Model 2 from Cuevas et al. (2007)
Model 2 from Cuevas et al. (2007)
Processes:
X(t) = m_0(t) + e(t), m_0(t) = 30*(1-t)*t^2 + 0.5*abs(sin(20*pi*t))
Y(t) = an 8-knot spline approximation of X
e(t): Gaussian with mean = 0, cov(X(s), X(t)) = 0.2*exp(-abs(s - t)/0.3)
the processes are discretized at numDiscrets equally distant points on [0, 1].
dataf.sim.2.CFF07(numTrain = 100, numTest = 50, numDiscrets = 51, plot = FALSE)
numTrain |
number of objects in the training sample |
numTest |
number of objects in the test sample |
numDiscrets |
number of points for each object |
plot |
if TRUE the training sample is plotted |
A data strusture containing $learn and $test functional data.
The functional data are given as data structures.
datafThe functional data as a list of objects. Each object is characterized by two coordinates.
argsa numeric vector
valsa numeric vector
labelsThe classes of the objects: 0 for X(t), 1 for Y(t)
Cuevas, A., Febrero, M. and Fraiman, R. (2007). Robust estimation and classification for functional data via projection-based depth notions. Computational Statistics 22 481-496.
dataf.* for other functional data sets
plot.functional for building plots of functional data
## load the dataset dataf = dataf.sim.2.CFF07(numTrain = 100, numTest = 50, numDiscrets = 51) learn = dataf$learn test = dataf$test ## view the classes unique(learn$labels) ## access the 5th point of the 2nd object learn$dataf[[2]]$args[5] learn$dataf[[2]]$vals[5] ## Not run: ## plot the data plot(learn) plot(test) ## or dataf = dataf.sim.2.CFF07(numTrain = 100, numTest = 50, numDiscrets = 51, plot = TRUE) ## End(Not run)
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