ddalpha: ddalphaf.getErrorRatePart – R documentation

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ddalphaf.getErrorRatePart

Test Functional DD-Classifier

Description

Performs a benchmark procedure by partitioning the given data. On each of times steps size observations are removed from the data, the functional DD-classifier is trained on these data and tested on the removed observations.

Usage

ddalphaf.getErrorRatePart(dataf, labels, size = 0.3, times = 10, 
                          disc.type = c("LS", "comp"),  ...)

Arguments

`dataf`	list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively
`labels`	list of output labels of the functional observations
`size`	the excluded sequences size. Either an integer between 1 and n, or a fraction of data between 0 and 1.
`times`	the number of times the classifier is trained.
`disc.type`	type of the used discretization scheme. "LS" for `ddalphaf.train`, "comp" for for `compclassf.train`
`...`	additional parameters passed to `ddalphaf.train`

Value

`errors`	the part of incorrectly classified data (mean)
`errors_sd`	the standard deviation of errors
`errors_vec`	vector of errors
`time`	the mean training time
`time_sd`	the standard deviation of training time

Examples

# load the fdata
df = dataf.growth()

stat <- ddalphaf.getErrorRatePart(dataf = df$dataf, labels = df$labels, 
                          size = 0.3, times = 5,
                          adc.args = list(instance = "avr", 
                                         numFcn = 2, 
                                         numDer = 2))

cat("Classification error rate: ", stat$errors, ".\n", sep = "")

ddalpha

Depth-Based Classification and Calculation of Data Depth

v1.3.11

GPL-2

Authors

Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]

Initial release

2020-01-09