"covQual"Covariance kernel for qualitative inputs.
Objects can be created by calls of the form new("covQual", ...).
covLevels:Object of class "function". This function has
arguments 'par' and optional arguments lowerSQRT and
compGrad. It returns the covariance matrix for an input
corresponding to all the levels.
covLevMat:Object of class "matrix". This is the result returned by the
function covLevels (former slot) with lowerSQRT = FALSE
and gradient = FALSE.
hasGrad:Object of class "logical". When TRUE, the
covariance matrix returned by the function in slot
covLevels must compute the gradients. The returned
covariance matrix must have a "gradient" attribute;
this must be an array with dimension c(m, m, np) where
m stands for the number of levels and \(np\) is the
number of parameters.
acceptLowerSQRT:Object of class "logical". When TRUE, the function
in slot covLevels must have a formal lowerSQRT
which can receive a logical value. When the value is TRUE
the Cholesky (lower) root of the covariance is returned instead of
the covariance.
label:Object of class "character". A description of the kernel
which will remained attached with it.
d:Object of class "integer". The dimension or number of
(qualitative) inputs of the kernel.
inputNames:Object of class "character". The names of the (qualitative)
inputs. These will be matched against the columns of a data frame
when the kernel will be evaluated.
nlevels:Object of class "integer". A vector with length
d giving the number of levels for each of the d
inputs.
levels:Object of class "list". A list of length d
containing the d character vectors of levels for
the d (qualitative) inputs.
parLower:Object of class "numeric". Vector of parN lower
values for the parameters of the structure. The value
-Inf can be used when needed.
parUpper:Object of class "numeric". Vector of parN upper
values for the parameters of the structure. The value
Inf can be used when needed.
par:Object of class "numeric". Vector of parN
current values for the structure.
parN:Object of class "integer". Number of parameters for the
structure, as returned by the npar method.
kernParNames:Object of class "character". Vector of length parN
giving the names of the parameters. E.g. "range",
"var", "sigma2" are popular names.
ordered:Vector of class "logical" indicating whether the factors
are ordered or not.
intAsChar:Object of class "logical" indicating how to cope with an
integer input. When intAsChar is TRUE the input is
coerced into a character; the values taken by this character
vector should then match the levels in the covQual object
as given by levels(object)[[1]]. If instead
intAsChar is FALSE, the integer values are assumed
to correspond to the levels of the covQual object in the
same order.
signature(object = "covQual", 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 = "covQual", 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 = "covQual"): replace the whole vector of
coefficients, as required during ML estimation.
signature(object = "covQual"): replacement method for lower
bounds on covQual coefficients.
signature(object = "covQual"): extracts the numeric values of
the lower bounds.
signature(object = "covQual"): extracts the numeric values
of the covariance parameters.
signature(object = "covQual"): replacement method for upper
bounds on covQual coefficients.
signature(object = "covQual"): ...
signature(object = "covQual"): build the covariance matrix
or the cross covariance matrix between two sets of locations for a
covQual object.
signature(object = "covQual"): returns the number of
parameters.
signature(x = "covQual"): see plot,covQual-method.
signature(object = "covQual"): 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 = "covQual"): 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 = "covQual"): build the variance vector
corresponding to a set locations for a covQual object.
See covMan for a comparable structure dedicated
to kernels with continuous inputs.