| Title: | Individual Dynamic Latent Factor Model |
|---|---|
| Description: | A personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series. |
| Authors: | Siyang Liu [aut, cre], Jiuchen Zhang [aut], Annie Qu [aut] |
| Maintainer: | Siyang Liu <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.2 |
| Built: | 2024-12-05 12:11:10 UTC |
| Source: | CRAN |
This function generates simulated data in multiple time series with heterogeneity and non-stationarity. It includes 3 settings in Setion 5.3.
generate_data(n_patients, n_var, time, idx_x, idx_y, rank, k, N)generate_data(n_patients, n_var, time, idx_x, idx_y, rank, k, N)
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 splineS |
A list is returned, containing output_x and output_y as sparse matrices of x_data and y_data, spline knots, individualized dynamic latent factor, shared latent factor for X and Y.
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.
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 <- randomSparseArray(c(I, 1, time), density=0.2) idx_x <- array(runif(length(idx_x), 0, 1), dim = dim(idx_x)) idx_y <- array(runif(length(idx_y), 0, 1), dim = dim(idx_y)) generate_data(I, J, time, idx_x, idx_y, R, k, N)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 <- randomSparseArray(c(I, 1, time), density=0.2) idx_x <- array(runif(length(idx_x), 0, 1), dim = dim(idx_x)) idx_y <- array(runif(length(idx_y), 0, 1), dim = dim(idx_y)) generate_data(I, J, time, idx_x, idx_y, R, k, N)
This function implements the individualized dynamic latent factor model.
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 )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 )
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 |
A list is returned, containing the model weights, factor matrix, spline knots, predicted X and Y.
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.
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_x <- array(runif(length(idx_x), 0, 1), dim = dim(idx_x)) idx_y_train <- randomSparseArray(c(I, 1, time), density=0.2) idx_y_train <- array(runif(length(idx_y_train), 0, 1), dim = dim(idx_y_train)) idx_y_test <- randomSparseArray(c(I, 1, time), density=0.2) idx_y_test <- array(runif(length(idx_y_test), 0, 1), dim = dim(idx_y_test)) 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)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_x <- array(runif(length(idx_x), 0, 1), dim = dim(idx_x)) idx_y_train <- randomSparseArray(c(I, 1, time), density=0.2) idx_y_train <- array(runif(length(idx_y_train), 0, 1), dim = dim(idx_y_train)) idx_y_test <- randomSparseArray(c(I, 1, time), density=0.2) idx_y_test <- array(runif(length(idx_y_test), 0, 1), dim = dim(idx_y_test)) 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)