poLCA: An R package for polytomous variable latent class analysis

Journal of Statistical Software

Volumn 42, Issue 10, 2011, Pages 1-29

  Linzer, Drew A ^a   Lewis, Jeffrey B ^b  

^a EMORY UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Categorical; Concomitant; Latent class analysis; Latent class regression; Polytomous

Indexed keywords

EID: 79961221925     PISSN: None     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v042.i10     Document Type: Article

References (38)

1. Agresti A (2002). Categorical Data Analysis. John Wiley & Sons, Hoboken.
2. Akaike H (1973). Information Theory and an Extension of the Maximum Likelihood Principle." In B Petrov, F Csake (eds.), Second International Symposium on Information Theory, pp. 267-281. Akademiai Kiado, Budapest, Hungary.
3. Bandeen-Roche K, Miglioretti DL, Zeger SL, Rathouz PJ (1997). Latent Variable Regression for Multiple Discrete Outcomes." Journal of the American Statistical Association, 92(440), 1375-1386.
4. Beath K (2011). randomLCA: Random Effects Latent Class Analysis. R package version 0.7- 4, URL http://CRAN.R-project.org/package=randomLCA.
5. Blaydes L, Linzer DA (2008). The Political Economy ofWomen's Support for Fundamentalist Islam." World Politics, 60(4), 576-609.
6. Bolck A, Croon M, Hagenaars J (2004). Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators. Political Analysis, 12(1), 3-27.
7. Breen R (2000).Why Is Support for Extreme Parties Underestimated by Surveys? A Latent Class Analysis. British Journal of Political Science, 30(2), 375-382.
8. Clinton J, Jackman S, Rivers D (2004). The Statistical Analysis of Roll Call Data."American Political Science Review, 98(2), 355-370.
9. Clogg CC, Goodman LA (1986). On Scaling Models Applied to Data from Several Groups. Psychometrika, 51(1), 123-135.
10. Dayton CM (1998). Latent Class Scaling Analysis. Sage Publications, Thousand Oaks, CA.
11. Dayton CM, Macready GB (1988). Concomitant-Variable Latent-Class Models." Journal of the American Statistical Association, 83(401), 173-178.
12. Dempster AP, Laird NM, Rubin DB (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society B, 39, 1-38.
13. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A (2011). e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-25, URL http://CRAN.R-project.org/package=e1071.
14. Duffy D (2010). gllm: Generalised log-linear model. R package version 0.33. C code in emgllmfitter by Andreas Borg, URL http://CRAN.R-project.org/package=gllm.
15. Everitt BS (1984). An Introduction to Latent Variable Models. Chapman and Hall, London.
16. Everitt BS, Hand DJ (1981). Finite Mixture Distributions. Chapman and Hall, London.
17. Feick LF (1989). Latent Class Analysis of Survey Questions that Include Don't Know Responses." Public Opinion Quarterly, 53(4), 525-547.
18. Forster MR (2000). Key Concepts in Model Selection: Performance and Generalizability. Journal of Mathematical Psychology, 44, 205-231.
19. Goodman L (1970). The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications." Journal of the American Statistical Association, 65, 226-256.
20. Goodman L (1974). Exploratory Latent Structure Analysis using both Identiffable and Unidentifiable Models." Biometrika, 61, 315-231.
21. Grün B, Leisch F (2008). FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters." Journal of Statistical Software, 28(4), 1-35. URL http://www.jstatsoft.org/v28/i04/.
22. Hagenaars JA, McCutcheon AL (eds.) (2002). Applied Latent Class Analysis. Cambridge University Press, Cambridge.
23. Hennig C (2010). fpc: Flexible Procedures for Clustering. R package version 2.0-3, URL http://CRAN.R-project.org/package=fpc.
24. Hill JL, Kriesi H (2001a). Classification by Opinion-Changing Behavior: A Mixture Model Approach." Political Analysis, 9(4), 301-324.
25. Hill JL, Kriesi H (2001b). An Extension and Test of Converse's 'Black-and-White' Model of Response Stability." American Political Science Review, 95(2), 397-413.
26. Lazarsfeld PF (1950). The Logical and Mathematical Foundations of Latent Structure Analysis." In SA Stouffer (ed.), Measurement and Prediction, pp. 362-412. John Wiley & Sons, New York.
27. Leisch F (2004). FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R." Journal of Statistical Software, 11(8), 1-18. URL http://www.jstatsoft.org/v11/i08/.
28. Lin TH, Dayton CM (1997). Model Selection Information Criteria for Non-Nested Latent Class Models." Journal of Educational and Behavioral Statistics, 22(3), 249-264.
29. Linzer DA (2011). Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators." Political Analysis, 19(2).
30. Linzer DA, Lewis J (2011). poLCA: Polytomous Variable Latent Class Analysis. R package version 1.3, URL http://CRAN.R-project.org/package=poLCA.
31. McCutcheon AL (1985). A Latent Class Analysis of Tolerance for Nonconformity in the American Public." The Public Opinion Quarterly, 49(5), 474-488.
32. McCutcheon AL (1987). Latent Class Analysis. Sage Publications, Newbury Park.
33. McLachlan G, Peel D (2000). Finite Mixture Models. John Wiley & Sons, New York.
34. McLachlan GJ, Krishnan T (1997). The EM Algorithm and Extensions. John Wiley & Sons, New York.
35. Meilijson I (1989). A Fast Improvement to the EM Algorithm on its Own Terms." Journal of the Royal Statistical Society B, 51(1), 127-138.
36. R Development Core Team (2010). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
37. Schwartz G (1978). Estimating the Dimension of a Model." The Annals of Statistics, 6, 461-464.
38. The National Election Studies (2000). The 2000 National Election Study dataset. University of Michigan, Center for Political Studies, producer and distributor, Ann Arbor, MI. URL http://www.electionstudies.org/.