IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v058b01.html
   My bibliography  Save this article

Analyzing Spatial Models of Choice and Judgment with R

Author

Listed:
  • Evans, Gary

Abstract

Abstracts not available for BookReviews

Suggested Citation

  • Evans, Gary, 2014. "Analyzing Spatial Models of Choice and Judgment with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(b01).
  • Handle: RePEc:jss:jstsof:v:058:b01
    DOI: http://hdl.handle.net/10.18637/jss.v058.b01
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v058b01/v58b01.pdf
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v058.b01?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. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    2. Poole, Keith & Lewis, Jeffrey B. & Lo, James & Carroll, Royce, 2011. "Scaling Roll Call Votes with wnominate in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i14).
    3. Wand, Jonathan & King, Gary & Lau, Olivia, 2011. "anchors: Software for Anchoring Vignette Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i03).
    4. de Leeuw, Jan & Mair, Patrick, 2009. "Multidimensional Scaling Using Majorization: SMACOF in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i03).
    Full references (including those not matched with items on IDEAS)

    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. Groenen, Patrick J. F. & van de Velden, Michel, 2016. "Multidimensional Scaling by Majorization: A Review," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i08).
    2. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
    3. Funk, Patrick & Davis, Alex & Vaishnav, Parth & Dewitt, Barry & Fuchs, Erica, 2020. "Individual inconsistency and aggregate rationality: Overcoming inconsistencies in expert judgment at the technical frontier," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    4. Michael J. Greenacre & Patrick J. F. Groenen, 2016. "Weighted Euclidean Biplots," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 442-459, October.
    5. Cloléry, Héloïse, 2023. "Legislators in the crossfire: Strategic non-voting and the effect of transparency," European Journal of Political Economy, Elsevier, vol. 78(C).
    6. Walesiak Marek & Dudek Andrzej, 2017. "Selecting the Optimal Multidimensional Scaling Procedure for Metric Data With R Environment," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 521-540, September.
    7. Seokho Lee & Hyejin Shin & Sang Han Lee, 2016. "Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study," Biometrics, The International Biometric Society, vol. 72(4), pages 1325-1335, December.
    8. Tasos Kalandrakis, 2006. "Roll Call Data and Ideal Points," Wallis Working Papers WP42, University of Rochester - Wallis Institute of Political Economy.
    9. Yang Liu, 2020. "A Riemannian Optimization Algorithm for Joint Maximum Likelihood Estimation of High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 439-468, June.
    10. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    11. Dzemyda, Gintautas & Sabaliauskas, Martynas, 2021. "Geometric multidimensional scaling: A new approach for data dimensionality reduction," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    12. Zongfeng Sun & Jintao Li, 2019. "Citizens’ Satisfaction with Air Quality and Key Factors in China—Using the Anchoring Vignettes Method," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    13. Tangian, Andranik S., 2017. "Selection of questions for VAAs and the VAA-based elections," Working Paper Series in Economics 100, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    14. Paula Clerici, 2021. "Legislative Territorialization: The Impact of a Decentralized Party System on Individual Legislative Behavior in Argentina," Publius: The Journal of Federalism, CSF Associates Inc., vol. 51(1), pages 104-130.
    15. van Dijk, A. & van Rosmalen, J.M. & Paap, R., 2009. "A Bayesian approach to two-mode clustering," Econometric Institute Research Papers EI 2009-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. repec:jss:jstsof:42:i09 is not listed on IDEAS
    17. Tom Everaert & Adriaan Spruyt & Jan De Houwer, 2018. "To IMPRES or to EXPRES? Exploiting comparative judgments to measure and visualize implicit and explicit preferences," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-14, January.
    18. Sayan Chakraborty & Arnab Bhattacharjee & Taps Maiti, 2021. "Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 185-206, November.
    19. Monika Mühlböck & Nikoleta Yordanova, 2017. "When legislators choose not to decide: Abstentions in the European Parliament," European Union Politics, , vol. 18(2), pages 323-336, June.
    20. Reza Mousavi & Bin Gu, 2019. "The Impact of Twitter Adoption on Lawmakers’ Voting Orientations," Service Science, INFORMS, vol. 30(1), pages 133-153, March.
    21. repec:jss:jstsof:34:i10 is not listed on IDEAS
    22. Robin, Geneviève & Josse, Julie & Moulines, Éric & Sardy, Sylvain, 2019. "Low-rank model with covariates for count data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 416-434.

    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:058:b01. 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.