Test for nonlinear dynamics
PredictNonlinear uses SMap to evaluate
prediction accuracy as a function of the localisation parameter
theta.
PredictNonlinear(pathIn = "./", dataFile = "", dataFrame = NULL, pathOut = "./", predictFile = "", lib = "", pred = "", theta = "", E = 1, Tp = 1, knn = 0, tau = -1, columns = "", target = "", embedded = FALSE, verbose = FALSE, numThreads = 4, showPlot = TRUE)
pathIn |
path to |
dataFile |
.csv format data file name. The first column must be a time index or time values. The first row must be column names. |
dataFrame |
input data.frame. The first column must be a time index or time values. The columns must be named. |
pathOut |
path for |
predictFile |
output file name. |
lib |
string with start and stop indices of input data rows used to create the library of observations. A single contiguous range is supported. |
pred |
string with start and stop indices of input data rows used for predictions. A single contiguous range is supported. |
theta |
A whitespace delimeted string with values of the S-map
localisation parameter. An empty string will use default values of
|
E |
embedding dimension. |
Tp |
prediction horizon (number of time column rows). |
knn |
number of nearest neighbors. If knn=0, knn is set to the library size. |
tau |
lag of time delay embedding specified as number of time column rows. |
columns |
string of whitespace separated column name(s) in the input data used to create the library. |
target |
column name in the input data used for prediction. |
embedded |
logical specifying if the input data are embedded. |
verbose |
logical to produce additional console reporting. |
numThreads |
number of parallel threads for computation. |
showPlot |
logical to plot results. |
The localisation parameter theta weights nearest
neighbors according to exp( (-theta D / D_avg) ) where D is the
distance between the observation vector and neighbor, D_avg the mean
distance. If theta = 0, weights are uniformally unity corresponding
to a global autoregressive model. As theta increases, neighbors in
closer proximity to the observation are considered.
A data.frame with columns Theta, rho.
data(TentMapNoise) theta.rho <- PredictNonlinear( dataFrame=TentMapNoise, E=2,lib="1 100", pred="201 500", columns="TentMap", target="TentMap", showPlot = FALSE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.