lsirm12pl: An R package for latent space item response modeling
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866912264810921984 |
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| author | Go, Dongyoung Kim, Gwanghee Park, Jina Park, Junyong Jeon, Minjeong Jin, Ick Hoon |
| author_facet | Go, Dongyoung Kim, Gwanghee Park, Jina Park, Junyong Jeon, Minjeong Jin, Ick Hoon |
| contents | The item response model in latent space (LSIRM; Jeon et al., 2021) uncovers unobserved interactions between respondents and items in the item response data by embedding both in a shared latent metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for various response types, base model specifications, and missing data handling. Furthermore, lsirm12pl package provides methods to improve model utilization and interpretation, such as clustering item positions on an estimated interaction map. The package also offers convenient summary and plotting options to evaluate and process the estimated results. In this paper, we provide an overview of the LSIRM's methodological foundation and describe several extensions included in the package. We then demonstrate the use of the package with real data examples contained within it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_06989 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | lsirm12pl: An R package for latent space item response modeling Go, Dongyoung Kim, Gwanghee Park, Jina Park, Junyong Jeon, Minjeong Jin, Ick Hoon Methodology Computation The item response model in latent space (LSIRM; Jeon et al., 2021) uncovers unobserved interactions between respondents and items in the item response data by embedding both in a shared latent metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for various response types, base model specifications, and missing data handling. Furthermore, lsirm12pl package provides methods to improve model utilization and interpretation, such as clustering item positions on an estimated interaction map. The package also offers convenient summary and plotting options to evaluate and process the estimated results. In this paper, we provide an overview of the LSIRM's methodological foundation and describe several extensions included in the package. We then demonstrate the use of the package with real data examples contained within it. |
| title | lsirm12pl: An R package for latent space item response modeling |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2205.06989 |