Open Access
Path analysis with logistic regression models
Nobuoki Eshima,Minoru Tabata,Geng Zhi +2 more
- 01 Jan 2002
About: The article was published on 01 Jan 2002. and is currently open access. The article focuses on the topics: Logistic regression & Logistic distribution.
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Citations
Standards for Standardized Logistic Regression Coefficients
TL;DR: The authors reviewed the state of knowledge regarding the use of standardized coefficients in general and standardized logistic regression regression coefficients in particular, and made specific recommendations on how to best use (and avoid abusing) standardized Logistic regression coefficients.
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Fire mediated edge effects in bayhead tree islands
TL;DR: The spatial patterns of tree survival and resprouting observed are likely due to gradients in intensity of peat smoldering and aboveground burning, coupled with differential susceptibility to these two types of fire.
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Path Analysis for Recursive Generalized Linear Model Systems
Nobuoki Eshima,Hasama Yufu,Minoru Tabata,Tetsuji Ohyama +3 more
- 01 Jan 2011
TL;DR: In this paper, the authors proposed a path analysis method for categorical variables based on generalized linear models (GLMs), which is an analogy to the LISREL approach for path analysis of continuous variables.
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References
Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
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Lisrel 8: User's Reference Guide
Karl G. Jöreskog,Dag Sörbom +1 more
- 01 Mar 1997
TL;DR: This ebook offers full option of this ebook in doc, DjVu, PDF, ePub, txt forms, and on the site you can reading the instructions and other artistic eBooks online, either download them as well.
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Analysis of Binary Data
A. Lancaster,David Cox +1 more
Abstract: Binary response variables special logistical analyses some complications some related approaches more complex responses. Appendices: Theoretical background Choice of explanatory variables in multiple regression Review of computational aspects Further results and exercises.
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