IDLFM: Individualized Dynamic Latent Factor Model

In IDLFM: Individual Dynamic Latent Factor Model

Individualized Dynamic Latent Factor Model

Description

This function implements the individualized dynamic latent factor model.

Usage

IDLFM(
  X,
  Y,
  n_patients,
  n_var,
  time,
  idx_x,
  idx_y,
  rank,
  k,
  N,
  lambda1 = 1,
  lambda2 = 1,
  Niter = 100,
  alpha = 0.001,
  ebs = 1e-04,
  l = 1,
  verbose
)

Arguments

X	a sparse matrix for predictor variables
Y	a sparse matrix for response variables
n_patients	the number of patients
n_var	the number of X variables
time	maximum time
idx_x	indices for the X data, a sparse matrix
idx_y	indices for the Y data, a sparse matrix
rank	rank for the random matrices
k	spline smoothness
N	number of knots in the spline
lambda1	regularization parameter for fused lasso, with the default value 1
lambda2	regularization parameter for total variation, with the default value 1
Niter	number of iterations for the Adam optimizer, with the default value 100
alpha	learning rate for the Adam optimizer, with the default value 0.001
ebs	convergence threshold, with the default value 0.0001
l	regularization parameter, with the default value 1
verbose	to control the console output

Value

A list is returned, containing the model weights, factor matrix, spline knots, predicted X and Y.

References

Zhang, J., F. Xue, Q. Xu, J. Lee, and A. Qu. "Individualized dynamic latent factor model for multi-resolutional data with application to mobile health." Biometrika (2024): asae015.

Examples

library(splines)
#if (!require("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
#BiocManager::install("SparseArray")
library(SparseArray)

I <- 3
J <- 5
time <- 1000
R <- 3
k <- 3
N <- 300
idx_x <- randomSparseArray(c(I, J, time), density=0.8)
idx_y_train <- randomSparseArray(c(I, 1, time), density=0.2)
idx_y_test <- randomSparseArray(c(I, 1, time), density=0.2)
data <- generate_data(I, J, time, idx_x, idx_y_train, R, k, N)
output_x <- data[[1]]
output_y <- data[[2]]
knots <- data[[3]]
weights <- data[[4]]
Fx <- data[[5]]
Fy <- data[[6]]
IDLFM(X = output_x, Y = output_y, n_patients = I, n_var = J, time = time,
idx_x = idx_x, idx_y = idx_y_train, rank = R, k = k, N = N, verbose = FALSE)

IDLFM documentation built on June 8, 2025, 1 p.m.