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1. |
Record Nr. |
UNISA990000804540203316 |
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Autore |
CHIOSSO, Giorgio |
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Titolo |
Profilo storico della pedagogia cristiana in Italia, (19. e 20. secolo) / Giorgio Chiosso |
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Pubbl/distr/stampa |
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Brescia : La Scuola, copyr.2001 |
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ISBN |
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Descrizione fisica |
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Collana |
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Disciplina |
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Soggetti |
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Pedagogia cristiana - Italia - Sec. 19.-20 |
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Collocazione |
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II.2.2764(XIV Coll 245/1) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910457693303321 |
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Autore |
Berridge Damon M. |
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Titolo |
Multivariate generalized linear mixed models using R / / Damon M. Berridge, Robert Crouchley |
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Pubbl/distr/stampa |
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Boca Raton, Fla. : , : CRC Press, , 2011 |
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ISBN |
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0-429-19160-X |
1-4987-4070-7 |
1-4398-1327-2 |
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Descrizione fisica |
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1 online resource (284 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Social sciences - Research - Mathematical models |
Social sciences - Research - Statistical methods |
Social sciences - Research - Data processing |
Multivariate analysis |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Front Cover; Contents; List of Figures; List of Tables; List of Applications; List of Datasets; Preface; Acknowledgments; 1. Introduction; 2.Generalized linear models for continuous/interval scale data; 3. Generalized linear models for other types of data; 4. Family of generalized linear models; 5. Mixed models for continuous/interval scale data; 6. Mixed models for binary data; 7. Mixed models for ordinal data; 8. Mixed models for count data; 9. Family of two-level generalized linear models; 10. Three-level generalized linear models; 11. Models for multivariate data |
12. Models for duration and event history data13. Stayers, non-susceptibles and endpoints; 14. Handling initial conditions/state dependence in binary data; 15. Incidental parameters: an empirical comparison of fixed effects and random effects models; A. SabreR installation, SabreR commands, quadrature, estimation, endogenous effects; B. Introduction to R for Sabre; References |
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Sommario/riassunto |
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To provide researchers with the ability to analyze large and complex data sets using robust models, this book presents a unified framework |
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for a broad class of models that can be applied using a dedicated R package (Sabre). The first five chapters cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs). The next few chapters extend to multivariate GLMMs and the last chapters address more specialized topics, such as parallel computing for large-scale analyses. Each chapter includes many real-world examples implemented using Sabre as well as exercises and |
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