rEDM (version 2.0.2)

PredictNonlinear: 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)

Value

A data.frame with columns Theta and rho.

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.

Examples

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