NLDSD_TransientLorenzAttractor:

In faustogc/streamChaos: Analysis of Non Linear Data Streams

Usage

NLDSD_TransientLorenzAttractor(N, start.x = c(5, 5, 5), parm.range = list(t0 = c(10, 28, 8/3), tn = c(10, 268, 8/3)), dt = 0.1, warming.up.num = 0, range = NULL, noise.type = "None", noise.parms = list())

Arguments

N
start.x
parm.range
dt
warming.up.num
range
noise.type
noise.parms

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (N, start.x = c(5, 5, 5), parm.range = list(t0 = c(10, 
    28, 8/3), tn = c(10, 268, 8/3)), dt = 0.1, warming.up.num = 0, 
    range = NULL, noise.type = "None", noise.parms = list()) 
{
    if (is.null(range)) {
        range <- matrix(c(-54, 56), ncol = 2, byrow = T)
    }
    if (N < 1) 
        stop("invalid number of observations")
    counter <- 0
    warmed.up <- F
    parms <- parm.range$t0
    parms.mod <- (parm.range$tn - parm.range$t0)/N
    state <- new.env()
    assign("counter", counter, envir = state)
    assign("start.x", start.x, envir = state)
    assign("parms", parms, envir = state)
    assign("warmed.up", warmed.up, envir = state)
    change.times <- c(100, 145, 166, 214.4)
    time.changes <- c((change.times - parms[2])/parms.mod[2]) * 
        dt
    l <- list(description = "Transient Lorenz Attractor Stream", 
        N = N, range = range, dt = dt, syst = LorenzAttractor, 
        is.transient = T, runge.kutta = T, warming.up.num = warming.up.num, 
        noise.type = noise.type, noise.parms = noise.parms, parms.mod = parms.mod, 
        time.changes = time.changes, state = state)
    class(l) <- c("NLDSD_TransientLorenzAttractor", "NLDSD", 
        "DSD_R", "DSD_data.frame", "DSD")
    l
  }

faustogc/streamChaos documentation built on May 8, 2019, 9:22 a.m.