mongrad: Evaluate the gradient of a monotone function

In fda: Functional Data Analysis

Evaluate the gradient of a monotone function

Description

Evaluates the gradient of a monotone function with respect to the coefficients defining the log-first derivative $W(t)$ at each of a set of argument values.

Usage

mongrad(x, Wfdobj, basislist=vector("list",JMAX), 
                    returnMatrix=FALSE)

Arguments

x	A numerical vector at which function and derivative are evaluated.
Wfdobj	A functional data object.
basislist	A list containing values of basis functions.
returnMatrix	logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package.

Value

A matrix with as many rows as argument values and as many columns as basis functions defining $W$.

Author(s)

J. O. Ramsay

References

Ramsay, James O., Hooker, G. and Graves, S. (2009), Functional Data Analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

See Also

monfn, monhess, landmarkreg, smooth.morph

fda documentation built on May 31, 2023, 9:19 p.m.