deriv_rw: Analytic D matrix Random Walk (RW) Process
In schoi355/gmwm: Generalized Method of Wavelet Moments
deriv_rw R Documentation
Analytic D matrix Random Walk (RW) Process
Description
Obtain the first derivative of the Random Walk (RW) process.
Usage
deriv_rw(tau)
Arguments
tau
A vec containing the scales e.g. 2^tau
Value
A matrix with the first column containing
the partial derivative with respect to gamma^2.
Process Haar WV First Derivative
Taking the derivative with respect to gamma^2 yields:
d/dgamma2 nu[j]^2 (gamma2) = (tau[j]^2 + 2)/(12*tau[j])
Author(s)
James Joseph Balamuta (JJB)
Examples
deriv_rw(2^(1:5))
schoi355/gmwm documentation built on April 11, 2022, 1:21 a.m.
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| deriv_rw | R Documentation |
Analytic D matrix Random Walk (RW) Process
Description
Obtain the first derivative of the Random Walk (RW) process.
Usage
deriv_rw(tau)
Arguments
tau |
A |
Value
A matrix with the first column containing
the partial derivative with respect to gamma^2.
Process Haar WV First Derivative
Taking the derivative with respect to gamma^2 yields:
d/dgamma2 nu[j]^2 (gamma2) = (tau[j]^2 + 2)/(12*tau[j])
Author(s)
James Joseph Balamuta (JJB)
Examples
deriv_rw(2^(1:5))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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