sisal (version 0.48)

testSisal: Testing the Sequential Input Selection Algorithm

Description

Tests sisal with example datasets or time series data. The function uses the training part of an example dataset or user-supplied numeric data interpreted as a time series.

Usage

testSisal(dataset = c("tsToy", "laser", "poland", "toy"), nData = Inf,
          FUN = "sisal", lags = NULL, stepsAhead = 1,
          noiseSd = 0.2, verbose = 1, …)

Arguments

dataset

the dataset to use. A numeric vector containing time series data or one of "tsToy" (the default), "laser", "poland" and "toy".

nData

a numeric value containing the number of observations to use. If larger than the number of observations in the dataset, all of the data will be used (the default).

FUN

which function to call. By default, acts as a front end to sisal. This can be any function that accepts arguments named "X", "y" and "verbose". See match.fun for legal values.

lags

a numeric or integer vector. When using time series data (dataset is numeric, "laser", "poland" or "tsToy"), the function creates lagged versions of the time series to be used as input variables in sisal. The lags are specified here. These are non-negative integral values where 0 means the latest observation, 1 is the previous observation etc. The default values for "laser", "poland" and "tsToy" are 0:19, 0:14 and 0:9, respectively.

stepsAhead

an integral value specifying how many steps ahead to predict in a time series setting. The default is 1.

noiseSd

standard deviation of noise to be used with the "toy" dataset. The base noise is always the same (stored with the dataset) and only scaled to match this setting.

verbose

a numeric or integer verbosity level. This function only has two verbosity levels (0 and larger than 0), but the value is also propagated to FUN.

…

arguments passed to FUN.

Value

The value returned by function FUN, when called with the given dataset (processed by this function) and parameters. See the help page of the relevant function, e.g. sisal.

Details

The function recognizes if a numeric dataset is the "laser" or "poland" dataset. In case repeated experiments will be performed on those datasets, it is best to explicitly fetch them with sisalData before using this function. Doing so reduces the amount of network traffic and makes offline work possible.

See Also

See sisalData, toy.learn and tsToy.learn for documentation on the datasets.

The performance of the models returned by this functions can be evaluated using bootMSE, which uses a separate test part of the dataset.

Examples

# NOT RUN {
foo <- testSisal(dataset="toy", hbranches=2, max.width=2, Mtimes=5,
                 use.ridge=TRUE)
print(foo)
names(foo)
# }