pGARMA: Cumulative distribution function for the stationary GARMA...

In ts.extend: Stationary Gaussian ARMA Processes and Other Time-Series Utilities

Description

This function computes the cumulative distribution function from the stationary Gaussian auto-regressive moving-average (GARMA) distribution. The user specifies a vector x giving a single time-series vector, or a matrix x giving one time-series vector in each row, and the function returns the vector of cumulative probabilities corresponding to the input time-series vectors. By default the function generates from the marginal GARMA distribution, but the user may give conditioning indicators in the cond vector to compute the conditional density where some of the elements in the input vectors are conditioning values.

Usage

pGARMA(
  x,
  cond = FALSE,
  mean = 0,
  errorvar = 1,
  ar = numeric(0),
  ma = numeric(0),
  log = FALSE
)

Arguments

x	A vector or matrix of time-series values (if a matrix, each time-series should be one row of the matrix)
cond	Either a single logical value FALSE or a logical vector with the same number of elements; as each time-series vector; each logical value indicates whether the density is conditional on the associated time-series value in x.
mean	The mean parameter
errorvar	The error variance parameter
ar	Vector of auto-regressive coefficients (all roots of AR characteristic polynomial must be outside the unit circle)
ma	Vector of moving-average coefficients
log	Logical; if TRUE the function returns the log-probability; if FALSE the function returns the probability data(garma) AR <- c(0.8, -0.2) MA <- c(0.6, 0.3) #Compute the cumulative probability of the GARMA output (PROBS <- pGARMA(SERIES, ar = AR, ma = MA))

ts.extend documentation built on Nov. 15, 2020, 1:06 a.m.