sym.knn: Symbolic k-Nearest Neighbor Regression

In RSDA: R to Symbolic Data Analysis

Symbolic k-Nearest Neighbor Regression

Description

Symbolic k-Nearest Neighbor Regression

Usage

sym.knn(
  formula,
  sym.data,
  method = c("cm", "crm"),
  scale = TRUE,
  kmax = 20,
  kernel = "triangular"
)

Arguments

formula	a formula object.
sym.data	symbolc data.table
method	cm or crm
scale	logical, scale variable to have equal sd.
kmax	maximum number of k, if ks is not specified.
kernel	kernel to use. Possible choices are "rectangular" (which is standard unweighted knn), "triangular", "epanechnikov" (or beta(2,2)), "biweight" (or beta(3,3)), "triweight" (or beta(4,4)), "cos", "inv", "gaussian" and "optimal".

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

RSDA documentation built on Nov. 10, 2023, 5:06 p.m.