PredictNonlinear: Prediction skill versus S-map localisation

In rEDM: Empirical Dynamic Modeling ('EDM')

Prediction skill versus S-map localisation

Description

PredictNonlinear uses SMap to evaluate prediction accuracy as a function of the state-space localization parameter theta.

Usage

PredictNonlinear(dataFrame = NULL, columns, target, lib, pred, theta = NULL, E = NULL,
                 Tp = 1, knn = 0, tau = -1, exclusionRadius = 0,
                 embedded = FALSE, validLib = logical(0), noTime = FALSE,
                 ignoreNan = TRUE, numProcess = 4, backend = "RANN",
                 pathIn = "./", dataFile = "", pathOut = "./", predictFile = "",
                 parameterList = FALSE, showPlot = FALSE, verbose = FALSE)

Arguments

dataFrame	A data.frame of input data. The first column must be a time index or time values unless noTime = TRUE. The columns must be named.
columns	Column name(s) to build the embedding: character vector or space-separated string.
target	Target column name to predict.
lib	Library (training) index range as (start end) pairs.
pred	Prediction index range as (start end) pairs.
theta	S-map localisation parameter (0 = global linear map).
E	Embedding dimension. Required (no default); must be a positive integer unless embedded = TRUE, in which case it is inferred as the number of columns.
Tp	Forecast interval (prediction horizon).
knn	Number of nearest neighbours. 0 selects the method default.
tau	Embedding delay (negative selects past lags).
exclusionRadius	Temporal (Theiler) exclusion radius around each prediction point.
embedded	If TRUE, columns already form the embedding.
validLib	Logical vector marking admissible library rows (or length 0 for all).
noTime	If TRUE, synthesise a 1..N time index instead of using column 1.
ignoreNan	Remove rows with NaN in the embedding from the library and prediction sets.
numProcess	Number of worker processes for the parameter sweep or task grid.
backend	Nearest-neighbour backend: "RANN" (default) or "brute".
pathIn	File path for input dataFile.
dataFile	Input dataFile, .csv format.
pathOut	Output file path for predictFile
predictFile	Output file name, .csv format.
parameterList	Append named list of parameters/values to return.
showPlot	If TRUE, draw a base-graphics plot of the result.
verbose	Not used.

Value

A data.frame with columns Theta and rho.

Examples

data(TentMapNoise)
theta.rho = PredictNonlinear( dataFrame = TentMapNoise, E = 2,
lib = "1 100", pred = "201 500", columns = "TentMap",
target = "TentMap", showPlot = FALSE )

rEDM documentation built on Aug. 30, 2026, 9:07 a.m.