covOrd-class: Class '"covOrd"'

In kergp: Gaussian Process Laboratory

Class "covOrd"

Description

Covariance kernel for qualitative ordered inputs obtained by warping.

Let u be an ordered factor with levels u_1, \dots, u_L. Let k_1 be a 1-dimensional stationary kernel (with no or fixed parameters), F a warping function i.e. an increasing function on the interval [0,1] and \theta a scale parameter. Then k is defined by:

k(u_i, u_j) = k_1([F(z_i) - F(z_{j})]/\theta)

where z_1, \dots, z_L form a regular sequence from 0 to 1 (included). Notice that an example of warping is a distribution function (cdf) restricted to [0,1].

Objects from the Class

Objects can be created by calls of the form new("covOrd", ...).

Slots

covLevels:

Same as for covQual-class.

covLevMat:

Same as for covQual-class.

hasGrad:

Same as for covQual-class.

acceptLowerSQRT:

Same as for covQual-class.

label:

Same as for covQual-class.

d:

Same as for covQual-class. Here equal to 1.

inputNames:

Same as for covQual-class.

nlevels:

Same as for covQual-class.

levels:

Same as for covQual-class.

parLower:

Same as for covQual-class.

parUpper:

Same as for covQual-class.

par:

Same as for covQual-class.

parN:

Same as for covQual-class.

kernParNames:

Same as for covQual-class.

k1Fun1:

A function representing a 1-dimensional stationary kernel function, with no or fixed parameters.

warpFun:

A cumulative density function representing a warping.

cov:

Object of class "integer". The value 0L corresponds to a correlation kernel while 1L is for a covariance kernel.

parNk1:

Object of class "integer". Number of parameters of k1Fun1. Equal to 0 at this stage.

parNwarp:

Object of class "integer". Number of parameters of warpFun.

k1ParNames:

Object of class "character". Parameter names of k1Fun1.

warpParNames:

Object of class "character". Parameter names of warpFun.

warpKnots:

Object of class "numeric". Parameters of warpFun.

ordered:

Object of class "logical". TRUE for an ordinal input.

intAsChar:

Object of class "logical". If TRUE (default), an integer-valued input will be coerced into a character. Otherwise, it will be coerced into a factor.

Methods

checkX

signature(object = "covOrd", X = "data.frame"): check that the inputs exist with suitable column names and suitable factor content. The levels should match the prescribed levels. Returns a matrix with the input columns in the order prescribed by object.

signature(object = "covOrd", X = "matrix"): check that the inputs exist with suitable column names and suitable numeric content for coercion into a factor with the prescribed levels. Returns a data frame with the input columns in the order prescribed by object.

coef<-

signature(object = "covOrd"): replace the whole vector of coefficients, as required during ML estimation.

coefLower<-

signature(object = "covOrd"): replacement method for lower bounds on covOrd coefficients.

coefLower

signature(object = "covOrd"): extracts the numeric values of the lower bounds.

coef

signature(object = "covOrd"): extracts the numeric values of the covariance parameters.

coefUpper<-

signature(object = "covOrd"): replacement method for upper bounds on covOrd coefficients.

coefUpper

signature(object = "covOrd"): ...

covMat

signature(object = "covOrd"): build the covariance matrix or the cross covariance matrix between two sets of locations for a covOrd object.

npar

signature(object = "covOrd"): returns the number of parameters.

scores

signature(object = "covOrd"): return the vector of scores, i.e. the derivative of the log-likelihood w.r.t. the parameter vector at the current parameter values.

simulate

signature(object = "covOrd"): simulate nsim paths from a Gaussian Process having the covariance structure. The paths are indexed by the finite set of levels of factor inputs, and they are returned as columns of a matrix.

varVec

signature(object = "covOrd"): build the variance vector corresponding to a set locations for a covOrd object.

Note

This class is to be regarded as experimental. The slot names or list may be changed in the future. The methods npar, inputNames or `inputNames<-` should provide a more robust access to some slot values.

See Also

See covMan for a comparable structure dedicated to kernels with continuous inputs.

Examples

showClass("covOrd")

kergp documentation built on April 4, 2025, 2:29 a.m.