0)
}
}
}
# Update the values.
p = length(ar)
q = length(ma)
P = length(sar)
Q = length(sma)
out = structure(list(process.desc = c(rep("AR", p), rep("MA",q), rep("SAR", P), rep("SMA",Q), "SIGMA2"),
theta = c(ar, ma, sar, sma, sigma2),
plength = p + q + P + Q + 1,
desc = "SARIMA",
obj.desc = list(c(p, q, P, Q, 1, s, i, si)),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Multiple a ts.model by constant
#' @description Sets up the necessary backend for creating multiple model objects.
#' @method * ts.model
#' @param x A \code{numeric} value
#' @param y A \code{ts.model} object
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{y desc replicated x times}
#' \item{theta}{y theta replicated x times}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(c(1,1),c(1,1))}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @keywords internal
#' @export
#' @examples
#' 4*DR()+2*WN()
#' DR()*4 + WN()*2
#' AR1(phi=.3,sigma=.2)*3
`*.ts.model` = function(x, y) {
# Handles the ts.model()*c case
if(!is.numeric(x)){
temp = x
x = y
y = temp
}
out = structure(list(process.desc = rep(y$process.desc,x),
theta = rep(y$theta,x),
plength = y$plength*x,
desc = rep(y$desc,x),
obj.desc = rep(y$obj.desc,x),
starting = y$starting), class = "ts.model")
invisible(out)
}
#' @title Add ts.model objects together
#' @description Sets up the necessary backend for combining ts.model objects.
#' @method + ts.model
#' @param x A \code{ts.model} object
#' @param y A \code{ts.model} object
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{process.desc}{combined x, y desc}
#' \item{theta}{combined x, y theta}
#' \item{plength}{Number of Parameters}
#' \item{desc}{Add process to queue e.g. c("AR1","WN")}
#' \item{obj.desc}{Depth of Parameters e.g. list(1, c(1,1), c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @export
#' @keywords internal
#' @examples
#' DR()+WN()
#' AR1(phi=.3,sigma=.2)
`+.ts.model` = function(x, y) {
starting = FALSE
if(y$starting & x$starting){
starting = TRUE
}
out = structure(list(process.desc = c(x$process.desc, y$process.desc),
theta = c(x$theta,y$theta),
plength = x$plength + y$plength,
desc = c(x$desc, y$desc),
obj.desc = c(x$obj.desc, y$obj.desc),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Multiple a ts.model by constant
#' @description Sets up the necessary backend for creating multiple model objects.
#' @method print ts.model
#' @export
#' @param x A \code{numeric} value
#' @param ... further arguments passed to or from other methods.
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{desc}
#' \item{theta}
#' }
#' @keywords internal
#' @author JJB
#' @examples
#' # Creates a parameter space for guessing
#' QN() + DR() + WN() + RW() + AR1() + ARMA(1,2)
#'
#' # Creates a user-specified starting value model
#' AR1(phi = .9, sigma2 = .1) + WN(sigma2 = 1)
#'
#' # Similarly, with the addition of a generic ARMA
#' RW(gamma2 = .3) + DR(omega = .5) + QN(q2 = .9) + ARMA(ar = c(.3,.1), ma = c(.3,.2), sigma2 = .99)
#'
#' # In a similar vein, this example highlights the lack of need for specifying parameters.
#' AR1(.9,.1) + WN(1) + RW(.3) + DR(.5) + QN(.9) + ARMA(c(.3,.1), c(.3,.2), .99)
print.ts.model = function(x, ...){
desctable = data.frame("Terms" = x$process.desc, "Initial Values" = x$theta, stringsAsFactors = FALSE);
cat("\nGuess Starting Values:", x$starting, "\n")
if(x$starting){
cat("The program will attempt to guess starting values for...\n")
print(desctable[,1], row.names = FALSE)
cat("To have the option of using your own starting values, please supply values for each parameter.\n")
}else{
print(desctable, row.names = FALSE)
cat("The model will be initiated using the initial values you supplied.\n")
}
}
#' @title Create a ts.model from desc string
#' @description Sets up the necessary backend for using Cpp functions to build R ts.model objects
#' @param desc A \code{character} vector containing: \code{"AR1"},\code{"DR"},\code{"WN"},\code{"RW"},\code{"QN"}
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{desc}
#' \item{theta}
#' }
#' @author JJB
#' @keywords internal
#' @examples
#' desc.to.ts.model(c("AR1","WN"))
desc.to.ts.model = function(desc){
theta = .Call('_gmwm_model_theta', PACKAGE = 'gmwm', desc)
out = structure(list(process.desc = .Call('_gmwm_model_process_desc', PACKAGE='gmwm', desc),
theta = theta,
plength = length(theta),
desc = desc,
obj.desc = .Call('_gmwm_model_objdesc', PACKAGE = 'gmwm', desc),
starting = TRUE), class = "ts.model")
invisible(out)
}
SMAC-Group/gmwm documentation built on Sept. 11, 2021, 10:06 a.m.
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Defines functions desc.to.ts.model print.ts.model `+.ts.model` `*.ts.model` SARIMA SARMA ARIMA ARMA MA AR DR RW WN QN GM ARMA11 MA1 AR1
Documented in AR AR1 ARIMA ARMA ARMA11 desc.to.ts.model DR GM MA MA1 print.ts.model QN RW SARIMA SARMA WN
#' Create an Autoregressive 1 [AR(1)] Process
#'
#' Setups the necessary backend for the AR1 process.
#' @param phi A \code{double} value for the \eqn{\phi}{phi} of an AR1 process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "AR1","SIGMA2"}
#' \item{theta}{\eqn{\phi}{phi}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"AR1"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' AR1()
#' AR1(phi=.32, sigma2=1.3)
AR1 = function(phi = NULL, sigma2 = 1) {
starting = FALSE;
if(is.null(phi)){
phi = 0;
sigma2 = 1;
starting = TRUE;
}
if(length(phi) != 1 & length(sigma2) != 1){
stop("Bad AR1 model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("AR1","SIGMA2"),
theta = c(phi,sigma2),
plength = 2,
desc = "AR1",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Moving Average 1 [MA(1)] Process
#'
#' Setups the necessary backend for the MA1 process.
#' @param theta A \code{double} value for the \eqn{\phi}{phi} of an MA1 process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "MA1","SIGMA2"}
#' \item{theta}{\eqn{\theta}{theta}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"MA1"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' MA1()
#' MA1(theta=.32, sigma2=1.3)
MA1 = function(theta = NULL, sigma2 = 1) {
starting = FALSE;
if(is.null(theta)){
theta = 0;
sigma2 = 1;
starting = TRUE;
}
if(length(theta) != 1 & length(sigma2) != 1){
stop("Bad MA1 model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("MA1","SIGMA2"),
theta = c(theta,sigma2),
plength = 2,
desc = "MA1",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Autoregressive Order 1 - Moving Average Order 1 (ARMA(1,1)) Process
#'
#' Sets up the necessary backend for the ARMA(1,1) process.
#' @param phi A \code{double} containing the coefficients for \eqn{\phi _1}{phi[1]}'s for the Autoregressive 1 (AR1) term.
#' @param theta A \code{double} containing the coefficients for \eqn{\theta _1}{theta[1]}'s for the Moving Average 1 (MA1) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR1}, \eqn{MA1}, \eqn{SIGMA2}}
#' \item{theta}{\eqn{\phi}{phi}, \eqn{\theta}{theta}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters: 3}
#' \item{obj.desc}{Depth of Parameters e.g. list(c(1,1,1))}
#' \item{starting}{Guess Starting values? \code{TRUE} or \code{FALSE} (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Creates an ARMA(1,1) process with predefined coefficients.
#' ARMA11(phi = .23, theta = .1, sigma2 = 1)
#'
#' # Creates an ARMA(1,1) process with values to be guessed on callibration.
#' ARMA11()
ARMA11 = function(phi = NULL, theta = NULL, sigma2 = 1.0) {
# Assume the user specified data
starting = FALSE
if(is.null(phi) || is.null(theta)){
phi = 0
theta = 0;
sigma2 = 1;
starting = TRUE;
}else if(length(phi) != 1 || length(theta) != 1){
stop("`phi` and `theta` must have only one value.")
}
out = structure(list(process.desc = c("AR1","MA1","SIGMA2"),
theta = c(phi, theta, sigma2),
plength = 3,
desc = "ARMA11",
obj.desc = list(c(1,1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create a Gauss-Markov (GM) Process
#'
#' Setups the necessary backend for the GM process.
#' @param beta A \code{double} value for the \eqn{\beta}{beta} of an GM process.
#' @param sigma2_gm A \code{double} value for the variance, \eqn{\sigma ^2_{gm}}{sigma^2[gm]}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "BETA","SIGMA2"}
#' \item{theta}{\eqn{\beta}{beta}, \eqn{\sigma ^2_{gm}}{sigma^2[gm]}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"GM"}
#' \item{obj.desc}{Depth of Parameters e.g. list(1,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' When supplying values for \eqn{\beta}{beta} and \eqn{\sigma ^2_{gm}}{sigma^2[gm]},
#' these parameters should be of a GM process and NOT of an AR1. That is,
#' do not supply AR1 parameters such as \eqn{\phi}{phi}, \eqn{\sigma^2}{sigma^2}.
#'
#' Internally, GM parameters are converted to AR1 using the `freq`
#' supplied when creating data objects (\link{imu}, \link{gts})
#' or specifying a `freq` parameter in \link{gmwm} or \link{gmwm.imu}.
#'
#' The `freq` of a data object takes precedence over the `freq` set when modeling.
#' @author JJB
#' @examples
#' GM()
#' GM(beta=.32, sigma2_gm=1.3)
GM = function(beta = NULL, sigma2_gm = 1) {
starting = FALSE;
if(is.null(beta)){
beta = 0;
sigma2_gm = 1;
starting = TRUE;
}
if(length(beta) != 1 & length(sigma2_gm) != 1){
stop("Bad GM model submitted. Must be double values for two parameters.")
}
out = structure(list(process.desc = c("BETA","SIGMA2_GM"),
theta = c(beta,sigma2_gm),
plength = 2,
desc = "GM",
obj.desc = list(c(1,1)),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Quantisation Noise (QN) Process
#'
#' Sets up the necessary backend for the QN process.
#' @param q2 A \code{double} value for the \eqn{Q^2}{Q^2} of a QN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "QN"}
#' \item{theta}{\eqn{Q^2}{Q^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' QN()
#' QN(q2=3.4)
QN = function(q2 = NULL) {
starting = FALSE
if(is.null(q2)){
q2 = 2
starting = TRUE
}
if(length(q2) != 1){
stop("Bad QN model submitted. Must be a double that indicates the Q2 value.")
}
out = structure(list(process.desc = "QN",
theta = q2,
plength = 1,
desc = "QN",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an White Noise (WN) Process
#'
#' Sets up the necessary backend for the WN process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "WN"}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' WN()
#' WN(sigma2=3.4)
WN = function(sigma2 = NULL) {
starting = FALSE
if(is.null(sigma2)){
sigma2 = 3
starting = TRUE
}
if(length(sigma2) != 1){
stop("Bad WN model submitted. Must be a double that indicates the standard deviation.")
}
out = structure(list(process.desc = "WN",
theta = sigma2,
plength = 1,
desc = "WN",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Random Walk (RW) Process
#'
#' Sets up the necessary backend for the RW process.
#' @param gamma2 A \code{double} value for the variance \eqn{\gamma ^2}{gamma^2}
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "RW"}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' RW()
#' RW(gamma2=3.4)
RW = function(gamma2 = NULL) {
starting = FALSE
if(is.null(gamma2)){
gamma2 = 4
starting = TRUE
}
if(length(gamma2) != 1){
stop("Bad RW model submitted. Must be a double that indicates the standard deviation.")
}
out = structure(list(process.desc = "RW",
theta = gamma2,
plength = 1,
desc = "RW",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' Create an Drift (DR) Process
#'
#' Sets up the necessary backend for the DR process.
#' @param omega A \code{double} value for the slope of a DR process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "DR"}
#' \item{theta}{slope}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' DR()
#' DR(omega=3.4)
DR = function(omega = NULL) {
starting = FALSE
if(is.null(omega)){
omega = 5
starting = TRUE
}
if(length(omega) != 1){
stop("Bad Drift model submitted. Must be a double that indicates a slope.")
}
out = structure(list(process.desc = "DR",
theta = omega,
plength = 1,
desc = "DR",
obj.desc = list(1),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Create an Autoregressive P [AR(P)] Process
#' @description Setups the necessary backend for the AR(P) process.
#' @param phi A \code{vector} with double values for the \eqn{\phi}{phi} of an AR(P) process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "AR-1","AR-2", ..., "AR-P", "SIGMA2"}
#' \item{theta}{\eqn{\phi_1}{phi[[1]]}, \eqn{\phi_2}{phi[[2]]}, ..., \eqn{\phi_p}{phi[[p]]}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"AR"}
#' \item{obj.desc}{Depth of Parameters e.g. list(p,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' AR(1) # Slower version of AR1()
#' AR(phi=.32, sigma=1.3) # Slower version of AR1()
#' AR(2) # Equivalent to ARMA(2,0).
AR = function(phi = NULL, sigma2 = 1) {
if(is.null(phi)){
stop("`phi` must either be a whole number or a vector of numbers for phi parameter in AR().")
}
out = SARIMA(ar = phi, i = 0, ma = 0, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' Create an Moving Average Q [MA(Q)] Process
#'
#' Setups the necessary backend for the MA(Q) process.
#' @param theta A \code{vector} with double values for the \eqn{\theta}{theta} of an MA(Q) process.
#' @param sigma2 A \code{double} value for the variance, \eqn{\sigma ^2}{sigma^2}, of a WN process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{Used in summary: "MA-1","MA-2", ..., "MA-Q", "SIGMA2"}
#' \item{theta}{\eqn{\theta_1}{theta[[1]]}, \eqn{\theta_2}{theta[[2]]}, ..., \eqn{\theta_q}{theta[[q]]}, \eqn{\sigma^2}{sigma^2}}
#' \item{plength}{Number of Parameters}
#' \item{desc}{"MA"}
#' \item{obj.desc}{Depth of Parameters e.g. list(q,1)}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @examples
#' MA(1) # One theta
#' MA(2) # Two thetas!
#'
#' MA(theta=.32, sigma=1.3) # 1 theta with a specific value.
#' MA(theta=c(.3,.5), sigma=.3) # 2 thetas with specific values.
MA = function(theta = NULL, sigma2 = 1) {
if(is.null(theta)){
stop("`theta` must be either a whole number or a vector of doubles for theta parameter in MA().")
}
out = SARIMA(ar = 0, i = 0, ma = theta, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create an Autoregressive Moving Average (ARMA) Process
#' @description Sets up the necessary backend for the ARMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Create an ARMA(1,2) process
#' ARMA(ar=1,2)
#' # Creates an ARMA(3,2) process with predefined coefficients.
#' ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))
#'
#' # Creates an ARMA(3,2) process with predefined coefficients and standard deviation
#' ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)
ARMA = function(ar = 1, ma = 1, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = 0, ma = ma, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create an Autoregressive Integrated Moving Average (ARIMA) Process
#' @description Sets up the necessary backend for the ARIMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param i An \code{integer} containing the number of differences to be done.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the ARIMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation like R.
#' @author JJB
#' @examples
#' # Create an ARMA(1,2) process
#' ARIMA(ar=1,2)
#' # Creates an ARMA(3,2) process with predefined coefficients.
#' ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))
#'
#' # Creates an ARMA(3,2) process with predefined coefficients and standard deviation
#' ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)
ARIMA = function(ar = 1, i = 0, ma = 1, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = i, ma = ma, sar = 0, si = 0, sma = 0, s = 0, sigma2 = sigma2)
invisible(out)
}
#' @title Create a Seasonal Autoregressive Moving Average (SARMA) Process
#' @description Sets up the necessary backend for the SARMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Phi}{PHI}'s or the process number \eqn{P} for the Seasonal Autoregressive (SAR) term.
#' @param sma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Theta}{THETA}'s or the process number \eqn{Q} for the Seasonal Moving Average (SMA) term.
#' @param s A \code{integer} indicating the seasonal value of the data.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the SARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}, \eqn{SAR*P}{SAR x P}, \eqn{SMA*Q}{SMA x Q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation unlike R.
#' @author JJB
#' @examples
#' # Create an SARMA(1,2)x(1,1) process
#' SARMA(ar = 1, ma = 2,sar = 1, sma =1)
#'
#' # Creates an SARMA(1,1)x(1,1) process with predefined coefficients.
#' SARMA(ar=0.23, ma=0.4, sar = .3, sma = .3)
SARMA = function(ar = 1, ma = 1, sar = 1, sma = 1, s = 12, sigma2 = 1.0) {
out = SARIMA(ar = ar, i = 0, ma = ma, sar = sar, si = 0, sma = sma, s = 12, sigma2 = sigma2)
invisible(out)
}
#' @title Create a Seasonal Autoregressive Integrated Moving Average (SARIMA) Process
#' @description Sets up the necessary backend for the SARIMA process.
#' @param ar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\phi}{phi}'s or the process number \eqn{p} for the Autoregressive (AR) term.
#' @param i An \code{integer} containing the number of differences to be done.
#' @param ma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\theta}{theta}'s or the process number \eqn{q} for the Moving Average (MA) term.
#' @param sar A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Phi}{PHI}'s or the process number \eqn{P} for the Seasonal Autoregressive (SAR) term.
#' @param si An \code{integer} containing the number of seasonal differences to be done.
#' @param sma A \code{vector} or \code{integer} containing either the coefficients for \eqn{\Theta}{THETA}'s or the process number \eqn{Q} for the Seasonal Moving Average (SMA) term.
#' @param s An \code{integer} containing the seasonality.
#' @param sigma2 A \code{double} value for the standard deviation, \eqn{\sigma}{sigma}, of the SARMA process.
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{\eqn{AR*p}{AR x p}, \eqn{MA*q}{MA x q}, \eqn{SAR*P}{SAR x P}, \eqn{SMA*Q}{SMA x Q}}
#' \item{theta}{\eqn{\sigma}{sigma}}
#' \item{plength}{Number of Parameters}
#' \item{obj.desc}{y desc replicated x times}
#' \item{obj}{Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1, i, si) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @details
#' A variance is required since the model generation statements utilize
#' randomization functions expecting a variance instead of a standard deviation unlike R.
#' @author JJB
#' @examples
#' # Create an SARIMA(1,1,2)x(1,0,1) process
#' SARIMA(ar = 1, i = 1, ma = 2, sar = 1, si = 0, sma =1)
#'
#' # Creates an SARMA(1,0,1)x(1,1,1) process with predefined coefficients.
#' SARIMA(ar=0.23, i = 0, ma=0.4, sar = .3, sma = .3)
SARIMA = function(ar = 1, i = 0, ma = 1, sar = 1, si = 0, sma = 1, s = 12, sigma2 = 1.0) {
# Assume the user specified data
starting = FALSE
# Get initial parameters
p = length(ar)
q = length(ma)
P = length(sar)
Q = length(sma)
d = length(i)
ds = length(si)
if(d > 1 || ds > 1){stop("`i` or `si` must have a length of 1.")}
else if(!is.wholenumber(i) || !is.wholenumber(si)){stop("`i` or `si` must be integers.")}
# If P or Q == 1, this implies we might have a starting guess.
if( p == 1 || q == 1 || P == 1 || Q == 1 ){
if(p == 1){
if(is.wholenumber(ar) & ar != 0){
ar = rep(-1, ar)
starting = TRUE
}else if(ar == 0){
ar = numeric(0) # creates a size 0 vector
}
}
if(q == 1){
if(is.wholenumber(ma) & ma != 0){
ma = rep(-2, ma)
starting = TRUE
}else if(ma == 0){
ma = numeric(0)
}
}
if(P == 1){
if(is.wholenumber(sar) & sar != 0){
sar = rep(-1, sar)
starting = TRUE
}else if(sar == 0){
sar = numeric(0) # creates a size 0 vector
}
}
if(Q == 1){
if(is.wholenumber(sma) & sma != 0){
sma = rep(-2, sma)
starting = TRUE
}else if(sma == 0){
sma = numeric(0)
}
}
}
# Update the values.
p = length(ar)
q = length(ma)
P = length(sar)
Q = length(sma)
out = structure(list(process.desc = c(rep("AR", p), rep("MA",q), rep("SAR", P), rep("SMA",Q), "SIGMA2"),
theta = c(ar, ma, sar, sma, sigma2),
plength = p + q + P + Q + 1,
desc = "SARIMA",
obj.desc = list(c(p, q, P, Q, 1, s, i, si)),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Multiple a ts.model by constant
#' @description Sets up the necessary backend for creating multiple model objects.
#' @method * ts.model
#' @param x A \code{numeric} value
#' @param y A \code{ts.model} object
#' @return An S3 object with called ts.model with the following structure:
#' \describe{
#' \item{process.desc}{y desc replicated x times}
#' \item{theta}{y theta replicated x times}
#' \item{plength}{Number of Parameters}
#' \item{desc}{y desc replicated x times}
#' \item{obj.desc}{Depth of Parameters e.g. list(c(1,1),c(1,1))}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @keywords internal
#' @export
#' @examples
#' 4*DR()+2*WN()
#' DR()*4 + WN()*2
#' AR1(phi=.3,sigma=.2)*3
`*.ts.model` = function(x, y) {
# Handles the ts.model()*c case
if(!is.numeric(x)){
temp = x
x = y
y = temp
}
out = structure(list(process.desc = rep(y$process.desc,x),
theta = rep(y$theta,x),
plength = y$plength*x,
desc = rep(y$desc,x),
obj.desc = rep(y$obj.desc,x),
starting = y$starting), class = "ts.model")
invisible(out)
}
#' @title Add ts.model objects together
#' @description Sets up the necessary backend for combining ts.model objects.
#' @method + ts.model
#' @param x A \code{ts.model} object
#' @param y A \code{ts.model} object
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{process.desc}{combined x, y desc}
#' \item{theta}{combined x, y theta}
#' \item{plength}{Number of Parameters}
#' \item{desc}{Add process to queue e.g. c("AR1","WN")}
#' \item{obj.desc}{Depth of Parameters e.g. list(1, c(1,1), c(length(ar),length(ma),1) )}
#' \item{starting}{Guess Starting values? TRUE or FALSE (e.g. specified value)}
#' }
#' @author JJB
#' @export
#' @keywords internal
#' @examples
#' DR()+WN()
#' AR1(phi=.3,sigma=.2)
`+.ts.model` = function(x, y) {
starting = FALSE
if(y$starting & x$starting){
starting = TRUE
}
out = structure(list(process.desc = c(x$process.desc, y$process.desc),
theta = c(x$theta,y$theta),
plength = x$plength + y$plength,
desc = c(x$desc, y$desc),
obj.desc = c(x$obj.desc, y$obj.desc),
starting = starting), class = "ts.model")
invisible(out)
}
#' @title Multiple a ts.model by constant
#' @description Sets up the necessary backend for creating multiple model objects.
#' @method print ts.model
#' @export
#' @param x A \code{numeric} value
#' @param ... further arguments passed to or from other methods.
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{desc}
#' \item{theta}
#' }
#' @keywords internal
#' @author JJB
#' @examples
#' # Creates a parameter space for guessing
#' QN() + DR() + WN() + RW() + AR1() + ARMA(1,2)
#'
#' # Creates a user-specified starting value model
#' AR1(phi = .9, sigma2 = .1) + WN(sigma2 = 1)
#'
#' # Similarly, with the addition of a generic ARMA
#' RW(gamma2 = .3) + DR(omega = .5) + QN(q2 = .9) + ARMA(ar = c(.3,.1), ma = c(.3,.2), sigma2 = .99)
#'
#' # In a similar vein, this example highlights the lack of need for specifying parameters.
#' AR1(.9,.1) + WN(1) + RW(.3) + DR(.5) + QN(.9) + ARMA(c(.3,.1), c(.3,.2), .99)
print.ts.model = function(x, ...){
desctable = data.frame("Terms" = x$process.desc, "Initial Values" = x$theta, stringsAsFactors = FALSE);
cat("\nGuess Starting Values:", x$starting, "\n")
if(x$starting){
cat("The program will attempt to guess starting values for...\n")
print(desctable[,1], row.names = FALSE)
cat("To have the option of using your own starting values, please supply values for each parameter.\n")
}else{
print(desctable, row.names = FALSE)
cat("The model will be initiated using the initial values you supplied.\n")
}
}
#' @title Create a ts.model from desc string
#' @description Sets up the necessary backend for using Cpp functions to build R ts.model objects
#' @param desc A \code{character} vector containing: \code{"AR1"},\code{"DR"},\code{"WN"},\code{"RW"},\code{"QN"}
#' @return An S3 object with called ts.model with the following structure:
#' \itemize{
#' \item{desc}
#' \item{theta}
#' }
#' @author JJB
#' @keywords internal
#' @examples
#' desc.to.ts.model(c("AR1","WN"))
desc.to.ts.model = function(desc){
theta = .Call('_gmwm_model_theta', PACKAGE = 'gmwm', desc)
out = structure(list(process.desc = .Call('_gmwm_model_process_desc', PACKAGE='gmwm', desc),
theta = theta,
plength = length(theta),
desc = desc,
obj.desc = .Call('_gmwm_model_objdesc', PACKAGE = 'gmwm', desc),
starting = TRUE), class = "ts.model")
invisible(out)
}
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