Multivariate kernel density estimation
Multivariate kernel density estimation.
mkde(x, h, thumb = "silverman")
x |
A matrix with Euclidean (continuous) data. |
h |
The bandwidh value. It can be a single value, which is turned into a vector and then into a diagonal matrix, or a vector which is turned into a diagonal matrix. |
thumb |
Do you want to use a rule of thumb for the bandwidth parameter? If no, leave it "none", or else put "estim" for maximum likelihood cross-validation, "scott" or "silverman" for Scott's and Silverman's rules of thumb respectively. |
The multivariate kernel density estimate is calculated with a (not necssarily given) bandwidth value. It is used a wrapper for the function comp.kerncontour.
A vector with the density estimates calculated for every vector.
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives. The kedd R package.
M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.
B.W. Silverman (1986). Density estimation for statistics and data analysis, pages 76-78.
mkde( as.matrix(iris[, 1:4]), thumb = "scott" ) mkde( as.matrix(iris[, 1:4]), thumb = "silverman" )
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