A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.
Package details |
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Author | Haijiang Qin [aut, cre, cph] (ORCID: <https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph] (ORCID: <https://orcid.org/0000-0002-8273-3587>) |
Maintainer | Haijiang Qin <haijiang133@outlook.com> |
License | GPL-3 |
Version | 1.0.0 |
URL | https://haijiangqin.com/LSTMfactors/ |
Package repository | View on CRAN |
Installation |
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