Functional Depth-Based Shape Outlier Detection
Detects functional outliers of first three orders, based on the order extended integrated depth for functional data.
shape.fd.outliers(dataf, range = NULL, d = 101, q = 0.05,
method = c("halfspace", "simplicial"), approx = 100, print = FALSE,
plotpairs = FALSE, max.order = 3, exclude.out = TRUE,
output = c("matrix", "list"), identifiers = NULL)dataf |
Functional dataset, represented by a |
range |
The common range of the domain where the fucntions |
d |
Grid size to which all the functional data are transformed. For depth computation,
all functional observations are first transformed into vectors of their functional values of length |
q |
The quantile presenting a threshold for the first order outlier detection. Functions with first order integrated depth
smaller than the |
method |
The depth that is used in the diagnostic plot. possible values are |
approx |
For the computation of the third order integrated depth,
the number of approximations used in the computation of the order extended depth. By default
this is set to |
print |
If the rows of |
plotpairs |
If set to |
max.order |
Maximal order of shape outlyingness to be computed, can be set to |
exclude.out |
Logical variable; exclude the detected lower order outliers in the flagging process? By default |
output |
Output method, can be set to |
identifiers |
A vector of names for the data observations. Facilitates identification of outlying functions. |
Using the procedure described in Nagy et al. (2016), the function uses the order extended integrated depths for functions,
see depthf.fd1 and shape.fd.analysis, to perform informal functional shape outlier detection.
Outliers of the first order (horizontal shift outliers) are found as the functions with q % of smallest (first order)
integrated depth values. Second and third order outliers (shape outliers) are found using the extension of the boxplot method
for depths as described in the paper Nagy et al. (2016).
A matrix of logical values of size n*4, where n is the sample size. In the first three rows indicators of outlyingness
of the corresponding functions for orders 1, 2 and 3 are given, in the fourth row the indicator of outlyingness
with respect to the comparison of the first, and third order depths is given. That is, the fist row corresponds to the first order outliers,
the second row to the second order outliers, and the last two rows formally to the third order outliers. Please consult Nagy et al. (2016)
to interpret the notion of shape outlyingness.
Stanislav Nagy, nagy at karlin.mff.cuni.cz
Nagy, S., Gijbels, I. and Hlubinka, D. (2017). Depth-based recognition of shape outlying functions. Journal of Computational and Graphical Statistics, 26 (4), 883–893.
n = 30 dataf = dataf.population()$dataf[1:n] shape.fd.outliers(dataf,print=TRUE,plotpairs=TRUE, identifiers=unlist(dataf.population()$identifier)[1:n])
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