mkde.tune

A matrix with Euclidean (continuous) data.

The minimum value to search for the optimal bandwidth value.

The maximum value to search for the optimal bandwidth value.

A covariance matrix. By default it is equal to the covariance matrix of the data, but can change to a robust covariance matrix, MCD for example.

Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation.

 kernel density 

 maximum likelihood cross validation 

 bandwidth tuning 

Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation

Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included.
The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include:
a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis.
b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534.
c) Tsagris M. (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444.
d) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57.
e) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>.
f) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>.
g) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>.
h) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>.
i) Tsagris M. and Stewart C. (2020). A folded model for compositional data analysis. Australian and New Zealand Journal of Statistics, 62(2):249--277. <doi:10.1111/anzs.12289>.
j) Tsagris M., Alenazi A. and Stewart C. (2021). Non-parametric regression models for compositional data. <arXiv:2002.05137>.
k) Alenazi A. (2021). Alenazi, A. (2021). A review of compositional data analysis and recent advances. Communications in Statistics-Theory and Methods (Accepted for publication). <doi:10.1080/03610926.2021.2014890>.
We further include functions for percentages (or proportions).

Michail Tsagris

Compositional

Compositional Data Analysis

Giorgos Athineou

Abdulaziz Alenazi

Christos Adam

Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation function

Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation — Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation

Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation: Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation

Description

Usage

Arguments

Value

Details

References

See Also

Examples