This is a repository for the implementation of the paper "Smooth Tensor Decomposition with Application to Ambulatory Blood Pressure Monitoring Data". The implementation depends on the SmoothHOOI R package.
Data used in HYPNOS Application are confidential. Synthetically generated ABPM data that mimics the characteristics of data analyzed in the paper is presented in Synthetic Example.
This folder includes code for reproducing the results in Section 4 of the paper.
1a_Data Preprocessing.R
: data preprocessing, including low-quality data detection and removal1b_Tensor Generation.R
: organization of original data into tensor structure2_Hyperparameter Tuning.R
: hyperparameter tuning, including rank reduction for parsimony3_Algorithm Run.R
: implementation of SmoothHOOI algorithm, with optimal hyperparameter applied4a_Result Visualization.R
: visualization of temporal components and estimated curves4b_Chronotype Analysis.R
: validation of the interpretation of the third temporal component (plot of g score vs sleep times)4c_Regression.R
: regression analysis4d_Regression Interpretation.R
: visualization of effect sizes and estimation of DBP, SBP, and HR values for different groups of subjects
This folder includes code for reproducing the results in Section 3 of the paper.
In Case 1-Fixed ranks
and Case 2-Flexible ranks
folders, the following abbreviations were used to name the files:
missing_rate
: random missingnessmissing_struc
: structured missingnessnoise_level
: noise levelp
: sample size
sim_analysis.R
includes the code for plotting the figures related to simulation studies.
This folder presents a synthetic example that shows the workflow of this study and the usage of the SmoothHOOI R package.
synthetic_raw.Rda
: L, R, mean of G scores, covariance of G scores, and empirical residuals generated from HYPNOS Application, used to generatesynthetic_data.Rda
synthetic_data.Rda
: synthetic dataSynthetic Example.Rmd
: code for this synthetic exampleSynthetic-Example.pdf
: output for this synthetic example