gausspr-class {kernlab} | R Documentation |
The Gaussian Processes object class
Objects can be created by calls of the form new("gausspr", ...)
.
or by calling the gausspr
function
tol
:"numeric"
contains
tolerance of termination criteriakernelf
:"kfunction"
contains
the kernel function usedkpar
:"list"
contains the
kernel parameter used kcall
:"list"
contains the used
function call type
:"character"
contains
type of problem terms
:"ANY"
contains the
terms representation of the symbolic model used (when using a formula)xmatrix
:"input"
containing
the data matrix used ymatrix
:"output"
containing the
response matrixfitted
:"output"
containing the
fitted values lev
:"vector"
containing the
levels of the response (in case of classification) nclass
:"numeric"
containing
the number of classes (in case of classification) alpha
:"listI"
containing the
computes alpha values alphaindex
"list"
containing
the indexes for the alphas in various classes (in multi-class
problems).scaling
"ANY"
containing
the scaling coefficients of the data (when case scaled = TRUE
is used).nvar
:"numeric"
containing the
computed varianceerror
:"numeric"
containing the
training errorcross
:"numeric"
containing the
cross validation errorn.action
:"ANY"
containing the
action performed in NA signature(object = "gausspr")
: returns the alpha
vectorsignature(object = "gausspr")
: returns the cross
validation error signature(object = "gausspr")
: returns the
training error signature(object = "vm")
: returns the fitted values signature(object = "gausspr")
: returns the call performedsignature(object = "gausspr")
: returns the
kernel function usedsignature(object = "gausspr")
: returns the kernel
parameter usedsignature(object = "gausspr")
: returns the
response levels (in classification) signature(object = "gausspr")
: returns the type
of problemsignature(object = "gausspr")
: returns the
data matrix usedsignature(object = "gausspr")
: returns the
response matrix usedsignature(object = "gausspr")
: returns the
scaling coefficients of the data (when scaled = TRUE
is used)
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
# train model data(iris) test <- gausspr(Species~.,data=iris,var=2) test alpha(test) error(test) lev(test)