View source: R/twonn_dec_depr.R
twonn_decimated | R Documentation |
TWO-NN
evolution with halving steps or vector of
proportionsThe estimation of the id
is related to the scale of the
dataset. To escape the local reach of the TWO-NN
estimator,
Facco et al. (2017)
proposed to subsample the original dataset in order to induce greater
distances between the data points. By investigating the estimates' evolution
as a function of the size of the neighborhood, it is possible to obtain
information about the validity of the modeling assumptions and the robustness
of the model in the presence of noise.
twonn_decimated(
X,
method = c("steps", "proportions"),
steps = 0,
proportions = 1,
seed = NULL
)
X |
data matrix with |
method |
method to use for decimation:
|
steps |
number of times the dataset is halved. |
proportions |
vector containing the fractions of the dataset to be considered. |
seed |
random seed controlling the sequence of sub-sampled observations. |
list containing the TWO-NN
evolution
(maximum likelihood estimation and confidence intervals), the average
distance from the second NN, and the vector of proportions that were
considered. According to the chosen estimation method, it is accompanied with
the vector of proportions or halving steps considered.
Facco E, D'Errico M, Rodriguez A, Laio A (2017). "Estimating the intrinsic dimension of datasets by a minimal neighborhood information." Scientific Reports, 7(1). ISSN 20452322, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-017-11873-y")}.
Denti F, Doimo D, Laio A, Mira A (2022). "The generalized ratios intrinsic dimension estimator." Scientific Reports, 12(20005). ISSN 20452322, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-022-20991-1")}.
twonn
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.