Calculate gradient of ACMTF model.

Usage

acmtfr_gradient(
  x,
  Z,
  Y,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06
)

Arguments

x: Vectorized parameters of the CMTF model.
Z: Z object as generated by setupCMTFdata().
Y: Dependent variable (regression part).
alpha: Alpha value of the loss function as specified by Acar et al., 2014
beta: Beta value of the loss function as specified by Acar et al., 2014
epsilon: Epsilon value of the loss function as specified by Acar et al., 2014
pi: Pi value of the loss function as specified by Van der Ploeg et al., 2025.
mu: Ridge term parameter for calculation of the regression coefficients rho (default = 1e-6).

Value

Vectorized gradient of the ACMTF regression model.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)
Y = A[,1]

init = initializeACMTF(Z, 2, output="vect")
g = acmtfr_gradient(init, Z, Y)