These are computing engines called by NGeDS and
GGeDS, needed for the underlying fitting procedures.
BivariateFitter(
X,
Y,
Z,
W,
weights = rep(1, length(X)),
Indicator,
beta = 0.5,
phi = 0.99,
min.intknots = 0,
max.intknots = 300,
q = 2,
Xextr = range(X),
Yextr = range(Y),
show.iters = TRUE,
tol = as.double(1e-12),
stoptype = c("SR", "RD", "LR"),
higher_order = TRUE,
Xintknots = NULL,
Yintknots = NULL
)GenBivariateFitter(
X,
Y,
Z,
W,
family = family,
weights = rep(1, length(X)),
Indicator,
beta = 0.5,
phi = 0.5,
min.intknots = 0,
max.intknots = 300,
q = 2,
Xextr = range(X),
Yextr = range(Y),
show.iters = TRUE,
tol = as.double(1e-12),
stoptype = c("SR", "RD", "LR"),
higher_order = TRUE
)
A GeDS-Class object, but without the Formula,
extcall, terms and znames slots.
a numeric vector containing \(N\) sample values of the first independent variable chosen to enter the spline regression component of the predictor model.
a numeric vector containing \(N\) sample values of the second independent variable chosen to enter the spline regression component of the predictor model.
a vector of size \(N\) containing the observed values of the response variable.
a design matrix with \(N\) rows containing other covariates
selected to enter the parametric component of the predictor model (see
formula). If no such covariates are selected, it
is set to NULL by default.
an optional vector of size \(N\) of `prior weights' to be
put on the observations in the fitting process in case the user requires
weighted GeDS fitting. It is NULL by default.
contingency table (i.e., frequency of observations) for the
independent variables X and Y.
numeric parameter in the interval \([0,1]\) tuning the knot
placement in stage A of GeDS. See the description of NGeDS or
GGeDS.
numeric parameter in the interval \([0,1]\) specifying the
threshold for the stopping rule (model selector) in stage A of GeDS. See
also stoptype and details in the description of NGeDS or
GGeDS.
optional parameter specifying the minimum number of internal knots required in Stage A's fit. Default is zero.
optional parameter allowing the user to set a maximum number of internal knots to be added in Stage A by the GeDS estimation algorithm. Default equals the number of internal knots \(\kappa\) for the saturated GeDS model (i.e. \(\kappa=N-2\)).
numeric parameter which allows to fine-tune the stopping rule of
stage A of GeDS, by default equal to 2. See details in the description of
NGeDS or GGeDS.
boundary knots in the X direction. By default equal to
the range of X.
boundary knots in the Y direction. By default equal to
the range of Y.
logical variable indicating whether or not to print fitting
information at each step. Default is FALSE.
numeric value indicating the tolerance to be used in checking whether two knots should be considered different during the knot placement steps in stage A.
a character string indicating the type of GeDS stopping rule
to be used. It should be either "SR", "RD" or "LR",
partial match allowed. See details of NGeDS or
GGeDS.
a logical defining whether to compute the higher
order fits (quadratic and cubic) after stage A is run. Default is
TRUE.
a vector of starting internal knots in the X direction.
Allows the user to begin Stage A's GeDS algorithm with a linear spline fit
using a predefined vector of internal X knots, instead of starting with
a straight line fit. Default is NULL.
a vector of starting internal knots in the Y direction.
Allows the user to begin Stage A's GeDS algorithm with a linear spline fit
using a predefined vector of internal X knots, instead of starting with
a straight line fit. Default is NULL.
a description of the error distribution and link function to be
used in the model. This can be a character string naming a family function
(e.g. "gaussian"), the family function itself (e.g.
gaussian) or the result of a call to a family function
(e.g. gaussian()). See family for details on family
functions.
Dimitrova, D. S., Kaishev, V. K., Lattuada, A. and Verrall, R. J. (2023).
Geometrically designed variable knot splines in generalized (non-)linear
models.
Applied Mathematics and Computation, 436.
DOI: tools:::Rd_expr_doi("10.1016/j.amc.2022.127493")
NGeDS, GGeDS and UnivariateFitters.