Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data
As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including ana...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01431/full |
| _version_ | 1828181748010188800 |
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| author | Ji Hoon Ryoo Jeffrey D. Long Greg W. Welch Arthur Reynolds Susan M. Swearer |
| author_facet | Ji Hoon Ryoo Jeffrey D. Long Greg W. Welch Arthur Reynolds Susan M. Swearer |
| author_sort | Ji Hoon Ryoo |
| collection | DOAJ |
| description | As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM) that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications. |
| first_indexed | 2024-04-12T06:06:37Z |
| format | Article |
| id | doaj.art-d0b44d7478ac42729191337e2d1afd76 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-12T06:06:37Z |
| publishDate | 2017-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-d0b44d7478ac42729191337e2d1afd762022-12-22T03:44:51ZengFrontiers Media S.A.Frontiers in Psychology1664-10782017-08-01810.3389/fpsyg.2017.01431260983Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal DataJi Hoon Ryoo0Jeffrey D. Long1Greg W. Welch2Arthur Reynolds3Susan M. Swearer4Educational Leadership, Foundations, and Policy, University of VirginiaCharlottesville, VA, United StatesDepartment of Psychiatry, University of IowaIowa City, IA, United StatesBuffett Early Childhood Institute, University of NebraskaLincoln, NE, United StatesInstitute of Child Development, University of MinnesotaMinneapolis, MN, United StatesDepartment of Educational Psychology, University of NebraskaLincoln, NE, United StatesAs in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM) that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications.http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01431/fullfractional polynomialgeneralized additive modelNon-Gaussian longitudinal dataChicago longitudinal studyreading of the mind |
| spellingShingle | Ji Hoon Ryoo Jeffrey D. Long Greg W. Welch Arthur Reynolds Susan M. Swearer Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data Frontiers in Psychology fractional polynomial generalized additive model Non-Gaussian longitudinal data Chicago longitudinal study reading of the mind |
| title | Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data |
| title_full | Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data |
| title_fullStr | Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data |
| title_full_unstemmed | Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data |
| title_short | Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data |
| title_sort | fitting the fractional polynomial model to non gaussian longitudinal data |
| topic | fractional polynomial generalized additive model Non-Gaussian longitudinal data Chicago longitudinal study reading of the mind |
| url | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01431/full |
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