gradientICA: Calculates the gradient for the objectiveICA function
In ica4fts: ica4fts package

Description Usage Arguments Details Value Author(s) See Also Examples

Uses a series of C functions to calculate the derivative of the objectiveICA function. Offers a choice between the Huber substitution function or the logCosh function.

1	gradientICA(T, E, N, C, PH, method = c("Huber", "Cosh"))

`T`	Vector of angles of length p
`E`	Financial time series data of dimension nxd
`N`	Vector of lags that MUST be arranged in ascending order
`C`	Real number C which is used for Huber substitution or Logcosh substitution
`PH`	Phi matrix of weights of dimension q*q
`method`	Choice between use of Huber or logCosh substitution methods

More details to help above

Gradient of objective value

Erjie Ang ea75@cornell.edu

See

##
d = 2
n = 10000
p = d*(d-1)/2
E = matrix(rnorm(d*n),n,d)
T = as.matrix(rep(0,p))
N = as.matrix(c(1,2))
q = 2*2*p
C = 1
PH = matrix(rnorm(q*q),q,q)
HCans = gradientICA(T,E,N,C,PH,method = "Huber")
LSans = gradientICA(T,E,N,C,PH,method = "Cosh")