fgpm-class | R Documentation |
This is the formal representation of Gaussian process models within the funGp package. Gaussian process models are useful statistical tools in the modeling of complex input-output relationships.
Main methods
fgpm: creation of funGp regression models
predict,fgpm-method: output estimation at new input points based on a fgpm
model
simulate,fgpm-method: random sampling from a fgpm
model
update,fgpm-method: modification of data and hyperparameters of a fgpm
model
Plotters
plot,fgpm-method: validation plot for a fgpm
model
plot.predict.fgpm: plot of predictions based on a fgpm
model
plot.simulate.fgpm: plot of simulations based on a fgpm
model
howCalled
Object of class "modelCall"
. User call reminder.
type
Object of class "character"
. Type of model based on type of inputs. To be set from
"scalar", "functional", "hybrid".
ds
Object of class "numeric"
. Number of scalar inputs.
df
Object of class "numeric"
. Number of functional inputs.
f_dims
Object of class "numeric"
. An array with the original dimension of each functional
input.
sIn
Object of class "matrix"
. The scalar input points. Variables are arranged by columns and
coordinates by rows.
fIn
Object of class "list"
. The functional input points. Each element of the list contains
a functional input in the form of a matrix. In each matrix, curves representing functional coordinates
are arranged by rows.
sOut
Object of class "matrix"
. The scalar output values at the coordinates specified by sIn
and/or fIn.
n.tot
Object of class "integer"
. Number of observed points used to compute the training-training
and training-prediction covariance matrices.
n.tr
Object of class "integer"
. Among all the points loaded in the model, the amount used for
training.
f_proj
Object of class "fgpProj"
. Data structures related to the projection of functional
inputs. Check fgpProj for more details.
kern
Object of class "fgpKern"
. Data structures related to the kernel of the Gaussian process
model. Check fgpKern for more details.
nugget
Object of class "numeric"
. Variance parameter standing for the homogeneous nugget effect.
preMats
Object of class "list"
. L and LInvY matrices pre-computed for prediction. L is a lower
diagonal matrix such that L'L
equals the training auto-covariance matrix K.tt
. On the other
hand, LInvY = L^(-1) * sOut
.
convergence
Object of class "numeric"
. Integer code either confirming convergence or indicating
an error. Check the convergence component of the Value returned by optim
.
negLogLik
Object of class "numeric"
. Negated log-likelihood obained by optim
during hyperparameter optimization.
José Betancourt, François Bachoc, Thierry Klein and Jérémy Rohmer
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