FTR_CP: Frequentist Tensor Regression with the CP decomposition

In danieladamspencer/bayestensorreg: Bayesian Tensor Regression

Frequentist Tensor Regression with the CP decomposition

Description

Frequentist Tensor Regression with the CP decomposition

Usage

FTR_CP(input, rank = 1, epsilon = 1e-08, max.iter = 1000)

Arguments

input	An object of class TR_data that contains (at least) the elements y (a vector of response values) and X (an array of covariate values). Optionally, eta (a matrix of nuisance covariates) can also be included. Other list elements will be ignored.
rank	The rank of the CP decomposition to be used
epsilon	a value for the stopping rule of the algorithm. Specifically, this is the upper bound for the differences in the log-likelihood between two iterations of the algorithm.
max.iter	the maximum number of iterations to use before stopping the algorithm

Value

A list with elements gam (vector coefficient result), betas (tensor decomposition components), B (the tensor , coefficient), and total_time (time spent to complete the analysis).

Examples

## Not run: 
input <- TR_simulated_data()
results <- FTR_CP(input)

## End(Not run)

danieladamspencer/bayestensorreg documentation built on July 23, 2024, 10:14 a.m.