Description Usage Arguments Details Value References See Also Examples
View source: R/fun_heavy_tailed.R
It generates a heavy tail independent functional observations of sample size n
1 | fun_heavy_tailed(n, nbasis, df = 3, basis = NULL, rangeval = c(0, 1), ...)
|
n |
Sample size of generated functional data. A strictly positive integer |
nbasis |
Number of basis functions used to represent functional observations |
df |
Degrees of freedom of the T-distribution to construct the functional observations. The default value is 3 |
basis |
A functional basis object defining the basis. It can be the class of
|
rangeval |
A vector of length 2 containing the initial and final values of the interval over which the functional data object can be evaluated. As a default it is set to be [0,1]. |
... |
Further arguments to pass |
The implementation of this function is very similar to fun_IID
. The heavy tail functional observations are generated
based on a linear combination of basis functions where the i-th linear combination coefficient has a t-distribution with df
- degrees
of freedom.
An independent functional data sample (class fd
) containing:
coefs |
The coefficient array |
basis |
A basis object |
fdnames |
A list containing names for the arguments, function values and variables |
Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.
Aue A., Rice G., Sonmez O. (2017), Detecting and dating structural breaks in functional data without dimension reduction (https://arxiv.org/pdf/1511.04020.pdf)
Data2fd, fun_IID, fun_AR, fun_MA
1 2 | fdata1 = fun_heavy_tailed(n=100, nbasis=25)
fdata2 = fun_heavy_tailed(n=100, nbasis=25, df=4)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.