View source: R/IntegrateDerive.R
Derive | R Documentation |
This function computes derivatives of a fitted GeDS regression model.
Derive(object, order = 1L, x, n = 3L)
object |
An object of class |
order |
Integer value indicating the order of differentiation required
(e.g. first, second or higher derivatives). Note that |
x |
Numeric vector containing values of the independent variable at
which the derivatives of order |
n |
Integer value (2, 3 or 4) specifying the order ( |
This function relies on the splineDesign
function to compute
the exact derivatives of the GeDS fit. Specifically, it leverages the well-known
property that the m
-th derivative of a spline (for m = 1, 2, \ldots
)
can be obtained by differentiating its B-spline basis functions. This property is
detailed, e.g., in De Boor (2001, Chapter X, formula (15)).
Note that the GeDS fit is a B-spline representation of the predictor. Consequently, the derivative is computed with respect to the predictor scale and not the response scale. This implies that, in the GNM(GLM) framework, the function does not return derivatives of the conditional mean on the response scale, but rather of the underlying linear predictor scale.
De Boor, C. (2001). A Practical Guide to Splines (Revised Edition). Springer, New York.
# Generate a data sample for the response variable
# Y and the covariate X
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))
# Specify a model for the mean of Y to include only
# a component non-linear in X, defined by the function f_1
means <- f_1(X)
# Add (Normal) noise to the mean of Y
Y <- rnorm(N, means, sd = 0.1)
# Fit GeDS regression with only a spline component in the predictor model
Gmod <- NGeDS(Y ~ f(X), beta = 0.6, phi = 0.995, Xextr = c(-2,2))
# Compute the second derivative of the cubic GeDS fit
# at the points 0, -1 and 1
Derive(Gmod, x = c(0, -1, 1), order = 2, n = 4)
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