"covOrd"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 can be created by calls of the form new("covOrd", ...).
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.
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.
signature(object = "covOrd"): replace the whole vector of
coefficients, as required during ML estimation.
signature(object = "covOrd"): replacement method for lower
bounds on covOrd coefficients.
signature(object = "covOrd"): extracts the numeric values of
the lower bounds.
signature(object = "covOrd"): extracts the numeric values
of the covariance parameters.
signature(object = "covOrd"): replacement method for upper
bounds on covOrd coefficients.
signature(object = "covOrd"): ...
signature(object = "covOrd"): build the covariance matrix
or the cross covariance matrix between two sets of locations for a
covOrd object.
signature(object = "covOrd"): returns the number of
parameters.
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.
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.
signature(object = "covOrd"): build the variance vector
corresponding to a set locations for a covOrd object.
See covMan for a comparable structure dedicated
to kernels with continuous inputs.