IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v077i07.html
   My bibliography  Save this article

Fitting Nonlinear Structural Equation Models in R with Package nlsem

Author

Listed:
  • Umbach, Nora
  • Naumann, Katharina
  • Brandt, Holger
  • Kelava, Augustin

Abstract

Structural equation mixture modeling (SEMM) has become a standard procedure in latent variable modeling over the last two decades (Jedidi, Jagpal, and DeSarbo'97b; Muthén and Shedden'99; Muthén 2001, 2004; Muthén and Asparouhov 2009). SEMM was proposed as a technique for the approximation of nonlinear latent variable relationships by finite mixtures of linear relationships (Bauer 2005, 2007; Bauer, Baldasaro, and Gottfredson 2012). In addition to this semiparametric approach to nonlinear latent variable modeling, there are numerous parametric nonlinear approaches for normally distributed variables (e.g., LMS in Mplus; Klein and Moosbrugger 2000). Recently, an additional semiparametric nonlinear structural equation mixture modeling (NSEMM) approach was proposed by Kelava, Nagengast, and Brandt (2014) that is capable of dealing with nonnormal predictors. In the nlsem package presented here, the SEMM, two distribution analytic (QML and LMS) and NSEMM approaches can be specified and estimated. We provide examples of how to use the package in the context of nonlinear latent variable modeling.

Suggested Citation

  • Umbach, Nora & Naumann, Katharina & Brandt, Holger & Kelava, Augustin, 2017. "Fitting Nonlinear Structural Equation Models in R with Package nlsem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i07).
    • Handle: RePEc:jss:jstsof:v:077:i07
    DOI: http://hdl.handle.net/10.18637/jss.v077.i07
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v077i07/v77i07.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v077i07/nlsem_0.8.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v077i07/v77i07-replication.zip
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v077.i07?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Conor Dolan & Han Maas, 1998. "Fitting multivariage normal finite mixtures subject to structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 227-253, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Slupphaug, KJell & Mehmetoglu, Mehmet & Mittner, Matthias, 2024. "modsem: An R package for estimating latent interactions and quadratic effects," OSF Preprints h3rpw, Center for Open Science.
    2. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    3. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "Understanding public transport satisfaction: Using Maslow's hierarchy of (transit) needs," Transport Policy, Elsevier, vol. 81(C), pages 75-94.
    4. Kim, Gwangsu & Choi, Taeryon, 2019. "Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 68-82.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
    2. Peter Halpin & Conor Dolan & Raoul Grasman & Paul Boeck, 2011. "On the Relation Between the Linear Factor Model and the Latent Profile Model," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 564-583, October.
    3. Bacci, Silvia & Bartolucci, Francesco & Pieroni, Luca, 2012. "A causal analysis of mother’s education on birth inequalities," MPRA Paper 38754, University Library of Munich, Germany.
    4. Eisenbeiss, Maik & Blechschmidt, Boris & Backhaus, Klaus & Freund, Philipp Alexander, 2012. "“The (Real) World Is Not Enough:” Motivational Drivers and User Behavior in Virtual Worlds," Journal of Interactive Marketing, Elsevier, vol. 26(1), pages 4-20.
    5. Edgar Merkle & Achim Zeileis, 2013. "Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 59-82, January.
    6. Sy-Miin Chow & Guangjian Zhang, 2013. "Nonlinear Regime-Switching State-Space (RSSS) Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 740-768, October.
    7. Edgar Merkle & Jinyan Fan & Achim Zeileis, 2014. "Testing for Measurement Invariance with Respect to an Ordinal Variable," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 569-584, October.
    8. Dylan Molenaar & Paul Boeck, 2018. "Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 279-297, June.
    9. Dylan Molenaar, 2015. "Heteroscedastic Latent Trait Models for Dichotomous Data," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 625-644, September.
    10. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
    11. Williams, John & Temme, Dirk & Hildebrandt, Lutz, 2002. "A Monte Carlo study of structural equation models for finite mixtures," SFB 373 Discussion Papers 2002,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    12. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.
    13. Zhou, Min & Zhao, Lindu & Kong, Nan & Campy, Kathryn S. & Xu, Ge & Zhu, Guiju & Cao, Xianye & Wang, Song, 2020. "Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    14. Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
    15. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    16. B. Karmakar & K. Dhara & K. Dey & A. Basu & A. Ghosh, 2015. "Tests for statistical significance of a treatment effect in the presence of hidden sub-populations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 97-119, March.
    17. Li, Yun-Xian & Kano, Yutaka & Pan, Jun-Hao & Song, Xin-Yuan, 2012. "A criterion-based model comparison statistic for structural equation models with heterogeneous data," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 92-107.
    18. Heike Heidemeier & Anja Göritz, 2013. "Individual Differences in How Work and Nonwork Life Domains Contribute to Life Satisfaction: Using Factor Mixture Modeling for Classification," Journal of Happiness Studies, Springer, vol. 14(6), pages 1765-1788, December.
    19. Jolynn Pek & R. Philip Chalmers & Bethany E. Kok & Diane Losardo, 2015. "Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations Among Latent Variables," Journal of Educational and Behavioral Statistics, , vol. 40(4), pages 402-423, August.
    20. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:077:i07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.