Package: DIFM 1.0
DIFM: Dynamic ICAR Spatiotemporal Factor Models
Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.
Authors:
DIFM_1.0.tar.gz
DIFM_1.0.zip(r-4.5)DIFM_1.0.zip(r-4.4)DIFM_1.0.zip(r-4.3)
DIFM_1.0.tgz(r-4.4-x86_64)DIFM_1.0.tgz(r-4.4-arm64)DIFM_1.0.tgz(r-4.3-x86_64)DIFM_1.0.tgz(r-4.3-arm64)
DIFM_1.0.tar.gz(r-4.5-noble)DIFM_1.0.tar.gz(r-4.4-noble)
DIFM_1.0.tgz(r-4.4-emscripten)DIFM_1.0.tgz(r-4.3-emscripten)
DIFM.pdf |DIFM.html✨
DIFM/json (API)
| # Install 'DIFM' in R: |
| install.packages('DIFM', repos = c('https://shwasoo.r-universe.dev', 'https://cloud.r-project.org')) |
- Property - Property crime in United States
- Violent - Violent crime data in United States
- WestStates - Westen states in United States
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 months agofrom:3a6353b057. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 11 2024 |
| R-4.5-win-x86_64 | OK | Sep 11 2024 |
| R-4.5-linux-x86_64 | OK | Sep 11 2024 |
| R-4.4-win-x86_64 | OK | Sep 11 2024 |
| R-4.4-mac-x86_64 | OK | Sep 11 2024 |
| R-4.4-mac-aarch64 | OK | Sep 11 2024 |
| R-4.3-win-x86_64 | OK | Sep 11 2024 |
| R-4.3-mac-x86_64 | OK | Sep 11 2024 |
| R-4.3-mac-aarch64 | OK | Sep 11 2024 |
Exports:buildHdifm.hyp.parmdifm.model.attributesDIFMcppDIFMRmarginal_d_cppmarginal.dpermutation.orderpermutation.scaleplot_B.CIplot_B.spatialplot_sigma2.CIplot_tau.CIplot_X.CI
Dependencies:bootclassclassIntcliDBIdeldire1071gluegridExtragtableKernSmoothLaplacesDemonlatticelifecyclemagrittrMASSMatrixproxyRcppRcppArmadillorlangs2sfspspDataspdepunitswk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Dynamic ICAR Spatiotemporal Factor Models | DIFM-package DIFM |
| Spatial dependence matrix of the factor loadings | buildH |
| Hyperparameters for DIFM | difm.hyp.parm |
| Initialize model attributes for DIFM | difm.model.attributes |
| Run Dynamic ICAR Factors Model (DIFM), with C++ codes | DIFMcpp |
| Run Dynamic ICAR Factors Model (DIFM) | DIFMR |
| Marginal predictive density | marginal_d_cpp |
| Marginal predictive density | marginal.d |
| Order of permutation by the largest absolute value in each eigenvector | permutation.order |
| Permute the dataset by the largest absolute value in each eigenvector, and scale | permutation.scale |
| Credible interval plot of factor loadings | plot_B.CI |
| Spatial plots of factor loadings | plot_B.spatial |
| A credible interval plot of posterior of sigma squared | plot_sigma2.CI |
| Credible interval plot of factor loadings variance | plot_tau.CI |
| Credible interval plot of common factors | plot_X.CI |
| Property crime in United States | Property |
| Violent crime data in United States | Violent |
| Westen states in United States | WestStates |
