Description Usage Arguments Details Value See Also Examples
Methods for the functions coef
and coefficients
that allow to extract the
estimated coefficients of a fitted GeDS regression from a GeDS-Class
object.
1 2 3 4 5 |
object |
the |
n |
integer value (2, 3 or 4) specifying the order (= degree + 1) of the GeDS fit
whose coefficients should be extracted.
By default equal to |
onlySpline |
logical variable specifying whether only the coefficients for the GeDS component of the fitted multivariate regression model should be extracted or alternatively also the coefficients of the parametric component should also be extracted. |
... |
potentially further arguments (required by the definition of the generic function). They will be ignored, but with a warning. |
These are simple methods for the functions coef
and coefficients
.
As GeDS-class
objects contain three different fits (linear, quadratic and cubic), it is possible
to specify the order of the fit for which GeDS regression coefficients are required via the input argument n
.
As mentioned in the details of formula
, the predictor model may be multivariate and it
may include a GeD
spline component whereas the remaining variables may be part of a
parametric component. If the onlySpline
argument is set to TRUE
(the default value),
only the coefficients corresponding to the GeD spline component of order n
of the multivariate
predictor model are extracted.
A named vector containing the required coefficients of the fitted multivariate predictor model.
The coefficients corresponding to the variables that enter the
parametric component of the fitted multivariate predictor model
are named as the variables themselves. The coefficients of the GeDS component
are coded as "N
" followed by the index of the corresponding B-spline.
coef
for the standard definition; NGeDS
for examples.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Generate a data sample for the response variable
# and the covariates
set.seed(123)
N <- 500
f_1 <- function(x) (10*x/(1+100*x^2))*4+4
X <- sort(runif(N ,min = -2, max = 2))
Z <- runif(N)
# Specify a model for the mean of the response Y to be a superposition of
# a non-linear component f_1(X), a linear component 2*Z and a
# free term 1, i.e.
means <- f_1(X) + 2*Z + 1
# Add normal noise to the mean of y
Y <- rnorm(N, means, sd = 0.1)
# Fit to this sample a predictor model of the form f(X) + Z, where
# f(X) is the GeDS component and Z is the linear (additive) component
# see ?formula.GeDS for details
(Gmod <- NGeDS(Y ~ f(X) + Z, beta = 0.6, phi = 0.995, Xextr = c(-2,2)))
# Extract the GeD spline regression coefficients
coef(Gmod, n = 3)
# Extract all the coefficients, including the one for the linear component
coef(Gmod, onlySpline = FALSE, n = 3)
|
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